diff --git a/.github/workflows/dependency-review.yml b/.github/workflows/dependency-review.yml
index 7673349..1f3afba 100644
--- a/.github/workflows/dependency-review.yml
+++ b/.github/workflows/dependency-review.yml
@@ -5,7 +5,7 @@
 # Source repository: https://github.com/actions/dependency-review-action
 # Public documentation: https://docs.github.com/en/code-security/supply-chain-security/understanding-your-software-supply-chain/about-dependency-review#dependency-review-enforcement
 name: 'Dependency Review'
-on: pull_request
+on: [pull_request, push]
 
 permissions:
   contents: read
diff --git a/.gitignore b/.gitignore
index 65ddec8..4127bb8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -89,4 +89,5 @@ Samples/*
 /scc.exe
 /SCC_Auto.cmd
 /Microsoft.PowerShell_profile_Nvidia_smi.ps1
-/Data/image_SUB_generator.pkl
\ No newline at end of file
+/Data/image_SUB_generator.pkl
+/GPU_Info.txt
\ No newline at end of file
diff --git a/BETA_E_Model_T&T.ipynb b/BETA_E_Model_T&T.ipynb
index 062ce0c..f11dee8 100644
--- a/BETA_E_Model_T&T.ipynb
+++ b/BETA_E_Model_T&T.ipynb
@@ -69,6 +69,7 @@
     "import glob \n",
     "import keras\n",
     "import pprint\n",
+    "import numba\n",
     "import random\n",
     "import shutil\n",
     "import gzip\n",
@@ -137,6 +138,30 @@
     "    tf.config.experimental.set_memory_growth(gpu_instance, True)\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numba.cuda.cudadrv import enums\n",
+    "from numba import cuda\n",
+    "\n",
+    "device = cuda.get_current_device()\n",
+    "attribs = [name.replace(\"CU_DEVICE_ATTRIBUTE_\", \"\") for name in dir(enums) if name.startswith(\"CU_DEVICE_ATTRIBUTE_\")]\n",
+    "\n",
+    "with open('GPU_Info.txt', 'w') as f:\n",
+    "    for attr in attribs:\n",
+    "        f.write(f\"{attr} = {getattr(device, attr)}\\n\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -215,13 +240,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<Policy \"float32\">\n"
+      "<Policy \"mixed_float16\">\n"
      ]
     }
    ],
    "source": [
     "SAVE_TYPE = 'H5'\n",
-    "Use_mixed_float16 = False\n",
+    "Use_mixed_float16 = True\n",
     "#Other\n",
     "if Use_mixed_float16:\n",
     "    tf.keras.mixed_precision.set_global_policy('mixed_float16')\n",
@@ -261,15 +286,7 @@
       "\u001b[0;33mMaking categorical data...\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;33mGenerating augmented data \u001b[0m\u001b[0;36m[\u001b[0m\u001b[0;32mADBD: \u001b[0m\u001b[0;31m0\u001b[0m\u001b[0;36m]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;33mNormalizing image data...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0mData type: \u001b[0m\u001b[0;32mfloat16\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0mRGB Range: \u001b[0m\u001b[0;34mMin = 0.0\u001b[0m\u001b[0m | \u001b[0m\u001b[0;31mMax = 1.0\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0mLabel ratio: \u001b[0m\u001b[0;31m49.35% PNEUMONIA \u001b[0m\u001b[0;35m| \u001b[0m\u001b[0;32m50.65% NORMAL\u001b[0m\n",
-      "\u001b[0;33mSetting LNTS...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0mOriginal num_samples: \u001b[0m\u001b[0;32m23681\u001b[0m\n",
-      "\u001b[0;33mshuffling data...\u001b[0m\n",
-      "\u001b[0;33mSaving TS...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2024_m02_d16-h17_m33_s49\u001b[0m\n",
-      "\u001b[0;32mDone.\u001b[0m\n"
+      "\u001b[0m\u001b[0m\u001b[0mData type: \u001b[0m\u001b[0;32mfloat16\u001b[0m\n"
      ]
     }
    ],
@@ -684,7 +701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-28T02:31:27.380088800Z",
@@ -706,15 +723,37 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Data Analyzation (Removed temporary⚠️)"
+    "## Data Analyzation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1500 with 32 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "fig, axes = plt.subplots(4, 8, figsize=(20, 15))\n",
+    "\n",
+    "for i, ax in enumerate(axes.flat):\n",
+    "    ax.imshow((x_train[i] * 255).astype('uint8'))\n",
+    "    ax.set_title(f'Label: {np.argmax(y_train[i])}')\n",
+    "    ax.axis('off')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -816,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-27T17:34:12.077394600Z",
@@ -3168,234 +3207,4383 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from keras_efficientnet_v2 import EfficientNetV2XL\n",
-    "\n",
-    "EfficientNet_M = EfficientNetV2XL(input_shape=(img_res[0], img_res[1], img_res[2]), pretrained='imagenet21k-ft1k', num_classes=2, dropout=0.4)\n",
-    "# define new model\n",
-    "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n",
-    "\n",
-    "# compile model\n",
-    "opt = SGD(momentum=0.9)\n",
-    "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False)\n",
-    "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n",
-    "# opt = Adam()\n",
-    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n",
-    "\n",
-    "freeze_layers = 0\n",
-    "model.summary(show_trainable=True, expand_nested=True)\n",
-    "print('done.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Rev1.5 (The best one)\n",
-    "```\n",
-    "recommended: ✅\n",
-    "statuses: Ready\n",
-    "Working: ✅\n",
-    "Max fine tuned acc: 95.3\n",
-    "Max fine tuned acc TLRev2: 97.12\n",
-    "type: transfer learning>>>(EfficientNetB4::CCL)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from efficientnet.keras import EfficientNetB4 as KENB4\n",
-    "# FUNC\n",
-    "def Eff_B4_NS(freeze_layers):\n",
-    "    base_model = KENB4(input_shape=(\n",
-    "        img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n",
-    "    print('Total layers in the base model: ', len(base_model.layers))\n",
-    "    print(f'Freezing {freeze_layers} layers in the base model...')\n",
-    "    # Freeze the specified number of layers\n",
-    "    for layer in base_model.layers[:freeze_layers]:\n",
-    "        layer.trainable = False\n",
-    "\n",
-    "    # Unfreeze the rest\n",
-    "    for layer in base_model.layers[freeze_layers:]:\n",
-    "        layer.trainable = True\n",
-    "\n",
-    "    # Calculate the percentage of the model that is frozen\n",
-    "    frozen_percentage = ((freeze_layers + 1e-10) /\n",
-    "                         len(base_model.layers)) * 100\n",
-    "    print(\n",
-    "        f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n",
-    "    # adding CDL>>>\n",
-    "    #GlobalAveragePooling2D\n",
-    "    base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output)\n",
-    "    #Dense\n",
-    "    Dense_L1 = Dense(512, activation='relu',\n",
-    "                     kernel_regularizer=l2(0.02),\n",
-    "                     name='FC_C_Dense-L1-512'\n",
-    "                     )(base_model_FT)\n",
-    "    #Dropout\n",
-    "    Dropout_L1 = Dropout(0.1,\n",
-    "                         name='FC_C_Dropout-L1-0.1'\n",
-    "                         )(Dense_L1)\n",
-    "    #BatchNormalization\n",
-    "    BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L1'\n",
-    "                                      )(Dropout_L1)\n",
-    "    #Dense\n",
-    "    Dense_L2 = Dense(512, activation='relu',\n",
-    "                     kernel_regularizer=l2(0.01),\n",
-    "                     name='FC_C_Dense-L2-512'\n",
-    "                     )(BatchNorm_L2)\n",
-    "    #BatchNormalization\n",
-    "    BatchNorm_L3 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L2'\n",
-    "                                      )(Dense_L2)\n",
-    "    #Dense\n",
-    "    Dense_L3 = Dense(128, activation='relu',\n",
-    "                     name='FC_C_Dense-L3-128'\n",
-    "                     )(BatchNorm_L3)\n",
-    "    #Dense\n",
-    "    # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n",
-    "    predictions = Dense(2, activation='softmax',\n",
-    "                        name='FC_OUTPUT_Dense-2')(Dense_L3)\n",
-    "    # CDL<<<\n",
-    "    model_EfficientNetB4_NS = Model(\n",
-    "        inputs=base_model.input, outputs=predictions)\n",
-    "    print('Total model layers: ', len(model_EfficientNetB4_NS.layers))\n",
-    "    # OPT/compile\n",
-    "    opt = SGD(momentum=0.92, nesterov=False)\n",
-    "    # opt = Nadam()\n",
-    "    # opt = Adamax()\n",
-    "    # opt = RMSprop(momentum=0.9)\n",
-    "    # opt = Adagrad()\n",
-    "    # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n",
-    "    # opt = Yogi()\n",
-    "    model_EfficientNetB4_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])  # categorical_crossentropy / binary_crossentropy\n",
-    "\n",
-    "    return model_EfficientNetB4_NS\n",
-    "\n",
-    "print('Creating the model...')\n",
-    "# Main\n",
-    "freeze_layers = 0\n",
-    "model = Eff_B4_NS(freeze_layers)\n",
-    "model.summary(show_trainable=True, expand_nested=True)\n",
-    "print('done.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### V(T) Beta"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from efficientnet.keras import EfficientNetL2 as KENBL2\n",
-    "#FUNC\n",
-    "def Eff_B7_NS(freeze_layers):\n",
-    "    base_model = KENBL2(input_shape=(img_res[0], img_res[1], img_res[2]),\n",
-    "                        weights='./download/Models/EFN_L2/efficientnet-l2_noisy-student_notop.h5',\n",
-    "                        include_top=False,\n",
-    "                        drop_connect_rate=0)\n",
-    "    print('Total layers in the base model: ', len(base_model.layers))\n",
-    "    print(f'Freezing {freeze_layers} layers in the base model...')\n",
-    "    # Freeze the specified number of layers\n",
-    "    for layer in base_model.layers[:freeze_layers]:\n",
-    "        layer.trainable = False\n",
-    "\n",
-    "    # Unfreeze the rest\n",
-    "    for layer in base_model.layers[freeze_layers:]:\n",
-    "        layer.trainable = True\n",
-    "\n",
-    "    # Calculate the percentage of the model that is frozen\n",
-    "    frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100\n",
-    "    print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n",
-    "    # adding CDL\n",
-    "    base_model_FT = GlobalAveragePooling2D()(base_model.output)\n",
-    "    Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT)\n",
-    "    Dropout_L1 = Dropout(0.1)(Dense_L1) \n",
-    "    BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n",
-    "    Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2)\n",
-    "    BatchNorm_L3 = BatchNormalization()(Dense_L2)\n",
-    "    Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n",
-    "    predictions = Dense(2, activation='softmax')(Dense_L3)\n",
-    "\n",
-    "    model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions)   \n",
-    "    print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n",
-    "    #OPT/compile\n",
-    "    opt = SGD(momentum=0.9)\n",
-    "    # opt = Yogi()\n",
-    "    model_EfficientNetB7_NS.compile(optimizer = opt,  loss='categorical_crossentropy', metrics=['accuracy'])\n",
-    "\n",
-    "    return model_EfficientNetB7_NS\n",
-    "print('Creating the model...')\n",
-    "# Main\n",
-    "freeze_layers = 0\n",
-    "model = Eff_B7_NS(freeze_layers)\n",
-    "model.summary(show_trainable=True, expand_nested=True)\n",
-    "print('done.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 28,
    "metadata": {},
-   "source": [
-    "### V(T) Beta2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-12-28T02:31:32.994176700Z",
-     "start_time": "2023-12-28T02:31:27.381088600Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from efficientnet.keras import EfficientNetB7 as KENB7\n",
-    "# FUNC\n",
-    "def Eff_B7_NS(freeze_layers):\n",
-    "    base_model = KENB7(input_shape=(\n",
-    "        img_res[0], img_res[1], img_res[2]), weights=None, include_top=False)\n",
-    "    print('Total layers in the base model: ', len(base_model.layers))\n",
-    "    print(f'Freezing {freeze_layers} layers in the base model...')\n",
-    "    # Freeze the specified number of layers\n",
-    "    for layer in base_model.layers[:freeze_layers]:\n",
-    "        layer.trainable = False\n",
-    "\n",
-    "    # Unfreeze the rest\n",
-    "    for layer in base_model.layers[freeze_layers:]:\n",
-    "        layer.trainable = True\n",
-    "\n",
-    "    # Calculate the percentage of the model that is frozen\n",
-    "    frozen_percentage = ((freeze_layers + 1e-10) /\n",
-    "                         len(base_model.layers)) * 100\n",
-    "    print(\n",
-    "        f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n",
-    "    # adding CDL>>>\n",
-    "    #GlobalAveragePooling2D\n",
-    "    base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output)\n",
-    "    #Dense\n",
-    "    Dense_L1 = Dense(512, activation='relu',\n",
-    "                     kernel_regularizer=l2(0.02),\n",
-    "                     name='FC_C_Dense-L1-512'\n",
-    "                     )(base_model_FT)\n",
-    "    #Dropout\n",
-    "    Dropout_L1 = Dropout(0.1,\n",
-    "                         name='FC_C_Dropout-L1-0.1'\n",
-    "                         )(Dense_L1)\n",
-    "    #BatchNormalization\n",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\initializers\\initializers_v2.py:120: UserWarning: The initializer VarianceScaling is unseeded and being called multiple times, which will return identical values  each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initalizer instance more than once.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ">>>> Load pretrained from: C:\\Users\\aydin\\.keras\\models/efficientnetv2\\efficientnetv2-xl-21k-ft1k.h5\n",
+      "Model: \"model\"\n",
+      "_____________________________________________________________________________________________________________\n",
+      " Layer (type)                   Output Shape         Param #     Connected to                     Trainable  \n",
+      "=============================================================================================================\n",
+      " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               Y          \n",
+      "                                )]                                                                           \n",
+      "                                                                                                             \n",
+      " stem_conv (Conv2D)             (None, 112, 112, 32  864         ['input_1[0][0]']                Y          \n",
+      "                                )                                                                            \n",
+      "                                                                                                             \n",
+      " stem_bn (BatchNormalization)   (None, 112, 112, 32  128         ['stem_conv[0][0]']              Y          \n",
+      "                                )                                                                            \n",
+      "                                                                                                             \n",
+      " stem_swish (Activation)        (None, 112, 112, 32  0           ['stem_bn[0][0]']                Y          \n",
+      "                                )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_0_block0_fu_conv (Conv2D  (None, 112, 112, 32  9216       ['stem_swish[0][0]']             Y          \n",
+      " )                              )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_0_block0_fu_bn (BatchNor  (None, 112, 112, 32  128        ['stack_0_block0_fu_conv[0][0]'  Y          \n",
+      " malization)                    )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_0_block0_fu_swish (Activ  (None, 112, 112, 32  0          ['stack_0_block0_fu_bn[0][0]']   Y          \n",
+      " ation)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " add (Add)                      (None, 112, 112, 32  0           ['stem_swish[0][0]',             Y          \n",
+      "                                )                                 'stack_0_block0_fu_swish[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_0_block1_fu_conv (Conv2D  (None, 112, 112, 32  9216       ['add[0][0]']                    Y          \n",
+      " )                              )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_0_block1_fu_bn (BatchNor  (None, 112, 112, 32  128        ['stack_0_block1_fu_conv[0][0]'  Y          \n",
+      " malization)                    )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_0_block1_fu_swish (Activ  (None, 112, 112, 32  0          ['stack_0_block1_fu_bn[0][0]']   Y          \n",
+      " ation)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " add_1 (Add)                    (None, 112, 112, 32  0           ['add[0][0]',                    Y          \n",
+      "                                )                                 'stack_0_block1_fu_swish[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_0_block2_fu_conv (Conv2D  (None, 112, 112, 32  9216       ['add_1[0][0]']                  Y          \n",
+      " )                              )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_0_block2_fu_bn (BatchNor  (None, 112, 112, 32  128        ['stack_0_block2_fu_conv[0][0]'  Y          \n",
+      " malization)                    )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_0_block2_fu_swish (Activ  (None, 112, 112, 32  0          ['stack_0_block2_fu_bn[0][0]']   Y          \n",
+      " ation)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " add_2 (Add)                    (None, 112, 112, 32  0           ['add_1[0][0]',                  Y          \n",
+      "                                )                                 'stack_0_block2_fu_swish[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_0_block3_fu_conv (Conv2D  (None, 112, 112, 32  9216       ['add_2[0][0]']                  Y          \n",
+      " )                              )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_0_block3_fu_bn (BatchNor  (None, 112, 112, 32  128        ['stack_0_block3_fu_conv[0][0]'  Y          \n",
+      " malization)                    )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_0_block3_fu_swish (Activ  (None, 112, 112, 32  0          ['stack_0_block3_fu_bn[0][0]']   Y          \n",
+      " ation)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " add_3 (Add)                    (None, 112, 112, 32  0           ['add_2[0][0]',                  Y          \n",
+      "                                )                                 'stack_0_block3_fu_swish[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_1_block0_sortcut_conv (C  (None, 56, 56, 128)  36864      ['add_3[0][0]']                  Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block0_sortcut_bn (Bat  (None, 56, 56, 128)  512        ['stack_1_block0_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block0_sortcut_swish (  (None, 56, 56, 128)  0          ['stack_1_block0_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_1_block0_MB_pw_conv (Con  (None, 56, 56, 64)  8192        ['stack_1_block0_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_1_block0_MB_pw_bn (Batch  (None, 56, 56, 64)  256         ['stack_1_block0_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_1_block1_sortcut_conv (C  (None, 56, 56, 256)  147456     ['stack_1_block0_MB_pw_bn[0][0]  Y          \n",
+      " onv2D)                                                          ']                                          \n",
+      "                                                                                                             \n",
+      " stack_1_block1_sortcut_bn (Bat  (None, 56, 56, 256)  1024       ['stack_1_block1_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block1_sortcut_swish (  (None, 56, 56, 256)  0          ['stack_1_block1_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_1_block1_MB_pw_conv (Con  (None, 56, 56, 64)  16384       ['stack_1_block1_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_1_block1_MB_pw_bn (Batch  (None, 56, 56, 64)  256         ['stack_1_block1_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_4 (Add)                    (None, 56, 56, 64)   0           ['stack_1_block0_MB_pw_bn[0][0]  Y          \n",
+      "                                                                 ',                                          \n",
+      "                                                                  'stack_1_block1_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_1_block2_sortcut_conv (C  (None, 56, 56, 256)  147456     ['add_4[0][0]']                  Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block2_sortcut_bn (Bat  (None, 56, 56, 256)  1024       ['stack_1_block2_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block2_sortcut_swish (  (None, 56, 56, 256)  0          ['stack_1_block2_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_1_block2_MB_pw_conv (Con  (None, 56, 56, 64)  16384       ['stack_1_block2_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_1_block2_MB_pw_bn (Batch  (None, 56, 56, 64)  256         ['stack_1_block2_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_5 (Add)                    (None, 56, 56, 64)   0           ['add_4[0][0]',                  Y          \n",
+      "                                                                  'stack_1_block2_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_1_block3_sortcut_conv (C  (None, 56, 56, 256)  147456     ['add_5[0][0]']                  Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block3_sortcut_bn (Bat  (None, 56, 56, 256)  1024       ['stack_1_block3_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block3_sortcut_swish (  (None, 56, 56, 256)  0          ['stack_1_block3_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_1_block3_MB_pw_conv (Con  (None, 56, 56, 64)  16384       ['stack_1_block3_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_1_block3_MB_pw_bn (Batch  (None, 56, 56, 64)  256         ['stack_1_block3_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_6 (Add)                    (None, 56, 56, 64)   0           ['add_5[0][0]',                  Y          \n",
+      "                                                                  'stack_1_block3_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_1_block4_sortcut_conv (C  (None, 56, 56, 256)  147456     ['add_6[0][0]']                  Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block4_sortcut_bn (Bat  (None, 56, 56, 256)  1024       ['stack_1_block4_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block4_sortcut_swish (  (None, 56, 56, 256)  0          ['stack_1_block4_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_1_block4_MB_pw_conv (Con  (None, 56, 56, 64)  16384       ['stack_1_block4_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_1_block4_MB_pw_bn (Batch  (None, 56, 56, 64)  256         ['stack_1_block4_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_7 (Add)                    (None, 56, 56, 64)   0           ['add_6[0][0]',                  Y          \n",
+      "                                                                  'stack_1_block4_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_1_block5_sortcut_conv (C  (None, 56, 56, 256)  147456     ['add_7[0][0]']                  Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block5_sortcut_bn (Bat  (None, 56, 56, 256)  1024       ['stack_1_block5_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block5_sortcut_swish (  (None, 56, 56, 256)  0          ['stack_1_block5_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_1_block5_MB_pw_conv (Con  (None, 56, 56, 64)  16384       ['stack_1_block5_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_1_block5_MB_pw_bn (Batch  (None, 56, 56, 64)  256         ['stack_1_block5_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_8 (Add)                    (None, 56, 56, 64)   0           ['add_7[0][0]',                  Y          \n",
+      "                                                                  'stack_1_block5_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_1_block6_sortcut_conv (C  (None, 56, 56, 256)  147456     ['add_8[0][0]']                  Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block6_sortcut_bn (Bat  (None, 56, 56, 256)  1024       ['stack_1_block6_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block6_sortcut_swish (  (None, 56, 56, 256)  0          ['stack_1_block6_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_1_block6_MB_pw_conv (Con  (None, 56, 56, 64)  16384       ['stack_1_block6_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_1_block6_MB_pw_bn (Batch  (None, 56, 56, 64)  256         ['stack_1_block6_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_9 (Add)                    (None, 56, 56, 64)   0           ['add_8[0][0]',                  Y          \n",
+      "                                                                  'stack_1_block6_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_1_block7_sortcut_conv (C  (None, 56, 56, 256)  147456     ['add_9[0][0]']                  Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block7_sortcut_bn (Bat  (None, 56, 56, 256)  1024       ['stack_1_block7_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_1_block7_sortcut_swish (  (None, 56, 56, 256)  0          ['stack_1_block7_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_1_block7_MB_pw_conv (Con  (None, 56, 56, 64)  16384       ['stack_1_block7_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_1_block7_MB_pw_bn (Batch  (None, 56, 56, 64)  256         ['stack_1_block7_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_10 (Add)                   (None, 56, 56, 64)   0           ['add_9[0][0]',                  Y          \n",
+      "                                                                  'stack_1_block7_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_2_block0_sortcut_conv (C  (None, 28, 28, 256)  147456     ['add_10[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block0_sortcut_bn (Bat  (None, 28, 28, 256)  1024       ['stack_2_block0_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block0_sortcut_swish (  (None, 28, 28, 256)  0          ['stack_2_block0_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_2_block0_MB_pw_conv (Con  (None, 28, 28, 96)  24576       ['stack_2_block0_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_2_block0_MB_pw_bn (Batch  (None, 28, 28, 96)  384         ['stack_2_block0_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_2_block1_sortcut_conv (C  (None, 28, 28, 384)  331776     ['stack_2_block0_MB_pw_bn[0][0]  Y          \n",
+      " onv2D)                                                          ']                                          \n",
+      "                                                                                                             \n",
+      " stack_2_block1_sortcut_bn (Bat  (None, 28, 28, 384)  1536       ['stack_2_block1_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block1_sortcut_swish (  (None, 28, 28, 384)  0          ['stack_2_block1_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_2_block1_MB_pw_conv (Con  (None, 28, 28, 96)  36864       ['stack_2_block1_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_2_block1_MB_pw_bn (Batch  (None, 28, 28, 96)  384         ['stack_2_block1_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_11 (Add)                   (None, 28, 28, 96)   0           ['stack_2_block0_MB_pw_bn[0][0]  Y          \n",
+      "                                                                 ',                                          \n",
+      "                                                                  'stack_2_block1_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_2_block2_sortcut_conv (C  (None, 28, 28, 384)  331776     ['add_11[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block2_sortcut_bn (Bat  (None, 28, 28, 384)  1536       ['stack_2_block2_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block2_sortcut_swish (  (None, 28, 28, 384)  0          ['stack_2_block2_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_2_block2_MB_pw_conv (Con  (None, 28, 28, 96)  36864       ['stack_2_block2_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_2_block2_MB_pw_bn (Batch  (None, 28, 28, 96)  384         ['stack_2_block2_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_12 (Add)                   (None, 28, 28, 96)   0           ['add_11[0][0]',                 Y          \n",
+      "                                                                  'stack_2_block2_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_2_block3_sortcut_conv (C  (None, 28, 28, 384)  331776     ['add_12[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block3_sortcut_bn (Bat  (None, 28, 28, 384)  1536       ['stack_2_block3_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block3_sortcut_swish (  (None, 28, 28, 384)  0          ['stack_2_block3_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_2_block3_MB_pw_conv (Con  (None, 28, 28, 96)  36864       ['stack_2_block3_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_2_block3_MB_pw_bn (Batch  (None, 28, 28, 96)  384         ['stack_2_block3_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_13 (Add)                   (None, 28, 28, 96)   0           ['add_12[0][0]',                 Y          \n",
+      "                                                                  'stack_2_block3_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_2_block4_sortcut_conv (C  (None, 28, 28, 384)  331776     ['add_13[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block4_sortcut_bn (Bat  (None, 28, 28, 384)  1536       ['stack_2_block4_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block4_sortcut_swish (  (None, 28, 28, 384)  0          ['stack_2_block4_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_2_block4_MB_pw_conv (Con  (None, 28, 28, 96)  36864       ['stack_2_block4_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_2_block4_MB_pw_bn (Batch  (None, 28, 28, 96)  384         ['stack_2_block4_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_14 (Add)                   (None, 28, 28, 96)   0           ['add_13[0][0]',                 Y          \n",
+      "                                                                  'stack_2_block4_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_2_block5_sortcut_conv (C  (None, 28, 28, 384)  331776     ['add_14[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block5_sortcut_bn (Bat  (None, 28, 28, 384)  1536       ['stack_2_block5_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block5_sortcut_swish (  (None, 28, 28, 384)  0          ['stack_2_block5_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_2_block5_MB_pw_conv (Con  (None, 28, 28, 96)  36864       ['stack_2_block5_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_2_block5_MB_pw_bn (Batch  (None, 28, 28, 96)  384         ['stack_2_block5_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_15 (Add)                   (None, 28, 28, 96)   0           ['add_14[0][0]',                 Y          \n",
+      "                                                                  'stack_2_block5_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_2_block6_sortcut_conv (C  (None, 28, 28, 384)  331776     ['add_15[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block6_sortcut_bn (Bat  (None, 28, 28, 384)  1536       ['stack_2_block6_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block6_sortcut_swish (  (None, 28, 28, 384)  0          ['stack_2_block6_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_2_block6_MB_pw_conv (Con  (None, 28, 28, 96)  36864       ['stack_2_block6_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_2_block6_MB_pw_bn (Batch  (None, 28, 28, 96)  384         ['stack_2_block6_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_16 (Add)                   (None, 28, 28, 96)   0           ['add_15[0][0]',                 Y          \n",
+      "                                                                  'stack_2_block6_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_2_block7_sortcut_conv (C  (None, 28, 28, 384)  331776     ['add_16[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block7_sortcut_bn (Bat  (None, 28, 28, 384)  1536       ['stack_2_block7_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_2_block7_sortcut_swish (  (None, 28, 28, 384)  0          ['stack_2_block7_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_2_block7_MB_pw_conv (Con  (None, 28, 28, 96)  36864       ['stack_2_block7_sortcut_swish[  Y          \n",
+      " v2D)                                                            0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_2_block7_MB_pw_bn (Batch  (None, 28, 28, 96)  384         ['stack_2_block7_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_17 (Add)                   (None, 28, 28, 96)   0           ['add_16[0][0]',                 Y          \n",
+      "                                                                  'stack_2_block7_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_3_block0_sortcut_conv (C  (None, 28, 28, 384)  36864      ['add_17[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block0_sortcut_bn (Bat  (None, 28, 28, 384)  1536       ['stack_3_block0_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block0_sortcut_swish (  (None, 28, 28, 384)  0          ['stack_3_block0_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block0_MB_dw_ (Depthwi  (None, 14, 14, 384)  3456       ['stack_3_block0_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block0_MB_dw_bn (Batch  (None, 14, 14, 384)  1536       ['stack_3_block0_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_3_block0_MB_dw_swish (Ac  (None, 14, 14, 384)  0          ['stack_3_block0_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean (TFOpLambd  (None, 1, 1, 384)   0           ['stack_3_block0_MB_dw_swish[0]  Y          \n",
+      " a)                                                              [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block0_se_1_conv (Conv  (None, 1, 1, 24)    9240        ['tf.math.reduce_mean[0][0]']    Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation (Activation)        (None, 1, 1, 24)     0           ['stack_3_block0_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block0_se_2_conv (Conv  (None, 1, 1, 384)   9600        ['activation[0][0]']             Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_1 (Activation)      (None, 1, 1, 384)    0           ['stack_3_block0_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply (Multiply)            (None, 14, 14, 384)  0           ['stack_3_block0_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_1[0][0]']                      \n",
+      "                                                                                                             \n",
+      " stack_3_block0_MB_pw_conv (Con  (None, 14, 14, 192)  73728      ['multiply[0][0]']               Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block0_MB_pw_bn (Batch  (None, 14, 14, 192)  768        ['stack_3_block0_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block1_sortcut_conv (C  (None, 14, 14, 768)  147456     ['stack_3_block0_MB_pw_bn[0][0]  Y          \n",
+      " onv2D)                                                          ']                                          \n",
+      "                                                                                                             \n",
+      " stack_3_block1_sortcut_bn (Bat  (None, 14, 14, 768)  3072       ['stack_3_block1_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block1_sortcut_swish (  (None, 14, 14, 768)  0          ['stack_3_block1_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block1_MB_dw_ (Depthwi  (None, 14, 14, 768)  6912       ['stack_3_block1_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block1_MB_dw_bn (Batch  (None, 14, 14, 768)  3072       ['stack_3_block1_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_3_block1_MB_dw_swish (Ac  (None, 14, 14, 768)  0          ['stack_3_block1_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_1 (TFOpLam  (None, 1, 1, 768)   0           ['stack_3_block1_MB_dw_swish[0]  Y          \n",
+      " bda)                                                            [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block1_se_1_conv (Conv  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_1[0][0]']  Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_2 (Activation)      (None, 1, 1, 48)     0           ['stack_3_block1_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block1_se_2_conv (Conv  (None, 1, 1, 768)   37632       ['activation_2[0][0]']           Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_3 (Activation)      (None, 1, 1, 768)    0           ['stack_3_block1_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_1 (Multiply)          (None, 14, 14, 768)  0           ['stack_3_block1_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_3[0][0]']                      \n",
+      "                                                                                                             \n",
+      " stack_3_block1_MB_pw_conv (Con  (None, 14, 14, 192)  147456     ['multiply_1[0][0]']             Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block1_MB_pw_bn (Batch  (None, 14, 14, 192)  768        ['stack_3_block1_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_18 (Add)                   (None, 14, 14, 192)  0           ['stack_3_block0_MB_pw_bn[0][0]  Y          \n",
+      "                                                                 ',                                          \n",
+      "                                                                  'stack_3_block1_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_3_block2_sortcut_conv (C  (None, 14, 14, 768)  147456     ['add_18[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block2_sortcut_bn (Bat  (None, 14, 14, 768)  3072       ['stack_3_block2_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block2_sortcut_swish (  (None, 14, 14, 768)  0          ['stack_3_block2_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block2_MB_dw_ (Depthwi  (None, 14, 14, 768)  6912       ['stack_3_block2_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block2_MB_dw_bn (Batch  (None, 14, 14, 768)  3072       ['stack_3_block2_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_3_block2_MB_dw_swish (Ac  (None, 14, 14, 768)  0          ['stack_3_block2_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_2 (TFOpLam  (None, 1, 1, 768)   0           ['stack_3_block2_MB_dw_swish[0]  Y          \n",
+      " bda)                                                            [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block2_se_1_conv (Conv  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_2[0][0]']  Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_4 (Activation)      (None, 1, 1, 48)     0           ['stack_3_block2_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block2_se_2_conv (Conv  (None, 1, 1, 768)   37632       ['activation_4[0][0]']           Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_5 (Activation)      (None, 1, 1, 768)    0           ['stack_3_block2_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_2 (Multiply)          (None, 14, 14, 768)  0           ['stack_3_block2_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_5[0][0]']                      \n",
+      "                                                                                                             \n",
+      " stack_3_block2_MB_pw_conv (Con  (None, 14, 14, 192)  147456     ['multiply_2[0][0]']             Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block2_MB_pw_bn (Batch  (None, 14, 14, 192)  768        ['stack_3_block2_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_19 (Add)                   (None, 14, 14, 192)  0           ['add_18[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block2_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_3_block3_sortcut_conv (C  (None, 14, 14, 768)  147456     ['add_19[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block3_sortcut_bn (Bat  (None, 14, 14, 768)  3072       ['stack_3_block3_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block3_sortcut_swish (  (None, 14, 14, 768)  0          ['stack_3_block3_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block3_MB_dw_ (Depthwi  (None, 14, 14, 768)  6912       ['stack_3_block3_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block3_MB_dw_bn (Batch  (None, 14, 14, 768)  3072       ['stack_3_block3_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_3_block3_MB_dw_swish (Ac  (None, 14, 14, 768)  0          ['stack_3_block3_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_3 (TFOpLam  (None, 1, 1, 768)   0           ['stack_3_block3_MB_dw_swish[0]  Y          \n",
+      " bda)                                                            [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block3_se_1_conv (Conv  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_3[0][0]']  Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_6 (Activation)      (None, 1, 1, 48)     0           ['stack_3_block3_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block3_se_2_conv (Conv  (None, 1, 1, 768)   37632       ['activation_6[0][0]']           Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_7 (Activation)      (None, 1, 1, 768)    0           ['stack_3_block3_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_3 (Multiply)          (None, 14, 14, 768)  0           ['stack_3_block3_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_7[0][0]']                      \n",
+      "                                                                                                             \n",
+      " stack_3_block3_MB_pw_conv (Con  (None, 14, 14, 192)  147456     ['multiply_3[0][0]']             Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block3_MB_pw_bn (Batch  (None, 14, 14, 192)  768        ['stack_3_block3_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_20 (Add)                   (None, 14, 14, 192)  0           ['add_19[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block3_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_3_block4_sortcut_conv (C  (None, 14, 14, 768)  147456     ['add_20[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block4_sortcut_bn (Bat  (None, 14, 14, 768)  3072       ['stack_3_block4_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block4_sortcut_swish (  (None, 14, 14, 768)  0          ['stack_3_block4_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block4_MB_dw_ (Depthwi  (None, 14, 14, 768)  6912       ['stack_3_block4_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block4_MB_dw_bn (Batch  (None, 14, 14, 768)  3072       ['stack_3_block4_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_3_block4_MB_dw_swish (Ac  (None, 14, 14, 768)  0          ['stack_3_block4_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_4 (TFOpLam  (None, 1, 1, 768)   0           ['stack_3_block4_MB_dw_swish[0]  Y          \n",
+      " bda)                                                            [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block4_se_1_conv (Conv  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_4[0][0]']  Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_8 (Activation)      (None, 1, 1, 48)     0           ['stack_3_block4_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block4_se_2_conv (Conv  (None, 1, 1, 768)   37632       ['activation_8[0][0]']           Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_9 (Activation)      (None, 1, 1, 768)    0           ['stack_3_block4_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_4 (Multiply)          (None, 14, 14, 768)  0           ['stack_3_block4_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_9[0][0]']                      \n",
+      "                                                                                                             \n",
+      " stack_3_block4_MB_pw_conv (Con  (None, 14, 14, 192)  147456     ['multiply_4[0][0]']             Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block4_MB_pw_bn (Batch  (None, 14, 14, 192)  768        ['stack_3_block4_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_21 (Add)                   (None, 14, 14, 192)  0           ['add_20[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block4_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_3_block5_sortcut_conv (C  (None, 14, 14, 768)  147456     ['add_21[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block5_sortcut_bn (Bat  (None, 14, 14, 768)  3072       ['stack_3_block5_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block5_sortcut_swish (  (None, 14, 14, 768)  0          ['stack_3_block5_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block5_MB_dw_ (Depthwi  (None, 14, 14, 768)  6912       ['stack_3_block5_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block5_MB_dw_bn (Batch  (None, 14, 14, 768)  3072       ['stack_3_block5_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_3_block5_MB_dw_swish (Ac  (None, 14, 14, 768)  0          ['stack_3_block5_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_5 (TFOpLam  (None, 1, 1, 768)   0           ['stack_3_block5_MB_dw_swish[0]  Y          \n",
+      " bda)                                                            [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block5_se_1_conv (Conv  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_5[0][0]']  Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_10 (Activation)     (None, 1, 1, 48)     0           ['stack_3_block5_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block5_se_2_conv (Conv  (None, 1, 1, 768)   37632       ['activation_10[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_11 (Activation)     (None, 1, 1, 768)    0           ['stack_3_block5_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_5 (Multiply)          (None, 14, 14, 768)  0           ['stack_3_block5_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_11[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_3_block5_MB_pw_conv (Con  (None, 14, 14, 192)  147456     ['multiply_5[0][0]']             Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block5_MB_pw_bn (Batch  (None, 14, 14, 192)  768        ['stack_3_block5_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_22 (Add)                   (None, 14, 14, 192)  0           ['add_21[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block5_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_3_block6_sortcut_conv (C  (None, 14, 14, 768)  147456     ['add_22[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block6_sortcut_bn (Bat  (None, 14, 14, 768)  3072       ['stack_3_block6_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block6_sortcut_swish (  (None, 14, 14, 768)  0          ['stack_3_block6_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block6_MB_dw_ (Depthwi  (None, 14, 14, 768)  6912       ['stack_3_block6_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block6_MB_dw_bn (Batch  (None, 14, 14, 768)  3072       ['stack_3_block6_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_3_block6_MB_dw_swish (Ac  (None, 14, 14, 768)  0          ['stack_3_block6_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_6 (TFOpLam  (None, 1, 1, 768)   0           ['stack_3_block6_MB_dw_swish[0]  Y          \n",
+      " bda)                                                            [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block6_se_1_conv (Conv  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_6[0][0]']  Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_12 (Activation)     (None, 1, 1, 48)     0           ['stack_3_block6_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block6_se_2_conv (Conv  (None, 1, 1, 768)   37632       ['activation_12[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_13 (Activation)     (None, 1, 1, 768)    0           ['stack_3_block6_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_6 (Multiply)          (None, 14, 14, 768)  0           ['stack_3_block6_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_13[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_3_block6_MB_pw_conv (Con  (None, 14, 14, 192)  147456     ['multiply_6[0][0]']             Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block6_MB_pw_bn (Batch  (None, 14, 14, 192)  768        ['stack_3_block6_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_23 (Add)                   (None, 14, 14, 192)  0           ['add_22[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block6_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_3_block7_sortcut_conv (C  (None, 14, 14, 768)  147456     ['add_23[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block7_sortcut_bn (Bat  (None, 14, 14, 768)  3072       ['stack_3_block7_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block7_sortcut_swish (  (None, 14, 14, 768)  0          ['stack_3_block7_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block7_MB_dw_ (Depthwi  (None, 14, 14, 768)  6912       ['stack_3_block7_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block7_MB_dw_bn (Batch  (None, 14, 14, 768)  3072       ['stack_3_block7_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_3_block7_MB_dw_swish (Ac  (None, 14, 14, 768)  0          ['stack_3_block7_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_7 (TFOpLam  (None, 1, 1, 768)   0           ['stack_3_block7_MB_dw_swish[0]  Y          \n",
+      " bda)                                                            [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block7_se_1_conv (Conv  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_7[0][0]']  Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_14 (Activation)     (None, 1, 1, 48)     0           ['stack_3_block7_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block7_se_2_conv (Conv  (None, 1, 1, 768)   37632       ['activation_14[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_15 (Activation)     (None, 1, 1, 768)    0           ['stack_3_block7_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_7 (Multiply)          (None, 14, 14, 768)  0           ['stack_3_block7_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_15[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_3_block7_MB_pw_conv (Con  (None, 14, 14, 192)  147456     ['multiply_7[0][0]']             Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block7_MB_pw_bn (Batch  (None, 14, 14, 192)  768        ['stack_3_block7_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_24 (Add)                   (None, 14, 14, 192)  0           ['add_23[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block7_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_3_block8_sortcut_conv (C  (None, 14, 14, 768)  147456     ['add_24[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block8_sortcut_bn (Bat  (None, 14, 14, 768)  3072       ['stack_3_block8_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block8_sortcut_swish (  (None, 14, 14, 768)  0          ['stack_3_block8_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block8_MB_dw_ (Depthwi  (None, 14, 14, 768)  6912       ['stack_3_block8_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block8_MB_dw_bn (Batch  (None, 14, 14, 768)  3072       ['stack_3_block8_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_3_block8_MB_dw_swish (Ac  (None, 14, 14, 768)  0          ['stack_3_block8_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_8 (TFOpLam  (None, 1, 1, 768)   0           ['stack_3_block8_MB_dw_swish[0]  Y          \n",
+      " bda)                                                            [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block8_se_1_conv (Conv  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_8[0][0]']  Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_16 (Activation)     (None, 1, 1, 48)     0           ['stack_3_block8_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block8_se_2_conv (Conv  (None, 1, 1, 768)   37632       ['activation_16[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_17 (Activation)     (None, 1, 1, 768)    0           ['stack_3_block8_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_8 (Multiply)          (None, 14, 14, 768)  0           ['stack_3_block8_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_17[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_3_block8_MB_pw_conv (Con  (None, 14, 14, 192)  147456     ['multiply_8[0][0]']             Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block8_MB_pw_bn (Batch  (None, 14, 14, 192)  768        ['stack_3_block8_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_25 (Add)                   (None, 14, 14, 192)  0           ['add_24[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block8_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_3_block9_sortcut_conv (C  (None, 14, 14, 768)  147456     ['add_25[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block9_sortcut_bn (Bat  (None, 14, 14, 768)  3072       ['stack_3_block9_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block9_sortcut_swish (  (None, 14, 14, 768)  0          ['stack_3_block9_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block9_MB_dw_ (Depthwi  (None, 14, 14, 768)  6912       ['stack_3_block9_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block9_MB_dw_bn (Batch  (None, 14, 14, 768)  3072       ['stack_3_block9_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_3_block9_MB_dw_swish (Ac  (None, 14, 14, 768)  0          ['stack_3_block9_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_9 (TFOpLam  (None, 1, 1, 768)   0           ['stack_3_block9_MB_dw_swish[0]  Y          \n",
+      " bda)                                                            [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block9_se_1_conv (Conv  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_9[0][0]']  Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_18 (Activation)     (None, 1, 1, 48)     0           ['stack_3_block9_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block9_se_2_conv (Conv  (None, 1, 1, 768)   37632       ['activation_18[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_19 (Activation)     (None, 1, 1, 768)    0           ['stack_3_block9_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_9 (Multiply)          (None, 14, 14, 768)  0           ['stack_3_block9_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_19[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_3_block9_MB_pw_conv (Con  (None, 14, 14, 192)  147456     ['multiply_9[0][0]']             Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block9_MB_pw_bn (Batch  (None, 14, 14, 192)  768        ['stack_3_block9_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_26 (Add)                   (None, 14, 14, 192)  0           ['add_25[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block9_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_3_block10_sortcut_conv (  (None, 14, 14, 768)  147456     ['add_26[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block10_sortcut_bn (Ba  (None, 14, 14, 768)  3072       ['stack_3_block10_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block10_sortcut_swish   (None, 14, 14, 768)  0          ['stack_3_block10_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block10_MB_dw_ (Depthw  (None, 14, 14, 768)  6912       ['stack_3_block10_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_3_block10_MB_dw_bn (Batc  (None, 14, 14, 768)  3072       ['stack_3_block10_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_3_block10_MB_dw_swish (A  (None, 14, 14, 768)  0          ['stack_3_block10_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_10 (TFOpLa  (None, 1, 1, 768)   0           ['stack_3_block10_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block10_se_1_conv (Con  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_10[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_20 (Activation)     (None, 1, 1, 48)     0           ['stack_3_block10_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block10_se_2_conv (Con  (None, 1, 1, 768)   37632       ['activation_20[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_21 (Activation)     (None, 1, 1, 768)    0           ['stack_3_block10_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_10 (Multiply)         (None, 14, 14, 768)  0           ['stack_3_block10_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_21[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_3_block10_MB_pw_conv (Co  (None, 14, 14, 192)  147456     ['multiply_10[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block10_MB_pw_bn (Batc  (None, 14, 14, 192)  768        ['stack_3_block10_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_27 (Add)                   (None, 14, 14, 192)  0           ['add_26[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block10_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block11_sortcut_conv (  (None, 14, 14, 768)  147456     ['add_27[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block11_sortcut_bn (Ba  (None, 14, 14, 768)  3072       ['stack_3_block11_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block11_sortcut_swish   (None, 14, 14, 768)  0          ['stack_3_block11_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block11_MB_dw_ (Depthw  (None, 14, 14, 768)  6912       ['stack_3_block11_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_3_block11_MB_dw_bn (Batc  (None, 14, 14, 768)  3072       ['stack_3_block11_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_3_block11_MB_dw_swish (A  (None, 14, 14, 768)  0          ['stack_3_block11_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_11 (TFOpLa  (None, 1, 1, 768)   0           ['stack_3_block11_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block11_se_1_conv (Con  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_11[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_22 (Activation)     (None, 1, 1, 48)     0           ['stack_3_block11_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block11_se_2_conv (Con  (None, 1, 1, 768)   37632       ['activation_22[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_23 (Activation)     (None, 1, 1, 768)    0           ['stack_3_block11_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_11 (Multiply)         (None, 14, 14, 768)  0           ['stack_3_block11_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_23[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_3_block11_MB_pw_conv (Co  (None, 14, 14, 192)  147456     ['multiply_11[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block11_MB_pw_bn (Batc  (None, 14, 14, 192)  768        ['stack_3_block11_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_28 (Add)                   (None, 14, 14, 192)  0           ['add_27[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block11_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block12_sortcut_conv (  (None, 14, 14, 768)  147456     ['add_28[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block12_sortcut_bn (Ba  (None, 14, 14, 768)  3072       ['stack_3_block12_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block12_sortcut_swish   (None, 14, 14, 768)  0          ['stack_3_block12_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block12_MB_dw_ (Depthw  (None, 14, 14, 768)  6912       ['stack_3_block12_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_3_block12_MB_dw_bn (Batc  (None, 14, 14, 768)  3072       ['stack_3_block12_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_3_block12_MB_dw_swish (A  (None, 14, 14, 768)  0          ['stack_3_block12_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_12 (TFOpLa  (None, 1, 1, 768)   0           ['stack_3_block12_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block12_se_1_conv (Con  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_12[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_24 (Activation)     (None, 1, 1, 48)     0           ['stack_3_block12_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block12_se_2_conv (Con  (None, 1, 1, 768)   37632       ['activation_24[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_25 (Activation)     (None, 1, 1, 768)    0           ['stack_3_block12_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_12 (Multiply)         (None, 14, 14, 768)  0           ['stack_3_block12_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_25[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_3_block12_MB_pw_conv (Co  (None, 14, 14, 192)  147456     ['multiply_12[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block12_MB_pw_bn (Batc  (None, 14, 14, 192)  768        ['stack_3_block12_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_29 (Add)                   (None, 14, 14, 192)  0           ['add_28[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block12_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block13_sortcut_conv (  (None, 14, 14, 768)  147456     ['add_29[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block13_sortcut_bn (Ba  (None, 14, 14, 768)  3072       ['stack_3_block13_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block13_sortcut_swish   (None, 14, 14, 768)  0          ['stack_3_block13_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block13_MB_dw_ (Depthw  (None, 14, 14, 768)  6912       ['stack_3_block13_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_3_block13_MB_dw_bn (Batc  (None, 14, 14, 768)  3072       ['stack_3_block13_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_3_block13_MB_dw_swish (A  (None, 14, 14, 768)  0          ['stack_3_block13_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_13 (TFOpLa  (None, 1, 1, 768)   0           ['stack_3_block13_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block13_se_1_conv (Con  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_13[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_26 (Activation)     (None, 1, 1, 48)     0           ['stack_3_block13_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block13_se_2_conv (Con  (None, 1, 1, 768)   37632       ['activation_26[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_27 (Activation)     (None, 1, 1, 768)    0           ['stack_3_block13_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_13 (Multiply)         (None, 14, 14, 768)  0           ['stack_3_block13_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_27[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_3_block13_MB_pw_conv (Co  (None, 14, 14, 192)  147456     ['multiply_13[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block13_MB_pw_bn (Batc  (None, 14, 14, 192)  768        ['stack_3_block13_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_30 (Add)                   (None, 14, 14, 192)  0           ['add_29[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block13_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block14_sortcut_conv (  (None, 14, 14, 768)  147456     ['add_30[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block14_sortcut_bn (Ba  (None, 14, 14, 768)  3072       ['stack_3_block14_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block14_sortcut_swish   (None, 14, 14, 768)  0          ['stack_3_block14_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block14_MB_dw_ (Depthw  (None, 14, 14, 768)  6912       ['stack_3_block14_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_3_block14_MB_dw_bn (Batc  (None, 14, 14, 768)  3072       ['stack_3_block14_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_3_block14_MB_dw_swish (A  (None, 14, 14, 768)  0          ['stack_3_block14_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_14 (TFOpLa  (None, 1, 1, 768)   0           ['stack_3_block14_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block14_se_1_conv (Con  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_14[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_28 (Activation)     (None, 1, 1, 48)     0           ['stack_3_block14_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block14_se_2_conv (Con  (None, 1, 1, 768)   37632       ['activation_28[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_29 (Activation)     (None, 1, 1, 768)    0           ['stack_3_block14_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_14 (Multiply)         (None, 14, 14, 768)  0           ['stack_3_block14_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_29[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_3_block14_MB_pw_conv (Co  (None, 14, 14, 192)  147456     ['multiply_14[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block14_MB_pw_bn (Batc  (None, 14, 14, 192)  768        ['stack_3_block14_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_31 (Add)                   (None, 14, 14, 192)  0           ['add_30[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block14_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_3_block15_sortcut_conv (  (None, 14, 14, 768)  147456     ['add_31[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block15_sortcut_bn (Ba  (None, 14, 14, 768)  3072       ['stack_3_block15_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_3_block15_sortcut_swish   (None, 14, 14, 768)  0          ['stack_3_block15_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block15_MB_dw_ (Depthw  (None, 14, 14, 768)  6912       ['stack_3_block15_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_3_block15_MB_dw_bn (Batc  (None, 14, 14, 768)  3072       ['stack_3_block15_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_3_block15_MB_dw_swish (A  (None, 14, 14, 768)  0          ['stack_3_block15_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_15 (TFOpLa  (None, 1, 1, 768)   0           ['stack_3_block15_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_3_block15_se_1_conv (Con  (None, 1, 1, 48)    36912       ['tf.math.reduce_mean_15[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_30 (Activation)     (None, 1, 1, 48)     0           ['stack_3_block15_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_3_block15_se_2_conv (Con  (None, 1, 1, 768)   37632       ['activation_30[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_31 (Activation)     (None, 1, 1, 768)    0           ['stack_3_block15_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_15 (Multiply)         (None, 14, 14, 768)  0           ['stack_3_block15_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_31[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_3_block15_MB_pw_conv (Co  (None, 14, 14, 192)  147456     ['multiply_15[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_3_block15_MB_pw_bn (Batc  (None, 14, 14, 192)  768        ['stack_3_block15_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_32 (Add)                   (None, 14, 14, 192)  0           ['add_31[0][0]',                 Y          \n",
+      "                                                                  'stack_3_block15_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block0_sortcut_conv (C  (None, 14, 14, 1152  221184     ['add_32[0][0]']                 Y          \n",
+      " onv2D)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block0_sortcut_bn (Bat  (None, 14, 14, 1152  4608       ['stack_4_block0_sortcut_conv[0  Y          \n",
+      " chNormalization)               )                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block0_sortcut_swish (  (None, 14, 14, 1152  0          ['stack_4_block0_sortcut_bn[0][  Y          \n",
+      " Activation)                    )                                0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block0_MB_dw_ (Depthwi  (None, 14, 14, 1152  10368      ['stack_4_block0_sortcut_swish[  Y          \n",
+      " seConv2D)                      )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block0_MB_dw_bn (Batch  (None, 14, 14, 1152  4608       ['stack_4_block0_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                 )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block0_MB_dw_swish (Ac  (None, 14, 14, 1152  0          ['stack_4_block0_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                      )                                ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_16 (TFOpLa  (None, 1, 1, 1152)  0           ['stack_4_block0_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block0_se_1_conv (Conv  (None, 1, 1, 48)    55344       ['tf.math.reduce_mean_16[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_32 (Activation)     (None, 1, 1, 48)     0           ['stack_4_block0_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block0_se_2_conv (Conv  (None, 1, 1, 1152)  56448       ['activation_32[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_33 (Activation)     (None, 1, 1, 1152)   0           ['stack_4_block0_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_16 (Multiply)         (None, 14, 14, 1152  0           ['stack_4_block0_MB_dw_swish[0]  Y          \n",
+      "                                )                                [0]',                                       \n",
+      "                                                                  'activation_33[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block0_MB_pw_conv (Con  (None, 14, 14, 256)  294912     ['multiply_16[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block0_MB_pw_bn (Batch  (None, 14, 14, 256)  1024       ['stack_4_block0_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block1_sortcut_conv (C  (None, 14, 14, 1536  393216     ['stack_4_block0_MB_pw_bn[0][0]  Y          \n",
+      " onv2D)                         )                                ']                                          \n",
+      "                                                                                                             \n",
+      " stack_4_block1_sortcut_bn (Bat  (None, 14, 14, 1536  6144       ['stack_4_block1_sortcut_conv[0  Y          \n",
+      " chNormalization)               )                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block1_sortcut_swish (  (None, 14, 14, 1536  0          ['stack_4_block1_sortcut_bn[0][  Y          \n",
+      " Activation)                    )                                0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block1_MB_dw_ (Depthwi  (None, 14, 14, 1536  13824      ['stack_4_block1_sortcut_swish[  Y          \n",
+      " seConv2D)                      )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block1_MB_dw_bn (Batch  (None, 14, 14, 1536  6144       ['stack_4_block1_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                 )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block1_MB_dw_swish (Ac  (None, 14, 14, 1536  0          ['stack_4_block1_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                      )                                ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_17 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block1_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block1_se_1_conv (Conv  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_17[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_34 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block1_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block1_se_2_conv (Conv  (None, 1, 1, 1536)  99840       ['activation_34[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_35 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block1_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_17 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block1_MB_dw_swish[0]  Y          \n",
+      "                                )                                [0]',                                       \n",
+      "                                                                  'activation_35[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block1_MB_pw_conv (Con  (None, 14, 14, 256)  393216     ['multiply_17[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block1_MB_pw_bn (Batch  (None, 14, 14, 256)  1024       ['stack_4_block1_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_33 (Add)                   (None, 14, 14, 256)  0           ['stack_4_block0_MB_pw_bn[0][0]  Y          \n",
+      "                                                                 ',                                          \n",
+      "                                                                  'stack_4_block1_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_4_block2_sortcut_conv (C  (None, 14, 14, 1536  393216     ['add_33[0][0]']                 Y          \n",
+      " onv2D)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block2_sortcut_bn (Bat  (None, 14, 14, 1536  6144       ['stack_4_block2_sortcut_conv[0  Y          \n",
+      " chNormalization)               )                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block2_sortcut_swish (  (None, 14, 14, 1536  0          ['stack_4_block2_sortcut_bn[0][  Y          \n",
+      " Activation)                    )                                0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block2_MB_dw_ (Depthwi  (None, 14, 14, 1536  13824      ['stack_4_block2_sortcut_swish[  Y          \n",
+      " seConv2D)                      )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block2_MB_dw_bn (Batch  (None, 14, 14, 1536  6144       ['stack_4_block2_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                 )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block2_MB_dw_swish (Ac  (None, 14, 14, 1536  0          ['stack_4_block2_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                      )                                ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_18 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block2_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block2_se_1_conv (Conv  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_18[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_36 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block2_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block2_se_2_conv (Conv  (None, 1, 1, 1536)  99840       ['activation_36[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_37 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block2_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_18 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block2_MB_dw_swish[0]  Y          \n",
+      "                                )                                [0]',                                       \n",
+      "                                                                  'activation_37[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block2_MB_pw_conv (Con  (None, 14, 14, 256)  393216     ['multiply_18[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block2_MB_pw_bn (Batch  (None, 14, 14, 256)  1024       ['stack_4_block2_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_34 (Add)                   (None, 14, 14, 256)  0           ['add_33[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block2_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_4_block3_sortcut_conv (C  (None, 14, 14, 1536  393216     ['add_34[0][0]']                 Y          \n",
+      " onv2D)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block3_sortcut_bn (Bat  (None, 14, 14, 1536  6144       ['stack_4_block3_sortcut_conv[0  Y          \n",
+      " chNormalization)               )                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block3_sortcut_swish (  (None, 14, 14, 1536  0          ['stack_4_block3_sortcut_bn[0][  Y          \n",
+      " Activation)                    )                                0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block3_MB_dw_ (Depthwi  (None, 14, 14, 1536  13824      ['stack_4_block3_sortcut_swish[  Y          \n",
+      " seConv2D)                      )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block3_MB_dw_bn (Batch  (None, 14, 14, 1536  6144       ['stack_4_block3_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                 )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block3_MB_dw_swish (Ac  (None, 14, 14, 1536  0          ['stack_4_block3_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                      )                                ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_19 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block3_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block3_se_1_conv (Conv  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_19[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_38 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block3_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block3_se_2_conv (Conv  (None, 1, 1, 1536)  99840       ['activation_38[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_39 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block3_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_19 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block3_MB_dw_swish[0]  Y          \n",
+      "                                )                                [0]',                                       \n",
+      "                                                                  'activation_39[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block3_MB_pw_conv (Con  (None, 14, 14, 256)  393216     ['multiply_19[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block3_MB_pw_bn (Batch  (None, 14, 14, 256)  1024       ['stack_4_block3_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_35 (Add)                   (None, 14, 14, 256)  0           ['add_34[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block3_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_4_block4_sortcut_conv (C  (None, 14, 14, 1536  393216     ['add_35[0][0]']                 Y          \n",
+      " onv2D)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block4_sortcut_bn (Bat  (None, 14, 14, 1536  6144       ['stack_4_block4_sortcut_conv[0  Y          \n",
+      " chNormalization)               )                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block4_sortcut_swish (  (None, 14, 14, 1536  0          ['stack_4_block4_sortcut_bn[0][  Y          \n",
+      " Activation)                    )                                0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block4_MB_dw_ (Depthwi  (None, 14, 14, 1536  13824      ['stack_4_block4_sortcut_swish[  Y          \n",
+      " seConv2D)                      )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block4_MB_dw_bn (Batch  (None, 14, 14, 1536  6144       ['stack_4_block4_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                 )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block4_MB_dw_swish (Ac  (None, 14, 14, 1536  0          ['stack_4_block4_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                      )                                ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_20 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block4_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block4_se_1_conv (Conv  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_20[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_40 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block4_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block4_se_2_conv (Conv  (None, 1, 1, 1536)  99840       ['activation_40[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_41 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block4_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_20 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block4_MB_dw_swish[0]  Y          \n",
+      "                                )                                [0]',                                       \n",
+      "                                                                  'activation_41[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block4_MB_pw_conv (Con  (None, 14, 14, 256)  393216     ['multiply_20[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block4_MB_pw_bn (Batch  (None, 14, 14, 256)  1024       ['stack_4_block4_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_36 (Add)                   (None, 14, 14, 256)  0           ['add_35[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block4_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_4_block5_sortcut_conv (C  (None, 14, 14, 1536  393216     ['add_36[0][0]']                 Y          \n",
+      " onv2D)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block5_sortcut_bn (Bat  (None, 14, 14, 1536  6144       ['stack_4_block5_sortcut_conv[0  Y          \n",
+      " chNormalization)               )                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block5_sortcut_swish (  (None, 14, 14, 1536  0          ['stack_4_block5_sortcut_bn[0][  Y          \n",
+      " Activation)                    )                                0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block5_MB_dw_ (Depthwi  (None, 14, 14, 1536  13824      ['stack_4_block5_sortcut_swish[  Y          \n",
+      " seConv2D)                      )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block5_MB_dw_bn (Batch  (None, 14, 14, 1536  6144       ['stack_4_block5_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                 )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block5_MB_dw_swish (Ac  (None, 14, 14, 1536  0          ['stack_4_block5_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                      )                                ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_21 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block5_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block5_se_1_conv (Conv  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_21[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_42 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block5_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block5_se_2_conv (Conv  (None, 1, 1, 1536)  99840       ['activation_42[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_43 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block5_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_21 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block5_MB_dw_swish[0]  Y          \n",
+      "                                )                                [0]',                                       \n",
+      "                                                                  'activation_43[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block5_MB_pw_conv (Con  (None, 14, 14, 256)  393216     ['multiply_21[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block5_MB_pw_bn (Batch  (None, 14, 14, 256)  1024       ['stack_4_block5_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_37 (Add)                   (None, 14, 14, 256)  0           ['add_36[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block5_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_4_block6_sortcut_conv (C  (None, 14, 14, 1536  393216     ['add_37[0][0]']                 Y          \n",
+      " onv2D)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block6_sortcut_bn (Bat  (None, 14, 14, 1536  6144       ['stack_4_block6_sortcut_conv[0  Y          \n",
+      " chNormalization)               )                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block6_sortcut_swish (  (None, 14, 14, 1536  0          ['stack_4_block6_sortcut_bn[0][  Y          \n",
+      " Activation)                    )                                0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block6_MB_dw_ (Depthwi  (None, 14, 14, 1536  13824      ['stack_4_block6_sortcut_swish[  Y          \n",
+      " seConv2D)                      )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block6_MB_dw_bn (Batch  (None, 14, 14, 1536  6144       ['stack_4_block6_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                 )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block6_MB_dw_swish (Ac  (None, 14, 14, 1536  0          ['stack_4_block6_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                      )                                ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_22 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block6_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block6_se_1_conv (Conv  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_22[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_44 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block6_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block6_se_2_conv (Conv  (None, 1, 1, 1536)  99840       ['activation_44[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_45 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block6_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_22 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block6_MB_dw_swish[0]  Y          \n",
+      "                                )                                [0]',                                       \n",
+      "                                                                  'activation_45[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block6_MB_pw_conv (Con  (None, 14, 14, 256)  393216     ['multiply_22[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block6_MB_pw_bn (Batch  (None, 14, 14, 256)  1024       ['stack_4_block6_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_38 (Add)                   (None, 14, 14, 256)  0           ['add_37[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block6_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_4_block7_sortcut_conv (C  (None, 14, 14, 1536  393216     ['add_38[0][0]']                 Y          \n",
+      " onv2D)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block7_sortcut_bn (Bat  (None, 14, 14, 1536  6144       ['stack_4_block7_sortcut_conv[0  Y          \n",
+      " chNormalization)               )                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block7_sortcut_swish (  (None, 14, 14, 1536  0          ['stack_4_block7_sortcut_bn[0][  Y          \n",
+      " Activation)                    )                                0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block7_MB_dw_ (Depthwi  (None, 14, 14, 1536  13824      ['stack_4_block7_sortcut_swish[  Y          \n",
+      " seConv2D)                      )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block7_MB_dw_bn (Batch  (None, 14, 14, 1536  6144       ['stack_4_block7_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                 )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block7_MB_dw_swish (Ac  (None, 14, 14, 1536  0          ['stack_4_block7_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                      )                                ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_23 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block7_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block7_se_1_conv (Conv  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_23[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_46 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block7_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block7_se_2_conv (Conv  (None, 1, 1, 1536)  99840       ['activation_46[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_47 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block7_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_23 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block7_MB_dw_swish[0]  Y          \n",
+      "                                )                                [0]',                                       \n",
+      "                                                                  'activation_47[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block7_MB_pw_conv (Con  (None, 14, 14, 256)  393216     ['multiply_23[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block7_MB_pw_bn (Batch  (None, 14, 14, 256)  1024       ['stack_4_block7_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_39 (Add)                   (None, 14, 14, 256)  0           ['add_38[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block7_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_4_block8_sortcut_conv (C  (None, 14, 14, 1536  393216     ['add_39[0][0]']                 Y          \n",
+      " onv2D)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block8_sortcut_bn (Bat  (None, 14, 14, 1536  6144       ['stack_4_block8_sortcut_conv[0  Y          \n",
+      " chNormalization)               )                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block8_sortcut_swish (  (None, 14, 14, 1536  0          ['stack_4_block8_sortcut_bn[0][  Y          \n",
+      " Activation)                    )                                0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block8_MB_dw_ (Depthwi  (None, 14, 14, 1536  13824      ['stack_4_block8_sortcut_swish[  Y          \n",
+      " seConv2D)                      )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block8_MB_dw_bn (Batch  (None, 14, 14, 1536  6144       ['stack_4_block8_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                 )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block8_MB_dw_swish (Ac  (None, 14, 14, 1536  0          ['stack_4_block8_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                      )                                ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_24 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block8_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block8_se_1_conv (Conv  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_24[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_48 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block8_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block8_se_2_conv (Conv  (None, 1, 1, 1536)  99840       ['activation_48[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_49 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block8_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_24 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block8_MB_dw_swish[0]  Y          \n",
+      "                                )                                [0]',                                       \n",
+      "                                                                  'activation_49[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block8_MB_pw_conv (Con  (None, 14, 14, 256)  393216     ['multiply_24[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block8_MB_pw_bn (Batch  (None, 14, 14, 256)  1024       ['stack_4_block8_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_40 (Add)                   (None, 14, 14, 256)  0           ['add_39[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block8_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_4_block9_sortcut_conv (C  (None, 14, 14, 1536  393216     ['add_40[0][0]']                 Y          \n",
+      " onv2D)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block9_sortcut_bn (Bat  (None, 14, 14, 1536  6144       ['stack_4_block9_sortcut_conv[0  Y          \n",
+      " chNormalization)               )                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block9_sortcut_swish (  (None, 14, 14, 1536  0          ['stack_4_block9_sortcut_bn[0][  Y          \n",
+      " Activation)                    )                                0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block9_MB_dw_ (Depthwi  (None, 14, 14, 1536  13824      ['stack_4_block9_sortcut_swish[  Y          \n",
+      " seConv2D)                      )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block9_MB_dw_bn (Batch  (None, 14, 14, 1536  6144       ['stack_4_block9_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                 )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block9_MB_dw_swish (Ac  (None, 14, 14, 1536  0          ['stack_4_block9_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                      )                                ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_25 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block9_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block9_se_1_conv (Conv  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_25[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_50 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block9_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block9_se_2_conv (Conv  (None, 1, 1, 1536)  99840       ['activation_50[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_51 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block9_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_25 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block9_MB_dw_swish[0]  Y          \n",
+      "                                )                                [0]',                                       \n",
+      "                                                                  'activation_51[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block9_MB_pw_conv (Con  (None, 14, 14, 256)  393216     ['multiply_25[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block9_MB_pw_bn (Batch  (None, 14, 14, 256)  1024       ['stack_4_block9_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_41 (Add)                   (None, 14, 14, 256)  0           ['add_40[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block9_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_4_block10_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_41[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block10_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block10_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block10_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block10_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block10_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block10_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block10_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block10_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block10_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block10_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_26 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block10_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block10_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_26[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_52 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block10_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block10_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_52[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_53 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block10_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_26 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block10_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_53[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block10_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_26[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block10_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block10_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_42 (Add)                   (None, 14, 14, 256)  0           ['add_41[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block10_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block11_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_42[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block11_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block11_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block11_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block11_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block11_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block11_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block11_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block11_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block11_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block11_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_27 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block11_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block11_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_27[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_54 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block11_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block11_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_54[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_55 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block11_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_27 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block11_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_55[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block11_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_27[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block11_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block11_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_43 (Add)                   (None, 14, 14, 256)  0           ['add_42[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block11_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block12_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_43[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block12_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block12_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block12_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block12_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block12_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block12_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block12_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block12_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block12_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block12_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_28 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block12_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block12_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_28[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_56 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block12_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block12_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_56[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_57 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block12_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_28 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block12_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_57[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block12_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_28[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block12_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block12_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_44 (Add)                   (None, 14, 14, 256)  0           ['add_43[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block12_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block13_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_44[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block13_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block13_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block13_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block13_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block13_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block13_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block13_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block13_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block13_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block13_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_29 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block13_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block13_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_29[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_58 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block13_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block13_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_58[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_59 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block13_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_29 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block13_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_59[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block13_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_29[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block13_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block13_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_45 (Add)                   (None, 14, 14, 256)  0           ['add_44[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block13_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block14_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_45[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block14_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block14_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block14_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block14_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block14_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block14_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block14_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block14_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block14_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block14_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_30 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block14_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block14_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_30[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_60 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block14_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block14_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_60[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_61 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block14_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_30 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block14_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_61[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block14_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_30[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block14_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block14_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_46 (Add)                   (None, 14, 14, 256)  0           ['add_45[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block14_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block15_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_46[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block15_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block15_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block15_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block15_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block15_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block15_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block15_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block15_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block15_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block15_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_31 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block15_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block15_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_31[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_62 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block15_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block15_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_62[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_63 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block15_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_31 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block15_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_63[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block15_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_31[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block15_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block15_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_47 (Add)                   (None, 14, 14, 256)  0           ['add_46[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block15_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block16_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_47[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block16_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block16_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block16_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block16_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block16_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block16_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block16_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block16_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block16_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block16_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_32 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block16_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block16_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_32[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_64 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block16_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block16_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_64[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_65 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block16_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_32 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block16_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_65[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block16_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_32[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block16_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block16_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_48 (Add)                   (None, 14, 14, 256)  0           ['add_47[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block16_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block17_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_48[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block17_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block17_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block17_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block17_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block17_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block17_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block17_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block17_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block17_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block17_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_33 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block17_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block17_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_33[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_66 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block17_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block17_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_66[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_67 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block17_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_33 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block17_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_67[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block17_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_33[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block17_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block17_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_49 (Add)                   (None, 14, 14, 256)  0           ['add_48[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block17_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block18_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_49[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block18_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block18_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block18_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block18_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block18_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block18_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block18_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block18_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block18_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block18_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_34 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block18_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block18_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_34[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_68 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block18_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block18_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_68[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_69 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block18_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_34 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block18_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_69[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block18_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_34[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block18_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block18_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_50 (Add)                   (None, 14, 14, 256)  0           ['add_49[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block18_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block19_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_50[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block19_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block19_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block19_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block19_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block19_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block19_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block19_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block19_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block19_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block19_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_35 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block19_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block19_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_35[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_70 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block19_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block19_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_70[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_71 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block19_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_35 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block19_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_71[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block19_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_35[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block19_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block19_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_51 (Add)                   (None, 14, 14, 256)  0           ['add_50[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block19_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block20_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_51[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block20_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block20_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block20_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block20_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block20_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block20_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block20_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block20_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block20_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block20_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_36 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block20_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block20_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_36[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_72 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block20_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block20_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_72[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_73 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block20_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_36 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block20_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_73[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block20_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_36[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block20_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block20_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_52 (Add)                   (None, 14, 14, 256)  0           ['add_51[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block20_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block21_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_52[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block21_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block21_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block21_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block21_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block21_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block21_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block21_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block21_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block21_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block21_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_37 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block21_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block21_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_37[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_74 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block21_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block21_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_74[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_75 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block21_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_37 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block21_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_75[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block21_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_37[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block21_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block21_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_53 (Add)                   (None, 14, 14, 256)  0           ['add_52[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block21_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block22_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_53[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block22_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block22_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block22_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block22_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block22_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block22_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block22_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block22_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block22_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block22_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_38 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block22_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block22_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_38[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_76 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block22_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block22_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_76[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_77 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block22_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_38 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block22_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_77[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block22_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_38[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block22_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block22_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_54 (Add)                   (None, 14, 14, 256)  0           ['add_53[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block22_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_4_block23_sortcut_conv (  (None, 14, 14, 1536  393216     ['add_54[0][0]']                 Y          \n",
+      " Conv2D)                        )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_4_block23_sortcut_bn (Ba  (None, 14, 14, 1536  6144       ['stack_4_block23_sortcut_conv[  Y          \n",
+      " tchNormalization)              )                                0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_4_block23_sortcut_swish   (None, 14, 14, 1536  0          ['stack_4_block23_sortcut_bn[0]  Y          \n",
+      " (Activation)                   )                                [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block23_MB_dw_ (Depthw  (None, 14, 14, 1536  13824      ['stack_4_block23_sortcut_swish  Y          \n",
+      " iseConv2D)                     )                                [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_4_block23_MB_dw_bn (Batc  (None, 14, 14, 1536  6144       ['stack_4_block23_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                )                                ]                                           \n",
+      "                                                                                                             \n",
+      " stack_4_block23_MB_dw_swish (A  (None, 14, 14, 1536  0          ['stack_4_block23_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                     )                                ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_39 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_4_block23_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_4_block23_se_1_conv (Con  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_39[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_78 (Activation)     (None, 1, 1, 64)     0           ['stack_4_block23_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_4_block23_se_2_conv (Con  (None, 1, 1, 1536)  99840       ['activation_78[0][0]']          Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_79 (Activation)     (None, 1, 1, 1536)   0           ['stack_4_block23_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_39 (Multiply)         (None, 14, 14, 1536  0           ['stack_4_block23_MB_dw_swish[0  Y          \n",
+      "                                )                                ][0]',                                      \n",
+      "                                                                  'activation_79[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_4_block23_MB_pw_conv (Co  (None, 14, 14, 256)  393216     ['multiply_39[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_4_block23_MB_pw_bn (Batc  (None, 14, 14, 256)  1024       ['stack_4_block23_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_55 (Add)                   (None, 14, 14, 256)  0           ['add_54[0][0]',                 Y          \n",
+      "                                                                  'stack_4_block23_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block0_sortcut_conv (C  (None, 14, 14, 1536  393216     ['add_55[0][0]']                 Y          \n",
+      " onv2D)                         )                                                                            \n",
+      "                                                                                                             \n",
+      " stack_5_block0_sortcut_bn (Bat  (None, 14, 14, 1536  6144       ['stack_5_block0_sortcut_conv[0  Y          \n",
+      " chNormalization)               )                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block0_sortcut_swish (  (None, 14, 14, 1536  0          ['stack_5_block0_sortcut_bn[0][  Y          \n",
+      " Activation)                    )                                0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block0_MB_dw_ (Depthwi  (None, 7, 7, 1536)  13824       ['stack_5_block0_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block0_MB_dw_bn (Batch  (None, 7, 7, 1536)  6144        ['stack_5_block0_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_5_block0_MB_dw_swish (Ac  (None, 7, 7, 1536)  0           ['stack_5_block0_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_40 (TFOpLa  (None, 1, 1, 1536)  0           ['stack_5_block0_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block0_se_1_conv (Conv  (None, 1, 1, 64)    98368       ['tf.math.reduce_mean_40[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_80 (Activation)     (None, 1, 1, 64)     0           ['stack_5_block0_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block0_se_2_conv (Conv  (None, 1, 1, 1536)  99840       ['activation_80[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_81 (Activation)     (None, 1, 1, 1536)   0           ['stack_5_block0_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_40 (Multiply)         (None, 7, 7, 1536)   0           ['stack_5_block0_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_81[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_5_block0_MB_pw_conv (Con  (None, 7, 7, 512)   786432      ['multiply_40[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block0_MB_pw_bn (Batch  (None, 7, 7, 512)   2048        ['stack_5_block0_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block1_sortcut_conv (C  (None, 7, 7, 3072)  1572864     ['stack_5_block0_MB_pw_bn[0][0]  Y          \n",
+      " onv2D)                                                          ']                                          \n",
+      "                                                                                                             \n",
+      " stack_5_block1_sortcut_bn (Bat  (None, 7, 7, 3072)  12288       ['stack_5_block1_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block1_sortcut_swish (  (None, 7, 7, 3072)  0           ['stack_5_block1_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block1_MB_dw_ (Depthwi  (None, 7, 7, 3072)  27648       ['stack_5_block1_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block1_MB_dw_bn (Batch  (None, 7, 7, 3072)  12288       ['stack_5_block1_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_5_block1_MB_dw_swish (Ac  (None, 7, 7, 3072)  0           ['stack_5_block1_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_41 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block1_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block1_se_1_conv (Conv  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_41[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_82 (Activation)     (None, 1, 1, 128)    0           ['stack_5_block1_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block1_se_2_conv (Conv  (None, 1, 1, 3072)  396288      ['activation_82[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_83 (Activation)     (None, 1, 1, 3072)   0           ['stack_5_block1_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_41 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block1_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_83[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_5_block1_MB_pw_conv (Con  (None, 7, 7, 512)   1572864     ['multiply_41[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block1_MB_pw_bn (Batch  (None, 7, 7, 512)   2048        ['stack_5_block1_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_56 (Add)                   (None, 7, 7, 512)    0           ['stack_5_block0_MB_pw_bn[0][0]  Y          \n",
+      "                                                                 ',                                          \n",
+      "                                                                  'stack_5_block1_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_5_block2_sortcut_conv (C  (None, 7, 7, 3072)  1572864     ['add_56[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block2_sortcut_bn (Bat  (None, 7, 7, 3072)  12288       ['stack_5_block2_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block2_sortcut_swish (  (None, 7, 7, 3072)  0           ['stack_5_block2_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block2_MB_dw_ (Depthwi  (None, 7, 7, 3072)  27648       ['stack_5_block2_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block2_MB_dw_bn (Batch  (None, 7, 7, 3072)  12288       ['stack_5_block2_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_5_block2_MB_dw_swish (Ac  (None, 7, 7, 3072)  0           ['stack_5_block2_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_42 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block2_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block2_se_1_conv (Conv  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_42[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_84 (Activation)     (None, 1, 1, 128)    0           ['stack_5_block2_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block2_se_2_conv (Conv  (None, 1, 1, 3072)  396288      ['activation_84[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_85 (Activation)     (None, 1, 1, 3072)   0           ['stack_5_block2_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_42 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block2_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_85[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_5_block2_MB_pw_conv (Con  (None, 7, 7, 512)   1572864     ['multiply_42[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block2_MB_pw_bn (Batch  (None, 7, 7, 512)   2048        ['stack_5_block2_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_57 (Add)                   (None, 7, 7, 512)    0           ['add_56[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block2_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_5_block3_sortcut_conv (C  (None, 7, 7, 3072)  1572864     ['add_57[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block3_sortcut_bn (Bat  (None, 7, 7, 3072)  12288       ['stack_5_block3_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block3_sortcut_swish (  (None, 7, 7, 3072)  0           ['stack_5_block3_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block3_MB_dw_ (Depthwi  (None, 7, 7, 3072)  27648       ['stack_5_block3_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block3_MB_dw_bn (Batch  (None, 7, 7, 3072)  12288       ['stack_5_block3_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_5_block3_MB_dw_swish (Ac  (None, 7, 7, 3072)  0           ['stack_5_block3_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_43 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block3_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block3_se_1_conv (Conv  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_43[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_86 (Activation)     (None, 1, 1, 128)    0           ['stack_5_block3_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block3_se_2_conv (Conv  (None, 1, 1, 3072)  396288      ['activation_86[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_87 (Activation)     (None, 1, 1, 3072)   0           ['stack_5_block3_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_43 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block3_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_87[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_5_block3_MB_pw_conv (Con  (None, 7, 7, 512)   1572864     ['multiply_43[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block3_MB_pw_bn (Batch  (None, 7, 7, 512)   2048        ['stack_5_block3_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_58 (Add)                   (None, 7, 7, 512)    0           ['add_57[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block3_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_5_block4_sortcut_conv (C  (None, 7, 7, 3072)  1572864     ['add_58[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block4_sortcut_bn (Bat  (None, 7, 7, 3072)  12288       ['stack_5_block4_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block4_sortcut_swish (  (None, 7, 7, 3072)  0           ['stack_5_block4_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block4_MB_dw_ (Depthwi  (None, 7, 7, 3072)  27648       ['stack_5_block4_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block4_MB_dw_bn (Batch  (None, 7, 7, 3072)  12288       ['stack_5_block4_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_5_block4_MB_dw_swish (Ac  (None, 7, 7, 3072)  0           ['stack_5_block4_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_44 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block4_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block4_se_1_conv (Conv  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_44[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_88 (Activation)     (None, 1, 1, 128)    0           ['stack_5_block4_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block4_se_2_conv (Conv  (None, 1, 1, 3072)  396288      ['activation_88[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_89 (Activation)     (None, 1, 1, 3072)   0           ['stack_5_block4_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_44 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block4_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_89[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_5_block4_MB_pw_conv (Con  (None, 7, 7, 512)   1572864     ['multiply_44[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block4_MB_pw_bn (Batch  (None, 7, 7, 512)   2048        ['stack_5_block4_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_59 (Add)                   (None, 7, 7, 512)    0           ['add_58[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block4_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_5_block5_sortcut_conv (C  (None, 7, 7, 3072)  1572864     ['add_59[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block5_sortcut_bn (Bat  (None, 7, 7, 3072)  12288       ['stack_5_block5_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block5_sortcut_swish (  (None, 7, 7, 3072)  0           ['stack_5_block5_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block5_MB_dw_ (Depthwi  (None, 7, 7, 3072)  27648       ['stack_5_block5_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block5_MB_dw_bn (Batch  (None, 7, 7, 3072)  12288       ['stack_5_block5_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_5_block5_MB_dw_swish (Ac  (None, 7, 7, 3072)  0           ['stack_5_block5_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_45 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block5_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block5_se_1_conv (Conv  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_45[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_90 (Activation)     (None, 1, 1, 128)    0           ['stack_5_block5_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block5_se_2_conv (Conv  (None, 1, 1, 3072)  396288      ['activation_90[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_91 (Activation)     (None, 1, 1, 3072)   0           ['stack_5_block5_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_45 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block5_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_91[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_5_block5_MB_pw_conv (Con  (None, 7, 7, 512)   1572864     ['multiply_45[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block5_MB_pw_bn (Batch  (None, 7, 7, 512)   2048        ['stack_5_block5_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_60 (Add)                   (None, 7, 7, 512)    0           ['add_59[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block5_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_5_block6_sortcut_conv (C  (None, 7, 7, 3072)  1572864     ['add_60[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block6_sortcut_bn (Bat  (None, 7, 7, 3072)  12288       ['stack_5_block6_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block6_sortcut_swish (  (None, 7, 7, 3072)  0           ['stack_5_block6_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block6_MB_dw_ (Depthwi  (None, 7, 7, 3072)  27648       ['stack_5_block6_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block6_MB_dw_bn (Batch  (None, 7, 7, 3072)  12288       ['stack_5_block6_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_5_block6_MB_dw_swish (Ac  (None, 7, 7, 3072)  0           ['stack_5_block6_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_46 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block6_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block6_se_1_conv (Conv  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_46[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_92 (Activation)     (None, 1, 1, 128)    0           ['stack_5_block6_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block6_se_2_conv (Conv  (None, 1, 1, 3072)  396288      ['activation_92[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_93 (Activation)     (None, 1, 1, 3072)   0           ['stack_5_block6_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_46 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block6_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_93[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_5_block6_MB_pw_conv (Con  (None, 7, 7, 512)   1572864     ['multiply_46[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block6_MB_pw_bn (Batch  (None, 7, 7, 512)   2048        ['stack_5_block6_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_61 (Add)                   (None, 7, 7, 512)    0           ['add_60[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block6_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_5_block7_sortcut_conv (C  (None, 7, 7, 3072)  1572864     ['add_61[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block7_sortcut_bn (Bat  (None, 7, 7, 3072)  12288       ['stack_5_block7_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block7_sortcut_swish (  (None, 7, 7, 3072)  0           ['stack_5_block7_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block7_MB_dw_ (Depthwi  (None, 7, 7, 3072)  27648       ['stack_5_block7_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block7_MB_dw_bn (Batch  (None, 7, 7, 3072)  12288       ['stack_5_block7_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_5_block7_MB_dw_swish (Ac  (None, 7, 7, 3072)  0           ['stack_5_block7_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_47 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block7_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block7_se_1_conv (Conv  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_47[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_94 (Activation)     (None, 1, 1, 128)    0           ['stack_5_block7_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block7_se_2_conv (Conv  (None, 1, 1, 3072)  396288      ['activation_94[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_95 (Activation)     (None, 1, 1, 3072)   0           ['stack_5_block7_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_47 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block7_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_95[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_5_block7_MB_pw_conv (Con  (None, 7, 7, 512)   1572864     ['multiply_47[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block7_MB_pw_bn (Batch  (None, 7, 7, 512)   2048        ['stack_5_block7_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_62 (Add)                   (None, 7, 7, 512)    0           ['add_61[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block7_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_5_block8_sortcut_conv (C  (None, 7, 7, 3072)  1572864     ['add_62[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block8_sortcut_bn (Bat  (None, 7, 7, 3072)  12288       ['stack_5_block8_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block8_sortcut_swish (  (None, 7, 7, 3072)  0           ['stack_5_block8_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block8_MB_dw_ (Depthwi  (None, 7, 7, 3072)  27648       ['stack_5_block8_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block8_MB_dw_bn (Batch  (None, 7, 7, 3072)  12288       ['stack_5_block8_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_5_block8_MB_dw_swish (Ac  (None, 7, 7, 3072)  0           ['stack_5_block8_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_48 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block8_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block8_se_1_conv (Conv  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_48[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_96 (Activation)     (None, 1, 1, 128)    0           ['stack_5_block8_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block8_se_2_conv (Conv  (None, 1, 1, 3072)  396288      ['activation_96[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_97 (Activation)     (None, 1, 1, 3072)   0           ['stack_5_block8_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_48 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block8_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_97[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_5_block8_MB_pw_conv (Con  (None, 7, 7, 512)   1572864     ['multiply_48[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block8_MB_pw_bn (Batch  (None, 7, 7, 512)   2048        ['stack_5_block8_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_63 (Add)                   (None, 7, 7, 512)    0           ['add_62[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block8_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_5_block9_sortcut_conv (C  (None, 7, 7, 3072)  1572864     ['add_63[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block9_sortcut_bn (Bat  (None, 7, 7, 3072)  12288       ['stack_5_block9_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block9_sortcut_swish (  (None, 7, 7, 3072)  0           ['stack_5_block9_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block9_MB_dw_ (Depthwi  (None, 7, 7, 3072)  27648       ['stack_5_block9_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block9_MB_dw_bn (Batch  (None, 7, 7, 3072)  12288       ['stack_5_block9_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_5_block9_MB_dw_swish (Ac  (None, 7, 7, 3072)  0           ['stack_5_block9_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_49 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block9_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block9_se_1_conv (Conv  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_49[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_98 (Activation)     (None, 1, 1, 128)    0           ['stack_5_block9_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block9_se_2_conv (Conv  (None, 1, 1, 3072)  396288      ['activation_98[0][0]']          Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_99 (Activation)     (None, 1, 1, 3072)   0           ['stack_5_block9_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_49 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block9_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_99[0][0]']                     \n",
+      "                                                                                                             \n",
+      " stack_5_block9_MB_pw_conv (Con  (None, 7, 7, 512)   1572864     ['multiply_49[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block9_MB_pw_bn (Batch  (None, 7, 7, 512)   2048        ['stack_5_block9_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_64 (Add)                   (None, 7, 7, 512)    0           ['add_63[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block9_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_5_block10_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_64[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block10_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block10_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block10_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block10_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block10_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block10_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block10_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block10_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block10_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block10_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_50 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block10_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block10_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_50[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_100 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block10_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block10_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_100[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_101 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block10_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_50 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block10_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_101[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block10_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_50[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block10_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block10_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_65 (Add)                   (None, 7, 7, 512)    0           ['add_64[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block10_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block11_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_65[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block11_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block11_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block11_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block11_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block11_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block11_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block11_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block11_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block11_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block11_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_51 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block11_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block11_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_51[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_102 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block11_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block11_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_102[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_103 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block11_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_51 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block11_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_103[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block11_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_51[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block11_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block11_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_66 (Add)                   (None, 7, 7, 512)    0           ['add_65[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block11_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block12_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_66[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block12_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block12_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block12_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block12_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block12_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block12_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block12_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block12_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block12_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block12_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_52 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block12_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block12_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_52[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_104 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block12_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block12_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_104[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_105 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block12_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_52 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block12_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_105[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block12_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_52[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block12_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block12_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_67 (Add)                   (None, 7, 7, 512)    0           ['add_66[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block12_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block13_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_67[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block13_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block13_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block13_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block13_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block13_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block13_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block13_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block13_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block13_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block13_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_53 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block13_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block13_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_53[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_106 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block13_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block13_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_106[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_107 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block13_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_53 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block13_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_107[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block13_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_53[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block13_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block13_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_68 (Add)                   (None, 7, 7, 512)    0           ['add_67[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block13_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block14_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_68[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block14_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block14_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block14_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block14_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block14_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block14_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block14_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block14_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block14_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block14_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_54 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block14_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block14_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_54[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_108 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block14_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block14_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_108[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_109 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block14_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_54 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block14_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_109[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block14_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_54[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block14_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block14_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_69 (Add)                   (None, 7, 7, 512)    0           ['add_68[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block14_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block15_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_69[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block15_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block15_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block15_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block15_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block15_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block15_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block15_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block15_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block15_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block15_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_55 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block15_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block15_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_55[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_110 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block15_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block15_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_110[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_111 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block15_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_55 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block15_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_111[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block15_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_55[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block15_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block15_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_70 (Add)                   (None, 7, 7, 512)    0           ['add_69[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block15_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block16_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_70[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block16_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block16_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block16_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block16_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block16_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block16_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block16_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block16_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block16_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block16_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_56 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block16_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block16_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_56[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_112 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block16_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block16_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_112[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_113 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block16_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_56 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block16_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_113[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block16_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_56[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block16_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block16_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_71 (Add)                   (None, 7, 7, 512)    0           ['add_70[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block16_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block17_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_71[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block17_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block17_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block17_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block17_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block17_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block17_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block17_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block17_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block17_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block17_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_57 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block17_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block17_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_57[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_114 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block17_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block17_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_114[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_115 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block17_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_57 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block17_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_115[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block17_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_57[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block17_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block17_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_72 (Add)                   (None, 7, 7, 512)    0           ['add_71[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block17_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block18_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_72[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block18_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block18_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block18_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block18_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block18_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block18_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block18_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block18_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block18_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block18_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_58 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block18_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block18_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_58[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_116 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block18_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block18_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_116[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_117 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block18_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_58 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block18_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_117[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block18_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_58[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block18_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block18_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_73 (Add)                   (None, 7, 7, 512)    0           ['add_72[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block18_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block19_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_73[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block19_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block19_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block19_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block19_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block19_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block19_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block19_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block19_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block19_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block19_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_59 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block19_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block19_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_59[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_118 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block19_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block19_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_118[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_119 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block19_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_59 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block19_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_119[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block19_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_59[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block19_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block19_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_74 (Add)                   (None, 7, 7, 512)    0           ['add_73[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block19_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block20_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_74[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block20_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block20_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block20_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block20_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block20_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block20_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block20_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block20_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block20_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block20_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_60 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block20_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block20_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_60[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_120 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block20_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block20_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_120[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_121 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block20_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_60 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block20_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_121[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block20_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_60[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block20_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block20_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_75 (Add)                   (None, 7, 7, 512)    0           ['add_74[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block20_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block21_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_75[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block21_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block21_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block21_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block21_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block21_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block21_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block21_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block21_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block21_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block21_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_61 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block21_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block21_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_61[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_122 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block21_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block21_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_122[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_123 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block21_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_61 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block21_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_123[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block21_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_61[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block21_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block21_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_76 (Add)                   (None, 7, 7, 512)    0           ['add_75[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block21_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block22_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_76[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block22_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block22_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block22_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block22_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block22_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block22_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block22_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block22_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block22_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block22_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_62 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block22_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block22_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_62[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_124 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block22_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block22_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_124[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_125 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block22_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_62 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block22_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_125[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block22_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_62[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block22_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block22_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_77 (Add)                   (None, 7, 7, 512)    0           ['add_76[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block22_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block23_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_77[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block23_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block23_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block23_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block23_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block23_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block23_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block23_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block23_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block23_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block23_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_63 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block23_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block23_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_63[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_126 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block23_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block23_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_126[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_127 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block23_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_63 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block23_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_127[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block23_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_63[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block23_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block23_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_78 (Add)                   (None, 7, 7, 512)    0           ['add_77[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block23_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block24_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_78[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block24_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block24_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block24_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block24_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block24_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block24_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block24_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block24_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block24_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block24_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_64 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block24_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block24_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_64[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_128 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block24_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block24_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_128[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_129 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block24_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_64 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block24_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_129[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block24_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_64[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block24_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block24_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_79 (Add)                   (None, 7, 7, 512)    0           ['add_78[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block24_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block25_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_79[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block25_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block25_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block25_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block25_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block25_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block25_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block25_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block25_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block25_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block25_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_65 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block25_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block25_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_65[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_130 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block25_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block25_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_130[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_131 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block25_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_65 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block25_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_131[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block25_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_65[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block25_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block25_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_80 (Add)                   (None, 7, 7, 512)    0           ['add_79[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block25_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block26_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_80[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block26_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block26_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block26_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block26_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block26_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block26_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block26_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block26_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block26_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block26_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_66 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block26_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block26_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_66[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_132 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block26_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block26_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_132[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_133 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block26_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_66 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block26_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_133[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block26_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_66[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block26_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block26_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_81 (Add)                   (None, 7, 7, 512)    0           ['add_80[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block26_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block27_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_81[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block27_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block27_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block27_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block27_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block27_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block27_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block27_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block27_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block27_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block27_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_67 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block27_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block27_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_67[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_134 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block27_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block27_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_134[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_135 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block27_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_67 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block27_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_135[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block27_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_67[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block27_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block27_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_82 (Add)                   (None, 7, 7, 512)    0           ['add_81[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block27_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block28_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_82[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block28_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block28_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block28_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block28_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block28_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block28_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block28_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block28_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block28_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block28_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_68 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block28_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block28_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_68[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_136 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block28_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block28_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_136[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_137 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block28_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_68 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block28_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_137[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block28_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_68[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block28_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block28_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_83 (Add)                   (None, 7, 7, 512)    0           ['add_82[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block28_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block29_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_83[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block29_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block29_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block29_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block29_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block29_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block29_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block29_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block29_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block29_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block29_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_69 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block29_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block29_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_69[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_138 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block29_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block29_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_138[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_139 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block29_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_69 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block29_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_139[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block29_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_69[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block29_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block29_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_84 (Add)                   (None, 7, 7, 512)    0           ['add_83[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block29_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block30_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_84[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block30_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block30_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block30_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block30_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block30_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block30_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block30_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block30_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block30_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block30_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_70 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block30_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block30_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_70[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_140 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block30_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block30_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_140[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_141 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block30_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_70 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block30_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_141[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block30_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_70[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block30_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block30_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_85 (Add)                   (None, 7, 7, 512)    0           ['add_84[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block30_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_5_block31_sortcut_conv (  (None, 7, 7, 3072)  1572864     ['add_85[0][0]']                 Y          \n",
+      " Conv2D)                                                                                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block31_sortcut_bn (Ba  (None, 7, 7, 3072)  12288       ['stack_5_block31_sortcut_conv[  Y          \n",
+      " tchNormalization)                                               0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_5_block31_sortcut_swish   (None, 7, 7, 3072)  0           ['stack_5_block31_sortcut_bn[0]  Y          \n",
+      " (Activation)                                                    [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block31_MB_dw_ (Depthw  (None, 7, 7, 3072)  27648       ['stack_5_block31_sortcut_swish  Y          \n",
+      " iseConv2D)                                                      [0][0]']                                    \n",
+      "                                                                                                             \n",
+      " stack_5_block31_MB_dw_bn (Batc  (None, 7, 7, 3072)  12288       ['stack_5_block31_MB_dw_[0][0]'  Y          \n",
+      " hNormalization)                                                 ]                                           \n",
+      "                                                                                                             \n",
+      " stack_5_block31_MB_dw_swish (A  (None, 7, 7, 3072)  0           ['stack_5_block31_MB_dw_bn[0][0  Y          \n",
+      " ctivation)                                                      ]']                                         \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_71 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_5_block31_MB_dw_swish[0  Y          \n",
+      " mbda)                                                           ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_5_block31_se_1_conv (Con  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_71[0][0]'  Y          \n",
+      " v2D)                                                            ]                                           \n",
+      "                                                                                                             \n",
+      " activation_142 (Activation)    (None, 1, 1, 128)    0           ['stack_5_block31_se_1_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_5_block31_se_2_conv (Con  (None, 1, 1, 3072)  396288      ['activation_142[0][0]']         Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " activation_143 (Activation)    (None, 1, 1, 3072)   0           ['stack_5_block31_se_2_conv[0][  Y          \n",
+      "                                                                 0]']                                        \n",
+      "                                                                                                             \n",
+      " multiply_71 (Multiply)         (None, 7, 7, 3072)   0           ['stack_5_block31_MB_dw_swish[0  Y          \n",
+      "                                                                 ][0]',                                      \n",
+      "                                                                  'activation_143[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_5_block31_MB_pw_conv (Co  (None, 7, 7, 512)   1572864     ['multiply_71[0][0]']            Y          \n",
+      " nv2D)                                                                                                       \n",
+      "                                                                                                             \n",
+      " stack_5_block31_MB_pw_bn (Batc  (None, 7, 7, 512)   2048        ['stack_5_block31_MB_pw_conv[0]  Y          \n",
+      " hNormalization)                                                 [0]']                                       \n",
+      "                                                                                                             \n",
+      " add_86 (Add)                   (None, 7, 7, 512)    0           ['add_85[0][0]',                 Y          \n",
+      "                                                                  'stack_5_block31_MB_pw_bn[0][0             \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_6_block0_sortcut_conv (C  (None, 7, 7, 3072)  1572864     ['add_86[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block0_sortcut_bn (Bat  (None, 7, 7, 3072)  12288       ['stack_6_block0_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block0_sortcut_swish (  (None, 7, 7, 3072)  0           ['stack_6_block0_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block0_MB_dw_ (Depthwi  (None, 7, 7, 3072)  27648       ['stack_6_block0_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_6_block0_MB_dw_bn (Batch  (None, 7, 7, 3072)  12288       ['stack_6_block0_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_6_block0_MB_dw_swish (Ac  (None, 7, 7, 3072)  0           ['stack_6_block0_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_72 (TFOpLa  (None, 1, 1, 3072)  0           ['stack_6_block0_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_6_block0_se_1_conv (Conv  (None, 1, 1, 128)   393344      ['tf.math.reduce_mean_72[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_144 (Activation)    (None, 1, 1, 128)    0           ['stack_6_block0_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_6_block0_se_2_conv (Conv  (None, 1, 1, 3072)  396288      ['activation_144[0][0]']         Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_145 (Activation)    (None, 1, 1, 3072)   0           ['stack_6_block0_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_72 (Multiply)         (None, 7, 7, 3072)   0           ['stack_6_block0_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_145[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_6_block0_MB_pw_conv (Con  (None, 7, 7, 640)   1966080     ['multiply_72[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block0_MB_pw_bn (Batch  (None, 7, 7, 640)   2560        ['stack_6_block0_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block1_sortcut_conv (C  (None, 7, 7, 3840)  2457600     ['stack_6_block0_MB_pw_bn[0][0]  Y          \n",
+      " onv2D)                                                          ']                                          \n",
+      "                                                                                                             \n",
+      " stack_6_block1_sortcut_bn (Bat  (None, 7, 7, 3840)  15360       ['stack_6_block1_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block1_sortcut_swish (  (None, 7, 7, 3840)  0           ['stack_6_block1_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block1_MB_dw_ (Depthwi  (None, 7, 7, 3840)  34560       ['stack_6_block1_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_6_block1_MB_dw_bn (Batch  (None, 7, 7, 3840)  15360       ['stack_6_block1_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_6_block1_MB_dw_swish (Ac  (None, 7, 7, 3840)  0           ['stack_6_block1_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_73 (TFOpLa  (None, 1, 1, 3840)  0           ['stack_6_block1_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_6_block1_se_1_conv (Conv  (None, 1, 1, 160)   614560      ['tf.math.reduce_mean_73[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_146 (Activation)    (None, 1, 1, 160)    0           ['stack_6_block1_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_6_block1_se_2_conv (Conv  (None, 1, 1, 3840)  618240      ['activation_146[0][0]']         Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_147 (Activation)    (None, 1, 1, 3840)   0           ['stack_6_block1_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_73 (Multiply)         (None, 7, 7, 3840)   0           ['stack_6_block1_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_147[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_6_block1_MB_pw_conv (Con  (None, 7, 7, 640)   2457600     ['multiply_73[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block1_MB_pw_bn (Batch  (None, 7, 7, 640)   2560        ['stack_6_block1_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_87 (Add)                   (None, 7, 7, 640)    0           ['stack_6_block0_MB_pw_bn[0][0]  Y          \n",
+      "                                                                 ',                                          \n",
+      "                                                                  'stack_6_block1_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_6_block2_sortcut_conv (C  (None, 7, 7, 3840)  2457600     ['add_87[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block2_sortcut_bn (Bat  (None, 7, 7, 3840)  15360       ['stack_6_block2_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block2_sortcut_swish (  (None, 7, 7, 3840)  0           ['stack_6_block2_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block2_MB_dw_ (Depthwi  (None, 7, 7, 3840)  34560       ['stack_6_block2_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_6_block2_MB_dw_bn (Batch  (None, 7, 7, 3840)  15360       ['stack_6_block2_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_6_block2_MB_dw_swish (Ac  (None, 7, 7, 3840)  0           ['stack_6_block2_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_74 (TFOpLa  (None, 1, 1, 3840)  0           ['stack_6_block2_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_6_block2_se_1_conv (Conv  (None, 1, 1, 160)   614560      ['tf.math.reduce_mean_74[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_148 (Activation)    (None, 1, 1, 160)    0           ['stack_6_block2_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_6_block2_se_2_conv (Conv  (None, 1, 1, 3840)  618240      ['activation_148[0][0]']         Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_149 (Activation)    (None, 1, 1, 3840)   0           ['stack_6_block2_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_74 (Multiply)         (None, 7, 7, 3840)   0           ['stack_6_block2_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_149[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_6_block2_MB_pw_conv (Con  (None, 7, 7, 640)   2457600     ['multiply_74[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block2_MB_pw_bn (Batch  (None, 7, 7, 640)   2560        ['stack_6_block2_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_88 (Add)                   (None, 7, 7, 640)    0           ['add_87[0][0]',                 Y          \n",
+      "                                                                  'stack_6_block2_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_6_block3_sortcut_conv (C  (None, 7, 7, 3840)  2457600     ['add_88[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block3_sortcut_bn (Bat  (None, 7, 7, 3840)  15360       ['stack_6_block3_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block3_sortcut_swish (  (None, 7, 7, 3840)  0           ['stack_6_block3_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block3_MB_dw_ (Depthwi  (None, 7, 7, 3840)  34560       ['stack_6_block3_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_6_block3_MB_dw_bn (Batch  (None, 7, 7, 3840)  15360       ['stack_6_block3_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_6_block3_MB_dw_swish (Ac  (None, 7, 7, 3840)  0           ['stack_6_block3_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_75 (TFOpLa  (None, 1, 1, 3840)  0           ['stack_6_block3_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_6_block3_se_1_conv (Conv  (None, 1, 1, 160)   614560      ['tf.math.reduce_mean_75[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_150 (Activation)    (None, 1, 1, 160)    0           ['stack_6_block3_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_6_block3_se_2_conv (Conv  (None, 1, 1, 3840)  618240      ['activation_150[0][0]']         Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_151 (Activation)    (None, 1, 1, 3840)   0           ['stack_6_block3_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_75 (Multiply)         (None, 7, 7, 3840)   0           ['stack_6_block3_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_151[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_6_block3_MB_pw_conv (Con  (None, 7, 7, 640)   2457600     ['multiply_75[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block3_MB_pw_bn (Batch  (None, 7, 7, 640)   2560        ['stack_6_block3_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_89 (Add)                   (None, 7, 7, 640)    0           ['add_88[0][0]',                 Y          \n",
+      "                                                                  'stack_6_block3_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_6_block4_sortcut_conv (C  (None, 7, 7, 3840)  2457600     ['add_89[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block4_sortcut_bn (Bat  (None, 7, 7, 3840)  15360       ['stack_6_block4_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block4_sortcut_swish (  (None, 7, 7, 3840)  0           ['stack_6_block4_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block4_MB_dw_ (Depthwi  (None, 7, 7, 3840)  34560       ['stack_6_block4_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_6_block4_MB_dw_bn (Batch  (None, 7, 7, 3840)  15360       ['stack_6_block4_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_6_block4_MB_dw_swish (Ac  (None, 7, 7, 3840)  0           ['stack_6_block4_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_76 (TFOpLa  (None, 1, 1, 3840)  0           ['stack_6_block4_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_6_block4_se_1_conv (Conv  (None, 1, 1, 160)   614560      ['tf.math.reduce_mean_76[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_152 (Activation)    (None, 1, 1, 160)    0           ['stack_6_block4_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_6_block4_se_2_conv (Conv  (None, 1, 1, 3840)  618240      ['activation_152[0][0]']         Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_153 (Activation)    (None, 1, 1, 3840)   0           ['stack_6_block4_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_76 (Multiply)         (None, 7, 7, 3840)   0           ['stack_6_block4_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_153[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_6_block4_MB_pw_conv (Con  (None, 7, 7, 640)   2457600     ['multiply_76[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block4_MB_pw_bn (Batch  (None, 7, 7, 640)   2560        ['stack_6_block4_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_90 (Add)                   (None, 7, 7, 640)    0           ['add_89[0][0]',                 Y          \n",
+      "                                                                  'stack_6_block4_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_6_block5_sortcut_conv (C  (None, 7, 7, 3840)  2457600     ['add_90[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block5_sortcut_bn (Bat  (None, 7, 7, 3840)  15360       ['stack_6_block5_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block5_sortcut_swish (  (None, 7, 7, 3840)  0           ['stack_6_block5_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block5_MB_dw_ (Depthwi  (None, 7, 7, 3840)  34560       ['stack_6_block5_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_6_block5_MB_dw_bn (Batch  (None, 7, 7, 3840)  15360       ['stack_6_block5_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_6_block5_MB_dw_swish (Ac  (None, 7, 7, 3840)  0           ['stack_6_block5_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_77 (TFOpLa  (None, 1, 1, 3840)  0           ['stack_6_block5_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_6_block5_se_1_conv (Conv  (None, 1, 1, 160)   614560      ['tf.math.reduce_mean_77[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_154 (Activation)    (None, 1, 1, 160)    0           ['stack_6_block5_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_6_block5_se_2_conv (Conv  (None, 1, 1, 3840)  618240      ['activation_154[0][0]']         Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_155 (Activation)    (None, 1, 1, 3840)   0           ['stack_6_block5_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_77 (Multiply)         (None, 7, 7, 3840)   0           ['stack_6_block5_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_155[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_6_block5_MB_pw_conv (Con  (None, 7, 7, 640)   2457600     ['multiply_77[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block5_MB_pw_bn (Batch  (None, 7, 7, 640)   2560        ['stack_6_block5_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_91 (Add)                   (None, 7, 7, 640)    0           ['add_90[0][0]',                 Y          \n",
+      "                                                                  'stack_6_block5_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_6_block6_sortcut_conv (C  (None, 7, 7, 3840)  2457600     ['add_91[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block6_sortcut_bn (Bat  (None, 7, 7, 3840)  15360       ['stack_6_block6_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block6_sortcut_swish (  (None, 7, 7, 3840)  0           ['stack_6_block6_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block6_MB_dw_ (Depthwi  (None, 7, 7, 3840)  34560       ['stack_6_block6_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_6_block6_MB_dw_bn (Batch  (None, 7, 7, 3840)  15360       ['stack_6_block6_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_6_block6_MB_dw_swish (Ac  (None, 7, 7, 3840)  0           ['stack_6_block6_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_78 (TFOpLa  (None, 1, 1, 3840)  0           ['stack_6_block6_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_6_block6_se_1_conv (Conv  (None, 1, 1, 160)   614560      ['tf.math.reduce_mean_78[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_156 (Activation)    (None, 1, 1, 160)    0           ['stack_6_block6_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_6_block6_se_2_conv (Conv  (None, 1, 1, 3840)  618240      ['activation_156[0][0]']         Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_157 (Activation)    (None, 1, 1, 3840)   0           ['stack_6_block6_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_78 (Multiply)         (None, 7, 7, 3840)   0           ['stack_6_block6_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_157[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_6_block6_MB_pw_conv (Con  (None, 7, 7, 640)   2457600     ['multiply_78[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block6_MB_pw_bn (Batch  (None, 7, 7, 640)   2560        ['stack_6_block6_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_92 (Add)                   (None, 7, 7, 640)    0           ['add_91[0][0]',                 Y          \n",
+      "                                                                  'stack_6_block6_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " stack_6_block7_sortcut_conv (C  (None, 7, 7, 3840)  2457600     ['add_92[0][0]']                 Y          \n",
+      " onv2D)                                                                                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block7_sortcut_bn (Bat  (None, 7, 7, 3840)  15360       ['stack_6_block7_sortcut_conv[0  Y          \n",
+      " chNormalization)                                                ][0]']                                      \n",
+      "                                                                                                             \n",
+      " stack_6_block7_sortcut_swish (  (None, 7, 7, 3840)  0           ['stack_6_block7_sortcut_bn[0][  Y          \n",
+      " Activation)                                                     0]']                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block7_MB_dw_ (Depthwi  (None, 7, 7, 3840)  34560       ['stack_6_block7_sortcut_swish[  Y          \n",
+      " seConv2D)                                                       0][0]']                                     \n",
+      "                                                                                                             \n",
+      " stack_6_block7_MB_dw_bn (Batch  (None, 7, 7, 3840)  15360       ['stack_6_block7_MB_dw_[0][0]']  Y          \n",
+      " Normalization)                                                                                              \n",
+      "                                                                                                             \n",
+      " stack_6_block7_MB_dw_swish (Ac  (None, 7, 7, 3840)  0           ['stack_6_block7_MB_dw_bn[0][0]  Y          \n",
+      " tivation)                                                       ']                                          \n",
+      "                                                                                                             \n",
+      " tf.math.reduce_mean_79 (TFOpLa  (None, 1, 1, 3840)  0           ['stack_6_block7_MB_dw_swish[0]  Y          \n",
+      " mbda)                                                           [0]']                                       \n",
+      "                                                                                                             \n",
+      " stack_6_block7_se_1_conv (Conv  (None, 1, 1, 160)   614560      ['tf.math.reduce_mean_79[0][0]'  Y          \n",
+      " 2D)                                                             ]                                           \n",
+      "                                                                                                             \n",
+      " activation_158 (Activation)    (None, 1, 1, 160)    0           ['stack_6_block7_se_1_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " stack_6_block7_se_2_conv (Conv  (None, 1, 1, 3840)  618240      ['activation_158[0][0]']         Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " activation_159 (Activation)    (None, 1, 1, 3840)   0           ['stack_6_block7_se_2_conv[0][0  Y          \n",
+      "                                                                 ]']                                         \n",
+      "                                                                                                             \n",
+      " multiply_79 (Multiply)         (None, 7, 7, 3840)   0           ['stack_6_block7_MB_dw_swish[0]  Y          \n",
+      "                                                                 [0]',                                       \n",
+      "                                                                  'activation_159[0][0]']                    \n",
+      "                                                                                                             \n",
+      " stack_6_block7_MB_pw_conv (Con  (None, 7, 7, 640)   2457600     ['multiply_79[0][0]']            Y          \n",
+      " v2D)                                                                                                        \n",
+      "                                                                                                             \n",
+      " stack_6_block7_MB_pw_bn (Batch  (None, 7, 7, 640)   2560        ['stack_6_block7_MB_pw_conv[0][  Y          \n",
+      " Normalization)                                                  0]']                                        \n",
+      "                                                                                                             \n",
+      " add_93 (Add)                   (None, 7, 7, 640)    0           ['add_92[0][0]',                 Y          \n",
+      "                                                                  'stack_6_block7_MB_pw_bn[0][0]             \n",
+      "                                                                 ']                                          \n",
+      "                                                                                                             \n",
+      " post_conv (Conv2D)             (None, 7, 7, 1280)   819200      ['add_93[0][0]']                 Y          \n",
+      "                                                                                                             \n",
+      " post_bn (BatchNormalization)   (None, 7, 7, 1280)   5120        ['post_conv[0][0]']              Y          \n",
+      "                                                                                                             \n",
+      " post_swish (Activation)        (None, 7, 7, 1280)   0           ['post_bn[0][0]']                Y          \n",
+      "                                                                                                             \n",
+      " avg_pool (GlobalAveragePooling  (None, 1280)        0           ['post_swish[0][0]']             Y          \n",
+      " 2D)                                                                                                         \n",
+      "                                                                                                             \n",
+      " dropout (Dropout)              (None, 1280)         0           ['avg_pool[0][0]']               Y          \n",
+      "                                                                                                             \n",
+      " predictions (Dense)            (None, 2)            2562        ['dropout[0][0]']                Y          \n",
+      "                                                                                                             \n",
+      "=============================================================================================================\n",
+      "Total params: 207,618,394\n",
+      "Trainable params: 206,841,370\n",
+      "Non-trainable params: 777,024\n",
+      "_____________________________________________________________________________________________________________\n",
+      "done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras_efficientnet_v2 import EfficientNetV2XL\n",
+    "\n",
+    "EfficientNet_M = EfficientNetV2XL(input_shape=(img_res[0], img_res[1], img_res[2]), pretrained='imagenet21k-ft1k', num_classes=2, dropout=0.4)\n",
+    "# define new model\n",
+    "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n",
+    "\n",
+    "# compile model\n",
+    "opt = SGD(momentum=0.9)\n",
+    "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False)\n",
+    "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n",
+    "# opt = Adam()\n",
+    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "freeze_layers = 0\n",
+    "model.summary(show_trainable=True, expand_nested=True)\n",
+    "print('done.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rev1.5 (The best one)\n",
+    "```\n",
+    "recommended: ✅\n",
+    "statuses: Ready\n",
+    "Working: ✅\n",
+    "Max fine tuned acc: 95.3\n",
+    "Max fine tuned acc TLRev2: 97.12\n",
+    "type: transfer learning>>>(EfficientNetB4::CCL)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from efficientnet.keras import EfficientNetB4 as KENB4\n",
+    "# FUNC\n",
+    "def Eff_B4_NS(freeze_layers):\n",
+    "    base_model = KENB4(input_shape=(\n",
+    "        img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n",
+    "    print('Total layers in the base model: ', len(base_model.layers))\n",
+    "    print(f'Freezing {freeze_layers} layers in the base model...')\n",
+    "    # Freeze the specified number of layers\n",
+    "    for layer in base_model.layers[:freeze_layers]:\n",
+    "        layer.trainable = False\n",
+    "\n",
+    "    # Unfreeze the rest\n",
+    "    for layer in base_model.layers[freeze_layers:]:\n",
+    "        layer.trainable = True\n",
+    "\n",
+    "    # Calculate the percentage of the model that is frozen\n",
+    "    frozen_percentage = ((freeze_layers + 1e-10) /\n",
+    "                         len(base_model.layers)) * 100\n",
+    "    print(\n",
+    "        f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n",
+    "    # adding CDL>>>\n",
+    "    #GlobalAveragePooling2D\n",
+    "    base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output)\n",
+    "    #Dense\n",
+    "    Dense_L1 = Dense(512, activation='relu',\n",
+    "                     kernel_regularizer=l2(0.02),\n",
+    "                     name='FC_C_Dense-L1-512'\n",
+    "                     )(base_model_FT)\n",
+    "    #Dropout\n",
+    "    Dropout_L1 = Dropout(0.1,\n",
+    "                         name='FC_C_Dropout-L1-0.1'\n",
+    "                         )(Dense_L1)\n",
+    "    #BatchNormalization\n",
+    "    BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L1'\n",
+    "                                      )(Dropout_L1)\n",
+    "    #Dense\n",
+    "    Dense_L2 = Dense(512, activation='relu',\n",
+    "                     kernel_regularizer=l2(0.01),\n",
+    "                     name='FC_C_Dense-L2-512'\n",
+    "                     )(BatchNorm_L2)\n",
+    "    #BatchNormalization\n",
+    "    BatchNorm_L3 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L2'\n",
+    "                                      )(Dense_L2)\n",
+    "    #Dense\n",
+    "    Dense_L3 = Dense(128, activation='relu',\n",
+    "                     name='FC_C_Dense-L3-128'\n",
+    "                     )(BatchNorm_L3)\n",
+    "    #Dense\n",
+    "    # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n",
+    "    predictions = Dense(2, activation='softmax',\n",
+    "                        name='FC_OUTPUT_Dense-2')(Dense_L3)\n",
+    "    # CDL<<<\n",
+    "    model_EfficientNetB4_NS = Model(\n",
+    "        inputs=base_model.input, outputs=predictions)\n",
+    "    print('Total model layers: ', len(model_EfficientNetB4_NS.layers))\n",
+    "    # OPT/compile\n",
+    "    opt = SGD(momentum=0.92, nesterov=False)\n",
+    "    # opt = Nadam()\n",
+    "    # opt = Adamax()\n",
+    "    # opt = RMSprop(momentum=0.9)\n",
+    "    # opt = Adagrad()\n",
+    "    # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n",
+    "    # opt = Yogi()\n",
+    "    model_EfficientNetB4_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])  # categorical_crossentropy / binary_crossentropy\n",
+    "\n",
+    "    return model_EfficientNetB4_NS\n",
+    "\n",
+    "print('Creating the model...')\n",
+    "# Main\n",
+    "freeze_layers = 0\n",
+    "model = Eff_B4_NS(freeze_layers)\n",
+    "model.summary(show_trainable=True, expand_nested=True)\n",
+    "print('done.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### V(T) Beta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from efficientnet.keras import EfficientNetL2 as KENBL2\n",
+    "#FUNC\n",
+    "def Eff_B7_NS(freeze_layers):\n",
+    "    base_model = KENBL2(input_shape=(img_res[0], img_res[1], img_res[2]),\n",
+    "                        weights='./download/Models/EFN_L2/efficientnet-l2_noisy-student_notop.h5',\n",
+    "                        include_top=False,\n",
+    "                        drop_connect_rate=0)\n",
+    "    print('Total layers in the base model: ', len(base_model.layers))\n",
+    "    print(f'Freezing {freeze_layers} layers in the base model...')\n",
+    "    # Freeze the specified number of layers\n",
+    "    for layer in base_model.layers[:freeze_layers]:\n",
+    "        layer.trainable = False\n",
+    "\n",
+    "    # Unfreeze the rest\n",
+    "    for layer in base_model.layers[freeze_layers:]:\n",
+    "        layer.trainable = True\n",
+    "\n",
+    "    # Calculate the percentage of the model that is frozen\n",
+    "    frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100\n",
+    "    print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n",
+    "    # adding CDL\n",
+    "    base_model_FT = GlobalAveragePooling2D()(base_model.output)\n",
+    "    Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT)\n",
+    "    Dropout_L1 = Dropout(0.1)(Dense_L1) \n",
+    "    BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n",
+    "    Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2)\n",
+    "    BatchNorm_L3 = BatchNormalization()(Dense_L2)\n",
+    "    Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n",
+    "    predictions = Dense(2, activation='softmax')(Dense_L3)\n",
+    "\n",
+    "    model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions)   \n",
+    "    print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n",
+    "    #OPT/compile\n",
+    "    opt = SGD(momentum=0.9)\n",
+    "    # opt = Yogi()\n",
+    "    model_EfficientNetB7_NS.compile(optimizer = opt,  loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "    return model_EfficientNetB7_NS\n",
+    "print('Creating the model...')\n",
+    "# Main\n",
+    "freeze_layers = 0\n",
+    "model = Eff_B7_NS(freeze_layers)\n",
+    "model.summary(show_trainable=True, expand_nested=True)\n",
+    "print('done.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### V(T) Beta2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-28T02:31:32.994176700Z",
+     "start_time": "2023-12-28T02:31:27.381088600Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from efficientnet.keras import EfficientNetB7 as KENB7\n",
+    "# FUNC\n",
+    "def Eff_B7_NS(freeze_layers):\n",
+    "    base_model = KENB7(input_shape=(\n",
+    "        img_res[0], img_res[1], img_res[2]), weights=None, include_top=False)\n",
+    "    print('Total layers in the base model: ', len(base_model.layers))\n",
+    "    print(f'Freezing {freeze_layers} layers in the base model...')\n",
+    "    # Freeze the specified number of layers\n",
+    "    for layer in base_model.layers[:freeze_layers]:\n",
+    "        layer.trainable = False\n",
+    "\n",
+    "    # Unfreeze the rest\n",
+    "    for layer in base_model.layers[freeze_layers:]:\n",
+    "        layer.trainable = True\n",
+    "\n",
+    "    # Calculate the percentage of the model that is frozen\n",
+    "    frozen_percentage = ((freeze_layers + 1e-10) /\n",
+    "                         len(base_model.layers)) * 100\n",
+    "    print(\n",
+    "        f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n",
+    "    # adding CDL>>>\n",
+    "    #GlobalAveragePooling2D\n",
+    "    base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output)\n",
+    "    #Dense\n",
+    "    Dense_L1 = Dense(512, activation='relu',\n",
+    "                     kernel_regularizer=l2(0.02),\n",
+    "                     name='FC_C_Dense-L1-512'\n",
+    "                     )(base_model_FT)\n",
+    "    #Dropout\n",
+    "    Dropout_L1 = Dropout(0.1,\n",
+    "                         name='FC_C_Dropout-L1-0.1'\n",
+    "                         )(Dense_L1)\n",
+    "    #BatchNormalization\n",
     "    BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-Pooling-L1'\n",
     "                                      )(Dropout_L1)\n",
     "    #Dense\n",
@@ -3835,7 +8023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-28T07:04:23.573633300Z",
@@ -3850,47 +8038,49 @@
       "Training the model...\n",
       "\u001b[0;33m\n",
       "Setup Verbose:\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m02_d16-h15_m47_s56]\u001b[0m\u001b[0;36m...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m02_d21-h19_m45_s50]\u001b[0m\u001b[0;36m...\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mUse_OneCycleLr \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mUsing the modified ReduceLROnPlateau.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
       "\u001b[0;33mSetup Verbose END.\u001b[0m\n",
       "\u001b[0m\n",
       "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;33mPreparing train data...\u001b[0m\n",
-      "\u001b[0;33m- Loading fitted ImageDataGenerator...\u001b[0m\n",
+      "\u001b[0;33m- Fitting ImageDataGenerator...\u001b[0m\n",
       "\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n",
       "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
       "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m02_d16-h15_m48_s53\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m02_d21-h19_m50_s38\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;32mTraining on subset...\u001b[0m\n",
       "Epoch 1/6\n",
-      "256/256 [==============================] - 97s 299ms/step - loss: 7.0258 - accuracy: 0.6128 - val_loss: 4.5567 - val_accuracy: 0.7260 - lr: 0.0100\n",
+      "256/256 [==============================] - 90s 217ms/step - loss: 7.0960 - accuracy: 0.6514 - val_loss: 4.3509 - val_accuracy: 0.8782 - lr: 0.0100\n",
       "Epoch 2/6\n",
-      "256/256 [==============================] - 72s 281ms/step - loss: 3.1391 - accuracy: 0.8040 - val_loss: 2.1327 - val_accuracy: 0.8093 - lr: 0.0100\n",
+      "256/256 [==============================] - 53s 207ms/step - loss: 3.1833 - accuracy: 0.8086 - val_loss: 2.0124 - val_accuracy: 0.8926 - lr: 0.0100\n",
       "Epoch 3/6\n",
-      "256/256 [==============================] - 71s 276ms/step - loss: 1.5847 - accuracy: 0.8499 - val_loss: 1.2597 - val_accuracy: 0.7917 - lr: 0.0100\n",
+      "256/256 [==============================] - 53s 205ms/step - loss: 1.5983 - accuracy: 0.8572 - val_loss: 1.5372 - val_accuracy: 0.6795 - lr: 0.0100\n",
       "Epoch 4/6\n",
-      "256/256 [==============================] - 71s 278ms/step - loss: 0.9246 - accuracy: 0.8735 - val_loss: 0.6738 - val_accuracy: 0.8878 - lr: 0.0100\n",
+      "256/256 [==============================] - 53s 206ms/step - loss: 0.9135 - accuracy: 0.8821 - val_loss: 0.6788 - val_accuracy: 0.9215 - lr: 0.0100\n",
       "Epoch 5/6\n",
-      "256/256 [==============================] - 72s 281ms/step - loss: 0.6152 - accuracy: 0.8870 - val_loss: 0.5023 - val_accuracy: 0.9119 - lr: 0.0100\n",
+      "256/256 [==============================] - 52s 203ms/step - loss: 0.5820 - accuracy: 0.9023 - val_loss: 0.4907 - val_accuracy: 0.8878 - lr: 0.0100\n",
       "Epoch 6/6\n",
-      "256/256 [==============================] - 71s 276ms/step - loss: 0.4109 - accuracy: 0.9106 - val_loss: 0.9721 - val_accuracy: 0.7516 - lr: 0.0100\n",
+      "256/256 [==============================] - 52s 202ms/step - loss: 0.4026 - accuracy: 0.9194 - val_loss: 0.3878 - val_accuracy: 0.8814 - lr: 0.0100\n",
       "\u001b[0;32mSubset training done.\u001b[0m\n",
       "\u001b[0;33mLoading the best weights...\u001b[0m\n",
-      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-005-0.9119.h5...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9119\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5023\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.000000 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.911859\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-004-0.9215.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9215\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.6788\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.000000 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.921474\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.5022610426\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.6788101792\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m533.01 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m455.33 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.68 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m659.14 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m353.51 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m305.63 \u001b[0m\u001b[0;36msec\u001b[0m\n",
       "\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n",
       "\u001b[0m\n",
       "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
@@ -3901,29 +8091,30 @@
       "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;32mTraining on subset...\u001b[0m\n",
       "Epoch 7/12\n",
-      "256/256 [==============================] - 77s 285ms/step - loss: 0.5345 - accuracy: 0.8616 - val_loss: 0.4416 - val_accuracy: 0.8718 - lr: 0.0100\n",
+      "256/256 [==============================] - 60s 204ms/step - loss: 0.6837 - accuracy: 0.8618 - val_loss: 0.7311 - val_accuracy: 0.7660 - lr: 0.0100\n",
       "Epoch 8/12\n",
-      "256/256 [==============================] - 75s 294ms/step - loss: 0.3936 - accuracy: 0.8936 - val_loss: 0.3475 - val_accuracy: 0.9103 - lr: 0.0100\n",
+      "256/256 [==============================] - 51s 200ms/step - loss: 0.4632 - accuracy: 0.8940 - val_loss: 0.4718 - val_accuracy: 0.9135 - lr: 0.0100\n",
       "Epoch 9/12\n",
-      "256/256 [==============================] - 76s 294ms/step - loss: 0.3501 - accuracy: 0.9067 - val_loss: 0.2652 - val_accuracy: 0.9327 - lr: 0.0100\n",
+      "256/256 [==============================] - 51s 198ms/step - loss: 0.3834 - accuracy: 0.8992 - val_loss: 0.3400 - val_accuracy: 0.8878 - lr: 0.0100\n",
       "Epoch 10/12\n",
-      "256/256 [==============================] - 75s 292ms/step - loss: 0.2607 - accuracy: 0.9290 - val_loss: 0.2976 - val_accuracy: 0.9119 - lr: 0.0100\n",
+      "256/256 [==============================] - 52s 202ms/step - loss: 0.2943 - accuracy: 0.9253 - val_loss: 0.2833 - val_accuracy: 0.9359 - lr: 0.0100\n",
       "Epoch 11/12\n",
-      "256/256 [==============================] - 75s 291ms/step - loss: 0.2242 - accuracy: 0.9307 - val_loss: 0.2092 - val_accuracy: 0.9327 - lr: 0.0100\n",
+      "256/256 [==============================] - 51s 198ms/step - loss: 0.2666 - accuracy: 0.9253 - val_loss: 0.2520 - val_accuracy: 0.9295 - lr: 0.0100\n",
       "Epoch 12/12\n",
-      "256/256 [==============================] - 75s 291ms/step - loss: 0.2214 - accuracy: 0.9399 - val_loss: 0.4074 - val_accuracy: 0.9071 - lr: 0.0100\n",
+      "256/256 [==============================] - 51s 198ms/step - loss: 0.2069 - accuracy: 0.9382 - val_loss: 0.2819 - val_accuracy: 0.9199 - lr: 0.0100\n",
       "\u001b[0;32mSubset training done.\u001b[0m\n",
       "\u001b[0;33mLoading the best weights...\u001b[0m\n",
-      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-009-0.9327.h5...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2652\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.911859 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.932692\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-010-0.9359.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2833\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.921474 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.935897\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.5022610426 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2651945949\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.6788101792 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2832830250\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m517.22 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.58 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m63.64 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m377.80 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m316.57 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m61.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
       "\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n",
       "\u001b[0m\n",
       "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
@@ -3934,28 +8125,30 @@
       "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;32mTraining on subset...\u001b[0m\n",
       "Epoch 13/18\n",
-      "256/256 [==============================] - 81s 297ms/step - loss: 0.3348 - accuracy: 0.8870 - val_loss: 0.2732 - val_accuracy: 0.9199 - lr: 0.0100\n",
+      "256/256 [==============================] - 60s 206ms/step - loss: 0.3287 - accuracy: 0.8975 - val_loss: 0.2951 - val_accuracy: 0.8990 - lr: 0.0100\n",
       "Epoch 14/18\n",
-      "256/256 [==============================] - 75s 293ms/step - loss: 0.2810 - accuracy: 0.9111 - val_loss: 0.2527 - val_accuracy: 0.9215 - lr: 0.0100\n",
+      "256/256 [==============================] - 52s 203ms/step - loss: 0.2662 - accuracy: 0.9143 - val_loss: 0.2459 - val_accuracy: 0.9199 - lr: 0.0100\n",
       "Epoch 15/18\n",
-      "256/256 [==============================] - 75s 292ms/step - loss: 0.2533 - accuracy: 0.9197 - val_loss: 0.1895 - val_accuracy: 0.9327 - lr: 0.0100\n",
+      "256/256 [==============================] - 51s 199ms/step - loss: 0.2369 - accuracy: 0.9272 - val_loss: 0.3320 - val_accuracy: 0.8958 - lr: 0.0100\n",
       "Epoch 16/18\n",
-      "256/256 [==============================] - 75s 290ms/step - loss: 0.1958 - accuracy: 0.9458 - val_loss: 0.2797 - val_accuracy: 0.9199 - lr: 0.0100\n",
+      "256/256 [==============================] - 51s 199ms/step - loss: 0.2205 - accuracy: 0.9331 - val_loss: 0.3045 - val_accuracy: 0.8990 - lr: 0.0100\n",
       "Epoch 17/18\n",
-      "256/256 [==============================] - 74s 288ms/step - loss: 0.1814 - accuracy: 0.9426 - val_loss: 0.3444 - val_accuracy: 0.8750 - lr: 0.0100\n",
+      "256/256 [==============================] - 52s 202ms/step - loss: 0.1743 - accuracy: 0.9495 - val_loss: 0.2485 - val_accuracy: 0.9375 - lr: 0.0100\n",
       "Epoch 18/18\n",
-      "256/256 [==============================] - 75s 291ms/step - loss: 0.1870 - accuracy: 0.9458 - val_loss: 0.2474 - val_accuracy: 0.9263 - lr: 0.0100\n",
+      "256/256 [==============================] - 51s 199ms/step - loss: 0.1324 - accuracy: 0.9653 - val_loss: 0.3564 - val_accuracy: 0.8894 - lr: 0.0100\n",
       "\u001b[0;32mSubset training done.\u001b[0m\n",
       "\u001b[0;33mLoading the best weights...\u001b[0m\n",
-      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-015-0.9327.h5...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1895\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9326922894. Not saving model.\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2651945949 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1894630343\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-017-0.9375.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2485\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.935897 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.937500\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m531.48 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m455.38 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2832830250 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2485097945\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m383.51 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m318.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.00 \u001b[0m\u001b[0;36msec\u001b[0m\n",
       "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n",
       "\u001b[0m\n",
       "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
@@ -3966,12 +8159,560 @@
       "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;32mTraining on subset...\u001b[0m\n",
       "Epoch 19/24\n",
-      "256/256 [==============================] - 80s 295ms/step - loss: 0.3153 - accuracy: 0.8933 - val_loss: 0.2172 - val_accuracy: 0.9199 - lr: 0.0100\n",
+      "256/256 [==============================] - 61s 208ms/step - loss: 0.3077 - accuracy: 0.8970 - val_loss: 0.2433 - val_accuracy: 0.9151 - lr: 0.0100\n",
       "Epoch 20/24\n",
-      "133/256 [==============>...............] - ETA: 30s - loss: 0.2633 - accuracy: 0.9117\n",
-      "KeyboardInterrupt.\n",
-      "Training done.\n",
-      "\n"
+      "256/256 [==============================] - 52s 203ms/step - loss: 0.2331 - accuracy: 0.9307 - val_loss: 0.2300 - val_accuracy: 0.9279 - lr: 0.0100\n",
+      "Epoch 21/24\n",
+      "256/256 [==============================] - 52s 204ms/step - loss: 0.2139 - accuracy: 0.9336 - val_loss: 0.2418 - val_accuracy: 0.9407 - lr: 0.0100\n",
+      "Epoch 22/24\n",
+      "256/256 [==============================] - 52s 201ms/step - loss: 0.1581 - accuracy: 0.9531 - val_loss: 0.2810 - val_accuracy: 0.8862 - lr: 0.0100\n",
+      "Epoch 23/24\n",
+      "256/256 [==============================] - 52s 201ms/step - loss: 0.1658 - accuracy: 0.9526 - val_loss: 0.2898 - val_accuracy: 0.8910 - lr: 0.0100\n",
+      "Epoch 24/24\n",
+      "256/256 [==============================] - 52s 201ms/step - loss: 0.1613 - accuracy: 0.9539 - val_loss: 0.2693 - val_accuracy: 0.9231 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-021-0.9407.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2418\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.937500 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.940705\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2485097945 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2418088466\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m389.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m321.74 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.57 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 25/30\n",
+      "256/256 [==============================] - 63s 215ms/step - loss: 0.3060 - accuracy: 0.8970 - val_loss: 0.3489 - val_accuracy: 0.9183 - lr: 0.0100\n",
+      "Epoch 26/30\n",
+      "256/256 [==============================] - 54s 208ms/step - loss: 0.2544 - accuracy: 0.9197 - val_loss: 0.2685 - val_accuracy: 0.9006 - lr: 0.0100\n",
+      "Epoch 27/30\n",
+      "256/256 [==============================] - 53s 208ms/step - loss: 0.1866 - accuracy: 0.9417 - val_loss: 0.2319 - val_accuracy: 0.9167 - lr: 0.0100\n",
+      "Epoch 28/30\n",
+      "256/256 [==============================] - 53s 208ms/step - loss: 0.1715 - accuracy: 0.9480 - val_loss: 0.2892 - val_accuracy: 0.9167 - lr: 0.0100\n",
+      "Epoch 29/30\n",
+      "256/256 [==============================] - 54s 211ms/step - loss: 0.1400 - accuracy: 0.9600 - val_loss: 0.2882 - val_accuracy: 0.9327 - lr: 0.0100\n",
+      "Epoch 30/30\n",
+      "256/256 [==============================] - 54s 209ms/step - loss: 0.1331 - accuracy: 0.9651 - val_loss: 0.3342 - val_accuracy: 0.9071 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-029-0.9327.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2882\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9407051206. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.2418088466. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m398.30 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m331.69 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.61 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 31/36\n",
+      "256/256 [==============================] - 61s 209ms/step - loss: 0.2687 - accuracy: 0.9175 - val_loss: 0.2084 - val_accuracy: 0.9343 - lr: 0.0100\n",
+      "Epoch 32/36\n",
+      "256/256 [==============================] - 52s 202ms/step - loss: 0.1929 - accuracy: 0.9412 - val_loss: 0.2006 - val_accuracy: 0.9247 - lr: 0.0100\n",
+      "Epoch 33/36\n",
+      "256/256 [==============================] - 53s 205ms/step - loss: 0.1602 - accuracy: 0.9529 - val_loss: 0.2086 - val_accuracy: 0.9391 - lr: 0.0100\n",
+      "Epoch 34/36\n",
+      "256/256 [==============================] - 52s 202ms/step - loss: 0.1211 - accuracy: 0.9666 - val_loss: 0.2182 - val_accuracy: 0.9391 - lr: 0.0100\n",
+      "Epoch 35/36\n",
+      "256/256 [==============================] - 52s 202ms/step - loss: 0.1159 - accuracy: 0.9702 - val_loss: 0.3274 - val_accuracy: 0.9119 - lr: 0.0100\n",
+      "Epoch 36/36\n",
+      "256/256 [==============================] - 52s 202ms/step - loss: 0.0974 - accuracy: 0.9722 - val_loss: 0.2405 - val_accuracy: 0.9054 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-033-0.9391.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2086\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9407051206. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2418088466 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2086250037\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m392.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m322.32 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 37/42\n",
+      "256/256 [==============================] - 61s 208ms/step - loss: 0.2704 - accuracy: 0.9148 - val_loss: 0.2623 - val_accuracy: 0.9311 - lr: 0.0100\n",
+      "Epoch 38/42\n",
+      "256/256 [==============================] - 52s 201ms/step - loss: 0.2013 - accuracy: 0.9351 - val_loss: 0.3585 - val_accuracy: 0.9183 - lr: 0.0100\n",
+      "Epoch 39/42\n",
+      "256/256 [==============================] - 52s 202ms/step - loss: 0.1503 - accuracy: 0.9592 - val_loss: 0.2112 - val_accuracy: 0.9295 - lr: 0.0100\n",
+      "Epoch 40/42\n",
+      "256/256 [==============================] - 53s 206ms/step - loss: 0.1251 - accuracy: 0.9653 - val_loss: 0.2388 - val_accuracy: 0.9343 - lr: 0.0100\n",
+      "Epoch 41/42\n",
+      "256/256 [==============================] - 52s 201ms/step - loss: 0.0910 - accuracy: 0.9775 - val_loss: 0.2861 - val_accuracy: 0.9199 - lr: 0.0100\n",
+      "Epoch 42/42\n",
+      "256/256 [==============================] - 52s 202ms/step - loss: 0.0972 - accuracy: 0.9771 - val_loss: 0.2420 - val_accuracy: 0.9279 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-040-0.9343.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2388\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9407051206. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.2086250037. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m392.71 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m321.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.83 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 43/48\n",
+      "256/256 [==============================] - 61s 209ms/step - loss: 0.2648 - accuracy: 0.9199 - val_loss: 0.2181 - val_accuracy: 0.9279 - lr: 0.0100\n",
+      "Epoch 44/48\n",
+      "256/256 [==============================] - 52s 204ms/step - loss: 0.1892 - accuracy: 0.9390 - val_loss: 0.2879 - val_accuracy: 0.9423 - lr: 0.0100\n",
+      "Epoch 45/48\n",
+      "256/256 [==============================] - 51s 200ms/step - loss: 0.1339 - accuracy: 0.9602 - val_loss: 0.7077 - val_accuracy: 0.8894 - lr: 0.0100\n",
+      "Epoch 46/48\n",
+      "256/256 [==============================] - 52s 201ms/step - loss: 0.1364 - accuracy: 0.9634 - val_loss: 0.2454 - val_accuracy: 0.9375 - lr: 0.0100\n",
+      "Epoch 47/48\n",
+      "256/256 [==============================] - 52s 202ms/step - loss: 0.0885 - accuracy: 0.9756 - val_loss: 0.4411 - val_accuracy: 0.9391 - lr: 0.0100\n",
+      "Epoch 48/48\n",
+      "256/256 [==============================] - 52s 202ms/step - loss: 0.1021 - accuracy: 0.9724 - val_loss: 0.2602 - val_accuracy: 0.9311 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-044-0.9423.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2879\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.940705 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.942308\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.2086250037. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m397.25 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m321.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.75 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 49/54\n",
+      "256/256 [==============================] - 64s 217ms/step - loss: 0.2317 - accuracy: 0.9285 - val_loss: 0.2361 - val_accuracy: 0.9119 - lr: 0.0100\n",
+      "Epoch 50/54\n",
+      "256/256 [==============================] - 54s 212ms/step - loss: 0.1672 - accuracy: 0.9500 - val_loss: 0.2065 - val_accuracy: 0.9327 - lr: 0.0100\n",
+      "Epoch 51/54\n",
+      "256/256 [==============================] - 55s 213ms/step - loss: 0.1249 - accuracy: 0.9641 - val_loss: 0.2382 - val_accuracy: 0.9343 - lr: 0.0100\n",
+      "Epoch 52/54\n",
+      "256/256 [==============================] - 54s 208ms/step - loss: 0.1068 - accuracy: 0.9685 - val_loss: 0.2824 - val_accuracy: 0.8974 - lr: 0.0100\n",
+      "Epoch 53/54\n",
+      "256/256 [==============================] - 54s 208ms/step - loss: 0.0838 - accuracy: 0.9783 - val_loss: 0.3011 - val_accuracy: 0.9263 - lr: 0.0100\n",
+      "Epoch 54/54\n",
+      "256/256 [==============================] - 55s 213ms/step - loss: 0.0831 - accuracy: 0.9771 - val_loss: 0.2188 - val_accuracy: 0.9407 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-054-0.9407.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2188\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9423077106. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.2086250037. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m411.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m336.04 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.84 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 55/60\n",
+      "256/256 [==============================] - 62s 210ms/step - loss: 0.2614 - accuracy: 0.9189 - val_loss: 0.2734 - val_accuracy: 0.8974 - lr: 0.0100\n",
+      "Epoch 56/60\n",
+      "256/256 [==============================] - 53s 206ms/step - loss: 0.1772 - accuracy: 0.9470 - val_loss: 0.2006 - val_accuracy: 0.9183 - lr: 0.0100\n",
+      "Epoch 57/60\n",
+      "256/256 [==============================] - 52s 204ms/step - loss: 0.1454 - accuracy: 0.9558 - val_loss: 0.2368 - val_accuracy: 0.9038 - lr: 0.0100\n",
+      "Epoch 58/60\n",
+      "256/256 [==============================] - 52s 204ms/step - loss: 0.0926 - accuracy: 0.9741 - val_loss: 0.3055 - val_accuracy: 0.9022 - lr: 0.0100\n",
+      "Epoch 59/60\n",
+      "256/256 [==============================] - 53s 207ms/step - loss: 0.0743 - accuracy: 0.9817 - val_loss: 0.2830 - val_accuracy: 0.9247 - lr: 0.0100\n",
+      "Epoch 60/60\n",
+      "256/256 [==============================] - 52s 204ms/step - loss: 0.0661 - accuracy: 0.9841 - val_loss: 0.3475 - val_accuracy: 0.9183 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-059-0.9247.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2830\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9423077106. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.2086250037. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m403.49 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m325.68 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.82 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 61/66\n",
+      "256/256 [==============================] - 62s 213ms/step - loss: 0.2355 - accuracy: 0.9270 - val_loss: 0.1973 - val_accuracy: 0.9263 - lr: 0.0100\n",
+      "Epoch 62/66\n",
+      "256/256 [==============================] - 54s 208ms/step - loss: 0.1516 - accuracy: 0.9578 - val_loss: 0.2657 - val_accuracy: 0.9359 - lr: 0.0100\n",
+      "Epoch 63/66\n",
+      "256/256 [==============================] - 53s 205ms/step - loss: 0.1230 - accuracy: 0.9653 - val_loss: 0.3837 - val_accuracy: 0.8413 - lr: 0.0100\n",
+      "Epoch 64/66\n",
+      "256/256 [==============================] - 53s 205ms/step - loss: 0.0968 - accuracy: 0.9778 - val_loss: 0.3153 - val_accuracy: 0.9327 - lr: 0.0100\n",
+      "Epoch 65/66\n",
+      "256/256 [==============================] - 53s 206ms/step - loss: 0.0629 - accuracy: 0.9836 - val_loss: 0.2493 - val_accuracy: 0.9215 - lr: 0.0100\n",
+      "Epoch 66/66\n",
+      "256/256 [==============================] - 54s 209ms/step - loss: 0.0645 - accuracy: 0.9851 - val_loss: 0.1880 - val_accuracy: 0.9391 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-066-0.9391.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1879\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9423077106. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2086250037 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1879463345\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.15GB, used: 18.85GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m411.45 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m329.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m82.21 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 67/72\n",
+      "256/256 [==============================] - 76s 251ms/step - loss: 0.2183 - accuracy: 0.9319 - val_loss: 0.2818 - val_accuracy: 0.9391 - lr: 0.0100\n",
+      "Epoch 68/72\n",
+      "256/256 [==============================] - 62s 241ms/step - loss: 0.1548 - accuracy: 0.9556 - val_loss: 0.1632 - val_accuracy: 0.9359 - lr: 0.0100\n",
+      "Epoch 69/72\n",
+      "256/256 [==============================] - 61s 238ms/step - loss: 0.1195 - accuracy: 0.9700 - val_loss: 0.3300 - val_accuracy: 0.8862 - lr: 0.0100\n",
+      "Epoch 70/72\n",
+      "256/256 [==============================] - 61s 239ms/step - loss: 0.0721 - accuracy: 0.9839 - val_loss: 0.2902 - val_accuracy: 0.9359 - lr: 0.0100\n",
+      "Epoch 71/72\n",
+      "256/256 [==============================] - 61s 239ms/step - loss: 0.0623 - accuracy: 0.9858 - val_loss: 0.2715 - val_accuracy: 0.9263 - lr: 0.0100\n",
+      "Epoch 72/72\n",
+      "256/256 [==============================] - 62s 243ms/step - loss: 0.0523 - accuracy: 0.9883 - val_loss: 0.2548 - val_accuracy: 0.9327 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-067-0.9391.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2817\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9423077106. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1879463345. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.15GB, used: 18.85GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m498.81 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m385.01 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.80 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 73/78\n",
+      "256/256 [==============================] - 76s 252ms/step - loss: 0.2010 - accuracy: 0.9395 - val_loss: 0.1900 - val_accuracy: 0.9407 - lr: 0.0100\n",
+      "Epoch 74/78\n",
+      "256/256 [==============================] - 63s 243ms/step - loss: 0.1616 - accuracy: 0.9507 - val_loss: 0.3435 - val_accuracy: 0.8974 - lr: 0.0100\n",
+      "Epoch 75/78\n",
+      "256/256 [==============================] - 62s 240ms/step - loss: 0.1297 - accuracy: 0.9639 - val_loss: 0.3683 - val_accuracy: 0.8894 - lr: 0.0100\n",
+      "Epoch 76/78\n",
+      "256/256 [==============================] - 62s 242ms/step - loss: 0.0977 - accuracy: 0.9727 - val_loss: 0.2612 - val_accuracy: 0.9343 - lr: 0.0100\n",
+      "Epoch 77/78\n",
+      "256/256 [==============================] - 62s 240ms/step - loss: 0.0644 - accuracy: 0.9812 - val_loss: 0.5125 - val_accuracy: 0.8878 - lr: 0.0100\n",
+      "Epoch 78/78\n",
+      "256/256 [==============================] - 62s 243ms/step - loss: 0.0607 - accuracy: 0.9844 - val_loss: 0.3085 - val_accuracy: 0.9295 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-073-0.9407.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1900\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9423077106. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1879463345. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.15GB, used: 18.85GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m507.39 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m387.07 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m120.32 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 79/84\n",
+      "256/256 [==============================] - 75s 252ms/step - loss: 0.2236 - accuracy: 0.9336 - val_loss: 0.1998 - val_accuracy: 0.9247 - lr: 0.0100\n",
+      "Epoch 80/84\n",
+      "256/256 [==============================] - ETA: 0s - loss: 0.1527 - accuracy: 0.9507\n",
+      "Epoch 80: ReduceLROnPlateau reducing learning rate to 0.007999999821186066.\n",
+      "256/256 [==============================] - 62s 240ms/step - loss: 0.1527 - accuracy: 0.9507 - val_loss: 0.3333 - val_accuracy: 0.8814 - lr: 0.0100\n",
+      "Epoch 81/84\n",
+      "256/256 [==============================] - 62s 240ms/step - loss: 0.1094 - accuracy: 0.9680 - val_loss: 0.2392 - val_accuracy: 0.9103 - lr: 0.0080\n",
+      "Epoch 82/84\n",
+      "256/256 [==============================] - 63s 246ms/step - loss: 0.0869 - accuracy: 0.9768 - val_loss: 0.1866 - val_accuracy: 0.9471 - lr: 0.0080\n",
+      "Epoch 83/84\n",
+      "256/256 [==============================] - 61s 236ms/step - loss: 0.0570 - accuracy: 0.9861 - val_loss: 0.3123 - val_accuracy: 0.9343 - lr: 0.0080\n",
+      "Epoch 84/84\n",
+      "256/256 [==============================] - 63s 244ms/step - loss: 0.0551 - accuracy: 0.9856 - val_loss: 0.2779 - val_accuracy: 0.9423 - lr: 0.0080\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-082-0.9471.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1866\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.942308 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.947115\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1879463345 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1866419166\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m516.52 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m386.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m130.17 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 85/90\n",
+      "256/256 [==============================] - 76s 252ms/step - loss: 0.1981 - accuracy: 0.9370 - val_loss: 0.1788 - val_accuracy: 0.9407 - lr: 0.0080\n",
+      "Epoch 86/90\n",
+      "256/256 [==============================] - 63s 245ms/step - loss: 0.1321 - accuracy: 0.9641 - val_loss: 0.2799 - val_accuracy: 0.9119 - lr: 0.0080\n",
+      "Epoch 87/90\n",
+      "256/256 [==============================] - 62s 243ms/step - loss: 0.1001 - accuracy: 0.9700 - val_loss: 0.2444 - val_accuracy: 0.9295 - lr: 0.0080\n",
+      "Epoch 88/90\n",
+      "256/256 [==============================] - 62s 243ms/step - loss: 0.0655 - accuracy: 0.9805 - val_loss: 0.2372 - val_accuracy: 0.9311 - lr: 0.0080\n",
+      "Epoch 89/90\n",
+      "256/256 [==============================] - 64s 249ms/step - loss: 0.0549 - accuracy: 0.9856 - val_loss: 0.2542 - val_accuracy: 0.9423 - lr: 0.0080\n",
+      "Epoch 90/90\n",
+      "256/256 [==============================] - 64s 247ms/step - loss: 0.0418 - accuracy: 0.9902 - val_loss: 0.2532 - val_accuracy: 0.9471 - lr: 0.0080\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-090-0.9471.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2531\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9471153617. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1866419166. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.14GB, used: 18.86GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m524.30 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m392.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m132.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 91/96\n",
+      "256/256 [==============================] - 76s 253ms/step - loss: 0.2177 - accuracy: 0.9324 - val_loss: 0.3818 - val_accuracy: 0.8397 - lr: 0.0080\n",
+      "Epoch 92/96\n",
+      "256/256 [==============================] - 64s 248ms/step - loss: 0.1450 - accuracy: 0.9551 - val_loss: 0.2471 - val_accuracy: 0.9375 - lr: 0.0080\n",
+      "Epoch 93/96\n",
+      "256/256 [==============================] - 63s 246ms/step - loss: 0.1087 - accuracy: 0.9688 - val_loss: 0.2419 - val_accuracy: 0.9359 - lr: 0.0080\n",
+      "Epoch 94/96\n",
+      "256/256 [==============================] - 65s 252ms/step - loss: 0.0798 - accuracy: 0.9812 - val_loss: 0.2352 - val_accuracy: 0.9487 - lr: 0.0080\n",
+      "Epoch 95/96\n",
+      "256/256 [==============================] - 64s 249ms/step - loss: 0.0559 - accuracy: 0.9854 - val_loss: 0.3252 - val_accuracy: 0.9375 - lr: 0.0080\n",
+      "Epoch 96/96\n",
+      "256/256 [==============================] - 64s 247ms/step - loss: 0.0377 - accuracy: 0.9902 - val_loss: 0.3021 - val_accuracy: 0.9439 - lr: 0.0080\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-094-0.9487.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2352\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9471153617. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1866419166. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m530.90 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m395.79 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m135.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 97/102\n",
+      "256/256 [==============================] - 75s 251ms/step - loss: 0.1915 - accuracy: 0.9412 - val_loss: 0.1689 - val_accuracy: 0.9439 - lr: 0.0080\n",
+      "Epoch 98/102\n",
+      "256/256 [==============================] - 64s 247ms/step - loss: 0.1320 - accuracy: 0.9631 - val_loss: 0.2158 - val_accuracy: 0.9455 - lr: 0.0080\n",
+      "Epoch 99/102\n",
+      "256/256 [==============================] - 64s 250ms/step - loss: 0.0982 - accuracy: 0.9741 - val_loss: 0.2063 - val_accuracy: 0.9487 - lr: 0.0080\n",
+      "Epoch 100/102\n",
+      "256/256 [==============================] - 63s 247ms/step - loss: 0.0678 - accuracy: 0.9810 - val_loss: 0.2275 - val_accuracy: 0.9327 - lr: 0.0080\n",
+      "Epoch 101/102\n",
+      "256/256 [==============================] - 56s 219ms/step - loss: 0.0564 - accuracy: 0.9836 - val_loss: 0.3158 - val_accuracy: 0.9407 - lr: 0.0080\n",
+      "Epoch 102/102\n",
+      "256/256 [==============================] - 54s 209ms/step - loss: 0.0466 - accuracy: 0.9885 - val_loss: 0.2824 - val_accuracy: 0.9343 - lr: 0.0080\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-099-0.9487.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2062\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.947115 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.948718\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1866419166. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m512.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m376.74 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m135.37 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 103/108\n",
+      "256/256 [==============================] - 64s 216ms/step - loss: 0.2008 - accuracy: 0.9346 - val_loss: 0.2139 - val_accuracy: 0.9503 - lr: 0.0080\n",
+      "Epoch 104/108\n",
+      "256/256 [==============================] - 54s 208ms/step - loss: 0.1433 - accuracy: 0.9600 - val_loss: 0.2912 - val_accuracy: 0.9054 - lr: 0.0080\n",
+      "Epoch 105/108\n",
+      "256/256 [==============================] - 54s 209ms/step - loss: 0.1129 - accuracy: 0.9695 - val_loss: 0.1858 - val_accuracy: 0.9439 - lr: 0.0080\n",
+      "Epoch 106/108\n",
+      "256/256 [==============================] - 54s 208ms/step - loss: 0.0842 - accuracy: 0.9768 - val_loss: 0.2668 - val_accuracy: 0.9151 - lr: 0.0080\n",
+      "Epoch 107/108\n",
+      "256/256 [==============================] - 54s 208ms/step - loss: 0.0577 - accuracy: 0.9863 - val_loss: 0.2159 - val_accuracy: 0.9407 - lr: 0.0080\n",
+      "Epoch 108/108\n",
+      "256/256 [==============================] - 54s 209ms/step - loss: 0.0493 - accuracy: 0.9873 - val_loss: 0.2527 - val_accuracy: 0.9423 - lr: 0.0080\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-103-0.9503.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2139\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.948718 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.950321\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1866419166. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m431.91 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m332.66 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.25 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 109/114\n",
+      "256/256 [==============================] - 64s 218ms/step - loss: 0.1910 - accuracy: 0.9399 - val_loss: 0.1899 - val_accuracy: 0.9455 - lr: 0.0080\n",
+      "Epoch 110/114\n",
+      "256/256 [==============================] - 55s 213ms/step - loss: 0.1554 - accuracy: 0.9568 - val_loss: 0.1500 - val_accuracy: 0.9535 - lr: 0.0080\n",
+      "Epoch 111/114\n",
+      "256/256 [==============================] - 54s 211ms/step - loss: 0.0971 - accuracy: 0.9751 - val_loss: 0.1827 - val_accuracy: 0.9423 - lr: 0.0080\n",
+      "Epoch 112/114\n",
+      "256/256 [==============================] - 54s 210ms/step - loss: 0.0723 - accuracy: 0.9785 - val_loss: 0.1842 - val_accuracy: 0.9455 - lr: 0.0080\n",
+      "Epoch 113/114\n",
+      "256/256 [==============================] - 54s 210ms/step - loss: 0.0513 - accuracy: 0.9863 - val_loss: 0.1958 - val_accuracy: 0.9439 - lr: 0.0080\n",
+      "Epoch 114/114\n",
+      "256/256 [==============================] - 54s 211ms/step - loss: 0.0507 - accuracy: 0.9873 - val_loss: 0.2487 - val_accuracy: 0.9423 - lr: 0.0080\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-110-0.9535.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1500\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.950321 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.953526\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1866419166 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1500305533\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m440.61 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m336.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.19 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 115/120\n",
+      "256/256 [==============================] - 66s 224ms/step - loss: 0.1680 - accuracy: 0.9451 - val_loss: 0.1517 - val_accuracy: 0.9615 - lr: 0.0080\n",
+      "Epoch 116/120\n",
+      "256/256 [==============================] - 56s 217ms/step - loss: 0.1268 - accuracy: 0.9617 - val_loss: 0.1488 - val_accuracy: 0.9551 - lr: 0.0080\n",
+      "Epoch 117/120\n",
+      "256/256 [==============================] - 56s 217ms/step - loss: 0.0910 - accuracy: 0.9753 - val_loss: 0.1827 - val_accuracy: 0.9551 - lr: 0.0080\n",
+      "Epoch 118/120\n",
+      "256/256 [==============================] - 55s 216ms/step - loss: 0.0695 - accuracy: 0.9819 - val_loss: 0.1697 - val_accuracy: 0.9615 - lr: 0.0080\n",
+      "Epoch 119/120\n",
+      "256/256 [==============================] - 56s 217ms/step - loss: 0.0531 - accuracy: 0.9861 - val_loss: 0.2525 - val_accuracy: 0.9519 - lr: 0.0080\n",
+      "Epoch 120/120\n",
+      "256/256 [==============================] - 57s 220ms/step - loss: 0.0444 - accuracy: 0.9897 - val_loss: 0.1818 - val_accuracy: 0.9631 - lr: 0.0080\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-120-0.9631.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1817\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.953526 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.963141\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1500305533. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m452.17 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m346.20 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.97 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n"
      ]
     },
     {
@@ -4004,6 +8745,7 @@
     "DEC_LR = 0.00006 # DEC_LR: Learning rate decay.\n",
     "MIN_LR = 0.0005 # MIN_LR: Minimum learning rate.\n",
     "RES_LR = 0.006 # RES_LR: Resuming learning rate.\n",
+    "ReduceLROnPlateau_factor = 0.8 # ReduceLROnPlateau_factor: ReduceLROnPlateau factor. (Lr = Factor * Lr_prev)\n",
     "Use_OneCycleLr = False # Use_OneCycleLr: Use OneCycleLr if True. if false, use ReduceLROnPlateau.\n",
     "OneCycleLr_UFTS = False # OneCycleLr_UFTS: Set the OneCycleLr max epochs to the estimated full training SUB epochs. (DEC_LR and MIN_LR dont have any effect if True)\n",
     "Debug_OUTPUT_DPS = True # Debug_OUTPUT_DPS: Output debug image samples if True.\n",
@@ -4229,7 +8971,7 @@
     "# ReduceLROnPlateau\n",
     "if not Use_OneCycleLr:\n",
     "    learning_rate_schedule_SUB = ReduceLROnPlateau(monitor='val_accuracy',\n",
-    "                                                   factor=0.1,\n",
+    "                                                   factor=ReduceLROnPlateau_factor,\n",
     "                                                   patience=subset_epoch * 6,\n",
     "                                                   min_lr=MIN_LR,\n",
     "                                                   verbose=1)\n",
@@ -4244,6 +8986,7 @@
     "print_Color(f'~*Use_extended_tensorboard ~*[{Use_extended_tensorboard}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n",
     "print_Color(f'~*Debug_OUTPUT_DPS ~*[{Debug_OUTPUT_DPS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n",
     "print_Color(f'~*Use_OneCycleLr ~*[{Use_OneCycleLr}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n",
+    "print_Color(f'~*Using the modified ReduceLROnPlateau.', ['cyan'], advanced_mode=True)\n",
     "print_Color(f'~*OneCycleLr_UFTS ~*[{OneCycleLr_UFTS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n",
     "#warnings\n",
     "P_warning('RES_Train is True.') if RES_Train else None\n",
@@ -4440,7 +9183,8 @@
     "            print_Color_V2(f'<light_red>Model loss did not improve from {best_loss:.10f}. Not saving model.') \n",
     "        # Garbage Collection (memory)\n",
     "        gc.collect()\n",
-    "        tf.keras.backend.clear_session()   \n",
+    "        tf.keras.backend.clear_session()  \n",
+    "        GPU_memUsage()\n",
     "        # Epoch end\n",
     "        end_time = time.time()\n",
     "        epoch_time = end_time - start_FULL_time\n",
@@ -4683,6 +9427,41 @@
     "tf.keras.backend.clear_session()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Release all the GPU memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'GPU_memUsage' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[9], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cuda\n\u001b[0;32m      3\u001b[0m device \u001b[38;5;241m=\u001b[39m cuda\u001b[38;5;241m.\u001b[39mget_current_device()\n\u001b[1;32m----> 4\u001b[0m \u001b[43mGPU_memUsage\u001b[49m()\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRealising all memory...\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      6\u001b[0m device\u001b[38;5;241m.\u001b[39mreset()\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'GPU_memUsage' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "from numba import cuda\n",
+    "            \n",
+    "device = cuda.get_current_device()\n",
+    "GPU_memUsage()\n",
+    "print('Realising all memory...')\n",
+    "device.reset()\n",
+    "GPU_memUsage()\n",
+    "print('done.')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/Data/image_SUB_generator.pkl b/Data/image_SUB_generator.pkl
new file mode 100644
index 0000000..5c8d1fe
Binary files /dev/null and b/Data/image_SUB_generator.pkl differ
diff --git a/Model_T&T.ipynb b/Model_T&T.ipynb
index 01805c4..1da0c33 100644
--- a/Model_T&T.ipynb
+++ b/Model_T&T.ipynb
@@ -179,6 +179,7 @@
     "R_fill_mode = True\n",
     "add_img_grain = True\n",
     "Save_TS = True\n",
+    "Img_Data_type = 'float16' # float32 / float16\n",
     "Use_SMOTE = False # (⚠️Beta⚠️)\n",
     "ADBD = 0\n",
     "OP_HDC = False\n",
@@ -260,14 +261,14 @@
       "\u001b[0;33mMaking categorical data...\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;33mGenerating augmented data \u001b[0m\u001b[0;36m[\u001b[0m\u001b[0;32mADBD: \u001b[0m\u001b[0;31m0\u001b[0m\u001b[0;36m]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;33mNormalizing image data...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0mData type: \u001b[0m\u001b[0;32mfloat32\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0mData type: \u001b[0m\u001b[0;32mfloat16\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0mRGB Range: \u001b[0m\u001b[0;34mMin = 0.0\u001b[0m\u001b[0m | \u001b[0m\u001b[0;31mMax = 1.0\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0mLabel ratio: \u001b[0m\u001b[0;31m49.35% PNEUMONIA \u001b[0m\u001b[0;35m| \u001b[0m\u001b[0;32m50.65% NORMAL\u001b[0m\n",
       "\u001b[0;33mSetting LNTS...\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0mOriginal num_samples: \u001b[0m\u001b[0;32m23681\u001b[0m\n",
       "\u001b[0;33mshuffling data...\u001b[0m\n",
       "\u001b[0;33mSaving TS...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2024_m02_d11-h23_m13_s10\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2024_m02_d17-h23_m16_s45\u001b[0m\n",
       "\u001b[0;32mDone.\u001b[0m\n"
      ]
     }
@@ -275,21 +276,21 @@
    "source": [
     "#Z_SCORE_normalize\n",
     "def Z_SCORE_normalize(arr):\n",
-    "   arr = arr.astype('float32')\n",
+    "   arr = arr.astype(Img_Data_type)\n",
     "   mean = np.mean(arr)\n",
     "   std_dev = np.std(arr)\n",
     "   arr = (arr - mean) / std_dev\n",
     "   return arr\n",
     "#normalize_TO_RANGE\n",
     "def normalize_TO_RANGE(arr, min_val, max_val):\n",
-    "  arr = arr.astype('float32')\n",
+    "  arr = arr.astype(Img_Data_type)\n",
     "  arr = (arr - arr.min()) / (arr.max() - arr.min())\n",
     "  arr = arr * (max_val - min_val) + min_val\n",
     "  return arr\n",
     "#scale_data\n",
     "def scale_data_NP(data):\n",
     "    if scale_data_NP_M:\n",
-    "        data = data.astype('float32')\n",
+    "        data = data.astype(Img_Data_type)\n",
     "        data = (data - 127.5) / 127.5\n",
     "        return data\n",
     "    else:\n",
@@ -300,7 +301,7 @@
     "    noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8)\n",
     "\n",
     "    # Scale the noise array\n",
-    "    scaled_noise = (noise * intensity).astype(np.float32)\n",
+    "    scaled_noise = (noise * intensity).astype(np.float32 if Img_Data_type == 'float32' else 'float16')\n",
     "    # Add the noise to the image\n",
     "    noisy_image = cv2.add(image, scaled_noise)\n",
     "\n",
@@ -413,7 +414,8 @@
     "        featurewise_std_normalization=False,\n",
     "        interpolation_order=interpolation_order_IFG,\n",
     "        fill_mode='nearest', # constant\n",
-    "        preprocessing_function=noise_func\n",
+    "        preprocessing_function=noise_func,\n",
+    "        dtype=Img_Data_type\n",
     "    )\n",
     "else:\n",
     "    print_Color('Using Def IDG...', ['yellow'])\n",
@@ -431,7 +433,8 @@
     "        interpolation_order=interpolation_order_IFG,\n",
     "        featurewise_std_normalization=False,\n",
     "        fill_mode='nearest', # constant\n",
-    "        preprocessing_function=noise_func\n",
+    "        preprocessing_function=noise_func,\n",
+    "        dtype=Img_Data_type\n",
     "    )\n",
     "train_datagen_SM = ImageDataGenerator(\n",
     "    horizontal_flip=False,\n",
@@ -445,7 +448,8 @@
     "    channel_shift_range=0,\n",
     "    featurewise_center=False,\n",
     "    interpolation_order=interpolation_order_IFG,\n",
-    "    featurewise_std_normalization=False\n",
+    "    featurewise_std_normalization=False,\n",
+    "    dtype=Img_Data_type\n",
     ")\n",
     "# Create an iterator for the training set\n",
     "train_generator_SM = train_datagen_SM.flow_from_directory(\n",
@@ -460,6 +464,7 @@
     "        zoom_range = 0.01, \n",
     "        width_shift_range=0.01, \n",
     "        interpolation_order=interpolation_order_IFG,\n",
+    "        dtype=Img_Data_type,\n",
     "        height_shift_range=0.01)\n",
     "\n",
     "    # Create an iterator for the validation set\n",
@@ -476,6 +481,7 @@
     "        zoom_range = 0.01, \n",
     "        width_shift_range=0.01, \n",
     "        interpolation_order=interpolation_order_IFG,\n",
+    "        dtype=Img_Data_type,\n",
     "        height_shift_range=0.01)\n",
     "\n",
     "    # Create an iterator for the test set\n",
@@ -3844,248 +3850,2779 @@
       "Training the model...\n",
       "\u001b[0;33m\n",
       "Setup Verbose:\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m02_d11-h23_m13_s22]\u001b[0m\u001b[0;36m...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m02_d17-h23_m16_s58]\u001b[0m\u001b[0;36m...\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mUse_OneCycleLr \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
       "\u001b[0;33mSetup Verbose END.\u001b[0m\n",
       "\u001b[0m\n",
       "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[0;33mPreparing train data...\u001b[0m\n",
       "\u001b[0;33m- Fitting ImageDataGenerator...\u001b[0m\n",
       "\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n",
       "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
       "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m02_d12-h16_m22_s25\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[8]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m02_d17-h23_m22_s10\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;32mTraining on subset...\u001b[0m\n",
-      "Epoch 1/8\n",
-      "256/256 [==============================] - 95s 287ms/step - loss: 9.1551 - accuracy: 0.6196 - val_loss: 8.1979 - val_accuracy: 0.7308\n",
-      "Epoch 2/8\n",
-      "256/256 [==============================] - 72s 281ms/step - loss: 6.8418 - accuracy: 0.7876 - val_loss: 5.6343 - val_accuracy: 0.7644\n",
-      "Epoch 3/8\n",
-      "256/256 [==============================] - 72s 281ms/step - loss: 4.4641 - accuracy: 0.8574 - val_loss: 3.5514 - val_accuracy: 0.8734\n",
-      "Epoch 4/8\n",
-      "256/256 [==============================] - 71s 277ms/step - loss: 2.9271 - accuracy: 0.8877 - val_loss: 2.6880 - val_accuracy: 0.7436\n",
-      "Epoch 5/8\n",
-      "256/256 [==============================] - 70s 273ms/step - loss: 2.0127 - accuracy: 0.9141 - val_loss: 1.7827 - val_accuracy: 0.8734\n",
-      "Epoch 6/8\n",
-      "256/256 [==============================] - 74s 286ms/step - loss: 1.4831 - accuracy: 0.9236 - val_loss: 1.3144 - val_accuracy: 0.9311\n",
-      "Epoch 7/8\n",
-      "256/256 [==============================] - 71s 277ms/step - loss: 1.1651 - accuracy: 0.9495 - val_loss: 1.2676 - val_accuracy: 0.8846\n",
-      "Epoch 8/8\n",
-      "256/256 [==============================] - 70s 272ms/step - loss: 1.0656 - accuracy: 0.9487 - val_loss: 1.1422 - val_accuracy: 0.9151\n",
+      "Epoch 1/6\n",
+      "256/256 [==============================] - 112s 363ms/step - loss: 7.1283 - accuracy: 0.5090 - val_loss: 4.6667 - val_accuracy: 0.3750 - lr: 0.0100\n",
+      "Epoch 2/6\n",
+      "256/256 [==============================] - 96s 376ms/step - loss: 3.2733 - accuracy: 0.6890 - val_loss: 2.1362 - val_accuracy: 0.7853 - lr: 0.0100\n",
+      "Epoch 3/6\n",
+      "256/256 [==============================] - 101s 394ms/step - loss: 1.6445 - accuracy: 0.8176 - val_loss: 1.1380 - val_accuracy: 0.8814 - lr: 0.0100\n",
+      "Epoch 4/6\n",
+      "256/256 [==============================] - 101s 394ms/step - loss: 0.9876 - accuracy: 0.8521 - val_loss: 0.6707 - val_accuracy: 0.8926 - lr: 0.0100\n",
+      "Epoch 5/6\n",
+      "256/256 [==============================] - 99s 388ms/step - loss: 0.6460 - accuracy: 0.8723 - val_loss: 0.6975 - val_accuracy: 0.7612 - lr: 0.0100\n",
+      "Epoch 6/6\n",
+      "256/256 [==============================] - 100s 392ms/step - loss: 0.4603 - accuracy: 0.8994 - val_loss: 0.5359 - val_accuracy: 0.7724 - lr: 0.0100\n",
       "\u001b[0;32mSubset training done.\u001b[0m\n",
       "\u001b[0;33mLoading the best weights...\u001b[0m\n",
-      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-006-0.9311.h5...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.3144\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.000000 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.931090\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-004-0.8926.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8926\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.6707\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.000000 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.892628\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m1.3143811226\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.6707034707\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m62360.30 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m596.05 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m61764.25 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m941.73 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m611.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m330.32 \u001b[0m\u001b[0;36msec\u001b[0m\n",
       "\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n",
       "\u001b[0m\n",
-      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 8)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;33mPreparing train data...\u001b[0m\n",
       "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
       "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[8]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;32mTraining on subset...\u001b[0m\n",
-      "Epoch 9/16\n",
-      "256/256 [==============================] - 76s 279ms/step - loss: 1.3530 - accuracy: 0.8972 - val_loss: 1.2383 - val_accuracy: 0.9054\n",
-      "Epoch 10/16\n",
-      "256/256 [==============================] - 71s 275ms/step - loss: 1.0939 - accuracy: 0.9097 - val_loss: 0.9267 - val_accuracy: 0.9006\n",
-      "Epoch 11/16\n",
-      "256/256 [==============================] - 70s 274ms/step - loss: 0.8313 - accuracy: 0.9133 - val_loss: 0.8747 - val_accuracy: 0.8830\n",
-      "Epoch 12/16\n",
-      "256/256 [==============================] - 70s 274ms/step - loss: 0.6220 - accuracy: 0.9290 - val_loss: 0.7192 - val_accuracy: 0.8381\n",
-      "Epoch 13/16\n",
-      "256/256 [==============================] - 71s 278ms/step - loss: 0.4703 - accuracy: 0.9407 - val_loss: 0.4454 - val_accuracy: 0.9295\n",
-      "Epoch 14/16\n",
-      "256/256 [==============================] - 70s 275ms/step - loss: 0.3752 - accuracy: 0.9536 - val_loss: 0.4323 - val_accuracy: 0.9119\n",
-      "Epoch 15/16\n",
-      "256/256 [==============================] - 71s 276ms/step - loss: 0.3066 - accuracy: 0.9641 - val_loss: 0.5609 - val_accuracy: 0.8446\n",
-      "Epoch 16/16\n",
-      "256/256 [==============================] - 71s 277ms/step - loss: 0.2565 - accuracy: 0.9753 - val_loss: 0.5077 - val_accuracy: 0.8782\n",
+      "Epoch 7/12\n",
+      "256/256 [==============================] - 101s 380ms/step - loss: 0.6842 - accuracy: 0.8606 - val_loss: 0.4796 - val_accuracy: 0.9151 - lr: 0.0100\n",
+      "Epoch 8/12\n",
+      "256/256 [==============================] - 102s 399ms/step - loss: 0.5021 - accuracy: 0.8823 - val_loss: 0.6667 - val_accuracy: 0.7340 - lr: 0.0100\n",
+      "Epoch 9/12\n",
+      "256/256 [==============================] - 102s 397ms/step - loss: 0.4081 - accuracy: 0.8923 - val_loss: 0.5067 - val_accuracy: 0.9247 - lr: 0.0100\n",
+      "Epoch 10/12\n",
+      "256/256 [==============================] - 102s 398ms/step - loss: 0.3109 - accuracy: 0.9106 - val_loss: 0.3329 - val_accuracy: 0.9006 - lr: 0.0100\n",
+      "Epoch 11/12\n",
+      "256/256 [==============================] - 102s 396ms/step - loss: 0.2886 - accuracy: 0.9185 - val_loss: 0.2278 - val_accuracy: 0.9343 - lr: 0.0100\n",
+      "Epoch 12/12\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.2299 - accuracy: 0.9319 - val_loss: 0.6328 - val_accuracy: 0.8734 - lr: 0.0100\n",
       "\u001b[0;32mSubset training done.\u001b[0m\n",
       "\u001b[0;33mLoading the best weights...\u001b[0m\n",
-      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-013-0.9295.h5...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4454\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9310897589. Not saving model.\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m1.3143811226 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.4453735352\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-011-0.9343.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2278\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.892628 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.934295\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m635.30 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m572.22 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m63.08 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.6707034707 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2277684361\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m674.79 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m608.83 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.97 \u001b[0m\u001b[0;36msec\u001b[0m\n",
       "\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n",
       "\u001b[0m\n",
-      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 16)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 13/18\n",
+      "256/256 [==============================] - 101s 382ms/step - loss: 0.3353 - accuracy: 0.8936 - val_loss: 0.2317 - val_accuracy: 0.9359 - lr: 0.0100\n",
+      "Epoch 14/18\n",
+      "256/256 [==============================] - 100s 392ms/step - loss: 0.2979 - accuracy: 0.9004 - val_loss: 0.1880 - val_accuracy: 0.9423 - lr: 0.0100\n",
+      "Epoch 15/18\n",
+      "256/256 [==============================] - 96s 375ms/step - loss: 0.2394 - accuracy: 0.9255 - val_loss: 0.5667 - val_accuracy: 0.8542 - lr: 0.0100\n",
+      "Epoch 16/18\n",
+      "256/256 [==============================] - 97s 380ms/step - loss: 0.2147 - accuracy: 0.9312 - val_loss: 0.4449 - val_accuracy: 0.8542 - lr: 0.0100\n",
+      "Epoch 17/18\n",
+      "256/256 [==============================] - 97s 379ms/step - loss: 0.1955 - accuracy: 0.9404 - val_loss: 0.2868 - val_accuracy: 0.9359 - lr: 0.0100\n",
+      "Epoch 18/18\n",
+      "256/256 [==============================] - 102s 399ms/step - loss: 0.1741 - accuracy: 0.9478 - val_loss: 0.3708 - val_accuracy: 0.9327 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-014-0.9423.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1880\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.934295 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.942308\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2277684361 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1880175322\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m663.48 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m595.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.59 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;33mPreparing train data...\u001b[0m\n",
       "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
       "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[8]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;32mTraining on subset...\u001b[0m\n",
-      "Epoch 17/24\n",
-      "256/256 [==============================] - 78s 287ms/step - loss: 0.5226 - accuracy: 0.9058 - val_loss: 0.4769 - val_accuracy: 0.8990\n",
-      "Epoch 18/24\n",
-      "256/256 [==============================] - 71s 277ms/step - loss: 0.4608 - accuracy: 0.9092 - val_loss: 0.3894 - val_accuracy: 0.9343\n",
       "Epoch 19/24\n",
-      "256/256 [==============================] - 71s 277ms/step - loss: 0.3832 - accuracy: 0.9180 - val_loss: 0.3303 - val_accuracy: 0.9343\n",
+      "256/256 [==============================] - 101s 379ms/step - loss: 0.3215 - accuracy: 0.8926 - val_loss: 0.1962 - val_accuracy: 0.9423 - lr: 0.0100\n",
       "Epoch 20/24\n",
-      "256/256 [==============================] - 74s 287ms/step - loss: 0.3069 - accuracy: 0.9380 - val_loss: 0.2889 - val_accuracy: 0.9327\n",
+      "256/256 [==============================] - 101s 394ms/step - loss: 0.2743 - accuracy: 0.9155 - val_loss: 0.2456 - val_accuracy: 0.9199 - lr: 0.0100\n",
       "Epoch 21/24\n",
-      "256/256 [==============================] - 74s 288ms/step - loss: 0.2406 - accuracy: 0.9487 - val_loss: 0.2463 - val_accuracy: 0.9407\n",
+      "256/256 [==============================] - 103s 401ms/step - loss: 0.2349 - accuracy: 0.9250 - val_loss: 0.5406 - val_accuracy: 0.8926 - lr: 0.0100\n",
       "Epoch 22/24\n",
-      "256/256 [==============================] - 73s 284ms/step - loss: 0.2022 - accuracy: 0.9543 - val_loss: 0.3334 - val_accuracy: 0.9038\n",
+      "256/256 [==============================] - 101s 394ms/step - loss: 0.2349 - accuracy: 0.9250 - val_loss: 0.2288 - val_accuracy: 0.9199 - lr: 0.0100\n",
       "Epoch 23/24\n",
-      "256/256 [==============================] - 72s 282ms/step - loss: 0.1585 - accuracy: 0.9639 - val_loss: 0.2701 - val_accuracy: 0.9311\n",
+      "256/256 [==============================] - 103s 401ms/step - loss: 0.1787 - accuracy: 0.9485 - val_loss: 0.2014 - val_accuracy: 0.9455 - lr: 0.0100\n",
       "Epoch 24/24\n",
-      "256/256 [==============================] - 73s 283ms/step - loss: 0.1294 - accuracy: 0.9780 - val_loss: 0.2639 - val_accuracy: 0.9295\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1740 - accuracy: 0.9470 - val_loss: 0.4441 - val_accuracy: 0.8990 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-023-0.9455.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2014\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.942308 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.945513\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1880175322. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m675.00 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m608.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.87 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 25/30\n",
+      "256/256 [==============================] - 100s 375ms/step - loss: 0.2860 - accuracy: 0.9102 - val_loss: 0.2977 - val_accuracy: 0.9183 - lr: 0.0100\n",
+      "Epoch 26/30\n",
+      "256/256 [==============================] - 101s 394ms/step - loss: 0.2466 - accuracy: 0.9163 - val_loss: 0.2273 - val_accuracy: 0.9423 - lr: 0.0100\n",
+      "Epoch 27/30\n",
+      "256/256 [==============================] - 99s 386ms/step - loss: 0.2184 - accuracy: 0.9319 - val_loss: 0.2345 - val_accuracy: 0.9359 - lr: 0.0100\n",
+      "Epoch 28/30\n",
+      "256/256 [==============================] - 102s 398ms/step - loss: 0.1889 - accuracy: 0.9429 - val_loss: 0.2118 - val_accuracy: 0.9263 - lr: 0.0100\n",
+      "Epoch 29/30\n",
+      "256/256 [==============================] - 101s 395ms/step - loss: 0.1691 - accuracy: 0.9482 - val_loss: 0.3478 - val_accuracy: 0.9038 - lr: 0.0100\n",
+      "Epoch 30/30\n",
+      "256/256 [==============================] - 97s 380ms/step - loss: 0.1479 - accuracy: 0.9631 - val_loss: 0.2684 - val_accuracy: 0.9038 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-026-0.9423.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2272\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9455128312. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1880175322. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m666.94 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m601.54 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 31/36\n",
+      "256/256 [==============================] - 103s 387ms/step - loss: 0.2926 - accuracy: 0.9055 - val_loss: 0.1994 - val_accuracy: 0.9391 - lr: 0.0100\n",
+      "Epoch 32/36\n",
+      "256/256 [==============================] - 100s 390ms/step - loss: 0.2459 - accuracy: 0.9229 - val_loss: 0.1988 - val_accuracy: 0.9311 - lr: 0.0100\n",
+      "Epoch 33/36\n",
+      "256/256 [==============================] - 103s 400ms/step - loss: 0.1866 - accuracy: 0.9426 - val_loss: 0.2473 - val_accuracy: 0.9311 - lr: 0.0100\n",
+      "Epoch 34/36\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1694 - accuracy: 0.9526 - val_loss: 0.3695 - val_accuracy: 0.9295 - lr: 0.0100\n",
+      "Epoch 35/36\n",
+      "256/256 [==============================] - 100s 390ms/step - loss: 0.1620 - accuracy: 0.9573 - val_loss: 0.4695 - val_accuracy: 0.9263 - lr: 0.0100\n",
+      "Epoch 36/36\n",
+      "256/256 [==============================] - 97s 380ms/step - loss: 0.1678 - accuracy: 0.9514 - val_loss: 0.2931 - val_accuracy: 0.9167 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-031-0.9391.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1994\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9455128312. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1880175322. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m668.57 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m602.79 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.78 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 37/42\n",
+      "256/256 [==============================] - 102s 382ms/step - loss: 0.2835 - accuracy: 0.9060 - val_loss: 0.1842 - val_accuracy: 0.9455 - lr: 0.0100\n",
+      "Epoch 38/42\n",
+      "256/256 [==============================] - 98s 383ms/step - loss: 0.2643 - accuracy: 0.9167 - val_loss: 0.3530 - val_accuracy: 0.9183 - lr: 0.0100\n",
+      "Epoch 39/42\n",
+      "256/256 [==============================] - 96s 375ms/step - loss: 0.1828 - accuracy: 0.9465 - val_loss: 0.2193 - val_accuracy: 0.9311 - lr: 0.0100\n",
+      "Epoch 40/42\n",
+      "256/256 [==============================] - 96s 376ms/step - loss: 0.1743 - accuracy: 0.9465 - val_loss: 0.2400 - val_accuracy: 0.9391 - lr: 0.0100\n",
+      "Epoch 41/42\n",
+      "256/256 [==============================] - 96s 375ms/step - loss: 0.1513 - accuracy: 0.9578 - val_loss: 0.3145 - val_accuracy: 0.8766 - lr: 0.0100\n",
+      "Epoch 42/42\n",
+      "256/256 [==============================] - 98s 381ms/step - loss: 0.1342 - accuracy: 0.9646 - val_loss: 0.2169 - val_accuracy: 0.9391 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-037-0.9455.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1842\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9455128312. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1880175322 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1841763705\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m655.37 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m587.17 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.21 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 43/48\n",
+      "256/256 [==============================] - 100s 378ms/step - loss: 0.2630 - accuracy: 0.9138 - val_loss: 0.2486 - val_accuracy: 0.9119 - lr: 0.0100\n",
+      "Epoch 44/48\n",
+      "256/256 [==============================] - 102s 396ms/step - loss: 0.2565 - accuracy: 0.9182 - val_loss: 0.1904 - val_accuracy: 0.9535 - lr: 0.0100\n",
+      "Epoch 45/48\n",
+      "256/256 [==============================] - 101s 395ms/step - loss: 0.2104 - accuracy: 0.9380 - val_loss: 0.2425 - val_accuracy: 0.9487 - lr: 0.0100\n",
+      "Epoch 46/48\n",
+      "256/256 [==============================] - 104s 405ms/step - loss: 0.1861 - accuracy: 0.9478 - val_loss: 0.5963 - val_accuracy: 0.8205 - lr: 0.0100\n",
+      "Epoch 47/48\n",
+      "256/256 [==============================] - 100s 391ms/step - loss: 0.1398 - accuracy: 0.9595 - val_loss: 0.4671 - val_accuracy: 0.8878 - lr: 0.0100\n",
+      "Epoch 48/48\n",
+      "256/256 [==============================] - 98s 381ms/step - loss: 0.1285 - accuracy: 0.9661 - val_loss: 0.7364 - val_accuracy: 0.7885 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-044-0.9535.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1905\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.945513 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.953526\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1841763705. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m674.30 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m605.59 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.71 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 49/54\n",
+      "256/256 [==============================] - 103s 387ms/step - loss: 0.2792 - accuracy: 0.9106 - val_loss: 0.1871 - val_accuracy: 0.9407 - lr: 0.0100\n",
+      "Epoch 50/54\n",
+      "256/256 [==============================] - 95s 369ms/step - loss: 0.2003 - accuracy: 0.9343 - val_loss: 0.1856 - val_accuracy: 0.9343 - lr: 0.0100\n",
+      "Epoch 51/54\n",
+      "256/256 [==============================] - 95s 370ms/step - loss: 0.1818 - accuracy: 0.9475 - val_loss: 0.4352 - val_accuracy: 0.8782 - lr: 0.0100\n",
+      "Epoch 52/54\n",
+      "256/256 [==============================] - 96s 374ms/step - loss: 0.1485 - accuracy: 0.9531 - val_loss: 0.1813 - val_accuracy: 0.9359 - lr: 0.0100\n",
+      "Epoch 53/54\n",
+      "256/256 [==============================] - 97s 376ms/step - loss: 0.1220 - accuracy: 0.9658 - val_loss: 0.4157 - val_accuracy: 0.9247 - lr: 0.0100\n",
+      "Epoch 54/54\n",
+      "256/256 [==============================] - 97s 378ms/step - loss: 0.1259 - accuracy: 0.9641 - val_loss: 0.3071 - val_accuracy: 0.9391 - lr: 0.0100\n",
       "\u001b[0;32mSubset training done.\u001b[0m\n",
       "\u001b[0;33mLoading the best weights...\u001b[0m\n",
-      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-021-0.9407.h5...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-049-0.9407.h5...\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2463\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.931090 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.940705\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1871\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1841763705. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m650.75 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m582.73 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 55/60\n",
+      "256/256 [==============================] - 104s 393ms/step - loss: 0.2572 - accuracy: 0.9187 - val_loss: 0.2462 - val_accuracy: 0.9054 - lr: 0.0100\n",
+      "Epoch 56/60\n",
+      "256/256 [==============================] - 101s 394ms/step - loss: 0.2368 - accuracy: 0.9265 - val_loss: 0.2266 - val_accuracy: 0.9423 - lr: 0.0100\n",
+      "Epoch 57/60\n",
+      "256/256 [==============================] - 101s 393ms/step - loss: 0.1814 - accuracy: 0.9446 - val_loss: 0.1603 - val_accuracy: 0.9487 - lr: 0.0100\n",
+      "Epoch 58/60\n",
+      "256/256 [==============================] - 104s 404ms/step - loss: 0.1633 - accuracy: 0.9519 - val_loss: 0.1789 - val_accuracy: 0.9423 - lr: 0.0100\n",
+      "Epoch 59/60\n",
+      "256/256 [==============================] - 101s 396ms/step - loss: 0.1376 - accuracy: 0.9609 - val_loss: 0.2384 - val_accuracy: 0.9391 - lr: 0.0100\n",
+      "Epoch 60/60\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1100 - accuracy: 0.9712 - val_loss: 0.2274 - val_accuracy: 0.9471 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-057-0.9487.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1603\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1841763705 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1602820307\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.4453735352 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2462929189\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m681.69 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m611.45 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 61/66\n",
+      "256/256 [==============================] - 99s 370ms/step - loss: 0.2621 - accuracy: 0.9126 - val_loss: 0.1781 - val_accuracy: 0.9471 - lr: 0.0100\n",
+      "Epoch 62/66\n",
+      "256/256 [==============================] - 92s 359ms/step - loss: 0.1977 - accuracy: 0.9358 - val_loss: 0.2605 - val_accuracy: 0.9295 - lr: 0.0100\n",
+      "Epoch 63/66\n",
+      "256/256 [==============================] - 95s 372ms/step - loss: 0.1650 - accuracy: 0.9502 - val_loss: 0.2032 - val_accuracy: 0.9151 - lr: 0.0100\n",
+      "Epoch 64/66\n",
+      "256/256 [==============================] - 95s 370ms/step - loss: 0.1322 - accuracy: 0.9619 - val_loss: 0.3898 - val_accuracy: 0.8830 - lr: 0.0100\n",
+      "Epoch 65/66\n",
+      "256/256 [==============================] - 96s 373ms/step - loss: 0.1158 - accuracy: 0.9670 - val_loss: 0.2746 - val_accuracy: 0.9263 - lr: 0.0100\n",
+      "Epoch 66/66\n",
+      "256/256 [==============================] - 97s 379ms/step - loss: 0.1034 - accuracy: 0.9722 - val_loss: 0.3558 - val_accuracy: 0.9006 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-061-0.9471.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1781\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1602820307. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m644.29 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m574.95 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.34 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 67/72\n",
+      "256/256 [==============================] - 100s 375ms/step - loss: 0.2306 - accuracy: 0.9272 - val_loss: 0.1928 - val_accuracy: 0.9439 - lr: 0.0100\n",
+      "Epoch 68/72\n",
+      "256/256 [==============================] - 101s 392ms/step - loss: 0.2051 - accuracy: 0.9373 - val_loss: 0.1938 - val_accuracy: 0.9455 - lr: 0.0100\n",
+      "Epoch 69/72\n",
+      "256/256 [==============================] - 100s 389ms/step - loss: 0.1722 - accuracy: 0.9453 - val_loss: 0.2313 - val_accuracy: 0.9167 - lr: 0.0100\n",
+      "Epoch 70/72\n",
+      "256/256 [==============================] - 97s 377ms/step - loss: 0.1301 - accuracy: 0.9634 - val_loss: 0.2942 - val_accuracy: 0.8958 - lr: 0.0100\n",
+      "Epoch 71/72\n",
+      "256/256 [==============================] - 95s 369ms/step - loss: 0.1197 - accuracy: 0.9680 - val_loss: 0.2169 - val_accuracy: 0.9263 - lr: 0.0100\n",
+      "Epoch 72/72\n",
+      "256/256 [==============================] - 98s 381ms/step - loss: 0.1049 - accuracy: 0.9773 - val_loss: 0.3938 - val_accuracy: 0.9151 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-068-0.9455.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1938\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1602820307. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m660.00 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m589.83 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.17 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 73/78\n",
+      "256/256 [==============================] - 103s 388ms/step - loss: 0.2483 - accuracy: 0.9280 - val_loss: 0.1994 - val_accuracy: 0.9375 - lr: 0.0100\n",
+      "Epoch 74/78\n",
+      "256/256 [==============================] - 96s 376ms/step - loss: 0.1871 - accuracy: 0.9424 - val_loss: 0.1789 - val_accuracy: 0.9407 - lr: 0.0100\n",
+      "Epoch 75/78\n",
+      "256/256 [==============================] - 96s 376ms/step - loss: 0.1788 - accuracy: 0.9451 - val_loss: 0.1951 - val_accuracy: 0.9439 - lr: 0.0100\n",
+      "Epoch 76/78\n",
+      "256/256 [==============================] - 96s 375ms/step - loss: 0.1408 - accuracy: 0.9585 - val_loss: 0.2842 - val_accuracy: 0.9215 - lr: 0.0100\n",
+      "Epoch 77/78\n",
+      "256/256 [==============================] - 96s 373ms/step - loss: 0.1204 - accuracy: 0.9663 - val_loss: 0.2714 - val_accuracy: 0.9359 - lr: 0.0100\n",
+      "Epoch 78/78\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1016 - accuracy: 0.9724 - val_loss: 0.2670 - val_accuracy: 0.9215 - lr: 0.0100\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-075-0.9439.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1951\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1602820307. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m660.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m587.92 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.38 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 79/84\n",
+      "256/256 [==============================] - 104s 391ms/step - loss: 0.2218 - accuracy: 0.9324 - val_loss: 0.2859 - val_accuracy: 0.9135 - lr: 0.0100\n",
+      "Epoch 80/84\n",
+      "256/256 [==============================] - ETA: 0s - loss: 0.1733 - accuracy: 0.9497\n",
+      "Epoch 80: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
+      "256/256 [==============================] - 102s 397ms/step - loss: 0.1733 - accuracy: 0.9497 - val_loss: 0.3786 - val_accuracy: 0.9022 - lr: 0.0100\n",
+      "Epoch 81/84\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1299 - accuracy: 0.9644 - val_loss: 0.1974 - val_accuracy: 0.9327 - lr: 1.0000e-03\n",
+      "Epoch 82/84\n",
+      "256/256 [==============================] - 104s 405ms/step - loss: 0.1255 - accuracy: 0.9651 - val_loss: 0.1761 - val_accuracy: 0.9295 - lr: 1.0000e-03\n",
+      "Epoch 83/84\n",
+      "256/256 [==============================] - 101s 393ms/step - loss: 0.1087 - accuracy: 0.9688 - val_loss: 0.1990 - val_accuracy: 0.9359 - lr: 1.0000e-03\n",
+      "Epoch 84/84\n",
+      "256/256 [==============================] - 96s 375ms/step - loss: 0.0943 - accuracy: 0.9746 - val_loss: 0.1967 - val_accuracy: 0.9343 - lr: 1.0000e-03\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-083-0.9359.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1989\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1602820307. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m678.68 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m606.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.21 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 85/90\n",
+      "256/256 [==============================] - 103s 386ms/step - loss: 0.1920 - accuracy: 0.9392 - val_loss: 0.2229 - val_accuracy: 0.9279 - lr: 1.0000e-03\n",
+      "Epoch 86/90\n",
+      "256/256 [==============================] - 99s 388ms/step - loss: 0.1734 - accuracy: 0.9424 - val_loss: 0.2139 - val_accuracy: 0.9295 - lr: 1.0000e-03\n",
+      "Epoch 87/90\n",
+      "256/256 [==============================] - 102s 398ms/step - loss: 0.1657 - accuracy: 0.9475 - val_loss: 0.1940 - val_accuracy: 0.9327 - lr: 1.0000e-03\n",
+      "Epoch 88/90\n",
+      "256/256 [==============================] - 100s 391ms/step - loss: 0.1607 - accuracy: 0.9480 - val_loss: 0.1888 - val_accuracy: 0.9343 - lr: 1.0000e-03\n",
+      "Epoch 89/90\n",
+      "256/256 [==============================] - 102s 396ms/step - loss: 0.1523 - accuracy: 0.9517 - val_loss: 0.1611 - val_accuracy: 0.9423 - lr: 1.0000e-03\n",
+      "Epoch 90/90\n",
+      "256/256 [==============================] - 102s 396ms/step - loss: 0.1407 - accuracy: 0.9536 - val_loss: 0.1995 - val_accuracy: 0.9327 - lr: 1.0000e-03\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-089-0.9423.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1611\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1602820307. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m681.83 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m608.38 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m73.45 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 91/96\n",
+      "256/256 [==============================] - 102s 385ms/step - loss: 0.1897 - accuracy: 0.9363 - val_loss: 0.1646 - val_accuracy: 0.9391 - lr: 1.0000e-03\n",
+      "Epoch 92/96\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1915 - accuracy: 0.9380 - val_loss: 0.1689 - val_accuracy: 0.9407 - lr: 1.0000e-03\n",
+      "Epoch 93/96\n",
+      "256/256 [==============================] - 102s 399ms/step - loss: 0.1633 - accuracy: 0.9478 - val_loss: 0.1787 - val_accuracy: 0.9391 - lr: 1.0000e-03\n",
+      "Epoch 94/96\n",
+      "256/256 [==============================] - 100s 389ms/step - loss: 0.1642 - accuracy: 0.9429 - val_loss: 0.1624 - val_accuracy: 0.9375 - lr: 1.0000e-03\n",
+      "Epoch 95/96\n",
+      "256/256 [==============================] - 98s 384ms/step - loss: 0.1556 - accuracy: 0.9495 - val_loss: 0.1524 - val_accuracy: 0.9343 - lr: 1.0000e-03\n",
+      "Epoch 96/96\n",
+      "256/256 [==============================] - 99s 385ms/step - loss: 0.1447 - accuracy: 0.9514 - val_loss: 0.1603 - val_accuracy: 0.9423 - lr: 1.0000e-03\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-096-0.9423.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1603\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1602820307. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m676.36 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m601.68 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.68 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 97/102\n",
+      "256/256 [==============================] - 103s 386ms/step - loss: 0.1811 - accuracy: 0.9387 - val_loss: 0.1600 - val_accuracy: 0.9375 - lr: 1.0000e-03\n",
+      "Epoch 98/102\n",
+      "256/256 [==============================] - 100s 389ms/step - loss: 0.1574 - accuracy: 0.9446 - val_loss: 0.1712 - val_accuracy: 0.9407 - lr: 1.0000e-03\n",
+      "Epoch 99/102\n",
+      "256/256 [==============================] - 98s 382ms/step - loss: 0.1530 - accuracy: 0.9478 - val_loss: 0.1583 - val_accuracy: 0.9391 - lr: 1.0000e-03\n",
+      "Epoch 100/102\n",
+      "256/256 [==============================] - 96s 374ms/step - loss: 0.1421 - accuracy: 0.9526 - val_loss: 0.1583 - val_accuracy: 0.9407 - lr: 1.0000e-03\n",
+      "Epoch 101/102\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1336 - accuracy: 0.9551 - val_loss: 0.1562 - val_accuracy: 0.9343 - lr: 1.0000e-03\n",
+      "Epoch 102/102\n",
+      "256/256 [==============================] - 101s 394ms/step - loss: 0.1281 - accuracy: 0.9570 - val_loss: 0.1973 - val_accuracy: 0.9359 - lr: 1.0000e-03\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-098-0.9407.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1712\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1602820307. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m673.60 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m597.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 103/108\n",
+      "256/256 [==============================] - 103s 385ms/step - loss: 0.1703 - accuracy: 0.9429 - val_loss: 0.1672 - val_accuracy: 0.9375 - lr: 1.0000e-03\n",
+      "Epoch 104/108\n",
+      "256/256 [==============================] - 100s 392ms/step - loss: 0.1590 - accuracy: 0.9438 - val_loss: 0.1633 - val_accuracy: 0.9391 - lr: 1.0000e-03\n",
+      "Epoch 105/108\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1485 - accuracy: 0.9534 - val_loss: 0.1552 - val_accuracy: 0.9391 - lr: 1.0000e-03\n",
+      "Epoch 106/108\n",
+      "256/256 [==============================] - 103s 401ms/step - loss: 0.1381 - accuracy: 0.9536 - val_loss: 0.1532 - val_accuracy: 0.9407 - lr: 1.0000e-03\n",
+      "Epoch 107/108\n",
+      "256/256 [==============================] - 104s 406ms/step - loss: 0.1363 - accuracy: 0.9504 - val_loss: 0.1533 - val_accuracy: 0.9455 - lr: 1.0000e-03\n",
+      "Epoch 108/108\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1267 - accuracy: 0.9592 - val_loss: 0.1566 - val_accuracy: 0.9455 - lr: 1.0000e-03\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-107-0.9455.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1533\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1602820307 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1533284783\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m662.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m586.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.78 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m689.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m609.37 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m79.74 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n",
       "\u001b[0m\n",
-      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;33mPreparing train data...\u001b[0m\n",
       "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
       "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[8]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;32mTraining on subset...\u001b[0m\n",
-      "Epoch 25/32\n",
-      "256/256 [==============================] - 79s 291ms/step - loss: 0.3163 - accuracy: 0.9194 - val_loss: 0.2287 - val_accuracy: 0.9455\n",
-      "Epoch 26/32\n",
-      "256/256 [==============================] - 74s 288ms/step - loss: 0.2822 - accuracy: 0.9224 - val_loss: 0.2424 - val_accuracy: 0.9551\n",
-      "Epoch 27/32\n",
-      "256/256 [==============================] - 73s 284ms/step - loss: 0.2480 - accuracy: 0.9319 - val_loss: 0.4705 - val_accuracy: 0.7949\n",
-      "Epoch 28/32\n",
-      "256/256 [==============================] - 71s 277ms/step - loss: 0.2152 - accuracy: 0.9382 - val_loss: 0.2574 - val_accuracy: 0.9423\n",
-      "Epoch 29/32\n",
-      "256/256 [==============================] - 71s 276ms/step - loss: 0.1829 - accuracy: 0.9478 - val_loss: 0.2080 - val_accuracy: 0.9471\n",
-      "Epoch 30/32\n",
-      "256/256 [==============================] - 73s 283ms/step - loss: 0.1376 - accuracy: 0.9653 - val_loss: 0.1909 - val_accuracy: 0.9343\n",
-      "Epoch 31/32\n",
-      "256/256 [==============================] - 71s 276ms/step - loss: 0.0973 - accuracy: 0.9778 - val_loss: 0.2169 - val_accuracy: 0.9311\n",
-      "Epoch 32/32\n",
-      "256/256 [==============================] - 70s 274ms/step - loss: 0.0931 - accuracy: 0.9783 - val_loss: 0.2080 - val_accuracy: 0.9359\n",
+      "Epoch 109/114\n",
+      "256/256 [==============================] - 104s 389ms/step - loss: 0.1702 - accuracy: 0.9419 - val_loss: 0.1620 - val_accuracy: 0.9359 - lr: 1.0000e-03\n",
+      "Epoch 110/114\n",
+      "256/256 [==============================] - 97s 379ms/step - loss: 0.1555 - accuracy: 0.9426 - val_loss: 0.1721 - val_accuracy: 0.9439 - lr: 1.0000e-03\n",
+      "Epoch 111/114\n",
+      "256/256 [==============================] - 97s 379ms/step - loss: 0.1405 - accuracy: 0.9485 - val_loss: 0.1658 - val_accuracy: 0.9391 - lr: 1.0000e-03\n",
+      "Epoch 112/114\n",
+      "256/256 [==============================] - 99s 385ms/step - loss: 0.1367 - accuracy: 0.9502 - val_loss: 0.1994 - val_accuracy: 0.9343 - lr: 1.0000e-03\n",
+      "Epoch 113/114\n",
+      "256/256 [==============================] - 102s 397ms/step - loss: 0.1382 - accuracy: 0.9524 - val_loss: 0.1762 - val_accuracy: 0.9455 - lr: 1.0000e-03\n",
+      "Epoch 114/114\n",
+      "256/256 [==============================] - 100s 391ms/step - loss: 0.1252 - accuracy: 0.9556 - val_loss: 0.1941 - val_accuracy: 0.9359 - lr: 1.0000e-03\n",
       "\u001b[0;32mSubset training done.\u001b[0m\n",
       "\u001b[0;33mLoading the best weights...\u001b[0m\n",
-      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-026-0.9551.h5...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2424\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.940705 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.955128\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-113-0.9455.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1762\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533284783. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m678.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m599.93 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m78.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 115/120\n",
+      "256/256 [==============================] - 106s 398ms/step - loss: 0.1458 - accuracy: 0.9504 - val_loss: 0.2119 - val_accuracy: 0.9311 - lr: 1.0000e-03\n",
+      "Epoch 116/120\n",
+      "256/256 [==============================] - ETA: 0s - loss: 0.1433 - accuracy: 0.9543\n",
+      "Epoch 116: ReduceLROnPlateau reducing learning rate to 0.0005.\n",
+      "256/256 [==============================] - 104s 408ms/step - loss: 0.1433 - accuracy: 0.9543 - val_loss: 0.1867 - val_accuracy: 0.9343 - lr: 1.0000e-03\n",
+      "Epoch 117/120\n",
+      "256/256 [==============================] - 102s 398ms/step - loss: 0.1368 - accuracy: 0.9539 - val_loss: 0.1581 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 118/120\n",
+      "256/256 [==============================] - 98s 381ms/step - loss: 0.1289 - accuracy: 0.9578 - val_loss: 0.1651 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 119/120\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1296 - accuracy: 0.9597 - val_loss: 0.1606 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 120/120\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1272 - accuracy: 0.9583 - val_loss: 0.1724 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-117-0.9423.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1581\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533284783. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m689.55 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m609.08 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m80.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 121/126\n",
+      "256/256 [==============================] - 103s 389ms/step - loss: 0.1666 - accuracy: 0.9434 - val_loss: 0.1792 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 122/126\n",
+      "256/256 [==============================] - 101s 394ms/step - loss: 0.1495 - accuracy: 0.9470 - val_loss: 0.1726 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 123/126\n",
+      "256/256 [==============================] - 99s 388ms/step - loss: 0.1453 - accuracy: 0.9473 - val_loss: 0.1613 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 124/126\n",
+      "256/256 [==============================] - 102s 398ms/step - loss: 0.1471 - accuracy: 0.9453 - val_loss: 0.1650 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 125/126\n",
+      "256/256 [==============================] - 104s 404ms/step - loss: 0.1492 - accuracy: 0.9458 - val_loss: 0.1673 - val_accuracy: 0.9327 - lr: 5.0000e-04\n",
+      "Epoch 126/126\n",
+      "256/256 [==============================] - 103s 401ms/step - loss: 0.1380 - accuracy: 0.9497 - val_loss: 0.2337 - val_accuracy: 0.9183 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-123-0.9423.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1613\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533284783. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m695.36 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m613.07 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m82.29 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [21] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m22\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 126)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 127/132\n",
+      "256/256 [==============================] - 101s 378ms/step - loss: 0.1683 - accuracy: 0.9446 - val_loss: 0.1585 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 128/132\n",
+      "256/256 [==============================] - 98s 383ms/step - loss: 0.1610 - accuracy: 0.9429 - val_loss: 0.1632 - val_accuracy: 0.9359 - lr: 5.0000e-04\n",
+      "Epoch 129/132\n",
+      "256/256 [==============================] - 100s 391ms/step - loss: 0.1563 - accuracy: 0.9463 - val_loss: 0.1552 - val_accuracy: 0.9359 - lr: 5.0000e-04\n",
+      "Epoch 130/132\n",
+      "256/256 [==============================] - 103s 400ms/step - loss: 0.1579 - accuracy: 0.9478 - val_loss: 0.1670 - val_accuracy: 0.9311 - lr: 5.0000e-04\n",
+      "Epoch 131/132\n",
+      "256/256 [==============================] - 105s 411ms/step - loss: 0.1410 - accuracy: 0.9521 - val_loss: 0.1569 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 132/132\n",
+      "256/256 [==============================] - 104s 408ms/step - loss: 0.1415 - accuracy: 0.9502 - val_loss: 0.1735 - val_accuracy: 0.9311 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-131-0.9423.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1569\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533284783. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m695.82 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m612.87 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m82.94 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [22] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m23\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 132)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 133/138\n",
+      "256/256 [==============================] - 104s 390ms/step - loss: 0.1598 - accuracy: 0.9424 - val_loss: 0.1569 - val_accuracy: 0.9359 - lr: 5.0000e-04\n",
+      "Epoch 134/138\n",
+      "256/256 [==============================] - 104s 404ms/step - loss: 0.1534 - accuracy: 0.9451 - val_loss: 0.1549 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 135/138\n",
+      "256/256 [==============================] - 103s 402ms/step - loss: 0.1435 - accuracy: 0.9495 - val_loss: 0.1686 - val_accuracy: 0.9359 - lr: 5.0000e-04\n",
+      "Epoch 136/138\n",
+      "256/256 [==============================] - 102s 396ms/step - loss: 0.1486 - accuracy: 0.9485 - val_loss: 0.1449 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 137/138\n",
+      "256/256 [==============================] - 103s 402ms/step - loss: 0.1341 - accuracy: 0.9526 - val_loss: 0.1562 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 138/138\n",
+      "256/256 [==============================] - 102s 397ms/step - loss: 0.1393 - accuracy: 0.9529 - val_loss: 0.1488 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-134-0.9471.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1549\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533284783. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m701.34 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m617.30 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.04 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [23] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m24\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 138)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 139/144\n",
+      "256/256 [==============================] - 105s 395ms/step - loss: 0.1624 - accuracy: 0.9465 - val_loss: 0.1550 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 140/144\n",
+      "256/256 [==============================] - 104s 406ms/step - loss: 0.1580 - accuracy: 0.9487 - val_loss: 0.1615 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 141/144\n",
+      "256/256 [==============================] - 102s 399ms/step - loss: 0.1575 - accuracy: 0.9465 - val_loss: 0.1796 - val_accuracy: 0.9359 - lr: 5.0000e-04\n",
+      "Epoch 142/144\n",
+      "256/256 [==============================] - 98s 383ms/step - loss: 0.1485 - accuracy: 0.9480 - val_loss: 0.1562 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 143/144\n",
+      "256/256 [==============================] - 99s 388ms/step - loss: 0.1411 - accuracy: 0.9534 - val_loss: 0.1533 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 144/144\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1408 - accuracy: 0.9500 - val_loss: 0.1589 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-139-0.9423.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1550\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533284783. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m693.87 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m608.99 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [24] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m25\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 144)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 145/150\n",
+      "256/256 [==============================] - 104s 390ms/step - loss: 0.1449 - accuracy: 0.9512 - val_loss: 0.1658 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 146/150\n",
+      "256/256 [==============================] - 100s 390ms/step - loss: 0.1411 - accuracy: 0.9536 - val_loss: 0.1552 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 147/150\n",
+      "256/256 [==============================] - 103s 403ms/step - loss: 0.1394 - accuracy: 0.9507 - val_loss: 0.1540 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 148/150\n",
+      "256/256 [==============================] - 101s 394ms/step - loss: 0.1290 - accuracy: 0.9587 - val_loss: 0.1678 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 149/150\n",
+      "256/256 [==============================] - 99s 386ms/step - loss: 0.1241 - accuracy: 0.9587 - val_loss: 0.1934 - val_accuracy: 0.9247 - lr: 5.0000e-04\n",
+      "Epoch 150/150\n",
+      "256/256 [==============================] - 100s 389ms/step - loss: 0.1242 - accuracy: 0.9573 - val_loss: 0.1980 - val_accuracy: 0.9295 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-147-0.9407.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1540\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533284783. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m694.83 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m608.14 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.69 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [25] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m26\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 150)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 151/156\n",
+      "256/256 [==============================] - 106s 399ms/step - loss: 0.1489 - accuracy: 0.9514 - val_loss: 0.1560 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 152/156\n",
+      "256/256 [==============================] - 98s 383ms/step - loss: 0.1425 - accuracy: 0.9514 - val_loss: 0.1465 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 153/156\n",
+      "256/256 [==============================] - 98s 381ms/step - loss: 0.1340 - accuracy: 0.9541 - val_loss: 0.1592 - val_accuracy: 0.9279 - lr: 5.0000e-04\n",
+      "Epoch 154/156\n",
+      "256/256 [==============================] - 100s 390ms/step - loss: 0.1363 - accuracy: 0.9539 - val_loss: 0.1418 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 155/156\n",
+      "256/256 [==============================] - 100s 390ms/step - loss: 0.1274 - accuracy: 0.9585 - val_loss: 0.1495 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 156/156\n",
+      "256/256 [==============================] - 103s 403ms/step - loss: 0.1216 - accuracy: 0.9575 - val_loss: 0.1476 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-154-0.9487.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1418\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1533284783 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1417625248\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2462929189 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2424027622\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m696.48 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m606.10 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m90.38 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [26] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m27\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 156)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 157/162\n",
+      "256/256 [==============================] - 105s 393ms/step - loss: 0.1537 - accuracy: 0.9434 - val_loss: 0.1535 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 158/162\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1417 - accuracy: 0.9490 - val_loss: 0.1670 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 159/162\n",
+      "256/256 [==============================] - 99s 386ms/step - loss: 0.1487 - accuracy: 0.9487 - val_loss: 0.1720 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 160/162\n",
+      "256/256 [==============================] - 96s 376ms/step - loss: 0.1453 - accuracy: 0.9502 - val_loss: 0.1698 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 161/162\n",
+      "256/256 [==============================] - 99s 386ms/step - loss: 0.1390 - accuracy: 0.9487 - val_loss: 0.1609 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 162/162\n",
+      "256/256 [==============================] - 100s 392ms/step - loss: 0.1403 - accuracy: 0.9512 - val_loss: 0.1635 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1535}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1418}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1635\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1417625248. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m687.90 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m599.53 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m88.37 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [27] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m28\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 162)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 163/168\n",
+      "256/256 [==============================] - 110s 414ms/step - loss: 0.1375 - accuracy: 0.9521 - val_loss: 0.1853 - val_accuracy: 0.9311 - lr: 5.0000e-04\n",
+      "Epoch 164/168\n",
+      "256/256 [==============================] - 101s 393ms/step - loss: 0.1355 - accuracy: 0.9541 - val_loss: 0.1581 - val_accuracy: 0.9327 - lr: 5.0000e-04\n",
+      "Epoch 165/168\n",
+      "256/256 [==============================] - 98s 383ms/step - loss: 0.1434 - accuracy: 0.9509 - val_loss: 0.1691 - val_accuracy: 0.9311 - lr: 5.0000e-04\n",
+      "Epoch 166/168\n",
+      "256/256 [==============================] - 100s 388ms/step - loss: 0.1336 - accuracy: 0.9531 - val_loss: 0.1393 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 167/168\n",
+      "256/256 [==============================] - 99s 384ms/step - loss: 0.1288 - accuracy: 0.9592 - val_loss: 0.1829 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 168/168\n",
+      "256/256 [==============================] - 99s 386ms/step - loss: 0.1193 - accuracy: 0.9614 - val_loss: 0.1474 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-166-0.9471.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1393\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1417625248 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1392505765\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m655.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m582.56 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m73.40 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m698.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m606.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m92.00 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [28] END|--------------------------------------->\u001b[0m\n",
       "\u001b[0m\n",
-      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 32)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m29\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 168)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;33mPreparing train data...\u001b[0m\n",
       "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
       "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[8]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;32mTraining on subset...\u001b[0m\n",
-      "Epoch 33/40\n",
-      "256/256 [==============================] - 76s 281ms/step - loss: 0.2959 - accuracy: 0.9155 - val_loss: 0.3290 - val_accuracy: 0.8830\n",
-      "Epoch 34/40\n",
-      "256/256 [==============================] - 73s 283ms/step - loss: 0.2567 - accuracy: 0.9326 - val_loss: 0.1897 - val_accuracy: 0.9535\n",
-      "Epoch 35/40\n",
-      "256/256 [==============================] - 71s 278ms/step - loss: 0.2540 - accuracy: 0.9287 - val_loss: 0.2321 - val_accuracy: 0.9103\n",
-      "Epoch 36/40\n",
-      "256/256 [==============================] - 71s 277ms/step - loss: 0.1899 - accuracy: 0.9473 - val_loss: 0.2195 - val_accuracy: 0.9455\n",
-      "Epoch 37/40\n",
-      "256/256 [==============================] - 71s 276ms/step - loss: 0.1693 - accuracy: 0.9519 - val_loss: 0.2231 - val_accuracy: 0.9151\n",
-      "Epoch 38/40\n",
-      "256/256 [==============================] - 72s 282ms/step - loss: 0.1292 - accuracy: 0.9670 - val_loss: 0.2632 - val_accuracy: 0.9167\n",
-      "Epoch 39/40\n",
-      "256/256 [==============================] - 70s 272ms/step - loss: 0.1083 - accuracy: 0.9744 - val_loss: 0.3265 - val_accuracy: 0.8974\n",
-      "Epoch 40/40\n",
-      "256/256 [==============================] - 73s 284ms/step - loss: 0.0871 - accuracy: 0.9822 - val_loss: 0.2653 - val_accuracy: 0.9167\n",
+      "Epoch 169/174\n",
+      "256/256 [==============================] - 105s 395ms/step - loss: 0.1624 - accuracy: 0.9460 - val_loss: 0.1457 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 170/174\n",
+      "256/256 [==============================] - 104s 407ms/step - loss: 0.1434 - accuracy: 0.9502 - val_loss: 0.1541 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 171/174\n",
+      "256/256 [==============================] - 104s 406ms/step - loss: 0.1391 - accuracy: 0.9509 - val_loss: 0.1444 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 172/174\n",
+      "256/256 [==============================] - 101s 395ms/step - loss: 0.1378 - accuracy: 0.9553 - val_loss: 0.1459 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 173/174\n",
+      "256/256 [==============================] - 99s 387ms/step - loss: 0.1298 - accuracy: 0.9592 - val_loss: 0.1439 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 174/174\n",
+      "256/256 [==============================] - 101s 393ms/step - loss: 0.1236 - accuracy: 0.9565 - val_loss: 0.1387 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
       "\u001b[0;32mSubset training done.\u001b[0m\n",
       "\u001b[0;33mLoading the best weights...\u001b[0m\n",
-      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-034-0.9535.h5...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1897\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9551281929. Not saving model.\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2424027622 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1897365898\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-174-0.9503.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1387\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1392505765 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1387259513\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m644.91 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m577.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.45 \u001b[0m\u001b[0;36msec\u001b[0m\n",
-      "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m712.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m615.64 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m96.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [29] END|--------------------------------------->\u001b[0m\n",
       "\u001b[0m\n",
-      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 40)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m30\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 174)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
       "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
       "\u001b[0;33mPreparing train data...\u001b[0m\n",
       "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
       "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
-      "\n",
-      "KeyboardInterrupt.\n",
-      "Training done.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gc\n",
-    "# Garbage Collection (memory)\n",
-    "gc.collect()\n",
-    "tf.keras.backend.clear_session()\n",
-    "# CONF <-------------------------------------------------------------------------->\n",
-    "# Hyperparameters for training the model:\n",
-    "max_epoch = 489 # max_epoch: Maximum number of epochs to train for. Use >=256 for full fine-tuning of large models.\n",
-    "subset_epoch = 6 # subset_epoch: Number of epochs to train each subset.\n",
-    "subset_epoch_FT = 6 # subset_epoch_FT: subset_epoch after pre-training epochs.\n",
-    "PL_epoch = 26 # PL_epoch: Number of pre-training epochs. Use >=24 for large models or 0/1 for fine-tuning only.\n",
-    "subset_size = 4096 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 3200, 4096, 8192.\n",
-    "Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size.\n",
-    "RES_Train = False # RES_Train: Resume training if True.\n",
-    "MAX_LR = 0.01 # MAX_LR: Maximum learning rate.\n",
-    "DEC_LR = 0.00006 # DEC_LR: Learning rate decay.\n",
-    "MIN_LR = 0.0005 # MIN_LR: Minimum learning rate.\n",
-    "RES_LR = 0.006 # RES_LR: Resuming learning rate.\n",
-    "Use_OneCycleLr = False # Use_OneCycleLr: Use OneCycleLr if True. if false, use ReduceLROnPlateau.\n",
-    "OneCycleLr_UFTS = False # OneCycleLr_UFTS: Set the OneCycleLr max epochs to the estimated full training SUB epochs. (DEC_LR and MIN_LR dont have any effect if True)\n",
-    "Debug_OUTPUT_DPS = True # Debug_OUTPUT_DPS: Output debug image samples if True.\n",
-    "Debug_OUTPUT_DPS_freq = 42 # Debug_OUTPUT_DPS_freq: Debug image output frequency(epoch).\n",
-    "TerminateOnHighTemp_M = True # TerminateOnHighTemp_M: Terminate training on high GPU temp to prevent damage.\n",
-    "SAVE_FULLM = True # SAVE_FULLM: Save full model if True.\n",
-    "AdvSubsetC = True  # AdvSubsetC: Use advanced subset sampling to prevent overfitting if True.\n",
-    "AdvSubsetC_SHR = 32 # AdvSubsetC_SHR: Parameter for advanced subset sampling (shuffling data after n epochs).\n",
-    "load_SUB_BRW = True # load_SUB_BRW: Load previous subset weights to speed up training if True. May reduce max accuracy.\n",
-    "load_SUB_BRW_MODE = 'val_accuracy' # load_SUB_BRW_MODE: Previous subset weights loading mode - 'val_accuracy' or 'val_loss'.\n",
-    "load_SUB_BRW_LMODE = 0 # load_SUB_BRW_LMODE: Previous subset weights loading mode parameter (1 for only on imp and !1 for normal mode (for subset_epoch > 6 normal mode is better)).\n",
-    "load_SUB_BRW_LMODE_FN = True # load_SUB_BRW_LMODE_FN: Set load_SUB_BRW_LMODE=1 during fine-tuning if True.\n",
-    "ModelCheckpoint_mode = 'auto' # ModelCheckpoint_mode: 'auto', 'min', or 'max' - how to monitor ModelCheckpoint.\n",
-    "ModelCheckpoint_Reset_TO = 0.6251 # ModelCheckpoint_Reset_TO: Reset ModelCheckpoint monitor to this value, e.g. 0 or float('inf').\n",
-    "Auto_clear_cache = True # Auto_clear_cache: Clear cache during training if True to reduce memory usage.\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 175/180\n",
+      "256/256 [==============================] - 105s 395ms/step - loss: 0.1629 - accuracy: 0.9441 - val_loss: 0.1477 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 176/180\n",
+      "256/256 [==============================] - 103s 401ms/step - loss: 0.1530 - accuracy: 0.9490 - val_loss: 0.1343 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 177/180\n",
+      "256/256 [==============================] - 102s 399ms/step - loss: 0.1427 - accuracy: 0.9470 - val_loss: 0.1468 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 178/180\n",
+      "256/256 [==============================] - 98s 382ms/step - loss: 0.1420 - accuracy: 0.9495 - val_loss: 0.1458 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 179/180\n",
+      "256/256 [==============================] - 101s 393ms/step - loss: 0.1395 - accuracy: 0.9492 - val_loss: 0.1535 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 180/180\n",
+      "256/256 [==============================] - 101s 395ms/step - loss: 0.1394 - accuracy: 0.9526 - val_loss: 0.1343 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-176-0.9503.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1343\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1387259513 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1342520118\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m706.78 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m610.91 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.87 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 181/186\n",
+      "256/256 [==============================] - 108s 405ms/step - loss: 0.1364 - accuracy: 0.9502 - val_loss: 0.1472 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 182/186\n",
+      "256/256 [==============================] - 104s 404ms/step - loss: 0.1460 - accuracy: 0.9470 - val_loss: 0.1377 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 183/186\n",
+      "256/256 [==============================] - 101s 393ms/step - loss: 0.1337 - accuracy: 0.9543 - val_loss: 0.1531 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 184/186\n",
+      "256/256 [==============================] - 101s 392ms/step - loss: 0.1377 - accuracy: 0.9521 - val_loss: 0.1440 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 185/186\n",
+      "256/256 [==============================] - 103s 404ms/step - loss: 0.1223 - accuracy: 0.9565 - val_loss: 0.1675 - val_accuracy: 0.9327 - lr: 5.0000e-04\n",
+      "Epoch 186/186\n",
+      "256/256 [==============================] - 102s 397ms/step - loss: 0.1184 - accuracy: 0.9575 - val_loss: 0.1721 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1377}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1721\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m713.58 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m619.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33m└───Shuffling data...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 187/192\n",
+      "256/256 [==============================] - 108s 407ms/step - loss: 0.1455 - accuracy: 0.9536 - val_loss: 0.1698 - val_accuracy: 0.9279 - lr: 5.0000e-04\n",
+      "Epoch 188/192\n",
+      "256/256 [==============================] - 104s 407ms/step - loss: 0.1406 - accuracy: 0.9556 - val_loss: 0.1428 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 189/192\n",
+      "256/256 [==============================] - 99s 385ms/step - loss: 0.1346 - accuracy: 0.9558 - val_loss: 0.1725 - val_accuracy: 0.9311 - lr: 5.0000e-04\n",
+      "Epoch 190/192\n",
+      "256/256 [==============================] - 102s 398ms/step - loss: 0.1349 - accuracy: 0.9529 - val_loss: 0.1828 - val_accuracy: 0.9327 - lr: 5.0000e-04\n",
+      "Epoch 191/192\n",
+      "256/256 [==============================] - 104s 407ms/step - loss: 0.1196 - accuracy: 0.9587 - val_loss: 0.1557 - val_accuracy: 0.9311 - lr: 5.0000e-04\n",
+      "Epoch 192/192\n",
+      "256/256 [==============================] - 106s 413ms/step - loss: 0.1197 - accuracy: 0.9602 - val_loss: 0.1838 - val_accuracy: 0.9311 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.1428}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1838\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m723.55 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m624.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.08 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 193/198\n",
+      "256/256 [==============================] - 110s 414ms/step - loss: 0.1485 - accuracy: 0.9512 - val_loss: 0.1506 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 194/198\n",
+      "256/256 [==============================] - 109s 426ms/step - loss: 0.1418 - accuracy: 0.9490 - val_loss: 0.1592 - val_accuracy: 0.9359 - lr: 5.0000e-04\n",
+      "Epoch 195/198\n",
+      "256/256 [==============================] - 107s 418ms/step - loss: 0.1363 - accuracy: 0.9534 - val_loss: 0.1592 - val_accuracy: 0.9327 - lr: 5.0000e-04\n",
+      "Epoch 196/198\n",
+      "256/256 [==============================] - 104s 406ms/step - loss: 0.1322 - accuracy: 0.9529 - val_loss: 0.1766 - val_accuracy: 0.9295 - lr: 5.0000e-04\n",
+      "Epoch 197/198\n",
+      "256/256 [==============================] - 105s 408ms/step - loss: 0.1340 - accuracy: 0.9524 - val_loss: 0.1583 - val_accuracy: 0.9263 - lr: 5.0000e-04\n",
+      "Epoch 198/198\n",
+      "256/256 [==============================] - 105s 411ms/step - loss: 0.1263 - accuracy: 0.9534 - val_loss: 0.1569 - val_accuracy: 0.9279 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9359}, \u001b[0m\u001b[0;33mloss{0.1506}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1568\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m735.54 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m641.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [33] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m34\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 198)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 199/204\n",
+      "256/256 [==============================] - 106s 397ms/step - loss: 0.1480 - accuracy: 0.9507 - val_loss: 0.1465 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 200/204\n",
+      "256/256 [==============================] - 107s 418ms/step - loss: 0.1378 - accuracy: 0.9512 - val_loss: 0.1510 - val_accuracy: 0.9359 - lr: 5.0000e-04\n",
+      "Epoch 201/204\n",
+      "256/256 [==============================] - 101s 394ms/step - loss: 0.1357 - accuracy: 0.9531 - val_loss: 0.1666 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 202/204\n",
+      "256/256 [==============================] - 105s 408ms/step - loss: 0.1309 - accuracy: 0.9539 - val_loss: 0.1391 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 203/204\n",
+      "256/256 [==============================] - 105s 410ms/step - loss: 0.1302 - accuracy: 0.9536 - val_loss: 0.1705 - val_accuracy: 0.9311 - lr: 5.0000e-04\n",
+      "Epoch 204/204\n",
+      "256/256 [==============================] - 100s 389ms/step - loss: 0.1323 - accuracy: 0.9521 - val_loss: 0.2351 - val_accuracy: 0.9199 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1391}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2351\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m720.01 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m624.21 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.79 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [34] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m35\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 204)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 205/210\n",
+      "256/256 [==============================] - 108s 407ms/step - loss: 0.1462 - accuracy: 0.9485 - val_loss: 0.1811 - val_accuracy: 0.9295 - lr: 5.0000e-04\n",
+      "Epoch 206/210\n",
+      "256/256 [==============================] - 107s 416ms/step - loss: 0.1417 - accuracy: 0.9495 - val_loss: 0.1674 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 207/210\n",
+      "256/256 [==============================] - 107s 418ms/step - loss: 0.1324 - accuracy: 0.9519 - val_loss: 0.1653 - val_accuracy: 0.9295 - lr: 5.0000e-04\n",
+      "Epoch 208/210\n",
+      "256/256 [==============================] - 106s 414ms/step - loss: 0.1300 - accuracy: 0.9546 - val_loss: 0.1487 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 209/210\n",
+      "256/256 [==============================] - 106s 413ms/step - loss: 0.1257 - accuracy: 0.9558 - val_loss: 0.1639 - val_accuracy: 0.9327 - lr: 5.0000e-04\n",
+      "Epoch 210/210\n",
+      "256/256 [==============================] - 108s 420ms/step - loss: 0.1194 - accuracy: 0.9590 - val_loss: 0.1526 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1487}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1526\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m740.52 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m642.83 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.68 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [35] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m36\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 210)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 211/216\n",
+      "256/256 [==============================] - 108s 407ms/step - loss: 0.1443 - accuracy: 0.9514 - val_loss: 0.1569 - val_accuracy: 0.9295 - lr: 5.0000e-04\n",
+      "Epoch 212/216\n",
+      "256/256 [==============================] - 106s 412ms/step - loss: 0.1270 - accuracy: 0.9575 - val_loss: 0.1515 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 213/216\n",
+      "256/256 [==============================] - 102s 397ms/step - loss: 0.1296 - accuracy: 0.9573 - val_loss: 0.1453 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 214/216\n",
+      "256/256 [==============================] - 106s 415ms/step - loss: 0.1274 - accuracy: 0.9551 - val_loss: 0.1404 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 215/216\n",
+      "256/256 [==============================] - 105s 408ms/step - loss: 0.1214 - accuracy: 0.9590 - val_loss: 0.1439 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 216/216\n",
+      "256/256 [==============================] - 105s 410ms/step - loss: 0.1199 - accuracy: 0.9602 - val_loss: 0.1422 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1404}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1422\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m728.83 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m632.19 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m96.63 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [36] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m37\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 216)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 217/222\n",
+      "256/256 [==============================] - 108s 408ms/step - loss: 0.1481 - accuracy: 0.9497 - val_loss: 0.1465 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 218/222\n",
+      "256/256 [==============================] - 103s 402ms/step - loss: 0.1529 - accuracy: 0.9507 - val_loss: 0.1492 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 219/222\n",
+      "256/256 [==============================] - 98s 383ms/step - loss: 0.1456 - accuracy: 0.9502 - val_loss: 0.1492 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 220/222\n",
+      "256/256 [==============================] - 104s 408ms/step - loss: 0.1409 - accuracy: 0.9543 - val_loss: 0.1495 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 221/222\n",
+      "256/256 [==============================] - 102s 397ms/step - loss: 0.1360 - accuracy: 0.9507 - val_loss: 0.1987 - val_accuracy: 0.9295 - lr: 5.0000e-04\n",
+      "Epoch 222/222\n",
+      "256/256 [==============================] - 102s 396ms/step - loss: 0.1312 - accuracy: 0.9561 - val_loss: 0.1724 - val_accuracy: 0.9295 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1465}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1724\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m721.40 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m618.29 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [37] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m38\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 222)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 223/228\n",
+      "256/256 [==============================] - 108s 405ms/step - loss: 0.1378 - accuracy: 0.9556 - val_loss: 0.2100 - val_accuracy: 0.9263 - lr: 5.0000e-04\n",
+      "Epoch 224/228\n",
+      "256/256 [==============================] - 105s 408ms/step - loss: 0.1333 - accuracy: 0.9536 - val_loss: 0.1480 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 225/228\n",
+      "256/256 [==============================] - 108s 424ms/step - loss: 0.1297 - accuracy: 0.9568 - val_loss: 0.1816 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 226/228\n",
+      "256/256 [==============================] - 110s 430ms/step - loss: 0.1226 - accuracy: 0.9602 - val_loss: 0.1571 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 227/228\n",
+      "256/256 [==============================] - 108s 424ms/step - loss: 0.1118 - accuracy: 0.9626 - val_loss: 0.1858 - val_accuracy: 0.9263 - lr: 5.0000e-04\n",
+      "Epoch 228/228\n",
+      "256/256 [==============================] - 103s 401ms/step - loss: 0.1185 - accuracy: 0.9600 - val_loss: 0.1480 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.1480}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1480\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m745.20 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m643.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m101.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [38] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m39\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 228)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 229/234\n",
+      "256/256 [==============================] - 106s 400ms/step - loss: 0.1538 - accuracy: 0.9438 - val_loss: 0.1473 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 230/234\n",
+      "256/256 [==============================] - 108s 421ms/step - loss: 0.1451 - accuracy: 0.9473 - val_loss: 0.1454 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 231/234\n",
+      "256/256 [==============================] - 102s 399ms/step - loss: 0.1450 - accuracy: 0.9480 - val_loss: 0.1446 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 232/234\n",
+      "256/256 [==============================] - 107s 419ms/step - loss: 0.1358 - accuracy: 0.9478 - val_loss: 0.1593 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 233/234\n",
+      "256/256 [==============================] - 105s 411ms/step - loss: 0.1324 - accuracy: 0.9517 - val_loss: 0.1355 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 234/234\n",
+      "256/256 [==============================] - 107s 418ms/step - loss: 0.1236 - accuracy: 0.9548 - val_loss: 0.1445 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1355}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1445\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m742.91 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m637.60 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [39] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m40\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 234)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 235/240\n",
+      "256/256 [==============================] - 107s 404ms/step - loss: 0.1300 - accuracy: 0.9551 - val_loss: 0.1472 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 236/240\n",
+      "256/256 [==============================] - 108s 421ms/step - loss: 0.1294 - accuracy: 0.9531 - val_loss: 0.1501 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 237/240\n",
+      "256/256 [==============================] - 104s 405ms/step - loss: 0.1220 - accuracy: 0.9573 - val_loss: 0.1497 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 238/240\n",
+      "256/256 [==============================] - 106s 415ms/step - loss: 0.1184 - accuracy: 0.9592 - val_loss: 0.1666 - val_accuracy: 0.9359 - lr: 5.0000e-04\n",
+      "Epoch 239/240\n",
+      "256/256 [==============================] - 105s 410ms/step - loss: 0.1126 - accuracy: 0.9585 - val_loss: 0.1516 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 240/240\n",
+      "256/256 [==============================] - 105s 411ms/step - loss: 0.1112 - accuracy: 0.9624 - val_loss: 0.1953 - val_accuracy: 0.9279 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1472}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1953\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m740.18 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m636.92 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [40] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m41\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 240)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 241/246\n",
+      "256/256 [==============================] - 110s 413ms/step - loss: 0.1443 - accuracy: 0.9475 - val_loss: 0.1543 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 242/246\n",
+      "256/256 [==============================] - 109s 426ms/step - loss: 0.1426 - accuracy: 0.9487 - val_loss: 0.1537 - val_accuracy: 0.9359 - lr: 5.0000e-04\n",
+      "Epoch 243/246\n",
+      "256/256 [==============================] - 104s 408ms/step - loss: 0.1285 - accuracy: 0.9563 - val_loss: 0.1443 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 244/246\n",
+      "256/256 [==============================] - 103s 404ms/step - loss: 0.1259 - accuracy: 0.9578 - val_loss: 0.1438 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 245/246\n",
+      "256/256 [==============================] - 108s 421ms/step - loss: 0.1298 - accuracy: 0.9546 - val_loss: 0.1366 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 246/246\n",
+      "256/256 [==============================] - 101s 396ms/step - loss: 0.1247 - accuracy: 0.9565 - val_loss: 0.1419 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1366}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1420\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m742.37 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m637.08 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.29 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [41] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m42\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 246)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m02_d18-h07_m17_s23\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 247/252\n",
+      "256/256 [==============================] - 107s 402ms/step - loss: 0.1335 - accuracy: 0.9565 - val_loss: 0.1475 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 248/252\n",
+      "256/256 [==============================] - 111s 432ms/step - loss: 0.1345 - accuracy: 0.9551 - val_loss: 0.1511 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 249/252\n",
+      "256/256 [==============================] - 103s 402ms/step - loss: 0.1323 - accuracy: 0.9536 - val_loss: 0.1446 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 250/252\n",
+      "256/256 [==============================] - 110s 431ms/step - loss: 0.1230 - accuracy: 0.9587 - val_loss: 0.1382 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 251/252\n",
+      "256/256 [==============================] - 106s 415ms/step - loss: 0.1170 - accuracy: 0.9595 - val_loss: 0.1548 - val_accuracy: 0.9359 - lr: 5.0000e-04\n",
+      "Epoch 252/252\n",
+      "256/256 [==============================] - 108s 420ms/step - loss: 0.1169 - accuracy: 0.9600 - val_loss: 0.1475 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1382}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1475\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m764.78 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m647.53 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.25 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [42] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m43\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 252)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 253/258\n",
+      "256/256 [==============================] - 110s 413ms/step - loss: 0.1424 - accuracy: 0.9475 - val_loss: 0.1573 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 254/258\n",
+      "256/256 [==============================] - 106s 414ms/step - loss: 0.1364 - accuracy: 0.9524 - val_loss: 0.1766 - val_accuracy: 0.9295 - lr: 5.0000e-04\n",
+      "Epoch 255/258\n",
+      "256/256 [==============================] - 102s 399ms/step - loss: 0.1263 - accuracy: 0.9565 - val_loss: 0.1455 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 256/258\n",
+      "256/256 [==============================] - 101s 395ms/step - loss: 0.1297 - accuracy: 0.9587 - val_loss: 0.1510 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 257/258\n",
+      "256/256 [==============================] - 104s 406ms/step - loss: 0.1193 - accuracy: 0.9592 - val_loss: 0.2027 - val_accuracy: 0.9295 - lr: 5.0000e-04\n",
+      "Epoch 258/258\n",
+      "256/256 [==============================] - 114s 444ms/step - loss: 0.1128 - accuracy: 0.9631 - val_loss: 0.1584 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1455}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1584\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m745.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m638.32 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.09 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [43] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m44\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 258)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 259/264\n",
+      "256/256 [==============================] - 107s 403ms/step - loss: 0.1322 - accuracy: 0.9524 - val_loss: 0.1425 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 260/264\n",
+      "256/256 [==============================] - 110s 427ms/step - loss: 0.1253 - accuracy: 0.9565 - val_loss: 0.1471 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 261/264\n",
+      "256/256 [==============================] - 108s 421ms/step - loss: 0.1169 - accuracy: 0.9607 - val_loss: 0.1512 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 262/264\n",
+      "256/256 [==============================] - 109s 427ms/step - loss: 0.1253 - accuracy: 0.9558 - val_loss: 0.1425 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 263/264\n",
+      "256/256 [==============================] - 112s 439ms/step - loss: 0.1096 - accuracy: 0.9622 - val_loss: 0.1387 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 264/264\n",
+      "256/256 [==============================] - 109s 427ms/step - loss: 0.1138 - accuracy: 0.9604 - val_loss: 0.1380 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1380}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1380\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m765.83 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m656.73 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m109.10 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [44] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m45\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 264)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 265/270\n",
+      "256/256 [==============================] - 107s 403ms/step - loss: 0.1521 - accuracy: 0.9473 - val_loss: 0.1394 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 266/270\n",
+      "256/256 [==============================] - 107s 418ms/step - loss: 0.1423 - accuracy: 0.9502 - val_loss: 0.1367 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 267/270\n",
+      "256/256 [==============================] - 108s 422ms/step - loss: 0.1398 - accuracy: 0.9509 - val_loss: 0.1437 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 268/270\n",
+      "256/256 [==============================] - 102s 399ms/step - loss: 0.1370 - accuracy: 0.9534 - val_loss: 0.1367 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 269/270\n",
+      "256/256 [==============================] - 105s 409ms/step - loss: 0.1284 - accuracy: 0.9570 - val_loss: 0.1365 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 270/270\n",
+      "256/256 [==============================] - 110s 430ms/step - loss: 0.1233 - accuracy: 0.9551 - val_loss: 0.1439 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1365}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1439\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m749.86 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m640.75 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m109.10 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [45] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m46\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 270)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 271/276\n",
+      "256/256 [==============================] - 108s 406ms/step - loss: 0.1429 - accuracy: 0.9507 - val_loss: 0.1436 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 272/276\n",
+      "256/256 [==============================] - 108s 423ms/step - loss: 0.1344 - accuracy: 0.9492 - val_loss: 0.1485 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 273/276\n",
+      "256/256 [==============================] - 109s 425ms/step - loss: 0.1326 - accuracy: 0.9556 - val_loss: 0.1352 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 274/276\n",
+      "256/256 [==============================] - 114s 443ms/step - loss: 0.1323 - accuracy: 0.9531 - val_loss: 0.1410 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 275/276\n",
+      "256/256 [==============================] - 114s 443ms/step - loss: 0.1219 - accuracy: 0.9565 - val_loss: 0.1537 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 276/276\n",
+      "256/256 [==============================] - 111s 435ms/step - loss: 0.1216 - accuracy: 0.9558 - val_loss: 0.1362 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1352}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1343}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1362\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1342520118. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m773.36 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m664.84 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.52 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [46] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m47\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 276)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 277/282\n",
+      "256/256 [==============================] - 108s 406ms/step - loss: 0.1375 - accuracy: 0.9534 - val_loss: 0.1512 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 278/282\n",
+      "256/256 [==============================] - 105s 409ms/step - loss: 0.1274 - accuracy: 0.9563 - val_loss: 0.1450 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 279/282\n",
+      "256/256 [==============================] - 100s 389ms/step - loss: 0.1204 - accuracy: 0.9573 - val_loss: 0.1395 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 280/282\n",
+      "256/256 [==============================] - 107s 417ms/step - loss: 0.1208 - accuracy: 0.9573 - val_loss: 0.1345 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 281/282\n",
+      "256/256 [==============================] - 109s 425ms/step - loss: 0.1129 - accuracy: 0.9629 - val_loss: 0.1336 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 282/282\n",
+      "256/256 [==============================] - 107s 417ms/step - loss: 0.1096 - accuracy: 0.9639 - val_loss: 0.1296 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-281-0.9503.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1336\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1342520118 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1335873008\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m749.58 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m635.95 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.63 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [47] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m48\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 282)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 283/288\n",
+      "256/256 [==============================] - 108s 406ms/step - loss: 0.1374 - accuracy: 0.9541 - val_loss: 0.1349 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 284/288\n",
+      "256/256 [==============================] - 105s 408ms/step - loss: 0.1280 - accuracy: 0.9587 - val_loss: 0.1419 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 285/288\n",
+      "256/256 [==============================] - 114s 443ms/step - loss: 0.1278 - accuracy: 0.9595 - val_loss: 0.1444 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 286/288\n",
+      "256/256 [==============================] - 113s 441ms/step - loss: 0.1194 - accuracy: 0.9580 - val_loss: 0.1376 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 287/288\n",
+      "256/256 [==============================] - 117s 438ms/step - loss: 0.1185 - accuracy: 0.9612 - val_loss: 0.1398 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 288/288\n",
+      "256/256 [==============================] - 112s 436ms/step - loss: 0.1175 - accuracy: 0.9592 - val_loss: 0.1512 - val_accuracy: 0.9327 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1349}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1336}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1512\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1335873008. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m783.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m668.51 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.54 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [48] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m49\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 288)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 289/294\n",
+      "256/256 [==============================] - 109s 409ms/step - loss: 0.1448 - accuracy: 0.9507 - val_loss: 0.1683 - val_accuracy: 0.9311 - lr: 5.0000e-04\n",
+      "Epoch 290/294\n",
+      "256/256 [==============================] - 110s 429ms/step - loss: 0.1384 - accuracy: 0.9521 - val_loss: 0.1511 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 291/294\n",
+      "256/256 [==============================] - 106s 413ms/step - loss: 0.1269 - accuracy: 0.9570 - val_loss: 0.1993 - val_accuracy: 0.9279 - lr: 5.0000e-04\n",
+      "Epoch 292/294\n",
+      "256/256 [==============================] - 112s 439ms/step - loss: 0.1225 - accuracy: 0.9595 - val_loss: 0.1514 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 293/294\n",
+      "256/256 [==============================] - 113s 442ms/step - loss: 0.1213 - accuracy: 0.9619 - val_loss: 0.1649 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 294/294\n",
+      "256/256 [==============================] - 115s 449ms/step - loss: 0.1181 - accuracy: 0.9619 - val_loss: 0.1862 - val_accuracy: 0.9327 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1511}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1336}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1862\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1335873008. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m778.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m666.34 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.90 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [49] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m50\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 294)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 295/300\n",
+      "256/256 [==============================] - 108s 406ms/step - loss: 0.1277 - accuracy: 0.9543 - val_loss: 0.1805 - val_accuracy: 0.9311 - lr: 5.0000e-04\n",
+      "Epoch 296/300\n",
+      "256/256 [==============================] - 107s 417ms/step - loss: 0.1304 - accuracy: 0.9531 - val_loss: 0.1472 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 297/300\n",
+      "256/256 [==============================] - 106s 415ms/step - loss: 0.1264 - accuracy: 0.9578 - val_loss: 0.1501 - val_accuracy: 0.9359 - lr: 5.0000e-04\n",
+      "Epoch 298/300\n",
+      "256/256 [==============================] - 113s 441ms/step - loss: 0.1168 - accuracy: 0.9622 - val_loss: 0.1897 - val_accuracy: 0.9263 - lr: 5.0000e-04\n",
+      "Epoch 299/300\n",
+      "256/256 [==============================] - 112s 436ms/step - loss: 0.1095 - accuracy: 0.9619 - val_loss: 0.1492 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 300/300\n",
+      "256/256 [==============================] - 107s 417ms/step - loss: 0.1035 - accuracy: 0.9609 - val_loss: 0.1739 - val_accuracy: 0.9295 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1472}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1336}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1739\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1335873008. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m764.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m654.07 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.81 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [50] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m51\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 300)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 301/306\n",
+      "256/256 [==============================] - 110s 413ms/step - loss: 0.1430 - accuracy: 0.9490 - val_loss: 0.1395 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 302/306\n",
+      "256/256 [==============================] - 110s 429ms/step - loss: 0.1414 - accuracy: 0.9509 - val_loss: 0.1300 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 303/306\n",
+      "256/256 [==============================] - 104s 404ms/step - loss: 0.1330 - accuracy: 0.9563 - val_loss: 0.1336 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 304/306\n",
+      "256/256 [==============================] - 105s 408ms/step - loss: 0.1277 - accuracy: 0.9583 - val_loss: 0.1324 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 305/306\n",
+      "256/256 [==============================] - 106s 414ms/step - loss: 0.1241 - accuracy: 0.9551 - val_loss: 0.1352 - val_accuracy: 0.9535 - lr: 5.0000e-04\n",
+      "Epoch 306/306\n",
+      "256/256 [==============================] - 105s 410ms/step - loss: 0.1175 - accuracy: 0.9595 - val_loss: 0.1376 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-305-0.9535.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1352\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1335873008. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m754.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m639.79 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.17 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [51] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m52\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 306)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 307/312\n",
+      "256/256 [==============================] - 110s 412ms/step - loss: 0.1326 - accuracy: 0.9517 - val_loss: 0.1323 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 308/312\n",
+      "256/256 [==============================] - 106s 415ms/step - loss: 0.1339 - accuracy: 0.9514 - val_loss: 0.1342 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 309/312\n",
+      "256/256 [==============================] - 104s 407ms/step - loss: 0.1235 - accuracy: 0.9561 - val_loss: 0.1438 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 310/312\n",
+      "256/256 [==============================] - 104s 406ms/step - loss: 0.1237 - accuracy: 0.9583 - val_loss: 0.1388 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 311/312\n",
+      "256/256 [==============================] - 107s 419ms/step - loss: 0.1164 - accuracy: 0.9636 - val_loss: 0.1458 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 312/312\n",
+      "256/256 [==============================] - 108s 421ms/step - loss: 0.1093 - accuracy: 0.9626 - val_loss: 0.1324 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-307-0.9519.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1323\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1335873008 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1323240101\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m763.71 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m640.93 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.78 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [52] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m53\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 312)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 313/318\n",
+      "256/256 [==============================] - 115s 433ms/step - loss: 0.1242 - accuracy: 0.9573 - val_loss: 0.1352 - val_accuracy: 0.9535 - lr: 5.0000e-04\n",
+      "Epoch 314/318\n",
+      "256/256 [==============================] - 105s 408ms/step - loss: 0.1274 - accuracy: 0.9565 - val_loss: 0.1367 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 315/318\n",
+      "256/256 [==============================] - 105s 409ms/step - loss: 0.1214 - accuracy: 0.9580 - val_loss: 0.1460 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 316/318\n",
+      "256/256 [==============================] - 110s 430ms/step - loss: 0.1202 - accuracy: 0.9585 - val_loss: 0.1388 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 317/318\n",
+      "256/256 [==============================] - 113s 443ms/step - loss: 0.1147 - accuracy: 0.9604 - val_loss: 0.1518 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 318/318\n",
+      "256/256 [==============================] - 110s 428ms/step - loss: 0.1088 - accuracy: 0.9631 - val_loss: 0.1503 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1352}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1323}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1503\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1323240101. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m773.59 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m658.70 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.90 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [53] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m54\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 318)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 319/324\n",
+      "256/256 [==============================] - 109s 411ms/step - loss: 0.1392 - accuracy: 0.9521 - val_loss: 0.1395 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 320/324\n",
+      "256/256 [==============================] - 107s 419ms/step - loss: 0.1313 - accuracy: 0.9548 - val_loss: 0.1511 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 321/324\n",
+      "256/256 [==============================] - 107s 418ms/step - loss: 0.1324 - accuracy: 0.9558 - val_loss: 0.1355 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 322/324\n",
+      "256/256 [==============================] - 107s 419ms/step - loss: 0.1253 - accuracy: 0.9585 - val_loss: 0.1386 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 323/324\n",
+      "256/256 [==============================] - 108s 422ms/step - loss: 0.1253 - accuracy: 0.9553 - val_loss: 0.1568 - val_accuracy: 0.9359 - lr: 5.0000e-04\n",
+      "Epoch 324/324\n",
+      "256/256 [==============================] - 104s 408ms/step - loss: 0.1236 - accuracy: 0.9570 - val_loss: 0.1440 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1355}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9535}, loss{0.1323}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1440\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1323240101. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m763.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m645.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.44 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [54] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m55\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 324)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 325/330\n",
+      "256/256 [==============================] - 116s 438ms/step - loss: 0.1284 - accuracy: 0.9534 - val_loss: 0.1401 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 326/330\n",
+      "256/256 [==============================] - 106s 414ms/step - loss: 0.1300 - accuracy: 0.9543 - val_loss: 0.1394 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 327/330\n",
+      "256/256 [==============================] - 116s 455ms/step - loss: 0.1189 - accuracy: 0.9573 - val_loss: 0.1496 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 328/330\n",
+      "256/256 [==============================] - 118s 459ms/step - loss: 0.1190 - accuracy: 0.9575 - val_loss: 0.1359 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 329/330\n",
+      "256/256 [==============================] - 112s 436ms/step - loss: 0.1120 - accuracy: 0.9590 - val_loss: 0.1443 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 330/330\n",
+      "256/256 [==============================] - 115s 448ms/step - loss: 0.1024 - accuracy: 0.9634 - val_loss: 0.1376 - val_accuracy: 0.9583 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-330-0.9583.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1376\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m  0.953526 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m  0.958333\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1323240101. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m809.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m684.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m125.04 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [55] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m56\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 330)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 331/336\n",
+      "256/256 [==============================] - 110s 415ms/step - loss: 0.1295 - accuracy: 0.9565 - val_loss: 0.1299 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 332/336\n",
+      "256/256 [==============================] - 113s 440ms/step - loss: 0.1242 - accuracy: 0.9587 - val_loss: 0.1296 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 333/336\n",
+      "256/256 [==============================] - 115s 448ms/step - loss: 0.1220 - accuracy: 0.9580 - val_loss: 0.1297 - val_accuracy: 0.9551 - lr: 5.0000e-04\n",
+      "Epoch 334/336\n",
+      "256/256 [==============================] - 114s 446ms/step - loss: 0.1165 - accuracy: 0.9602 - val_loss: 0.1328 - val_accuracy: 0.9551 - lr: 5.0000e-04\n",
+      "Epoch 335/336\n",
+      "256/256 [==============================] - 116s 452ms/step - loss: 0.1093 - accuracy: 0.9626 - val_loss: 0.1367 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 336/336\n",
+      "256/256 [==============================] - 109s 425ms/step - loss: 0.1078 - accuracy: 0.9641 - val_loss: 0.1299 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-333-0.9551.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1296\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1323240101 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1296369284\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m799.45 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m677.55 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.90 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [56] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m57\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 336)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 337/342\n",
+      "256/256 [==============================] - 113s 426ms/step - loss: 0.1318 - accuracy: 0.9565 - val_loss: 0.1331 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 338/342\n",
+      "256/256 [==============================] - 112s 435ms/step - loss: 0.1256 - accuracy: 0.9590 - val_loss: 0.1347 - val_accuracy: 0.9535 - lr: 5.0000e-04\n",
+      "Epoch 339/342\n",
+      "256/256 [==============================] - 108s 421ms/step - loss: 0.1229 - accuracy: 0.9565 - val_loss: 0.1333 - val_accuracy: 0.9551 - lr: 5.0000e-04\n",
+      "Epoch 340/342\n",
+      "256/256 [==============================] - 114s 443ms/step - loss: 0.1162 - accuracy: 0.9585 - val_loss: 0.1347 - val_accuracy: 0.9535 - lr: 5.0000e-04\n",
+      "Epoch 341/342\n",
+      "256/256 [==============================] - 109s 425ms/step - loss: 0.1150 - accuracy: 0.9585 - val_loss: 0.1374 - val_accuracy: 0.9535 - lr: 5.0000e-04\n",
+      "Epoch 342/342\n",
+      "256/256 [==============================] - 111s 433ms/step - loss: 0.1039 - accuracy: 0.9629 - val_loss: 0.1319 - val_accuracy: 0.9551 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1319}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1296}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1319\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1296369284. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m791.51 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m667.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.04 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [57] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m58\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 342)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 343/348\n",
+      "256/256 [==============================] - 115s 434ms/step - loss: 0.1355 - accuracy: 0.9509 - val_loss: 0.1335 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 344/348\n",
+      "256/256 [==============================] - 107s 418ms/step - loss: 0.1265 - accuracy: 0.9539 - val_loss: 0.1431 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 345/348\n",
+      "256/256 [==============================] - 113s 440ms/step - loss: 0.1229 - accuracy: 0.9553 - val_loss: 0.1373 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 346/348\n",
+      "256/256 [==============================] - 112s 435ms/step - loss: 0.1226 - accuracy: 0.9578 - val_loss: 0.1559 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 347/348\n",
+      "256/256 [==============================] - 115s 448ms/step - loss: 0.1233 - accuracy: 0.9585 - val_loss: 0.1366 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 348/348\n",
+      "256/256 [==============================] - 110s 429ms/step - loss: 0.1146 - accuracy: 0.9617 - val_loss: 0.1468 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1335}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1296}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1468\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1296369284. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m796.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m672.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.07 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [58] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m59\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 348)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 349/354\n",
+      "256/256 [==============================] - 115s 433ms/step - loss: 0.1168 - accuracy: 0.9602 - val_loss: 0.1817 - val_accuracy: 0.9327 - lr: 5.0000e-04\n",
+      "Epoch 350/354\n",
+      "256/256 [==============================] - 108s 423ms/step - loss: 0.1101 - accuracy: 0.9609 - val_loss: 0.1440 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "Epoch 351/354\n",
+      "256/256 [==============================] - 117s 458ms/step - loss: 0.1151 - accuracy: 0.9617 - val_loss: 0.1437 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 352/354\n",
+      "256/256 [==============================] - 116s 452ms/step - loss: 0.1115 - accuracy: 0.9607 - val_loss: 0.1438 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 353/354\n",
+      "256/256 [==============================] - 115s 450ms/step - loss: 0.1054 - accuracy: 0.9646 - val_loss: 0.1643 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 354/354\n",
+      "256/256 [==============================] - 115s 450ms/step - loss: 0.1003 - accuracy: 0.9651 - val_loss: 0.1440 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1437}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1296}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1440\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1296369284. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m809.90 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m688.34 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.56 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [59] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m60\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 354)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 355/360\n",
+      "256/256 [==============================] - 115s 433ms/step - loss: 0.1408 - accuracy: 0.9512 - val_loss: 0.1382 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 356/360\n",
+      "256/256 [==============================] - 107s 419ms/step - loss: 0.1331 - accuracy: 0.9526 - val_loss: 0.1420 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 357/360\n",
+      "256/256 [==============================] - 116s 453ms/step - loss: 0.1319 - accuracy: 0.9539 - val_loss: 0.1673 - val_accuracy: 0.9343 - lr: 5.0000e-04\n",
+      "Epoch 358/360\n",
+      "256/256 [==============================] - 116s 454ms/step - loss: 0.1250 - accuracy: 0.9539 - val_loss: 0.1384 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 359/360\n",
+      "256/256 [==============================] - 115s 451ms/step - loss: 0.1205 - accuracy: 0.9595 - val_loss: 0.1436 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 360/360\n",
+      "256/256 [==============================] - 125s 489ms/step - loss: 0.1138 - accuracy: 0.9607 - val_loss: 0.1653 - val_accuracy: 0.9375 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1382}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1296}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1653\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1296369284. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m818.77 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m696.39 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.38 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [60] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m61\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 360)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 361/366\n",
+      "256/256 [==============================] - 114s 428ms/step - loss: 0.1249 - accuracy: 0.9570 - val_loss: 0.1380 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 362/366\n",
+      "256/256 [==============================] - 113s 439ms/step - loss: 0.1246 - accuracy: 0.9553 - val_loss: 0.1344 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 363/366\n",
+      "256/256 [==============================] - 109s 426ms/step - loss: 0.1171 - accuracy: 0.9629 - val_loss: 0.1459 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 364/366\n",
+      "256/256 [==============================] - 113s 443ms/step - loss: 0.1154 - accuracy: 0.9614 - val_loss: 0.1418 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 365/366\n",
+      "256/256 [==============================] - 115s 450ms/step - loss: 0.1125 - accuracy: 0.9629 - val_loss: 0.1385 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 366/366\n",
+      "256/256 [==============================] - 109s 424ms/step - loss: 0.1033 - accuracy: 0.9668 - val_loss: 0.1602 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1344}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1296}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1602\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1296369284. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m798.39 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m674.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.33 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [61] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m62\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 366)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 367/372\n",
+      "256/256 [==============================] - 117s 440ms/step - loss: 0.1346 - accuracy: 0.9487 - val_loss: 0.1389 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 368/372\n",
+      "256/256 [==============================] - 109s 424ms/step - loss: 0.1320 - accuracy: 0.9548 - val_loss: 0.1538 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 369/372\n",
+      "256/256 [==============================] - 118s 460ms/step - loss: 0.1329 - accuracy: 0.9536 - val_loss: 0.1406 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 370/372\n",
+      "256/256 [==============================] - 117s 457ms/step - loss: 0.1225 - accuracy: 0.9541 - val_loss: 0.1372 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 371/372\n",
+      "256/256 [==============================] - 115s 449ms/step - loss: 0.1210 - accuracy: 0.9590 - val_loss: 0.1396 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 372/372\n",
+      "256/256 [==============================] - 115s 448ms/step - loss: 0.1128 - accuracy: 0.9622 - val_loss: 0.1376 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1372}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1296}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1376\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1296369284. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m816.05 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m691.14 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.92 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [62] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m63\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 372)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 373/378\n",
+      "256/256 [==============================] - 111s 416ms/step - loss: 0.1235 - accuracy: 0.9595 - val_loss: 0.1299 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 374/378\n",
+      "256/256 [==============================] - 112s 436ms/step - loss: 0.1144 - accuracy: 0.9604 - val_loss: 0.1291 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 375/378\n",
+      "256/256 [==============================] - 106s 416ms/step - loss: 0.1118 - accuracy: 0.9629 - val_loss: 0.1379 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 376/378\n",
+      "256/256 [==============================] - 120s 469ms/step - loss: 0.1081 - accuracy: 0.9644 - val_loss: 0.1284 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 377/378\n",
+      "256/256 [==============================] - 114s 443ms/step - loss: 0.1032 - accuracy: 0.9624 - val_loss: 0.1248 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 378/378\n",
+      "256/256 [==============================] - 112s 439ms/step - loss: 0.0982 - accuracy: 0.9683 - val_loss: 0.1306 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-373-0.9487.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1299\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1296369284. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m801.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m676.18 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.87 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [63] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m64\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 378)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33m└───Shuffling data...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 379/384\n",
+      "256/256 [==============================] - 115s 435ms/step - loss: 0.1396 - accuracy: 0.9507 - val_loss: 0.1307 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 380/384\n",
+      "256/256 [==============================] - 109s 427ms/step - loss: 0.1309 - accuracy: 0.9531 - val_loss: 0.1346 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 381/384\n",
+      "256/256 [==============================] - 121s 471ms/step - loss: 0.1325 - accuracy: 0.9514 - val_loss: 0.1301 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 382/384\n",
+      "256/256 [==============================] - 117s 456ms/step - loss: 0.1230 - accuracy: 0.9558 - val_loss: 0.1278 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 383/384\n",
+      "256/256 [==============================] - 118s 461ms/step - loss: 0.1105 - accuracy: 0.9575 - val_loss: 0.1352 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 384/384\n",
+      "256/256 [==============================] - 114s 444ms/step - loss: 0.1097 - accuracy: 0.9597 - val_loss: 0.1332 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-384-0.9487.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1332\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1296369284. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m825.38 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m695.07 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m130.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [64] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m65\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 384)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 385/390\n",
+      "256/256 [==============================] - 112s 422ms/step - loss: 0.1268 - accuracy: 0.9556 - val_loss: 0.1295 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 386/390\n",
+      "256/256 [==============================] - 112s 436ms/step - loss: 0.1261 - accuracy: 0.9524 - val_loss: 0.1286 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 387/390\n",
+      "256/256 [==============================] - 118s 461ms/step - loss: 0.1168 - accuracy: 0.9597 - val_loss: 0.1291 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 388/390\n",
+      "256/256 [==============================] - 117s 456ms/step - loss: 0.1215 - accuracy: 0.9597 - val_loss: 0.1309 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 389/390\n",
+      "256/256 [==============================] - 120s 469ms/step - loss: 0.1119 - accuracy: 0.9626 - val_loss: 0.1310 - val_accuracy: 0.9535 - lr: 5.0000e-04\n",
+      "Epoch 390/390\n",
+      "256/256 [==============================] - 113s 442ms/step - loss: 0.1024 - accuracy: 0.9609 - val_loss: 0.1276 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-389-0.9535.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1310\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1296369284. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m820.75 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m693.25 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m127.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [65] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m66\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 390)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 391/396\n",
+      "256/256 [==============================] - 116s 438ms/step - loss: 0.1176 - accuracy: 0.9587 - val_loss: 0.1442 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 392/396\n",
+      "256/256 [==============================] - 109s 425ms/step - loss: 0.1150 - accuracy: 0.9624 - val_loss: 0.1318 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 393/396\n",
+      "256/256 [==============================] - 116s 451ms/step - loss: 0.1093 - accuracy: 0.9646 - val_loss: 0.1383 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 394/396\n",
+      "256/256 [==============================] - 114s 443ms/step - loss: 0.1006 - accuracy: 0.9670 - val_loss: 0.1405 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 395/396\n",
+      "256/256 [==============================] - 115s 450ms/step - loss: 0.1013 - accuracy: 0.9651 - val_loss: 0.1319 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 396/396\n",
+      "256/256 [==============================] - 112s 437ms/step - loss: 0.0978 - accuracy: 0.9680 - val_loss: 0.1382 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1318}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1296}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1382\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1296369284. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m811.05 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m682.53 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m128.52 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [66] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m67\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 396)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 397/402\n",
+      "256/256 [==============================] - 119s 447ms/step - loss: 0.1235 - accuracy: 0.9573 - val_loss: 0.1289 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 398/402\n",
+      "256/256 [==============================] - 109s 424ms/step - loss: 0.1171 - accuracy: 0.9604 - val_loss: 0.1259 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 399/402\n",
+      "256/256 [==============================] - 113s 442ms/step - loss: 0.1041 - accuracy: 0.9626 - val_loss: 0.1323 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 400/402\n",
+      "256/256 [==============================] - 118s 460ms/step - loss: 0.1094 - accuracy: 0.9636 - val_loss: 0.1302 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 401/402\n",
+      "256/256 [==============================] - 115s 448ms/step - loss: 0.1007 - accuracy: 0.9636 - val_loss: 0.1352 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 402/402\n",
+      "256/256 [==============================] - 114s 446ms/step - loss: 0.1097 - accuracy: 0.9612 - val_loss: 0.1393 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-398-0.9503.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1259\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1296369284 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1259101629\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m821.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m687.99 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m133.33 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [67] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m68\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 402)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 403/408\n",
+      "256/256 [==============================] - 112s 419ms/step - loss: 0.1364 - accuracy: 0.9521 - val_loss: 0.1278 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 404/408\n",
+      "256/256 [==============================] - 113s 441ms/step - loss: 0.1308 - accuracy: 0.9524 - val_loss: 0.1292 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 405/408\n",
+      "256/256 [==============================] - 117s 457ms/step - loss: 0.1224 - accuracy: 0.9551 - val_loss: 0.1285 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 406/408\n",
+      "256/256 [==============================] - 118s 460ms/step - loss: 0.1253 - accuracy: 0.9573 - val_loss: 0.1350 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 407/408\n",
+      "256/256 [==============================] - 113s 441ms/step - loss: 0.1173 - accuracy: 0.9600 - val_loss: 0.1307 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 408/408\n",
+      "256/256 [==============================] - 116s 452ms/step - loss: 0.1105 - accuracy: 0.9602 - val_loss: 0.1404 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1278}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1404\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m821.27 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m689.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m131.93 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [68] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m69\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 408)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 409/414\n",
+      "256/256 [==============================] - 112s 421ms/step - loss: 0.1357 - accuracy: 0.9485 - val_loss: 0.1557 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 410/414\n",
+      "256/256 [==============================] - 114s 444ms/step - loss: 0.1304 - accuracy: 0.9517 - val_loss: 0.1309 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 411/414\n",
+      "256/256 [==============================] - 120s 471ms/step - loss: 0.1222 - accuracy: 0.9583 - val_loss: 0.1527 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 412/414\n",
+      "256/256 [==============================] - 116s 452ms/step - loss: 0.1234 - accuracy: 0.9541 - val_loss: 0.1362 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 413/414\n",
+      "256/256 [==============================] - 116s 453ms/step - loss: 0.1192 - accuracy: 0.9565 - val_loss: 0.1313 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 414/414\n",
+      "256/256 [==============================] - 114s 447ms/step - loss: 0.1080 - accuracy: 0.9624 - val_loss: 0.1272 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1272}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1272\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m829.36 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m693.59 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m135.76 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [69] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m70\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 414)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 415/420\n",
+      "256/256 [==============================] - 114s 431ms/step - loss: 0.1368 - accuracy: 0.9536 - val_loss: 0.1283 - val_accuracy: 0.9535 - lr: 5.0000e-04\n",
+      "Epoch 416/420\n",
+      "256/256 [==============================] - 113s 442ms/step - loss: 0.1330 - accuracy: 0.9568 - val_loss: 0.1463 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 417/420\n",
+      "256/256 [==============================] - 118s 459ms/step - loss: 0.1244 - accuracy: 0.9543 - val_loss: 0.1317 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 418/420\n",
+      "256/256 [==============================] - 117s 458ms/step - loss: 0.1241 - accuracy: 0.9561 - val_loss: 0.1527 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 419/420\n",
+      "256/256 [==============================] - 120s 467ms/step - loss: 0.1183 - accuracy: 0.9629 - val_loss: 0.1653 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 420/420\n",
+      "256/256 [==============================] - 114s 445ms/step - loss: 0.1125 - accuracy: 0.9597 - val_loss: 0.1307 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1283}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1307\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m828.53 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m697.52 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m131.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [70] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m71\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 420)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 421/426\n",
+      "256/256 [==============================] - 114s 428ms/step - loss: 0.1202 - accuracy: 0.9556 - val_loss: 0.1381 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 422/426\n",
+      "256/256 [==============================] - 113s 439ms/step - loss: 0.1169 - accuracy: 0.9583 - val_loss: 0.1424 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 423/426\n",
+      "256/256 [==============================] - 118s 462ms/step - loss: 0.1149 - accuracy: 0.9600 - val_loss: 0.1406 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 424/426\n",
+      "256/256 [==============================] - 117s 457ms/step - loss: 0.1187 - accuracy: 0.9597 - val_loss: 0.1387 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 425/426\n",
+      "256/256 [==============================] - 114s 445ms/step - loss: 0.1104 - accuracy: 0.9612 - val_loss: 0.1349 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 426/426\n",
+      "256/256 [==============================] - 118s 460ms/step - loss: 0.1085 - accuracy: 0.9648 - val_loss: 0.1373 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1349}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1373\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m830.19 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m694.33 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m135.85 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [71] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m72\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 426)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 427/432\n",
+      "256/256 [==============================] - 112s 421ms/step - loss: 0.1240 - accuracy: 0.9570 - val_loss: 0.1357 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 428/432\n",
+      "256/256 [==============================] - 116s 454ms/step - loss: 0.1176 - accuracy: 0.9604 - val_loss: 0.1341 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 429/432\n",
+      "256/256 [==============================] - 121s 473ms/step - loss: 0.1142 - accuracy: 0.9629 - val_loss: 0.1354 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 430/432\n",
+      "256/256 [==============================] - 120s 468ms/step - loss: 0.1066 - accuracy: 0.9661 - val_loss: 0.1334 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 431/432\n",
+      "256/256 [==============================] - 120s 470ms/step - loss: 0.1024 - accuracy: 0.9673 - val_loss: 0.1596 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 432/432\n",
+      "256/256 [==============================] - 116s 453ms/step - loss: 0.1049 - accuracy: 0.9634 - val_loss: 0.1332 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1332}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1332\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m840.19 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m707.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m133.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [72] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m73\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 432)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 433/438\n",
+      "256/256 [==============================] - 118s 447ms/step - loss: 0.1157 - accuracy: 0.9607 - val_loss: 0.1305 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 434/438\n",
+      "256/256 [==============================] - 114s 446ms/step - loss: 0.1125 - accuracy: 0.9619 - val_loss: 0.1325 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 435/438\n",
+      "256/256 [==============================] - 118s 460ms/step - loss: 0.1077 - accuracy: 0.9624 - val_loss: 0.1490 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 436/438\n",
+      "256/256 [==============================] - 118s 460ms/step - loss: 0.1020 - accuracy: 0.9653 - val_loss: 0.1354 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 437/438\n",
+      "256/256 [==============================] - 117s 458ms/step - loss: 0.0988 - accuracy: 0.9656 - val_loss: 0.1410 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 438/438\n",
+      "256/256 [==============================] - 119s 463ms/step - loss: 0.0945 - accuracy: 0.9656 - val_loss: 0.1442 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1305}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1442\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m839.19 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m705.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m133.69 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [73] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m74\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 438)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 439/444\n",
+      "256/256 [==============================] - 122s 462ms/step - loss: 0.1340 - accuracy: 0.9548 - val_loss: 0.1560 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 440/444\n",
+      "256/256 [==============================] - 114s 445ms/step - loss: 0.1326 - accuracy: 0.9546 - val_loss: 0.1504 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 441/444\n",
+      "256/256 [==============================] - 119s 465ms/step - loss: 0.1280 - accuracy: 0.9536 - val_loss: 0.1411 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 442/444\n",
+      "256/256 [==============================] - 123s 480ms/step - loss: 0.1175 - accuracy: 0.9590 - val_loss: 0.1484 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 443/444\n",
+      "256/256 [==============================] - 119s 466ms/step - loss: 0.1167 - accuracy: 0.9585 - val_loss: 0.1598 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 444/444\n",
+      "256/256 [==============================] - 121s 472ms/step - loss: 0.1141 - accuracy: 0.9597 - val_loss: 0.1357 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1357}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1357\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m855.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m719.78 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m135.51 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [74] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m75\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 444)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 445/450\n",
+      "256/256 [==============================] - 120s 452ms/step - loss: 0.1429 - accuracy: 0.9497 - val_loss: 0.1452 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 446/450\n",
+      "256/256 [==============================] - 117s 456ms/step - loss: 0.1360 - accuracy: 0.9524 - val_loss: 0.1439 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 447/450\n",
+      "256/256 [==============================] - 113s 441ms/step - loss: 0.1238 - accuracy: 0.9573 - val_loss: 0.1608 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 448/450\n",
+      "256/256 [==============================] - 118s 460ms/step - loss: 0.1203 - accuracy: 0.9578 - val_loss: 0.1628 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 449/450\n",
+      "256/256 [==============================] - 122s 477ms/step - loss: 0.1206 - accuracy: 0.9595 - val_loss: 0.1459 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 450/450\n",
+      "256/256 [==============================] - 120s 467ms/step - loss: 0.1194 - accuracy: 0.9578 - val_loss: 0.1419 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1419}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1419\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m843.63 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m710.39 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m133.25 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [75] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m76\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 450)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 451/456\n",
+      "256/256 [==============================] - 116s 439ms/step - loss: 0.1256 - accuracy: 0.9541 - val_loss: 0.1506 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 452/456\n",
+      "256/256 [==============================] - 116s 452ms/step - loss: 0.1230 - accuracy: 0.9592 - val_loss: 0.1328 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 453/456\n",
+      "256/256 [==============================] - 116s 454ms/step - loss: 0.1093 - accuracy: 0.9619 - val_loss: 0.1442 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 454/456\n",
+      "256/256 [==============================] - 122s 477ms/step - loss: 0.1092 - accuracy: 0.9646 - val_loss: 0.1379 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 455/456\n",
+      "256/256 [==============================] - 121s 474ms/step - loss: 0.1032 - accuracy: 0.9658 - val_loss: 0.1522 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 456/456\n",
+      "256/256 [==============================] - 121s 472ms/step - loss: 0.1008 - accuracy: 0.9670 - val_loss: 0.1402 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1328}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1402\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m853.16 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m713.94 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m139.22 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [76] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m77\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 456)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 457/462\n",
+      "256/256 [==============================] - 116s 436ms/step - loss: 0.1157 - accuracy: 0.9624 - val_loss: 0.1307 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 458/462\n",
+      "256/256 [==============================] - 114s 446ms/step - loss: 0.1127 - accuracy: 0.9644 - val_loss: 0.1267 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 459/462\n",
+      "256/256 [==============================] - 115s 449ms/step - loss: 0.1058 - accuracy: 0.9656 - val_loss: 0.1634 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 460/462\n",
+      "256/256 [==============================] - 116s 452ms/step - loss: 0.1052 - accuracy: 0.9631 - val_loss: 0.1347 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 461/462\n",
+      "256/256 [==============================] - 113s 443ms/step - loss: 0.0994 - accuracy: 0.9670 - val_loss: 0.1402 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 462/462\n",
+      "256/256 [==============================] - 114s 445ms/step - loss: 0.1066 - accuracy: 0.9663 - val_loss: 0.1337 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1267}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1337\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m823.68 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m689.16 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m134.52 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [77] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m78\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 462)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 463/468\n",
+      "256/256 [==============================] - 121s 455ms/step - loss: 0.1259 - accuracy: 0.9561 - val_loss: 0.1316 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 464/468\n",
+      "256/256 [==============================] - 113s 442ms/step - loss: 0.1182 - accuracy: 0.9575 - val_loss: 0.1337 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 465/468\n",
+      "256/256 [==============================] - 111s 435ms/step - loss: 0.1069 - accuracy: 0.9646 - val_loss: 0.1417 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 466/468\n",
+      "256/256 [==============================] - 112s 436ms/step - loss: 0.1070 - accuracy: 0.9634 - val_loss: 0.1371 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 467/468\n",
+      "256/256 [==============================] - 120s 471ms/step - loss: 0.1033 - accuracy: 0.9656 - val_loss: 0.1398 - val_accuracy: 0.9391 - lr: 5.0000e-04\n",
+      "Epoch 468/468\n",
+      "256/256 [==============================] - 113s 440ms/step - loss: 0.1005 - accuracy: 0.9675 - val_loss: 0.1384 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1316}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1384\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m830.84 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m691.17 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m139.66 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [78] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m79\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 468)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 469/474\n",
+      "256/256 [==============================] - 116s 437ms/step - loss: 0.1162 - accuracy: 0.9600 - val_loss: 0.1399 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 470/474\n",
+      "256/256 [==============================] - 115s 449ms/step - loss: 0.1116 - accuracy: 0.9609 - val_loss: 0.1474 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 471/474\n",
+      "256/256 [==============================] - 117s 457ms/step - loss: 0.1055 - accuracy: 0.9675 - val_loss: 0.1533 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 472/474\n",
+      "256/256 [==============================] - 123s 480ms/step - loss: 0.1009 - accuracy: 0.9683 - val_loss: 0.1440 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 473/474\n",
+      "256/256 [==============================] - 124s 484ms/step - loss: 0.0955 - accuracy: 0.9678 - val_loss: 0.1496 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 474/474\n",
+      "256/256 [==============================] - 120s 470ms/step - loss: 0.0932 - accuracy: 0.9661 - val_loss: 0.1365 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1365}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1365\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m854.95 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m716.30 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m138.64 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [79] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m80\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 474)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 475/480\n",
+      "256/256 [==============================] - 114s 428ms/step - loss: 0.1309 - accuracy: 0.9556 - val_loss: 0.1307 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 476/480\n",
+      "256/256 [==============================] - 118s 460ms/step - loss: 0.1273 - accuracy: 0.9512 - val_loss: 0.1481 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 477/480\n",
+      "256/256 [==============================] - 112s 437ms/step - loss: 0.1189 - accuracy: 0.9563 - val_loss: 0.1338 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 478/480\n",
+      "256/256 [==============================] - 120s 468ms/step - loss: 0.1220 - accuracy: 0.9575 - val_loss: 0.1338 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 479/480\n",
+      "256/256 [==============================] - 118s 462ms/step - loss: 0.1020 - accuracy: 0.9617 - val_loss: 0.1388 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 480/480\n",
+      "256/256 [==============================] - 117s 455ms/step - loss: 0.1107 - accuracy: 0.9619 - val_loss: 0.1324 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1307}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1324\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m838.44 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m699.03 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m139.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [80] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m81\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 480)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 481/486\n",
+      "256/256 [==============================] - 117s 439ms/step - loss: 0.1326 - accuracy: 0.9497 - val_loss: 0.1386 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 482/486\n",
+      "256/256 [==============================] - 120s 467ms/step - loss: 0.1234 - accuracy: 0.9563 - val_loss: 0.1297 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 483/486\n",
+      "256/256 [==============================] - 123s 480ms/step - loss: 0.1239 - accuracy: 0.9563 - val_loss: 0.1299 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 484/486\n",
+      "256/256 [==============================] - 120s 470ms/step - loss: 0.1150 - accuracy: 0.9583 - val_loss: 0.1614 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 485/486\n",
+      "256/256 [==============================] - 113s 442ms/step - loss: 0.1101 - accuracy: 0.9580 - val_loss: 0.1347 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 486/486\n",
+      "256/256 [==============================] - 112s 437ms/step - loss: 0.1027 - accuracy: 0.9666 - val_loss: 0.1494 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1297}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1494\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m843.55 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m705.75 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m137.80 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [81] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m82\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 486)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 487/492\n",
+      "256/256 [==============================] - 125s 472ms/step - loss: 0.1278 - accuracy: 0.9536 - val_loss: 0.1490 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 488/492\n",
+      "256/256 [==============================] - 118s 462ms/step - loss: 0.1233 - accuracy: 0.9570 - val_loss: 0.1421 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 489/492\n",
+      "256/256 [==============================] - 117s 458ms/step - loss: 0.1143 - accuracy: 0.9592 - val_loss: 0.1458 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 490/492\n",
+      "256/256 [==============================] - 123s 481ms/step - loss: 0.1093 - accuracy: 0.9644 - val_loss: 0.1414 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "Epoch 491/492\n",
+      "256/256 [==============================] - 123s 482ms/step - loss: 0.1024 - accuracy: 0.9644 - val_loss: 0.1393 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 492/492\n",
+      "256/256 [==============================] - 123s 479ms/step - loss: 0.0969 - accuracy: 0.9688 - val_loss: 0.1520 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1393}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1521\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m869.79 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m730.91 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m138.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [82] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m83\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 492)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 493/498\n",
+      "256/256 [==============================] - 119s 449ms/step - loss: 0.1207 - accuracy: 0.9590 - val_loss: 0.1584 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 494/498\n",
+      "256/256 [==============================] - 115s 447ms/step - loss: 0.1118 - accuracy: 0.9636 - val_loss: 0.1564 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 495/498\n",
+      "256/256 [==============================] - 118s 459ms/step - loss: 0.1105 - accuracy: 0.9624 - val_loss: 0.1402 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 496/498\n",
+      "256/256 [==============================] - 124s 485ms/step - loss: 0.1010 - accuracy: 0.9663 - val_loss: 0.1544 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 497/498\n",
+      "256/256 [==============================] - 124s 485ms/step - loss: 0.1028 - accuracy: 0.9634 - val_loss: 0.1415 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 498/498\n",
+      "256/256 [==============================] - 114s 444ms/step - loss: 0.0972 - accuracy: 0.9680 - val_loss: 0.1623 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1402}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1623\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m851.68 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m714.70 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m136.98 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [83] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m84\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 498)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m02_d18-h16_m42_s58\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 499/504\n",
+      "256/256 [==============================] - 114s 431ms/step - loss: 0.1149 - accuracy: 0.9636 - val_loss: 0.1627 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 500/504\n",
+      "256/256 [==============================] - 121s 474ms/step - loss: 0.1087 - accuracy: 0.9617 - val_loss: 0.1481 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 501/504\n",
+      "256/256 [==============================] - 115s 450ms/step - loss: 0.1034 - accuracy: 0.9663 - val_loss: 0.1449 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 502/504\n",
+      "256/256 [==============================] - 120s 469ms/step - loss: 0.0959 - accuracy: 0.9685 - val_loss: 0.1414 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 503/504\n",
+      "256/256 [==============================] - 123s 481ms/step - loss: 0.0944 - accuracy: 0.9705 - val_loss: 0.1654 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 504/504\n",
+      "256/256 [==============================] - 120s 468ms/step - loss: 0.0950 - accuracy: 0.9705 - val_loss: 0.1441 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1414}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1441\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m864.61 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m715.23 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m149.38 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [84] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m85\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 504)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 505/510\n",
+      "256/256 [==============================] - 119s 447ms/step - loss: 0.1198 - accuracy: 0.9604 - val_loss: 0.1418 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 506/510\n",
+      "256/256 [==============================] - 119s 463ms/step - loss: 0.1087 - accuracy: 0.9631 - val_loss: 0.1449 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 507/510\n",
+      "256/256 [==============================] - 116s 455ms/step - loss: 0.1135 - accuracy: 0.9626 - val_loss: 0.1355 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 508/510\n",
+      "256/256 [==============================] - 124s 485ms/step - loss: 0.1021 - accuracy: 0.9644 - val_loss: 0.1490 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 509/510\n",
+      "256/256 [==============================] - 126s 492ms/step - loss: 0.1006 - accuracy: 0.9648 - val_loss: 0.1492 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 510/510\n",
+      "256/256 [==============================] - 126s 491ms/step - loss: 0.0989 - accuracy: 0.9644 - val_loss: 0.1517 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1355}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1517\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m870.72 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m730.86 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m139.86 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [85] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m86\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 510)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 511/516\n",
+      "256/256 [==============================] - 116s 436ms/step - loss: 0.1263 - accuracy: 0.9524 - val_loss: 0.1381 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 512/516\n",
+      "256/256 [==============================] - 121s 473ms/step - loss: 0.1297 - accuracy: 0.9531 - val_loss: 0.1368 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 513/516\n",
+      "256/256 [==============================] - 114s 446ms/step - loss: 0.1135 - accuracy: 0.9570 - val_loss: 0.1370 - val_accuracy: 0.9439 - lr: 5.0000e-04\n",
+      "Epoch 514/516\n",
+      "256/256 [==============================] - 117s 456ms/step - loss: 0.1095 - accuracy: 0.9602 - val_loss: 0.1555 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 515/516\n",
+      "256/256 [==============================] - 122s 475ms/step - loss: 0.1114 - accuracy: 0.9602 - val_loss: 0.1403 - val_accuracy: 0.9471 - lr: 5.0000e-04\n",
+      "Epoch 516/516\n",
+      "256/256 [==============================] - 126s 491ms/step - loss: 0.1059 - accuracy: 0.9634 - val_loss: 0.1528 - val_accuracy: 0.9407 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1368}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1529\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m857.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m716.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m141.68 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [86] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m87\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 516)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 517/522\n",
+      "256/256 [==============================] - 117s 440ms/step - loss: 0.1361 - accuracy: 0.9529 - val_loss: 0.1480 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 518/522\n",
+      "256/256 [==============================] - 115s 447ms/step - loss: 0.1249 - accuracy: 0.9575 - val_loss: 0.1787 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 519/522\n",
+      "256/256 [==============================] - 118s 459ms/step - loss: 0.1238 - accuracy: 0.9551 - val_loss: 0.1444 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 520/522\n",
+      "256/256 [==============================] - 130s 508ms/step - loss: 0.1196 - accuracy: 0.9600 - val_loss: 0.1674 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 521/522\n",
+      "256/256 [==============================] - 126s 494ms/step - loss: 0.1199 - accuracy: 0.9604 - val_loss: 0.1564 - val_accuracy: 0.9535 - lr: 5.0000e-04\n",
+      "Epoch 522/522\n",
+      "256/256 [==============================] - 127s 495ms/step - loss: 0.1047 - accuracy: 0.9634 - val_loss: 0.1868 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1444}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1869\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m874.34 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m733.72 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m140.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [87] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m88\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 522)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 523/528\n",
+      "256/256 [==============================] - 123s 463ms/step - loss: 0.1287 - accuracy: 0.9548 - val_loss: 0.1584 - val_accuracy: 0.9455 - lr: 5.0000e-04\n",
+      "Epoch 524/528\n",
+      "256/256 [==============================] - 126s 493ms/step - loss: 0.1342 - accuracy: 0.9536 - val_loss: 0.1393 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 525/528\n",
+      "256/256 [==============================] - 121s 473ms/step - loss: 0.1142 - accuracy: 0.9585 - val_loss: 0.1498 - val_accuracy: 0.9503 - lr: 5.0000e-04\n",
+      "Epoch 526/528\n",
+      "256/256 [==============================] - 124s 484ms/step - loss: 0.1131 - accuracy: 0.9597 - val_loss: 0.1935 - val_accuracy: 0.9423 - lr: 5.0000e-04\n",
+      "Epoch 527/528\n",
+      "256/256 [==============================] - 125s 488ms/step - loss: 0.1063 - accuracy: 0.9636 - val_loss: 0.1469 - val_accuracy: 0.9487 - lr: 5.0000e-04\n",
+      "Epoch 528/528\n",
+      "256/256 [==============================] - 127s 496ms/step - loss: 0.1000 - accuracy: 0.9656 - val_loss: 0.1559 - val_accuracy: 0.9535 - lr: 5.0000e-04\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1393}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9583}, loss{0.1259}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1559\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1259101629. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m901.71 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m748.01 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m153.70 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [88] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m89\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 528)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 529/534\n",
+      "256/256 [==============================] - 119s 448ms/step - loss: 0.1090 - accuracy: 0.9636 - val_loss: 0.1516 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 530/534\n",
+      "256/256 [==============================] - 115s 449ms/step - loss: 0.0971 - accuracy: 0.9697 - val_loss: 0.1533 - val_accuracy: 0.9519 - lr: 5.0000e-04\n",
+      "Epoch 531/534\n",
+      "256/256 [==============================] - 120s 467ms/step - loss: 0.0914 - accuracy: 0.9719 - val_loss: 0.1656 - val_accuracy: 0.9535 - lr: 5.0000e-04\n",
+      "Epoch 532/534\n",
+      "203/256 [======================>.......] - ETA: 25s - loss: 0.0931 - accuracy: 0.9704"
+     ]
+    }
+   ],
+   "source": [
+    "import gc\n",
+    "# Garbage Collection (memory)\n",
+    "gc.collect()\n",
+    "tf.keras.backend.clear_session()\n",
+    "# CONF <-------------------------------------------------------------------------->\n",
+    "# Hyperparameters for training the model:\n",
+    "max_epoch = 489 # max_epoch: Maximum number of epochs to train for. Use >=256 for full fine-tuning of large models.\n",
+    "subset_epoch = 6 # subset_epoch: Number of epochs to train each subset.\n",
+    "subset_epoch_FT = 6 # subset_epoch_FT: subset_epoch after pre-training epochs.\n",
+    "PL_epoch = 26 # PL_epoch: Number of pre-training epochs. Use >=24 for large models or 0/1 for fine-tuning only.\n",
+    "subset_size = 4096 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 3200, 4096, 8192.\n",
+    "Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size.\n",
+    "RES_Train = False # RES_Train: Resume training if True.\n",
+    "MAX_LR = 0.01 # MAX_LR: Maximum learning rate.\n",
+    "DEC_LR = 0.00006 # DEC_LR: Learning rate decay.\n",
+    "MIN_LR = 0.0005 # MIN_LR: Minimum learning rate.\n",
+    "RES_LR = 0.006 # RES_LR: Resuming learning rate.\n",
+    "Use_OneCycleLr = False # Use_OneCycleLr: Use OneCycleLr if True. if false, use ReduceLROnPlateau.\n",
+    "OneCycleLr_UFTS = False # OneCycleLr_UFTS: Set the OneCycleLr max epochs to the estimated full training SUB epochs. (DEC_LR and MIN_LR dont have any effect if True)\n",
+    "Debug_OUTPUT_DPS = True # Debug_OUTPUT_DPS: Output debug image samples if True.\n",
+    "Debug_OUTPUT_DPS_freq = 42 # Debug_OUTPUT_DPS_freq: Debug image output frequency(epoch).\n",
+    "TerminateOnHighTemp_M = True # TerminateOnHighTemp_M: Terminate training on high GPU temp to prevent damage.\n",
+    "SAVE_FULLM = True # SAVE_FULLM: Save full model if True.\n",
+    "AdvSubsetC = True  # AdvSubsetC: Use advanced subset sampling to prevent overfitting if True.\n",
+    "AdvSubsetC_SHR = 32 # AdvSubsetC_SHR: Parameter for advanced subset sampling (shuffling data after n epochs).\n",
+    "load_SUB_BRW = True # load_SUB_BRW: Load previous subset weights to speed up training if True. May reduce max accuracy.\n",
+    "load_SUB_BRW_MODE = 'val_accuracy' # load_SUB_BRW_MODE: Previous subset weights loading mode - 'val_accuracy' or 'val_loss'.\n",
+    "load_SUB_BRW_LMODE = 0 # load_SUB_BRW_LMODE: Previous subset weights loading mode parameter (1 for only on imp and !1 for normal mode (for subset_epoch > 6 normal mode is better)).\n",
+    "load_SUB_BRW_LMODE_FN = True # load_SUB_BRW_LMODE_FN: Set load_SUB_BRW_LMODE=1 during fine-tuning if True.\n",
+    "ModelCheckpoint_mode = 'auto' # ModelCheckpoint_mode: 'auto', 'min', or 'max' - how to monitor ModelCheckpoint.\n",
+    "ModelCheckpoint_Reset_TO = 0.6251 # ModelCheckpoint_Reset_TO: Reset ModelCheckpoint monitor to this value, e.g. 0 or float('inf').\n",
+    "Auto_clear_cache = True # Auto_clear_cache: Clear cache during training if True to reduce memory usage.\n",
     "Use_ES_ONSUBT = False # Use_ES_ONSUBT: Early stopping per subset (⚠️deprecated⚠️).\n",
     "EarlyStopping_P = 5 # EarlyStopping_P: Early stopping patience (⚠️deprecated⚠️).\n",
     "Use_tensorboard_profiler = False # Use_tensorboard_profiler: Enable tensorboard profiler.\n",
@@ -4215,6 +6752,8 @@
     "        super().on_epoch_end(epoch, logs)\n",
     "class DummyCallback(Callback):\n",
     "    pass\n",
+    "def DummyFunc(*Dummy_args, **Dummy_kwargs):\n",
+    "    pass\n",
     "# Define a function to plot the confusion matrix\n",
     "def plot_confusion_matrix_TensorBoard(epoch, logs):\n",
     "    # Use the model to predict the values from the test dataset.\n",
@@ -4298,6 +6837,7 @@
     "                                                   patience=subset_epoch * 6,\n",
     "                                                   min_lr=MIN_LR,\n",
     "                                                   verbose=1)\n",
+    "    learning_rate_schedule_SUB.on_train_begin = DummyFunc # Remove on_train_begin to make it work with subset training.\n",
     "#PRES\n",
     "# ...\n",
     "#MAIN\n",
@@ -4516,7 +7056,7 @@
     "        print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan'])\n",
     "        Total_SUB_epoch_C += C_subset_epoch # TO FIX TensorBoard\n",
     "except KeyboardInterrupt:\n",
-    "    print('\\nKeyboardInterrupt.')\n",
+    "    print('\\nKeyboardInterrupt. (Training stopped)')\n",
     "# End\n",
     "try:\n",
     "    history = {}\n",
@@ -4531,6 +7071,8 @@
     "try:\n",
     "    del train_SUB_datagen\n",
     "    del train_SUB_augmented_images\n",
+    "    del x_SUB_train\n",
+    "    del y_SUB_train\n",
     "except NameError:\n",
     "    pass"
    ]
diff --git a/Utils/Other.py b/Utils/Other.py
index dd4cdf2..d8e2686 100644
--- a/Utils/Other.py
+++ b/Utils/Other.py
@@ -2,10 +2,35 @@
 from Utils.print_color_V2_NEW import print_Color_V2
 from Utils.print_color_V1_OLD import print_Color
 from tabulate import tabulate
+from numba import cuda
 import numpy as np
 import pickle
 import gzip
+    
+def GPU_memUsage(Print=True):
+    """Prints GPU memory usage for each GPU.
 
+    Args:
+    Print (bool): Whether to print the memory usage. 
+        If True, prints the memory usage. 
+        If False, returns the free and total memory as a tuple.
+
+    Returns:
+    If Print is False, returns a tuple (free, total) with the free 
+    and total memory in bytes for the GPU.
+    """
+    gpus = cuda.gpus.lst
+    for gpu in gpus:
+        with gpu:
+            meminfo = cuda.current_context().get_memory_info()
+            if Print:
+                print_Color(
+                    f'~*(GPU-MEM)~*--{gpu}--[free: {meminfo.free / (1024 ** 3):.2f}GB, used: {meminfo.total / (1024 ** 3) - meminfo.free / (1024 ** 3):.2f}GB, total, {meminfo.total / (1024 ** 3):.2f}GB]',
+                    ['green', 'cyan'],
+                    advanced_mode=True)
+            else:
+                return meminfo.free, meminfo.total
+            
 def save_list(history, filename, compress=True):
     """Saves a list to a file.
 
diff --git a/env/Test_ENV3.ipynb b/env/Test_ENV3.ipynb
index 4e08b45..6501f1a 100644
--- a/env/Test_ENV3.ipynb
+++ b/env/Test_ENV3.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -108,24 +108,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from\u001b[0m\u001b[0;32m   0.458953\u001b[0m\u001b[0;33m to \u001b[0m\u001b[0;32m  0.458953\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n"
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 22.77GB, used: 1.23GB, total, 24.00GB]\u001b[0m\n",
+      "Realising all memory...\n",
+      "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m--<Managed Device 0>--[free: 22.77GB, used: 1.23GB, total, 24.00GB]\u001b[0m\n",
+      "done.\n"
      ]
     }
    ],
    "source": [
-    "# Copyright (c) 2024 Aydin Hamedi\n",
-    "# \n",
-    "# This software is released under the MIT License.\n",
-    "# https://opensource.org/licenses/MIT\n",
+    "from numba import cuda\n",
+    "    \n",
+    "def GPU_memUsage(Print=True):\n",
+    "    \"\"\"Prints GPU memory usage for each GPU.\n",
     "\n",
-    "print_Color_V2(f'<yellow>Improved model accuracy from<green>{0.4589532546:10f}<yellow> to <green>{0.4589532546:10f}<yellow>. <light_cyan>Saving model.')"
+    "    Args:\n",
+    "    Print (bool): Whether to print the memory usage. \n",
+    "        If True, prints the memory usage. \n",
+    "        If False, returns the free and total memory as a tuple.\n",
+    "\n",
+    "    Returns:\n",
+    "    If Print is False, returns a tuple (free, total) with the free \n",
+    "    and total memory in bytes for the GPU.\n",
+    "    \"\"\"\n",
+    "    gpus = cuda.gpus.lst\n",
+    "    for gpu in gpus:\n",
+    "        with gpu:\n",
+    "            meminfo = cuda.current_context().get_memory_info()\n",
+    "            if Print:\n",
+    "                print_Color(\n",
+    "                    f'~*(GPU-MEM)~*--{gpu}--[free: {meminfo.free / (1024 ** 3):.2f}GB, used: {meminfo.total / (1024 ** 3) - meminfo.free / (1024 ** 3):.2f}GB, total, {meminfo.total / (1024 ** 3):.2f}GB]',\n",
+    "                    ['green', 'cyan'],\n",
+    "                    advanced_mode=True)\n",
+    "            else:\n",
+    "                return meminfo.free, meminfo.total\n",
+    "            \n",
+    "device = cuda.get_current_device()\n",
+    "GPU_memUsage()\n",
+    "print('Realising all memory...')\n",
+    "device.reset()\n",
+    "GPU_memUsage()\n",
+    "print('done.')"
    ]
   }
  ],
diff --git a/requirements.txt b/requirements.txt
index 66ba448..e34a93e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -28,3 +28,4 @@ tensorflow==2.10.1
 tensorflow-addons==0.22.0
 tensorflow-model-optimization==0.7.5
 tqdm==4.66.1
+numba==0.59.0