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modified: BETA_E_Model_T&T.ipynb #82

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Jan 6, 2024
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modified: BETA_E_Model_T&T.ipynb #82

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55 changes: 30 additions & 25 deletions BETA_E_Model_T&T.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -878,7 +878,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-27T17:34:12.077394600Z",
Expand All @@ -898,14 +898,14 @@
"Freezing 0 layers in the base model...\n",
"Percentage of the base model that is frozen: 0.00%\n",
"Total model layers: 814\n",
"Model: \"model\"\n",
"Model: \"model_3\"\n",
"_____________________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to Trainable \n",
"=============================================================================================================\n",
" input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n",
" input_4 (InputLayer) [(None, 224, 224, 3 0 [] Y \n",
" )] \n",
" \n",
" stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n",
" stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_4[0][0]'] Y \n",
" ) \n",
" \n",
" stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n",
Expand Down Expand Up @@ -3016,25 +3016,24 @@
" \n",
" top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n",
" \n",
" global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n",
" alAveragePooling2D) \n",
" FC_INPUT_Avg-Pooling (GlobalAv (None, 2560) 0 ['top_activation[0][0]'] Y \n",
" eragePooling2D) \n",
" \n",
" dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n",
" ]'] \n",
" FC_C_Dense-L1-512 (Dense) (None, 512) 1311232 ['FC_INPUT_Avg-Pooling[0][0]'] Y \n",
" \n",
" dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n",
" FC_C_Dropout-L1-0.1 (Dropout) (None, 512) 0 ['FC_C_Dense-L1-512[0][0]'] Y \n",
" \n",
" batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n",
" FC_C_Avg-Pooling-L1 (BatchNorm (None, 512) 2048 ['FC_C_Dropout-L1-0.1[0][0]'] Y \n",
" alization) \n",
" \n",
" dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n",
" FC_C_Dense-L2-512 (Dense) (None, 512) 262656 ['FC_C_Avg-Pooling-L1[0][0]'] Y \n",
" \n",
" batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n",
" rmalization) \n",
" FC_C_Avg-Pooling-L2 (BatchNorm (None, 512) 2048 ['FC_C_Dense-L2-512[0][0]'] Y \n",
" alization) \n",
" \n",
" dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n",
" FC_C_Dense-L3-128 (Dense) (None, 128) 65664 ['FC_C_Avg-Pooling-L2[0][0]'] Y \n",
" \n",
" dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n",
" FC_OUTPUT_Dense-2 (Dense) (None, 2) 258 ['FC_C_Dense-L3-128[0][0]'] Y \n",
" \n",
"=============================================================================================================\n",
"Total params: 65,741,586\n",
Expand Down Expand Up @@ -9922,7 +9921,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-28T02:31:32.994176700Z",
Expand All @@ -9939,14 +9938,14 @@
"Freezing 0 layers in the base model...\n",
"Percentage of the base model that is frozen: 0.00%\n",
"Total model layers: 814\n",
"Model: \"model\"\n",
"Model: \"model_1\"\n",
"_____________________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to Trainable \n",
"=============================================================================================================\n",
" input_5 (InputLayer) [(None, 224, 224, 3 0 [] Y \n",
" input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n",
" )] \n",
" \n",
" stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_5[0][0]'] Y \n",
" stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_2[0][0]'] Y \n",
" ) \n",
" \n",
" stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n",
Expand Down Expand Up @@ -17993,7 +17992,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-28T07:04:23.573633300Z",
Expand All @@ -18008,7 +18007,7 @@
"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_m01_d06-h17_m48_s14]\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_m01_d06-h18_m12_s19]\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;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
Expand All @@ -18017,7 +18016,9 @@
"\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (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- Fitting ImageDataGenerator...\u001b[0m\n"
"\u001b[0;33m- Loading fitted ImageDataGenerator...\u001b[0m\n",
"\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n",
"\u001b[0;33m- Augmenting Image Data...\u001b[0m\n"
]
}
],
Expand Down Expand Up @@ -18227,11 +18228,15 @@
"# Define a function to plot the confusion matrix\n",
"def plot_confusion_matrix(epoch, logs):\n",
" # Use the model to predict the values from the test dataset.\n",
" test_pred_raw = model.predict(x_test)\n",
" test_pred = np.argmax(test_pred_raw, axis=1)\n",
" test_pred_raw = model.predict(x_test, verbose=0)\n",
" test_pred = np.argmax(test_pred_raw, axis=1) # Convert predictions from one-hot encoded to binary\n",
"\n",
" # Convert true labels from one-hot encoded to binary\n",
" y_true = np.argmax(y_test, axis=1)\n",
"\n",
" # Calculate the confusion matrix.\n",
" cm = confusion_matrix(y_test, test_pred)\n",
" cm = confusion_matrix(y_true, test_pred)\n",
" \n",
" # Log the confusion matrix as an image summary.\n",
" figure = plt.figure(figsize=(8, 8))\n",
" sns.heatmap(cm, annot=True, fmt=\"d\", cmap=plt.cm.Blues)\n",
Expand Down
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