Train_DCVAE_Style_MPS60.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "from Model.DCVAE import DCVAE_Style\n",
    "from Model.Utils import PlotHistory, PlotDataAE, Save_Model, MPS_Generator\n",
    "from Model.Utils import load_from_tfrecords, convert_to_tfrecords, load_numpy, model_plot\n",
    "from keras.optimizers import RMSprop,Adam\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import os\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_tfrecords = True\n",
    "create_tfrecords = False\n",
    "\n",
    "path_tfr = 'DataSet/MPS60'\n",
    "path_npy = '/share/GeoFacies/ModelosDeFacies/ModelMPS60/DataSet/DataSet.npy'\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "if load_tfrecords:\n",
    "    if create_tfrecords: \n",
    "        x_train, x_test = load_numpy(path_npy,random_state=0,split_data=0.30)\n",
    "        convert_to_tfrecords(path_tfr, x_train, x_test)\n",
    "        gen_train, gen_test = load_from_tfrecords(path_tfr, batch_size)\n",
    "    else:\n",
    "        try:\n",
    "            gen_train, gen_test = load_from_tfrecords(path_tfr, batch_size)\n",
    "        except:\n",
    "            print(\"Data not found. Change 'create_tfrecords' to True\")\n",
    "    \n",
    "    gen_train = MPS_Generator(os.path.join(path_tfr, 'train.tfrecords'), gen_train.num)\n",
    "    gen_test = MPS_Generator(os.path.join(path_tfr, 'test_val.tfrecords'), gen_test.num)\n",
    "    x_train = gen_train.get_numpy_batch().astype('float32')\n",
    "    x_test =gen_test.get_numpy_batch().astype('float32')\n",
    "else:\n",
    "    x_train, x_test = load_numpy(path_npy,random_state=0,split_data=0.30)\n",
    "\n",
    "x_train = np.argmax(x_train,axis=-1)   \n",
    "x_test = np.argmax(x_test,axis=-1)   \n",
    "\n",
    "x_train = np.expand_dims(x_train, axis=-1)\n",
    "x_train = 2*x_train-1\n",
    "x_test = np.expand_dims(x_test, axis=-1)\n",
    "x_test = 2*x_test-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((14000, 60, 60, 1), (6000, 60, 60, 1), -1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape,x_test.shape,x_train.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Convolutional Variational Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_weights = os.path.join(path_tfr, 'w60.hdf5')\n",
    "\n",
    "model = DCVAE_Style(input_shape=(60,60,1),filters=[128,64,32],strides=[2,2,1],KernelDim=(3,3,3),hidden_dim=2048,\n",
    "                    latent_dim=500,opt=Adam(lr=0.0001),dropout=0.1,epochs_drop=200,style_weight=np.linspace(30e-6,40e-6,9)[1],\n",
    "                    kl_weight=2.0, reconstruction_weight=1800, act='tanh',filepath=path_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up model...\n",
      "WARNING:tensorflow:From /work/HomeP100/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            (None, 60, 60, 1)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 30, 30, 128)  1280        input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 15, 15, 64)   73792       conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 15, 15, 32)   18464       conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 7200)         0           conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 2048)         14747648    flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 2048)         0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 500)          1024500     dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 500)          1024500     dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_2 (Lambda)               (None, 500)          0           dense_2[0][0]                    \n",
      "                                                                 dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 2048)         1026048     lambda_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 2048)         0           dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 7200)         14752800    dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 15, 15, 32)   0           dense_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 15, 15, 32)   9248        reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTrans (None, 30, 30, 64)   18496       conv2d_transpose_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTrans (None, 60, 60, 128)  73856       conv2d_transpose_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "bilinear_upsampling_1 (Bilinear (None, 60, 60, 128)  0           conv2d_transpose_3[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "generated (Conv2D)              (None, 60, 60, 1)    513         bilinear_upsampling_1[0][0]      \n",
      "==================================================================================================\n",
      "Total params: 32,771,145\n",
      "Trainable params: 32,771,145\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Completed model setup.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 14000 samples, validate on 6000 samples\n"
     ]
    },
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      "Epoch 1/500\n"
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      "14000/14000 [==============================] - 32s 2ms/step - loss: 1940.0180 - acc_pred: 0.7033 - sum_style_loss: 13984341.0526 - val_loss: 1741.4868 - val_acc_pred: 0.7654 - val_sum_style_loss: 12191310.5013\n",
      "Epoch 2/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1624.6674 - acc_pred: 0.8016 - sum_style_loss: 9393320.6857 - val_loss: 1494.8744 - val_acc_pred: 0.8336 - val_sum_style_loss: 6994639.4293\n",
      "Epoch 3/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1459.8453 - acc_pred: 0.8481 - sum_style_loss: 5903256.9914 - val_loss: 1412.1806 - val_acc_pred: 0.8576 - val_sum_style_loss: 5138831.0853\n",
      "Epoch 4/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1403.2187 - acc_pred: 0.8602 - sum_style_loss: 4893748.3823 - val_loss: 1373.9065 - val_acc_pred: 0.8630 - val_sum_style_loss: 4634848.5453\n",
      "Epoch 5/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1373.9068 - acc_pred: 0.8664 - sum_style_loss: 4487586.6606 - val_loss: 1345.9095 - val_acc_pred: 0.8690 - val_sum_style_loss: 4416635.2187\n",
      "Epoch 6/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1349.3471 - acc_pred: 0.8718 - sum_style_loss: 4235268.3186 - val_loss: 1322.9574 - val_acc_pred: 0.8762 - val_sum_style_loss: 4170209.0413\n",
      "Epoch 7/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1328.0880 - acc_pred: 0.8766 - sum_style_loss: 4026655.1803 - val_loss: 1311.7832 - val_acc_pred: 0.8792 - val_sum_style_loss: 4008179.4613\n",
      "Epoch 8/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1314.6449 - acc_pred: 0.8798 - sum_style_loss: 3894011.6851 - val_loss: 1294.3006 - val_acc_pred: 0.8817 - val_sum_style_loss: 3780265.5060\n",
      "Epoch 9/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1301.6938 - acc_pred: 0.8827 - sum_style_loss: 3793413.4060 - val_loss: 1286.6857 - val_acc_pred: 0.8871 - val_sum_style_loss: 3673990.1913\n",
      "Epoch 10/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1290.8582 - acc_pred: 0.8851 - sum_style_loss: 3711008.3874 - val_loss: 1275.2813 - val_acc_pred: 0.8858 - val_sum_style_loss: 3715989.6480\n",
      "Epoch 11/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1281.6568 - acc_pred: 0.8868 - sum_style_loss: 3655066.5757 - val_loss: 1267.5367 - val_acc_pred: 0.8872 - val_sum_style_loss: 3652806.7427\n",
      "Epoch 12/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1275.5036 - acc_pred: 0.8883 - sum_style_loss: 3603628.8349 - val_loss: 1259.4430 - val_acc_pred: 0.8892 - val_sum_style_loss: 3597974.1647\n",
      "Epoch 13/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1267.9340 - acc_pred: 0.8898 - sum_style_loss: 3548740.7891 - val_loss: 1251.2450 - val_acc_pred: 0.8916 - val_sum_style_loss: 3476313.7060\n",
      "Epoch 14/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1259.3741 - acc_pred: 0.8914 - sum_style_loss: 3506895.8766 - val_loss: 1243.1671 - val_acc_pred: 0.8924 - val_sum_style_loss: 3466355.2373\n",
      "Epoch 15/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1249.6503 - acc_pred: 0.8931 - sum_style_loss: 3450919.0914 - val_loss: 1236.5287 - val_acc_pred: 0.8963 - val_sum_style_loss: 3341908.2020\n",
      "Epoch 16/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1241.8648 - acc_pred: 0.8945 - sum_style_loss: 3400243.0734 - val_loss: 1231.3049 - val_acc_pred: 0.8965 - val_sum_style_loss: 3333187.9573\n",
      "Epoch 17/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 1232.7538 - acc_pred: 0.8959 - sum_style_loss: 3366369.1823 - val_loss: 1219.2973 - val_acc_pred: 0.8963 - val_sum_style_loss: 3379086.3247\n",
      "Epoch 18/500\n"
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      "14000/14000 [==============================] - 28s 2ms/step - loss: 955.8904 - acc_pred: 0.9414 - sum_style_loss: 2373676.2229 - val_loss: 1008.8629 - val_acc_pred: 0.9314 - val_sum_style_loss: 2436296.7993\n",
      "Epoch 134/500\n"
     ]
    },
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 955.4966 - acc_pred: 0.9414 - sum_style_loss: 2375380.4349 - val_loss: 1008.5482 - val_acc_pred: 0.9313 - val_sum_style_loss: 2433189.9020\n",
      "Epoch 135/500\n"
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    },
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 955.8634 - acc_pred: 0.9414 - sum_style_loss: 2372595.9180 - val_loss: 1008.4029 - val_acc_pred: 0.9312 - val_sum_style_loss: 2428923.1007\n",
      "Epoch 136/500\n"
     ]
    },
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 955.8049 - acc_pred: 0.9415 - sum_style_loss: 2371912.2557 - val_loss: 1009.1688 - val_acc_pred: 0.9313 - val_sum_style_loss: 2433493.6913\n",
      "Epoch 137/500\n"
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 955.4199 - acc_pred: 0.9415 - sum_style_loss: 2372455.1639 - val_loss: 1009.4787 - val_acc_pred: 0.9309 - val_sum_style_loss: 2435483.5427\n",
      "Epoch 138/500\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 956.0458 - acc_pred: 0.9415 - sum_style_loss: 2373446.9523 - val_loss: 1009.1518 - val_acc_pred: 0.9313 - val_sum_style_loss: 2426075.0007\n",
      "Epoch 139/500\n"
     ]
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 138', max=14000, style=ProgressStyle(description_width=…"
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 954.8266 - acc_pred: 0.9415 - sum_style_loss: 2369520.4523 - val_loss: 1009.1840 - val_acc_pred: 0.9310 - val_sum_style_loss: 2421383.8440\n",
      "Epoch 140/500\n"
     ]
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 139', max=14000, style=ProgressStyle(description_width=…"
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 955.2273 - acc_pred: 0.9416 - sum_style_loss: 2371241.7280 - val_loss: 1009.9840 - val_acc_pred: 0.9313 - val_sum_style_loss: 2422759.1293\n",
      "\n",
      "Epoch 00140: ReduceLROnPlateau reducing learning rate to 3.12499992105586e-06.\n",
      "Epoch 141/500\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 954.7324 - acc_pred: 0.9416 - sum_style_loss: 2370937.4706 - val_loss: 1009.6601 - val_acc_pred: 0.9310 - val_sum_style_loss: 2430892.3893\n",
      "Epoch 142/500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 954.5177 - acc_pred: 0.9416 - sum_style_loss: 2371177.7946 - val_loss: 1008.4200 - val_acc_pred: 0.9313 - val_sum_style_loss: 2433333.1907\n",
      "Epoch 143/500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 954.6762 - acc_pred: 0.9416 - sum_style_loss: 2370671.6366 - val_loss: 1008.0643 - val_acc_pred: 0.9314 - val_sum_style_loss: 2433455.4473\n",
      "Epoch 144/500\n"
     ]
    },
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 953.9225 - acc_pred: 0.9417 - sum_style_loss: 2370345.6800 - val_loss: 1009.5882 - val_acc_pred: 0.9310 - val_sum_style_loss: 2431320.2547\n",
      "Epoch 145/500\n"
     ]
    },
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 955.0270 - acc_pred: 0.9416 - sum_style_loss: 2369742.9814 - val_loss: 1008.7313 - val_acc_pred: 0.9313 - val_sum_style_loss: 2430357.7180\n",
      "Epoch 146/500\n"
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 954.7497 - acc_pred: 0.9417 - sum_style_loss: 2367887.1009 - val_loss: 1009.0395 - val_acc_pred: 0.9313 - val_sum_style_loss: 2422435.0033\n",
      "Epoch 147/500\n"
     ]
    },
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 954.2823 - acc_pred: 0.9418 - sum_style_loss: 2368334.3040 - val_loss: 1008.6982 - val_acc_pred: 0.9313 - val_sum_style_loss: 2427449.8093\n",
      "Epoch 148/500\n"
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 954.5934 - acc_pred: 0.9417 - sum_style_loss: 2367053.0509 - val_loss: 1008.3905 - val_acc_pred: 0.9312 - val_sum_style_loss: 2426868.8493\n",
      "\n",
      "Epoch 00148: ReduceLROnPlateau reducing learning rate to 1.56249996052793e-06.\n",
      "Epoch 149/500\n"
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    },
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 954.2935 - acc_pred: 0.9416 - sum_style_loss: 2368021.0454 - val_loss: 1008.5331 - val_acc_pred: 0.9314 - val_sum_style_loss: 2430449.1547\n",
      "Epoch 150/500\n"
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 954.4779 - acc_pred: 0.9417 - sum_style_loss: 2366842.2903 - val_loss: 1009.2331 - val_acc_pred: 0.9313 - val_sum_style_loss: 2424734.1553\n",
      "Epoch 151/500\n"
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 954.1251 - acc_pred: 0.9417 - sum_style_loss: 2366155.5000 - val_loss: 1009.0930 - val_acc_pred: 0.9312 - val_sum_style_loss: 2427572.0133\n",
      "Epoch 152/500\n"
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    },
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 954.5544 - acc_pred: 0.9417 - sum_style_loss: 2369695.6331 - val_loss: 1009.2257 - val_acc_pred: 0.9312 - val_sum_style_loss: 2425750.3247\n",
      "Epoch 153/500\n"
     ]
    },
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     "text": [
      "14000/14000 [==============================] - 28s 2ms/step - loss: 954.1547 - acc_pred: 0.9418 - sum_style_loss: 2365194.3229 - val_loss: 1009.1910 - val_acc_pred: 0.9313 - val_sum_style_loss: 2429438.6307\n",
      "\n",
      "Epoch 00153: ReduceLROnPlateau reducing learning rate to 7.81249980263965e-07.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 500\n",
    "model.fit(x_train, num_epochs=num_epochs, batch_size = batch_size, x_v=x_test,verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ploting Train History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss   :  [1008.5330903320313, 1009.2331061197916, 1009.0929816080729, 1009.2256640625]\n",
      "loss   :  [954.293544921875, 954.4779349190849, 954.1251244419643, 954.5544296875]\n"
     ]
    },
    {
     "data": {
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1UzDyfOg5Cj56EOprPYzMGGM6RrdOAunxkVw2oS+vLs5hQ365s+/wKfdD6VZYPt3r8Iwxpt116yQAcNNJA/H7Qnhs9jqn4LBTnCGjnz0KDfXeBmeMMe2s2yeB5Bg/Vx/bn7eW72DV9lJnRdGJP4XiTbDKJpAZY7q2bp8EAK6dOIC4CB9//MCtDQw+HVKHw5w/2EghY0yXZkkA6BEZxg0nDuR/a/JYvLXY6Rs4/k4oWAvrP/Q6PGOMaTeWBFxTj+5HUnT43r6BYWdBTE9Y8Iy3gRljTDtqMgmIyPMikiciKxuVPSIia0RkuYj8R0Ti3fJ+IlIlIkvd29ONXnOEiKwQkfUi8oRIcG3nFe33ccMJA/ns6wIWbCqC0DDIutqpCRR87XV4xhjTLppTE3gBmLRP2WxghKqOAtYBdzd6boOqjnFvNzQqfwq4Fhjk3vY9p+cum9CX5Bj/3trAEVdCSBgs+JuncRljTHtpMgmo6hygaJ+yD1R1z/jJ+cBBN7IVkV5AnKrOV2c/y5eAs1sWcvuJDA/lphMHMm9jIV9sKIDYNBh+Dix9Gap3ex2eMca0ubboE7gaeLfR4/4iskREPhWR492y3kBOo2Ny3LL9EpHrRCRbRLIPZTP5tnDJUZmkxTm1AVWFo2+C2jKY/9cOjcMYYzpCq5KAiNwD1APT3KIdQKaqjgXuBF4WkbhDPa+qPquqWaqalZKS0poQD1lEWCi3nHQYCzcXM3d9gTNx7PAp8MWfoaKgQ2Mxxpj21uIkICJXAmcAl7pNPKhqjaoWuvcXARuAwUAu324yynDLgtIFR/YhvUcEf/zArQ2cch/UVcFnf/Q6NGOMaVMtSgIiMgn4GTBFVSsblaeISKh7fwBOB/BGVd0B7BaRCe6ooCuAN1sdfTvx+0K57ZRBLN1Wwqxl2yF5EIy9FBY+ByXbvA7PGGPaTHOGiL4CzAOGiEiOiFwD/AWIBWbvMxR0IrBcRJYCrwI3qOqeTuWbgOeA9Tg1hMb9CEHn/Kw+jO4Tz6/f+oqSylo44eegCvP+4nVoxhjTZsRtyQlaWVlZmp2d7cl7r9peypS/fM4FWRn85txR8MZNsOo/8ONVEJXoSUzGGNMUEVmkqlnNOdZmDB/E8PQeXH1sP15ZsI2P1+bBsbdDXSUseNbr0Iwxpk1YEmjCnd8fwuG94rj9lSVsCcmAIZPhy2egtsLr0IwxptUsCTQhMjyUZy47AhHh+n8uouaoW6GqCBb/0+vQjDGm1SwJNENmUhSPXjCaNTvLeLOoD/SZ4HQQN9R5HZoxxrSKJYFmOnloKn0SI3lr+Q447g4o3QYrbdMZY0znZkmgmUSEM0el8/n6AgrTT4SUofD5486wUWOM6aQsCRyCKWPSaQgo76xyRwrlrbJNZ4wxnZolgUMwJC2WQakx/HfZdhjxQ4hOheznvQ7LGGNazJLAIRARzhydzsLNReyoaHCWklj3Huze7nVoxhjTIpYEDtGU0emowisLtsG4K0ADsGRa0y80xpggZEngEPVLjub7w9J4ad5mKmMyof8JsPglCDR4HZoxxhwySwItcMMJAyiprGPmwm3OFpSlW2H9R16HZYwxh8ySQAsc0TeRrL4J/O2zTdQPngxxveGT39hwUWNMp2NJoIWuP2EguSVVvL48H066B7YvhlU2ecwY07lYEmihU4amMjYznt+9u4bSQedB2gj48FdQX+N1aMYY02yWBFooJER46OwRFFfW8siHX8P3fwUlW2zegDGmU2nOzmLPi0ieiKxsVPaIiKwRkeUi8h8RiW/03N0isl5E1orIaY3KJ7ll60Xkrrb/KB1veHoPph7Tj2lfbmWZPwv6T4TPHoXayqZfbIwxQaA5NYEXgEn7lM0GRqjqKGAdcDeAiAwDLgKGu6/5q4iEuvsOPwmcDgwDLnaP7fTu/P5g4iPDePLj9XDiL6AiD7L/7nVYxhjTLE0mAVWdAxTtU/aBqta7D+cDGe79s4Dpqlqjqptw9hMe797Wq+pGVa0FprvHdnqxEWFcPD6T2at3sS12NAw4Ceb+yTadMcZ0Cm3RJ3A1ezeN7w1sa/Rcjlt2oPL9EpHrRCRbRLLz8/PbIMT2dfnRfQkV4cUvNsNJv4DKAljwN6/DMsaYJrUqCYjIPUA90KbrJqjqs6qapapZKSkpbXnqdtGrRySnj+zFjOxtlKeOg8O+5ywzXVPmdWjGGHNQLU4CInIlcAZwqeo3s6RygT6NDstwyw5U3mVcdWw/yqrreTV7m9M3UFVkG9IbY4Jei5KAiEwCfgZMUdXGQ2FmAReJiF9E+gODgAXAQmCQiPQXkXCczuNZrQs9uIzLTGBcZjx/+2wTdb3GwqBT4Ys/Q/Vur0MzxpgDas4Q0VeAecAQEckRkWuAvwCxwGwRWSoiTwOo6ipgJvAV8B5ws6o2uJ3ItwDvA6uBme6xXcqNJx5GbkkVby3fDifeDVXFsOAZr8MyxpgDEg3y9W6ysrI0Ozvb6zCaJRBQJj0+B4D3bp9IyPSLYesXcMcKiOjhcXTGmO5CRBapalZzjrUZw20oJES48cSBrNtVzkdr8uCku6G6FOY/7XVoxhizX5YE2tiZo9LJTIziD++vpT51JAw9A+Y9CVUlXodmjDHfYUmgjflCQ/jF5KGs3VXGKwu2wol3QU2pkwiMMSbIWBJoB6cN78nRA5L44+x1lMQNgeHnOvMG8td5HZoxxnyLJYF2ICLcP2UYu6vq+NOHX8Ok30JYJLx5s21DaYwJKpYE2snQnnFceGQm077cwtbaWDj9d5CzAL60IaPGmOBhSaAd3fG9QYSI8NiH62DUhc4Eso8fhrKdXodmjDGAJYF2lRYXwVXH9ueNpbms3lnmNAvV18BHD3odmjHGAJYE2t2NJwwk1u/jD++vhaSBcPRNsHQa5C7yOjRjjLEk0N56RIVxw4kD+WhNHgs3F8Hx/wfRqfDuXRDks7WNMV2fJYEOcNUx/UmN9fO7d9eg/lg45T6nk3jFq16HZozp5iwJdIDI8FBuPWUQ2VuK+XhtHoy5FHqNhg/vtx3IjDGesiTQQS46sg99k6J4YNZX5JXXwqTfwe5cZxKZMcZ4xJJABwkLDeGxC8dQUF7DJc99SUHSuL0ziW3IqDHGI5YEOtC4zASev/JIcoorufqFhTScdC801MFnf/Q6NGNMN2VJoINNGJDE784bxfKcUl7bHAZjL4Psf0DJVq9DM8Z0Q83ZWex5EckTkZWNys4XkVUiEhCRrEbl/USkyt1t7Jsdx9znjhCRFSKyXkSeEBFp+4/TOUwZnc6YPvH88YO1VB/zExCBT3/vdVjGmG6oOTWBF4BJ+5StBM4F5uzn+A2qOsa93dCo/CngWpx9hwft55zdhohwzw8OZ9fuGp5bXgtZ1zgTyDZ+4nVoxphupskkoKpzgKJ9ylar6trmvomI9ALiVHW+OvtZvgScfajBdiVH9kvktOFpPPXJBnZl/R8kD4Z/X2XNQsaYDtUefQL9RWSJiHwqIse7Zb2BnEbH5Lhl+yUi14lItohk5+fnt0OIweGeycOoCyi/nr0VLpwGgXqYcZnTWWyMMR2grZPADiBTVccCdwIvi0jcoZ5EVZ9V1SxVzUpJSWnjEINHZlIUN504kLeW7+DzkniY8gTsWAbLZ3gdmjGmm2jTJKCqNapa6N5fBGwABgO5QEajQzPcsm7vhhMGkpkYxb1vrqR28BToNQbm/AEa6r0OzRjTDbRpEhCRFBEJde8PwOkA3qiqO4DdIjLBHRV0BfBmW753ZxURFsr9Zw5jY34F//pyK5zwcyjeBCtmeh2aMaYbaM4Q0VeAecAQEckRkWtE5BwRyQGOBt4WkffdwycCy0VkKfAqcIOq7ulUvgl4DliPU0N4t40/S6d18tBUjh+UzOMffU1Jn1Og50iY84j1DRhj2p1okC9nnJWVpdnZ2V6H0e7W7izj9MfnMPWYftw/eCu8chEc+SP4gc0mNsYcGhFZpKpZTR9pM4aDxpCesVw0PpOX5m3hjcpRcMytsPA5WPA3r0MzxnRhlgSCyF2nD+XIfgncMWMpT4VdDkMmw7s/h82fex2aMaaLsiQQROIiwnjx6vFMGZ3O795fz7zRv4GEvvD6dVBV4nV4xpguyJJAkPH7Qnnk/FGkxfl5Yu5OOPc5KNsBb99p21EaY9qcJYEg5PeFcu3xA5i3sZBFDQPgpLth5Wuw5i2vQzPGdDGWBILUxeMzSYgK468fr4djfwwpQ2H2/TZs1BjTpiwJBKlov4+rju3PR2vymLOhGL73ABRtgMUveh2aMaYLsSQQxK4+rj9De8Zyw78WsTRyAvQ9Fj75LdSUeR2aMaaLsCQQxGL8Pl66ejxJMeFc9cJCto//BVTkO81CxhjTBiwJBLnUuAj+dc1RNASUmz8VAhNuhuy/w+r/eh2aMaYLsCTQCfRNiuahc0ayZGsJT4Ve5qw0+uYtULLN69CMMZ2cJYFOYsrodKaMTuexjzezbMJjzgY0r19rS04bY1rFkkAn8uuzRpCZGMXFr+Xx9ZEPwtZ5MMc2qDfGtJwlgU6kR1QY06+fQO/4SM6ck07BYec5S05v+szr0IwxnZQlgU4mNTaC6ddNICnaz4/yL0QTB8KrV0GpbdRmjDl0lgQ6oaQYP3dPHsrSXfW8O/wPUFcFM6+A+hqvQzPGdDLN2VnseRHJE5GVjcrOF5FVIhIQkax9jr9bRNaLyFoROa1R+SS3bL2I3NW2H6P7+cHIXmT1TeC+L+qo+sFfIDcb3vmp12EZYzqZ5tQEXgAm7VO2EjgXmNO4UESGARcBw93X/FVEQt19h58ETgeGARe7x5oWEhHuPWMYBeW1/GHbEDjuTmdJiUUveB2aMaYTaTIJqOocoGifstWqunY/h58FTFfVGlXdhLOf8Hj3tl5VN6pqLTDdPda0wug+8Vx6VCbPf76J7AE3wcBTnNpATtffjtMY0zbauk+gN9B4BlOOW3ag8v0SketEJFtEsvPz89s4xK7l7smH0zs+kp++voqqKc9CbC+YcTmU53kdmjGmEwjKjmFVfVZVs1Q1KyUlxetwglqM38fvfziKTQUVXP/aRvLPeB6qimHmVFt22hjTpLZOArlAn0aPM9yyA5WbNnDMwGR+ffYIFmwq5KR/FrJozIOw9Qtnf2LbjcwYcxBtnQRmAReJiF9E+gODgAXAQmCQiPQXkXCczuNZbfze3drlE/rywR0ncHivWM7/vDcbB//IWWju44e9Ds0YE8SaM0T0FWAeMEREckTkGhE5R0RygKOBt0XkfQBVXQXMBL4C3gNuVtUGVa0HbgHeB1YDM91jTRvKTIrixavHMzYzgdNWncyOAec7M4o/f8Lr0IwxQUo0yJsLsrKyNDvbRrscitKqOi7523y+3lnK7MyX6LvzfTjjT5B1ldehGWM6gIgsUtWspo8M0o5h0zo9IsOYft0Ejh2Uyvc2X8r6Hkejb/0YVrzqdWjGmCBjSaCLio0I47mpR3Lx0QP5wa7r2Rk/Dv5zPax9z+vQjDFBxJJAFxYaIjxw5nBOGZnJqbtupKTH4c4aQ5vmNP1iY0y3YEmgiwsJER69YAwDM9KZVHAbVbF94ZWLYfsSr0MzxgQBSwLdQERYKM9NzSIsNplzyv6Pen88TDsfCjd4HZoxxmOWBLqJ5Bg//7jySLY3xDOl9CeUVFST99fTKd6ysukXG2O6LEsC3chhqbH860dHMXbskTzf/w+E1FcT9sKpVHz1gdehGWM84vM6ANOxRmXEMyojHhjJvMVDiH/zCgbPvJCa7z+M/5gbQcTrEI0xHchqAt3Y0ePGknPOG/wvMA7/7Lupn3UHBBq8DssY04EsCXRz3x8zkPKz/8Ff66fgW/KCkwiCfBa5MabtWBIwnDMuk/gzH+Yv9WfhW/oS5e/e73VIxpgOYknAAHDJUZkMvuh3zNRTiFnwODum3wYN9V6HZYxpZ5YEzDdOHdGL0dc/z4yws+m15kW2PHE6dWW2s5sxXZklAfMtQ9LjOeP//s709LvoWbKYkkePYu28d7wOyxjTTiwJmO+I9vu46Lq7Wfz9f1NFOIPeu4SP/ngFC79aT7AvPW6MOTSWBMwBHX3cySTdOZ/l6T/kxLL/ctiME3jqz79lc0Gi2pi7AAAaPElEQVSF16EZY9pIc3YWe15E8kRkZaOyRBGZLSJfuz8T3PITRaRURJa6t/savWaSiKwVkfUiclf7fBzT1qJj4xlz/XPUXzuH2viB3FT0WxY8cRmvzl/rdWjGmDbQnJrAC8CkfcruAj5S1UHAR+7jPT5T1THu7UEAEQkFngROB4YBF4vIsNYGbzqOv/dI0m77HxXjb+OCkP8x4p3zeHP2/7wOyxjTSk0mAVWdAxTtU3wW8KJ7/0Xg7CZOMx5Yr6obVbUWmO6ew3QmoT6iJ/+a2ov/TbqvjO/PvYhZLzxCaWWd15EZY1qopX0Caaq6w72/E0hr9NzRIrJMRN4VkeFuWW9gW6Njctyy/RKR60QkW0Sy8/NtiGKwCR9yKhG3fsHOmMOZsvkhPvn9eTzw6gL+OX8Ly3NKaAhY57ExnUWrF5BTVRWRPb/1i4G+qlouIpOBN4BBLTjns8Cz4Gw039oYTdsLT+jNgDs/Iv/tBzlz8RNkrbiE/7f4Yu4NHEWsP4xjDkvitOE9OeXwNHpEhnkdrjHmAFpaE9glIr0A3J95AKq6W1XL3fvvAGEikgzkAn0avT7DLTOdWaiPlCkPEnLVO6T3TOPJ8CdY1PtRrhtUxrJtpdw5cxkTf/8xM7O32dBSY4JUS5PALGCqe38q8CaAiPQUcdYiFpHx7vkLgYXAIBHpLyLhwEXuOUxX0PcY5Po5cObjJFVt4db1P2LesNd5c+phDEmL5WevLueCZ+bx/qqd1DcEvI7WGNOINPUXmoi8ApwIJAO7gPtxmnlmApnAFuACVS0SkVuAG4F6oAq4U1W/cM8zGfgTEAo8r6oPNyfArKwszc7OPvRPZrxRXQpzHoH5T4MvgsAxt/J66Gn8cW4hO0qrSe8RwaUT+nLuuN4kRIXj94UgtoeBMW1KRBapalazjg32arolgU6qcAN8cC+sfRtC/QRGns+85PP565oIPl9f+M1hvhAhLS6CzMQobj35MI45LNnDoI3pGiwJmOCRtwYWPAPLpkNdJQw4kc3H/IaPd0VSWdtAeU09u0qrWbC5iJziKs4ak87ZY3ozpk88CdHhXkdvTKdkScAEn6piWPxPp6kIYNJvYMR5EBYJQHVdA09+vJ5nPt1IrdtvkBgdTmZiFCcMTuG8cRlkJkV5Fb0xnYolARO8irfA69fBtvkQFgWDvg/fewASBwBQUVPP8pxSlueUsLmwkq93lbFoazGqMCg1hqx+iQxKjSE9PoKhPePomxRlfQrG7MOSgAlugQbY+DGseQdW/BsC9XDKfTD0DOiR8Z3N7nNLqvjvsu3M31jIoi3FlFXv3ewmLc7PSUNSOXtsb8b3SyQkxBKCMZYETOdRmgOzboUN7jpEUUlOQhg39TvJAEBVKa6sY3tJFctySpi3oZCP1+RRUdtA7/hIzh6bzvD0HmwprMQXIpw1Jp3UuAgCAaWmPkBkeGgHf0BjOp4lAdO5qEJONuxYCqvegC1zYcgP4PTfQXyfJl9eWVvPB6t28fqSXOZ+nU/jVStCQ4TBabFsLaygoraBfklRjMqI5+iBSRzVP5E+iVGEhdqK6qZrsSRgOq9AAOb/FT76lZMcxlwMYy6DjCwIafqv+Lzd1eSV1ZCZFEVBWQ3TF25j9Y7dDEyJIT4qjNU7drNkawl5ZTWAU9lIifHTq0cEvXpE0is+gnT3Z68ekcRG+PD7QugdH4nPkoXpJCwJmM6vZBt8/jgsfgkaaiAy0elEHnQqDDkdwqNbfGpVZWNBBYu2FJNbXMWO0ip2lFazvcT5WVnb8J3XpMb6+eERGRyWGkNZdT1+XwhpcRHUB5Sc4koqaxuIiwyjb2IUxwxMwhcaQkllLduKqhjaK9ZqG6ZDWRIwXUdVidNfsO59WD8bKgshOhVO+BmMuwJ8/jZ9O1Vld1U920ur2FlaTUVtPeXV9cz+ahcfr82jOQukJseEMyA5hkVbi2kIKLF+H1n9EugVH0nv+EgmjejJwJSYQ44tEFAKKmrYUeIkrNiIMCYMSLQaivkOSwKmawo0wJYv4JPfwJbPISQM0oZBv+Nh5PnQa/R+O5PbSmF5DeU19UT7fdTUB9hZWk1oiNAnIZJov4/dVXUs3VbCf5bksq24khMGpzA4LZYvNxWxeEsxBeU1FFbUogpDe8ZSWdvAztJqBqREMzYznpTYCPy+EJZsLeHLjYWMyYzn/jOHkV9Wy2Oz17F0W8k3cyj2SIoO59ThPZk4KJljDku2FVsNYEnAdHWqsPET57Z9MWyZB4E6SB0GE25yEkJYhNdR7ldeWTVvLMnlk7X5JMX4SY31s25XGStySylxN+fpkxjJkf0Smf3VLipq6gko9OoRwZTR6fROiHT6LnpEkFtSxaxl2/l0bT7lNfXE+n389rxR/GBUL48/pfGaJQHTvVQWwVdvwsLnYNdKZxJa7yNg4MmQdRVEJngdYbMEAkp1fQORYaGICIXlNTw3dxPJMX4uPSqTiLD9d4zXNQRYuq2E//fOapZsLeGCrAyuP2EgA5KjWbV9N/M3FlJT79QgMhIiGZgSw/D0uCYn2QXcti+be9H5WBIw3ZMqbJoDa9+BrfNgxzIIj4Exl0Cfo5wRRgn9vI6y3dQ1BPjD+2t5bu4mGgJKcoyfgvKa/R57eK84rjq2Hxvyyvngq11U1znJp39yNKMy4tlSWMFHa/KormsgIyGS8f0Tuea4AQxMiWZHaTWlVXWkx0d+q/lpQ345K3NLOWloKnERe8vLquvYWlRJZFgoCVHhxEeF2SzvdmZJwBiAnSth7qOw+r/QUOuU9RwJI34IYy+D6K65YmleWTWvL85lRU4pxw9K5uTDU+kRGUYgANuKK1mytZhn5mxkY34FvhDhuEHJpMT4qaxtYO2uMtbnlRMfFcbJQ1NJjvGzuaCCT9flU1MfID4q7JtmK4CEqDCGp/cgoMoXG5zVYaPDQznl8DQKK2pYn1fOrt3fTkRxET4GpMRQHwhQVF5LfFQ4/ZOd0V5FFbXOrbIWX4iQGhdBWqyftLgIEqLDiQgLobY+wNbCSkqr6kjrEUFCVBilVXUUV9ZRUllLWXU9Eb5QovyhRPudIb5FFbXkl9UwMCWGYwYm0ScxitgIHzF+55ZXVsNXO3azvaSK4grn/0pCdDjR4T4UpbC8llXbd7NzdzXxkWH0io9k4qBkJg5OISXGT21DgA9X7+KzdQVkJkUxrFcc9QFld1UdUeGhJESHkxjtJkCE6roGthVXsj6vnPIaJ97QEKE+oAQCSoMqEb4Qrjy2f4v+D1gSMKax+lrIXwObP4NV/4GchRDqhxHnQv8ToM94SBrodZQdqiGgLNlazICUGBL3Wa3V+VIK+daoo8LyGv41fys7SqsYnh5HQnQ420uq2JBXwaodpZRX13PeuAyO7J/Iv7Nz+HRdPr0TIjksJYbDUmPomxRFbX2AwopaNhWUszG/Ar8vhITocIoratlcWEmIOIsGJkQ5twZVdu2uJm93DbvKqr+VfNJ7RNAjKpxdu6spqXQSSXxUGAlR4cRG+Kiua6CipoGK2nqqaxtIiA4nKcbP6h27yS/bf+1oj7gIZ9fd3Y2WJwkPDWFIz1h6x0dSWlXHxoK9yc0XIoSGCDX1AWL9Pspq6vd73kOVHOMn+5ffa9FrLQkYczD5a+HLZ2D5TKgtc8p6HwFZVzvzEGJSvY3P7FcgoNQ2BBABv29v/4iqNrt5SVXZVFBBfpkz0qu8pp7d1fUkRoUzLD2OPgl7JwXWNQSormsgRAT/PklRVVm1fTcLNxdRUF5DVW2AUw5PZcKAJMpr6lm3qwy/L4QekWFU1jZQXFFLYUUtxZW1CE78veIjGJwWS4/IMGrqAtQHAvhCQggJcWa6h4gcsB+oKW2eBETkeeAMIE9VR7hlicAMoB+wGWd3sWJ3e8nHgclAJXClqi52XzMV+KV72odU9cWm3tuSgGk3gQYnIWz8BBb9AwrWOeWJA50JaSPOhfRx7Trs1Jj20B5JYCJQDrzUKAn8HihS1d+KyF1Agqr+3N1G8lacJHAU8LiqHuUmjWwgC1BgEXCEqhYf7L0tCZgOoQq5i5z5B5s+cxJDoM7pSB5+DiT0Bw04Hcxpw7yO1piDOpQk4GvOQao6R0T67VN8Fs7ewwAvAp8AP3fLX1Inu8wXkXgR6eUeO1tVi9wgZwOTgFeaE4Mx7UrEGT2UkQXH3u5sgrPmbVj5Gnz+BOiepSQERv4QJv4MUgZ7GrIxbaFZSeAA0lR1h3t/J5Dm3u8NbGt0XI5bdqDy7xCR64DrADIzM1sRojEtFJngjCAae5mzdEVNmbPvweIXYf7Tzj4ImcdAv2OdZqXYnjBkcrNWPTUmmLQmCXxDVVVE2qyHWVWfBZ4Fpzmorc5rTItExjs3cHZBm3ATLH0ZlrjbZYb4nATx7s+cWcu9xjhLWPQa7QxJ9R/6OkHGdJTWJIFdItJLVXe4zT15bnku0PjPoQy3LJe9zUd7yj9pxfsb442YVDjuDue2R+EGWD0LNs91Frpb9rJT7otwlrE48hroOapZy2Eb05FakwRmAVOB37o/32xUfouITMfpGC51E8X7wP8TkT1z+E8F7m7F+xsTPJIGwnE/dm6qULYTdi6Hte/CsulOrcEXAamHQ9pw6Dkahp0FsWlNn9uYdtTc0UGv4PwVnwzsAu4H3gBmApnAFpwhokXuENG/4HT6VgJXqWq2e56rgV+4p31YVf/R1Hvb6CDT6VUWOUth71rp3HauhMoCpxlp8CSnCSk6xZmrkD7Gagum1WyymDHBTBUK18OiF5zRR2U7cUZNA/4e0O84GHACJA+CqGRIHhy0q6Ka4GRJwJjOJNAA5Xmw9QvY+KkzR6Fky97n/XFw+BQYcCIk9of4vs66RzaJzRxAm88TMMa0o5BQiOsFI85zbgAlW50tNst3wfoPnaWyl/5r72vCoiF9rLMH87CzbQSSaTGrCRjTGdTXQNEmKN7s1BKKNjnJofBrCA2HzKOdUUjjLvc6UhMErCZgTFfj80PqUOe2hyps+9JZKvvr2TDrFqirhKOu9y5O0+nYDtXGdFYikDkBTnsYbpoHQ34A7/7cWe7CmGayJGBMVxASCuc95/QTzLgMpl/q1A5qyr2OzAQ5aw4ypqsIj4LLXoMvnoDFL8Gat0BCnKUrsq6BURfaUFPzHdYxbExXVF/j7Lecs9DZc3nnCmdRvIR+EJnozFzuOQoS+kJcOsRlQIg1DHQVNk/AGLOXKmz61NlJrTwPKvIgbw00NNpmMTLR6V9IG+HMRYhKdmoN0alO4rAaRKdio4OMMXuJOBPNBpy4t6yhzpm1XJoDpdsgZxFsnQfr3nM2z9nXgJNgyp9tqewuyGoCxpi96mudiWrVJVBX5Sxpkb8GvnwaJBROudfZVCcyoelzGc9Yc5Axpm0VbYL/3ADb5kNImNPZHOJzOqMT+kHiAGcLzsQBzi0s0qlllGxx+h4i4rz+BN2KNQcZY9pWYn+4+j3YsRRWvAp5Xzl9DTW74atZUFX07ePDY6G2zLkf4nNWSI1JdcoTBzgd074IZx/ntBHWzOQhSwLGmOYRceYhpI/97nPVpU5toWiDs8FOeR6kDIH4TNg6H7Z8AQXrnaSxZ8OdxnofAb2znG06Y3t9+2dED1ssrx1ZEjDGtF5ED2cvhPQx331u8GnfflxTBvnrQBsAgc2fOUtfLJsONaXffb0v0kkG4THOshjgrKLqj3NqEojzODoVYlIgJm3vsNcevZ2mqfoap38jOsVpwjLfsCRgjOlY/ljIOGLv4z5HwvF3OvdrK5wv67KdULbj2z9rK5wvcA1AZaG7MU+Y87hoI1Tk700S33q/OCfx7NmzIT4TevRxmqfqa53XNdQ6zVb+GCeJ1Nc4o6dCQuGw7zlNVpWFTmd5XDrE9XY+hy8C6qudGJIGQni007G+bYHzXEyqk3iiU5xzVhVDaBhEJbqft9J5HJngnKOiwEls0angC2/Xf4Y9WpUEROR24FpAgL+p6p9E5AG3LN897Beq+o57/N3ANUADcJuqvt+a9zfGdDHh0c6XadLAlr2+ptxZfnv3dtid63ROl++CqCSneal8F+SvdRLLzhUQ6ndqEZEJzpdvTZkzVDbEB0mDoLYc5v7JrbU0xa2RVOQ3fei+QnxOH0vj90noB7cvO/RzHaIWJwERGYHzZT8eqAXeE5G33KcfU9U/7HP8MOAiYDiQDnwoIoNVm3V1jTGmaf4Y59bSJLI/VcVOMolJc1Zz3b3dSSI1ZVBX7UykU3WSS/Fm6DUa+h7jfKGX5zuT8yrynWatyASn1lFVBIhTs9lTGxFxElWIz3kc6JivxtbUBA4HvlTVSgAR+RQ49yDHnwVMV9UaYJOIrMdJIPNaEYMxxrSvyIRvz4uI6OGMbuoiWrNYyErgeBFJEpEoYDKwZ5zXLSKyXESeF5E9V683sK3R63Pcsu8QketEJFtEsvPzW1C1MsYY0ywtTgKquhr4HfAB8B6wFKet/ylgIDAG2AH8sQXnflZVs1Q1KyUlpaUhGmOMaUKrlg1U1b+r6hGqOhEoBtap6i5VbVDVAPA3nCYfgFz21hQAMtwyY4wxHmlVEhCRVPdnJk5/wMsi0qvRIefgNBsBzAIuEhG/iPQHBgELWvP+xhhjWqe18wReE5EkoA64WVVLROTPIjIGZ1DuZuB6AFVdJSIzga+Aevd4GxlkjDEealUSUNXj91N2+UGOfxh4uDXvaYwxpu3YVkLGGNONWRIwxphuLOj3ExCRfGBLC1+eDBS0YTjtoTPECJ0jToux7XSGODtDjOBNnH1VtVnj64M+CbSGiGQ3d2MFr3SGGKFzxGkxtp3OEGdniBGCP05rDjLGmG7MkoAxxnRjXT0JPOt1AM3QGWKEzhGnxdh2OkOcnSFGCPI4u3SfgDHGmIPr6jUBY4wxB2FJwBhjurEumQREZJKIrBWR9SJyl9fx7CEifUTkYxH5SkRWudtzIiKJIjJbRL52fyY0da4OiDVURJbs2S1ORPqLyJfuNZ0hIh2zAeqB44sXkVdFZI2IrBaRo4P0Ov7Y/bdeKSKviEhEMFxLd6+PPBFZ2ahsv9dPHE+48S4XkXEexviI+2++XET+IyLxjZ67241xrYictv+ztn+MjZ77iYioiCS7jz25jk3pcklAREKBJ4HTgWHAxe7WlsGgHviJqg4DJgA3u7HdBXykqoOAj9zHXrsdWN3o8e9wtg09DGfZ8Gs8iWqvx4H3VHUoMBon1qC6jiLSG7gNyFLVEUAozharwXAtXwAm7VN2oOt3Os6qv4OA63D2DPEqxtnACFUdBawD7obvbF87Cfir+13gRYyISB/gVGBro2KvruNBdbkkgLN/wXpV3aiqtcB0nK0tPaeqO1R1sXu/DOeLqzdOfC+6h70InO1NhA4RyQB+ADznPhbgZOBV9xBPYxSRHsBE4O8AqlqrqiUE2XV0+YBIEfEBUTgbLXl+LVV1DlC0T/GBrt9ZwEvqmA/E77NkfIfFqKofqGq9+3A+zr4ke2Kcrqo1qroJ2LN9bYfH6HoM+BnOasp7eHIdm9IVk0Czt7H0koj0A8YCXwJpqrrDfWonkOZRWHv8Cec/cMB9nASUNPrl8/qa9gfygX+4TVbPiUg0QXYdVTUX+APOX4M7gFJgEcF1LRs70PUL1t+pq4F33ftBE6OInAXkquqyfZ4Kmhgb64pJIOiJSAzwGnCHqu5u/Jw6Y3Y9G7crImcAeaq6yKsYmsEHjAOeUtWxQAX7NP14fR0B3Db1s3CSVjoQzX6aDoJRMFy/gxGRe3CaV6d5HUtj4uy3/gvgPq9jaa6umASCehtLEQnDSQDTVPV1t3jXnmqh+zPPq/iAY4EpIrIZpyntZJz293i3SQO8v6Y5QI6qfuk+fhUnKQTTdQT4HrBJVfNVtQ54Hef6BtO1bOxA1y+ofqdE5ErgDOBS3TvRKVhiHIiT9Je5v0MZwGIR6UnwxPgtXTEJLAQGuSMwwnE6i2Z5HBPwTdv634HVqvpoo6dmAVPd+1OBNzs6tj1U9W5VzVDVfjjX7n+qeinwMfBD9zCvY9wJbBORIW7RKTg71gXNdXRtBSaISJT7b78nzqC5lvs40PWbBVzhjm6ZAJQ2ajbqUCIyCaepcoqqVjZ6Kii2r1XVFaqaqqr93N+hHGCc+382aK7jt6hql7sBk3FGDmwA7vE6nkZxHYdTxV4OLHVvk3Ha3D8CvgY+BBK9jtWN90TgLff+AJxfqvXAvwG/x7GNAbLda/kGkBCM1xH4FbAGZ6/tfwL+YLiWwCs4/RR1OF9U1xzo+gGCM+JuA7ACZ7STVzGux2lX3/P783Sj4+9xY1wLnO5VjPs8vxlI9vI6NnWzZSOMMaYb64rNQcYYY5rJkoAxxnRjlgSMMaYbsyRgjDHdmCUBY4zpxiwJGNNOROREcVdhNSZYWRIwxphuzJKA6fZE5DIRWSAiS0XkGXH2UigXkcfcvQA+EpEU99gxIjK/0Xr2e9bcP0xEPhSRZSKyWEQGuqePkb37HkxzZw4bEzQsCZhuTUQOBy4EjlXVMUADcCnOYm/Zqjoc+BS4333JS8DP1VnPfkWj8mnAk6o6GjgGZxYpOCvF3oGzt8UAnLWDjAkavqYPMaZLOwU4Aljo/pEeibNwWgCY4R7zL+B1dx+DeFX91C1/Efi3iMQCvVX1PwCqWg3gnm+Bqua4j5cC/YC57f+xjGkeSwKmuxPgRVW9+1uFIvfuc1xL11epaXS/AfudM0HGmoNMd/cR8EMRSYVv9tnti/O7sWelz0uAuapaChSLyPFu+eXAp+rsEpcjIme75/C768obE/TsrxLTranqVyLyS+ADEQnBWQ3yZpyNasa7z+Xh9BuAs8Ty0+6X/EbgKrf8cuAZEXnQPcf5HfgxjGkxW0XUmP0QkXJVjfE6DmPamzUHGWNMN2Y1AWOM6casJmCMMd2YJQFjjOnGLAkYY0w3ZknAGGO6MUsCxhjTjf1/x52otazw46AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotHistory(model.history.history,listKeys=['val_loss','loss']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_acc_pred   :  [0.9313646299044291, 0.9313187956809997, 0.9312411144574483, 0.931190046151479]\n",
      "acc_pred   :  [0.9416467253140041, 0.9416592465809414, 0.9416934516770499, 0.9416729561941964]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotHistory(model.history.history,listKeys=['val_acc_pred','acc_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr   :  [1.5625e-06, 1.5625e-06, 1.5625e-06, 1.5625e-06]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotHistory(model.history.history,listKeys=['lr'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Model with Test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_plot(x_test, model, num_plots = 12, type_model = \"cvae_style\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate random sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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23Oc8HiAttnnAmtoxzNvyZw9ltIBzwDIt42SxT92XaTn5OYnnGTWD6bZlV1lGYGmgntoZ0J4DfKv6b9F+rAyoVhCPdlol/kuDsmBb61sZPF4YsarmWGE9WbFaLsuiTsy0oh2LkasKSvo+iecGkq171mfqvNoT154vrt8zkJF0XpYzexA9Blq0xivqw5YWxygS0f1q6sHSElwr5YjE4oxcT733udf5w55jK4nnERKr86zOgs4+uFlirW14E639WjjwelSzH9HaTo2eKzWI97aSVT2jk8/Z6rHnhevj750t1ha0XF3Q4vhK4okqlmdBPV65tprQl2hZdg91hoei11vr/Wvd37kAd6/HmHpp29RBfh3QdnVZObegz5yneW6d+6Rc7bZC4ondrA8U1u8lObTn6bcjeCorUMvD2HFs5Lhn6bkHI8fUdt+7NNqOD8yM7eP5uMwFiHu9xq7e7YXE84DnzorLvAxoXpLPVmW0XBezuvRUuhlEaZetE1APY9MxL2NqVDXxJ/m0qWdf8jRpoEXj/fCjkXgCBlldetu6PDMfUKyy1uZ6i9oma07iZm8LGKuu/S2ti+FCz7665/V7NTzePtWD1wtqj0YXAAAAAAAQGzOewlXdGXladnvIe1u1HOvZeW9btWZrkzlLGCO2hWhjqTdWV/FEYCGmPftXjxnQ0+Vfmn/PXpYf9Lll+sSztKJqG/hS9VfogXtJzuNeTkQQuf2VnNTPmHSeEjkRyn165QhelweiTM/+RXu6YSEJPSzDsvE7UyeevKcLd7zcxH6qfEv/YmSxHstcXtrGXlFOxCPX06V9uzTrQNL50Ez3vFq5yEryuZ+X+FlIhmbUM+6lY8p0iWdJBVgYoJGnVSebJcnY0mKAiho7HnAATaPbEyeFl0V4wqSX20ywrbatjb5wzQWHMTSWQe8ZO7omniMHvF5LaluZ5eCgsY6+RSeLnIAy8LdhZfag1lYbz1kqgza020/O9hkP2vCYlEY+zkVjtQ2Vym1rUfbXotKVL63Hh2EzntqJYMsrQqNFTUI9DCynylh71S5S3Z0Sff9O2WofwGw8Jl697B0TljbFMIXZr3x74+TxmBTl1g+PtNuLm6W2NL4b3mdZ7nh+51Tt30a+uhxxn2pZrudTbbflmHKub1iMB/LsrbvRS/xw3qgxa+YnDffY1whjLveIxuMm8ezBSye1fGKbY/bBI8rFA5xnrZ+O7nfW4oE81Nc8RvfRKEnG6LJH7bMeZ0EtP2F6lEejC2CFx0bgqfNZ47G+gVqXxgrGEgAAoC2t6zq6DAAAAACAwJjxBAAAAACoIvEEAAAAAKgi8QQAAAAAqCLxBAAAAACoIvEEAAAAAKgi8QQAAAAAqCLxBAAAAACoIvEEAAAAAKgi8QQAAAAAqCLxBAAAAACoIvEEAAAAAKgi8QQAAAAAqCLxBAAAAACoIvEEAAAAAKgi8QQAAAAAqCLxBAAAAACoIvEEAAAAAKgi8QQAAAAAqCLxBAAAAACoIvEEAAAAAKgi8QQAAAAAqCLxBAAAAACoIvEEAAAAAKj6P3UstLUmYlXuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x144 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_random = np.random.normal(0, 1, size=(20, 500))\n",
    "model_plot(x_random, model, num_plots = 12, mode = \"gen\", figsize = (60,60), type_model = \"cvae_style\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}