Train_DCVAE_Inc_MPS100_Keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "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_Inc\n",
    "from Model.Utils import PlotHistory, PlotDataAE, Save_Model\n",
    "from Model.Utils import load_from_tfrecords, convert_to_tfrecords, load_numpy\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/MPS100'\n",
    "path_npy = '/share/GeoFacies/DataSet/MPS100/MPS100.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",
    "else:\n",
    "    x_train, x_test = load_numpy(path_npy,random_state=0,split_data=0.30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Convolutional Variational Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_weights = os.path.join(path_tfr, 'w100_Inc.hdf5')\n",
    "\n",
    "model = DCVAE_Inc(input_shape=(100, 100, 2),kernel_init=64,latent_dim=500,opt=RMSprop(lr=0.0005),\n",
    "                  scheduler=True,filepath=path_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up model...\n",
      "WARNING:tensorflow:From /share/tfhome/.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_1 (InputLayer)            (None, 100, 100, 2)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 100, 100, 64) 192         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 100, 100, 32) 2080        conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 100, 100, 32) 128         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 100, 100, 32) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 100, 100, 64) 2112        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 100, 100, 64) 18496       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 100, 100, 64) 51264       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 100, 100, 64) 100416      activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 100, 100, 64) 256         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 100, 100, 64) 256         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 100, 100, 64) 256         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 100, 100, 64) 256         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 100, 100, 64) 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 100, 100, 64) 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 100, 100, 64) 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 100, 100, 64) 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 100, 100, 64) 0           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 100, 100, 64) 0           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 100, 100, 64) 0           activation_2[0][0]               \n",
      "                                                                 activation_3[0][0]               \n",
      "                                                                 activation_4[0][0]               \n",
      "                                                                 activation_5[0][0]               \n",
      "                                                                 max_pooling2d_1[0][0]            \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 50, 50, 64)   0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 50, 50, 128)  8320        average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 50, 50, 128)  512         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 50, 50, 128)  0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 50, 50, 64)   8256        activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 50, 50, 64)   256         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 50, 50, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 50, 50, 128)  8320        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 50, 50, 128)  73856       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 50, 50, 128)  204928      activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 50, 50, 128)  401536      activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 50, 50, 128)  512         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 50, 50, 128)  512         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 50, 50, 128)  512         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 50, 50, 128)  512         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 50, 50, 128)  0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 50, 50, 128)  0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 50, 50, 128)  0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 50, 50, 128)  0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 50, 50, 128)  0           activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 50, 50, 128)  0           activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 50, 50, 128)  0           activation_8[0][0]               \n",
      "                                                                 activation_9[0][0]               \n",
      "                                                                 activation_10[0][0]              \n",
      "                                                                 activation_11[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "                                                                 max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 25, 25, 128)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 25, 25, 256)  33024       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 25, 25, 256)  1024        conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 25, 25, 256)  0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 25, 25, 128)  32896       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 25, 25, 128)  512         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 25, 25, 128)  0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 25, 25, 256)  33024       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 25, 25, 256)  295168      activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 25, 25, 256)  819456      activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 25, 25, 256)  1605888     activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 25, 25, 256)  1024        conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 25, 25, 256)  1024        conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 25, 25, 256)  1024        conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 25, 25, 256)  1024        conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 25, 25, 256)  0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 25, 25, 256)  0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 25, 25, 256)  0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 25, 25, 256)  0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2D)  (None, 25, 25, 256)  0           activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2D)  (None, 25, 25, 256)  0           activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 25, 25, 256)  0           activation_14[0][0]              \n",
      "                                                                 activation_15[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "                                                                 activation_17[0][0]              \n",
      "                                                                 max_pooling2d_5[0][0]            \n",
      "                                                                 max_pooling2d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, 6, 6, 256)    0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 6, 6, 512)    131584      average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 6, 6, 512)    2048        conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 6, 6, 512)    0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 6, 6, 256)    131328      activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 6, 6, 256)    1024        conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 6, 6, 256)    0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 6, 6, 512)    131584      activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 6, 6, 512)    1180160     activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 6, 6, 512)    3277312     activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 6, 6, 512)    6423040     activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 6, 6, 512)    2048        conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 6, 6, 512)    2048        conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 6, 6, 512)    2048        conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 6, 6, 512)    2048        conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 6, 6, 512)    0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 6, 6, 512)    0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 6, 6, 512)    0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 6, 6, 512)    0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2D)  (None, 6, 6, 512)    0           activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2D)  (None, 6, 6, 512)    0           activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 6, 6, 512)    0           activation_20[0][0]              \n",
      "                                                                 activation_21[0][0]              \n",
      "                                                                 activation_22[0][0]              \n",
      "                                                                 activation_23[0][0]              \n",
      "                                                                 max_pooling2d_7[0][0]            \n",
      "                                                                 max_pooling2d_8[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, 1, 1, 512)    0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, 1, 512)       0           average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "lambda_2 (Lambda)               (None, 512)          0           lambda_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 512)          0           lambda_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 500)          256500      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 500)          256500      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_3 (Lambda)               (None, 500)          0           dense_1[0][0]                    \n",
      "                                                                 dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 512)          256512      lambda_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 512)          0           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_4 (Lambda)               (None, 1, 512)       0           dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_5 (Lambda)               (None, 1, 1, 512)    0           lambda_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, 7, 7, 512)    0           lambda_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 7, 7, 256)    131328      up_sampling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 7, 7, 256)    1024        conv2d_transpose_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 7, 7, 256)    0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTrans (None, 7, 7, 512)    131584      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTrans (None, 7, 7, 512)    1180160     activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_4 (Conv2DTrans (None, 7, 7, 512)    3277312     activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_5 (Conv2DTrans (None, 7, 7, 512)    6423040     activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 7, 7, 512)    2048        conv2d_transpose_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 7, 7, 512)    2048        conv2d_transpose_3[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 7, 7, 512)    2048        conv2d_transpose_4[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 7, 7, 512)    2048        conv2d_transpose_5[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 7, 7, 512)    0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 7, 7, 512)    0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 7, 7, 512)    0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 7, 7, 512)    0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2D)  (None, 7, 7, 512)    0           up_sampling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling2D) (None, 7, 7, 512)    0           up_sampling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 7, 7, 512)    0           activation_25[0][0]              \n",
      "                                                                 activation_26[0][0]              \n",
      "                                                                 activation_27[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "                                                                 max_pooling2d_9[0][0]            \n",
      "                                                                 max_pooling2d_10[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_6 (Conv2DTrans (None, 28, 28, 256)  2097408     add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 28, 28, 256)  1024        conv2d_transpose_6[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 28, 28, 256)  0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_7 (Conv2DTrans (None, 28, 28, 128)  32896       activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 28, 28, 128)  512         conv2d_transpose_7[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 28, 28, 128)  0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_8 (Conv2DTrans (None, 28, 28, 256)  33024       activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_9 (Conv2DTrans (None, 28, 28, 256)  295168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_10 (Conv2DTran (None, 28, 28, 256)  819456      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_11 (Conv2DTran (None, 28, 28, 256)  1605888     activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 28, 28, 256)  1024        conv2d_transpose_8[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 28, 28, 256)  1024        conv2d_transpose_9[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 28, 28, 256)  1024        conv2d_transpose_10[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 28, 28, 256)  1024        conv2d_transpose_11[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 28, 28, 256)  0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 28, 28, 256)  0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 28, 28, 256)  0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 28, 28, 256)  0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling2D) (None, 28, 28, 256)  0           activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling2D) (None, 28, 28, 256)  0           activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 256)  0           activation_31[0][0]              \n",
      "                                                                 activation_32[0][0]              \n",
      "                                                                 activation_33[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "                                                                 max_pooling2d_11[0][0]           \n",
      "                                                                 max_pooling2d_12[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_12 (Conv2DTran (None, 56, 56, 128)  131200      add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 56, 56, 128)  512         conv2d_transpose_12[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 56, 56, 128)  0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_13 (Conv2DTran (None, 56, 56, 64)   8256        activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 56, 56, 64)   256         conv2d_transpose_13[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 56, 56, 64)   0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_14 (Conv2DTran (None, 56, 56, 128)  8320        activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_15 (Conv2DTran (None, 56, 56, 128)  73856       activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_16 (Conv2DTran (None, 56, 56, 128)  204928      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_17 (Conv2DTran (None, 56, 56, 128)  401536      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 56, 56, 128)  512         conv2d_transpose_14[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 56, 56, 128)  512         conv2d_transpose_15[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 56, 56, 128)  512         conv2d_transpose_16[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 56, 56, 128)  512         conv2d_transpose_17[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 56, 56, 128)  0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 56, 56, 128)  0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 56, 56, 128)  0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 56, 56, 128)  0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling2D) (None, 56, 56, 128)  0           activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling2D) (None, 56, 56, 128)  0           activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 56, 56, 128)  0           activation_37[0][0]              \n",
      "                                                                 activation_38[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "                                                                 activation_40[0][0]              \n",
      "                                                                 max_pooling2d_13[0][0]           \n",
      "                                                                 max_pooling2d_14[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_18 (Conv2DTran (None, 112, 112, 64) 32832       add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 112, 112, 64) 256         conv2d_transpose_18[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 112, 112, 64) 0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_19 (Conv2DTran (None, 112, 112, 32) 2080        activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 112, 112, 32) 128         conv2d_transpose_19[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 112, 112, 32) 0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_20 (Conv2DTran (None, 112, 112, 64) 2112        activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_21 (Conv2DTran (None, 112, 112, 64) 18496       activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_22 (Conv2DTran (None, 112, 112, 64) 51264       activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_23 (Conv2DTran (None, 112, 112, 64) 100416      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 112, 112, 64) 256         conv2d_transpose_20[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 112, 112, 64) 256         conv2d_transpose_21[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 112, 112, 64) 256         conv2d_transpose_22[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 112, 112, 64) 256         conv2d_transpose_23[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 112, 112, 64) 0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 112, 112, 64) 0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 112, 112, 64) 0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 112, 112, 64) 0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling2D) (None, 112, 112, 64) 0           activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_16 (MaxPooling2D) (None, 112, 112, 64) 0           activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 112, 112, 64) 0           activation_43[0][0]              \n",
      "                                                                 activation_44[0][0]              \n",
      "                                                                 activation_45[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "                                                                 max_pooling2d_15[0][0]           \n",
      "                                                                 max_pooling2d_16[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bilinear_upsampling_1 (Bilinear (None, 100, 100, 64) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_24 (Conv2DTran (None, 100, 100, 2)  130         bilinear_upsampling_1[0][0]      \n",
      "==================================================================================================\n",
      "Total params: 32,846,378\n",
      "Trainable params: 32,826,410\n",
      "Non-trainable params: 19,968\n",
      "__________________________________________________________________________________________________\n",
      "Completed model setup.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    },
    {
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       "HBox(children=(IntProgress(value=0, description='Training', max=500, style=ProgressStyle(description_width='in…"
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     "text": [
      "Epoch 1/500\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 385s 440ms/step - loss: 344827.7252 - acc_pred: 0.6806 - val_loss: 8176.4038 - val_acc_pred: 0.6402\n",
      "Epoch 2/500\n"
     ]
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       "HBox(children=(IntProgress(value=0, description='Epoch 1', max=875, style=ProgressStyle(description_width='ini…"
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     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 367s 419ms/step - loss: 6175.6605 - acc_pred: 0.6842 - val_loss: 6390.6741 - val_acc_pred: 0.6711\n",
      "Epoch 3/500\n"
     ]
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       "HBox(children=(IntProgress(value=0, description='Epoch 2', max=875, style=ProgressStyle(description_width='ini…"
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     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 367s 420ms/step - loss: 5391.5981 - acc_pred: 0.7284 - val_loss: 5268.4818 - val_acc_pred: 0.7336\n",
      "Epoch 4/500\n"
     ]
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       "HBox(children=(IntProgress(value=0, description='Epoch 3', max=875, style=ProgressStyle(description_width='ini…"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 367s 420ms/step - loss: 4735.1798 - acc_pred: 0.7714 - val_loss: 5143.0874 - val_acc_pred: 0.7463\n",
      "Epoch 5/500\n"
     ]
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       "HBox(children=(IntProgress(value=0, description='Epoch 4', max=875, style=ProgressStyle(description_width='ini…"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 367s 420ms/step - loss: 4278.1515 - acc_pred: 0.8015 - val_loss: 4657.8631 - val_acc_pred: 0.7770\n",
      "Epoch 6/500\n"
     ]
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       "HBox(children=(IntProgress(value=0, description='Epoch 5', max=875, style=ProgressStyle(description_width='ini…"
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     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 367s 419ms/step - loss: 3951.9444 - acc_pred: 0.8222 - val_loss: 3883.0103 - val_acc_pred: 0.8239\n",
      "Epoch 7/500\n"
     ]
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       "HBox(children=(IntProgress(value=0, description='Epoch 6', max=875, style=ProgressStyle(description_width='ini…"
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     "text": [
      "875/875 [==============================] - 367s 420ms/step - loss: 3704.0130 - acc_pred: 0.8370 - val_loss: 3506.7778 - val_acc_pred: 0.8475\n",
      "Epoch 8/500\n"
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       "HBox(children=(IntProgress(value=0, description='Epoch 7', max=875, style=ProgressStyle(description_width='ini…"
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     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 367s 420ms/step - loss: 3513.0864 - acc_pred: 0.8478 - val_loss: 3371.4722 - val_acc_pred: 0.8583\n",
      "Epoch 9/500\n"
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       "HBox(children=(IntProgress(value=0, description='Epoch 8', max=875, style=ProgressStyle(description_width='ini…"
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     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 367s 419ms/step - loss: 3374.8343 - acc_pred: 0.8551 - val_loss: 3200.7371 - val_acc_pred: 0.8644\n",
      "Epoch 10/500\n"
     ]
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       "HBox(children=(IntProgress(value=0, description='Epoch 9', max=875, style=ProgressStyle(description_width='ini…"
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     "text": [
      "875/875 [==============================] - 365s 417ms/step - loss: 3246.2616 - acc_pred: 0.8618 - val_loss: 3129.2367 - val_acc_pred: 0.8681\n",
      "Epoch 11/500\n"
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       "HBox(children=(IntProgress(value=0, description='Epoch 10', max=875, style=ProgressStyle(description_width='in…"
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     "text": [
      "875/875 [==============================] - 365s 417ms/step - loss: 3131.2474 - acc_pred: 0.8678 - val_loss: 2835.8876 - val_acc_pred: 0.8838\n",
      "Epoch 12/500\n"
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       "HBox(children=(IntProgress(value=0, description='Epoch 11', max=875, style=ProgressStyle(description_width='in…"
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     "text": [
      "875/875 [==============================] - 365s 418ms/step - loss: 3040.2108 - acc_pred: 0.8724 - val_loss: 2904.2749 - val_acc_pred: 0.8799\n",
      "Epoch 13/500\n"
     ]
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       "HBox(children=(IntProgress(value=0, description='Epoch 12', max=875, style=ProgressStyle(description_width='in…"
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     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 417ms/step - loss: 2967.0027 - acc_pred: 0.8760 - val_loss: 2708.1266 - val_acc_pred: 0.8892\n",
      "Epoch 14/500\n"
     ]
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       "HBox(children=(IntProgress(value=0, description='Epoch 13', max=875, style=ProgressStyle(description_width='in…"
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     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 2901.9478 - acc_pred: 0.8791 - val_loss: 2891.1120 - val_acc_pred: 0.8799\n",
      "Epoch 15/500\n"
     ]
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       "HBox(children=(IntProgress(value=0, description='Epoch 14', max=875, style=ProgressStyle(description_width='in…"
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     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 2846.0260 - acc_pred: 0.8819 - val_loss: 2630.0876 - val_acc_pred: 0.8924\n",
      "Epoch 16/500\n"
     ]
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     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 2792.1798 - acc_pred: 0.8844 - val_loss: 2628.1507 - val_acc_pred: 0.8922\n",
      "Epoch 17/500\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 1710.4675 - acc_pred: 0.9341 - val_loss: 1783.6820 - val_acc_pred: 0.9309\n",
      "Epoch 107/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a9a87717f86459281e2b60551e0a596",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 106', max=875, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 1712.6260 - acc_pred: 0.9339 - val_loss: 1783.6707 - val_acc_pred: 0.9309\n",
      "Epoch 108/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1fd21246fef4a8c8b16c624081d19c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 107', max=875, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 1710.8922 - acc_pred: 0.9340 - val_loss: 1782.4219 - val_acc_pred: 0.9310\n",
      "Epoch 109/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d1dacfd2cae42d2a881f0b444e5f1f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 108', max=875, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 1710.3364 - acc_pred: 0.9340 - val_loss: 1783.7209 - val_acc_pred: 0.9309\n",
      "Epoch 110/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d954f54c68ad445a84aa8e53913f0d9d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 109', max=875, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 363s 415ms/step - loss: 1710.9231 - acc_pred: 0.9340 - val_loss: 1784.3173 - val_acc_pred: 0.9309\n",
      "Epoch 111/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8942a8bfa08749d9ba8c3f15f770a83b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 110', max=875, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 1709.1719 - acc_pred: 0.9341 - val_loss: 1783.9524 - val_acc_pred: 0.9309\n",
      "Epoch 112/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa3a9a2acdc64203b15266a614575f14",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 111', max=875, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 1710.1376 - acc_pred: 0.9341 - val_loss: 1783.6624 - val_acc_pred: 0.9309\n",
      "Epoch 113/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2268156c074f4ee9a7f574a248ae0244",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 112', max=875, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 1710.1088 - acc_pred: 0.9341 - val_loss: 1784.3332 - val_acc_pred: 0.9309\n",
      "\n",
      "Epoch 00113: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06.\n",
      "Epoch 114/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a8cf541403a4b4ea269415c16ba5715",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 113', max=875, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 1707.8597 - acc_pred: 0.9342 - val_loss: 1783.4599 - val_acc_pred: 0.9309\n",
      "Epoch 115/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "750a64d498534268b23c10b4d154accf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 114', max=875, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 363s 415ms/step - loss: 1709.2424 - acc_pred: 0.9341 - val_loss: 1783.8955 - val_acc_pred: 0.9309\n",
      "Epoch 116/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4fa84fe69a5c4ba0927d16088813b33b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 115', max=875, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 1707.9537 - acc_pred: 0.9342 - val_loss: 1783.0169 - val_acc_pred: 0.9309\n",
      "Epoch 117/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c48ffc5bc514abdbbcc7fd756d65b83",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 116', max=875, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 1708.2442 - acc_pred: 0.9342 - val_loss: 1783.5004 - val_acc_pred: 0.9309\n",
      "Epoch 118/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40b2ac8609894fa596af48240a24f0bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 117', max=875, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875/875 [==============================] - 364s 416ms/step - loss: 1708.2155 - acc_pred: 0.9341 - val_loss: 1783.6023 - val_acc_pred: 0.9309\n",
      "\n",
      "Epoch 00118: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 500\n",
    "\n",
    "if load_tfrecords:\n",
    "    x_train = gen_train.mps_generator()\n",
    "    x_val = gen_test.mps_generator()\n",
    "    model.fit_generator(x_train,\n",
    "                        num_epochs=num_epochs, verbose=1, \n",
    "                        steps_per_epoch = len(gen_train),\n",
    "                        val_set = x_val,\n",
    "                        validation_steps = len(gen_test))\n",
    "else:\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": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss   :  [1783.4598619791666, 1783.8955491536458, 1783.0168873697917, 1783.500425455729]\n",
      "loss   :  [1707.8597075892858, 1709.242426060268, 1707.9536665736607, 1708.2442138671875]\n"
     ]
    },
    {
     "data": {
      "image/png": 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A4cdahdvmaQJQSgW1bv8kMECY28mQHrEs3LIHUoeCOwLy5zd/oFJKdWNBkQAAcnvFszS/lDrjgB6jrBqAUkoFsaBJAKN7xVPn9bNyeylkHQs7loGnOtBhKaVUwARVAgCrI9h6IMwL25cEOCqllAqcoEkAKTFhZCWE2wmgviNYHwhTSgWvoEkAAKN6xrNk216ISoaEvpoAlFJBLagSwOD0GApLa9hbVQe9joets8HvD3RYSikVEEGVAAamxwCwurAcep0I1SU6PbRSKmgFVQIYlBYNwJodZdDzeKtw6w8BjEgppQInqBJAcnQoCZEhrCksh/hsiE6HLZoAlFLBKagSgIgwKD3aqgGIQK8TYMtsnRhOKRWUgioBAAxMi2FtUTk+v7Gagcq3Q8nmQIellFIdLggTQDQ1Hj+bd1daHcFgjQZSSqkgE3QJYJA9EmhNYTkkD4SwONjyfYCjUkqpjhd0CaBfShROh1j9AA7Hvn4ApZQKMkGXAMLcTvokRbK6sMwq6Hk87NkI5UWBDUwppTpY0CUAsB4IW11Ybr2p7wfY/F3gAlJKqQAIzgSQFk3B3mrKajzQYwSExkLejECHpZRSHSooE8BguyN47Y5ycDih9zgrAejzAEqpIBKUCWBguj0lRH0/QN9TrEXi9+QFMCqllOpYQZkA0mLCSIoKYdaGXVZBn1Osfzd+E7iglFKqgwVlAhARLhudxZerisgvqYKEPhDbU/sBlFJBJSgTAMB1x/dCRHhjzhZrXqC+J8Om78DnDXRoSinVIYI2AWTEhXPmkFTemreNqjqv1QxUWwrbFwc6NKWU6hBBmwAAbjqxN6XVHv67eDv0PgkQbQZSSgWNoE4Aub3iGdIjhtd+2ISJSID0YZA3PdBhKaVUhwjqBCAi3HhCNuuKKli0tcRqBto2F6r3Bjo0pZRqd0GdAADG9E4AIK+4EgZdAH4vrPk4wFEppVT7C/oEkBoTBkBRWQ1kjIK4nrDy/QBHpZRS7S/oE0CY20lchJsdZTXWcNAhF1v9AFV7Ah2aUkq1q6BPAGA9GbyjtNZ6M+QSqxlo9YeBDUoppdqZJgCsZqCishrrTfpw68ngle8FNiillGpnmgCwawD1CUDEqgVsmgkVxYENTCml2pEmACA1NoxdFbV4fH6rYOglYPywelpgA1NKqXakCQCrBmAMFJfb/QApgyFpACybEtjAlFKqHTWbAEQkTETmichSEVkpIr+zy3uLyFwR2SAiU0QkxC4Ptd9vsLdnNzrXQ3b5WhE5s70uqrXSYkMB9vUDiMDoG6yHwgqXBTAypZRqPy2pAdQCE4wxw4ERwFkiMhb4A/C0MaYfUALcbO9/M1Bilz9t74eIDAauBIYAZwH/EhFnW17MkUqJbvQsQL0RV4M7Aua/GKColFKqfTWbAIylwn7rtl8GmAC8Y5dPAi6yf77Qfo+9/VQREbv8LWNMrTFmE7ABGNMmV3GU0mKtBLCjtFECCI+HYVfAsrf1mQClVLfUoj4AEXGKyBJgJ/AlsBHYa4ypnzw/H8iwf84AtgHY20uBxMblTRzT+LNuFZEFIrKguLhjRuEkRITgdgo7ymr333DsLeCthiWTOyQOpZTqSC1KAMYYnzFmBJCJ9Vf7wPYKyBjzgjEm1xiTm5yc3F4fsx+HQ0iJDtu/CQggbSj0PAHmvwR+X4fEopRSHaVVo4CMMXuB6cDxQJyIuOxNmUCB/XMBkAVgb48Fdjcub+KYgEuLDdu/CajemFugZDOs/7LDY1JKqfbUklFAySISZ/8cDpwOrMZKBJfZu90AfGD/PM1+j739G2OMscuvtEcJ9Qb6A/Pa6kKOVlpMEzUAgEHnQ0wmfP+3jg9KKaXaUUtqAOnAdBFZBswHvjTGfAQ8ANwrIhuw2vhftvd/GUi0y+8FHgQwxqwEpgKrgM+AO4wxnaZdJdV+GtjKVY043XDCnbD1B9gyOzDBKaVUO3A1t4MxZhkwsonyPJoYxWOMqQEuP8S5Hgceb32Y7S8tNpSqOh/ltV5iwtz7bxx1Pcz8I8x6Cnq9HZgAlVKqjemTwLaGdQGa6gcIiYCxt8H6L/TBMKVUt6EJwJbWsDCMNRS0tNpDeY1n3w7H3gIh0TDr6UCEp5RSbU4TgK3hYbCyGvx+w8TnZ3Pv1KX7dgiPg2P/B1b9F4rXBihKpZRqO5oAbI2XhpyxbidrdpSzsqB0v30WZV5HhQml9rP/DUSISinVpjQB2BqWhiyt4cWZmwDYXlpDZa23YZ/vCuBfnvMJ3fgZbP4+UKEqpVSb0ATQSFpMGDPXFzM7bzeje8UDkFdc2bB9XVE5r/jOpiosFb74Nfj9gQpVKaWOmiaARlJjwtiyu4qoUBe/OncQABuLKxq2rysqp4ZQZmXdBtsX6bKRSqkuTRNAI/UjgSYem8XQHrE4HdKQAOq8fjbtsmoDX4ecBKk58PXvwNPEsFGllOoCNAE00jMxArdTuOnEbEJcDnolRDQkgM27K/H6raeEC0o9cObjsHcrzPlnIENWSqkjpgmgkZtOzOaze8aTGR8BQJ/kKDbstBLAuqJyqywpku17q6HPSTDgXJj5FyjfEbCYlVLqSGkCaCQixEXf5KiG931TItm8qwqvz8+6ogocAuP6J1Gwt9qaM+iM34OvDr7+fQCjVkqpI6MJ4DD6JUdR5/OTX1LNuh3l9EqMpHdSJLVeP7sr6yCxL4z9qbVgzPbFgQ5XKaVaRRPAYfRNsWoDG4srWLeznP4pUfSICwewmoEAxt8PkUnwyS91WKhSqkvRBHAYfZOsBLC6sIwtu6sYkBZ9cAIIi4XTfgv582DZlMAEqpRSR0ATwGHERrhJigrly1VF+PyG/qnRZMZbCaBgb6Phn8Ovhoxc+PJ/oab0EGdTSqnORRNAM/qlRLI037qpH5MaRWy4m4gQJwUl1ft2cjjg3D9DZTHMeDJAkSqlVOtoAmhG/aggp0PonRSJiNAjLnxfE1C9HiNh9I0w93ldM0Ap1SVoAmhGfQLITowg1OUEICMunO2l1QfvfOr/Wh3C790CdVUdGaZSSrWaJoBm1I8EOiY1uqGsyRoAQEQCXPwcFK+BL37VUSEqpdQR0QTQjH52AujfKAFkxIWxq6KOGk8Ta9r3nQAn3AULXoHVH3ZUmEop1WqaAJqRERfOk5fkcO1xPRvKDhoKeqAJj0D6CPjgZ1Ca3xFhKqVUq2kCaIErx/QkxZ4pFKykALB97yFmAnWFwGWvgN8L794CPm/T+ymlVABpAjgC9TWAgr2H6ehN7AvnPgVbf4CZf+qgyJRSquU0ARyBtNgwHHLAw2BNGT7Rekhs5h9h03cdE5xSSrWQJoAj4HY6SI0JO3QfQGPn/AkS+sD7P4Hqve0fnFJKtZAmgCPUIy6c/JIqa1rowwmNgktehIoi+OT+jglOKaVaQBPAEcqKD2dO3h76/epTRj76BW/N23ronTNGwfhfwvKpsELXEVZKdQ6aAI7Qz08/hofOHshtJ/UlIz6c//tkNRW1hxntM+4+yBgNH/0cygo7LlCllDoETQBHqFdiJD85qS+/OHMAj12UQ1mNl//M3XLoA5wuuPh5awWxN6+E2vKOC1YppZqgCaANjMiK44S+ibz03SZqvU08HVwvqT9c9irsWA5TrgNvXccFqZRSB9AE0EZuP7kfO8treW9RweF3HHAWXPB3yJsOH9yuq4gppQJGE0AbObFfIsMyY3n+2434/M2MDBp5rTVz6PK3YfpjHROgUkodQBNAGxERbj+5L5t3V/Hlqh3NH/Cje631A777Cyz+d7vHp5RSB9IE0IZOH5xGWkwYUxe0YAI4ETjnz9DnZPjwbtg0s73DU0qp/WgCaENOh3Dp6AxmrN1JUVkz00QAON1w+SRI6AtTroXide0fpFJK2ZpNACKSJSLTRWSViKwUkbvt8t+KSIGILLFf5zQ65iER2SAia0XkzEblZ9llG0Tkwfa5pMC6bHQWfkPzncH1wuPgmqngDIH/XA6Vu9o3QKWUsrWkBuAF7jPGDAbGAneIyGB729PGmBH26xMAe9uVwBDgLOBfIuIUESfwT+BsYDBwVaPzdBu9kyIZk53A2wu2NT9NRL34bLjqLSjfAW9eBZ4WzDGklFJHqdkEYIwpNMYssn8uB1YDGYc55ELgLWNMrTFmE7ABGGO/Nhhj8owxdcBb9r7dzuW5meTtqmThlhIAquq8Ta8e1lhmLlzyAuTPg7dvBE8LmpCUUuootKoPQESygZHAXLvoZyKyTEReEZF4uywD2NbosHy77FDlB37GrSKyQEQWFBcXtya8TuOcnHQiQ5w89+1GHn5/ObmPfcVVL87B39zw0MEXWmsIrPsMJl+mTwsrpdpVixOAiEQB7wL3GGPKgGeBvsAIoBD4S1sEZIx5wRiTa4zJTU5ObotTdrjIUBfnDkvnq9U7eXdhPiOy4li8dS8fLW/BHEDH3gwXvwBbfoDXL4LqkvYPWCkVlFqUAETEjXXzn2yMeQ/AGFNkjPEZY/zAi1hNPAAFQFajwzPtskOVd0u/OHMAf7xsGHMfPpV/33wcg9Jj+ONna5qcKmLx1hLeWdho6OjwiXDF67BjGUw6Hyp3d2DkSqlg0ZJRQAK8DKw2xjzVqDy90W4XAyvsn6cBV4pIqIj0BvoD84D5QH8R6S0iIVgdxdPa5jI6n5ToMK7IzSIuIgSHQ3j4nIHkl1TzxuyDJ4z70+drefj95dR5G00LMeg8uOpN2LUeJp0HFV2zOUwp1Xm1pAZwInAdMOGAIZ9/FJHlIrIMOAX4OYAxZiUwFVgFfAbcYdcUvMDPgM+xOpKn2vsGhXH9kxnXP4lnvtlAaZWnoby8xsO8TXuo8/pZV3RAm3+/0+DqKbBnE7x2DpRt7+ColVLdWUtGAc0yxogxZljjIZ/GmOuMMTl2+QXGmMJGxzxujOlrjBlgjPm0Ufknxphj7G2Pt9dFdVYPnT2IshoPr/6wqaFs1vpdeO3O4aX5TSwZ2edkuPZd6+b/6tlQsrlDYlVKdX/6JHAHGtwjhh/1S2Lq/G0NE8Z9s2YnMWEuYsPdLNtW2vSB2SfC9dOsNYVfOVufGFZKtQlNAB3sqjE92V5aw8z1xfj9hhnrihl/TDLDMmObrgHUyxwNN30Cfi+8fDrkzeiwmJVS3ZMmgA522qBUEiNDmDJvGyu3l1FcXsspA1IYnhnH+p0VVNcd5oGx1CFw8xcQnQ5vXALzXoSWPm2slFIH0ATQwUJcDi4dnclXq4uYumAbInDSAKsG4PMbVhUeohmoXkJvKwn0Px0++QU1H96vi8oopY6IJoAAmHhsFl6/4Y05WxiWGUdSVCjDs+IAWHqofoDGwmIovWASL3vPJmzRi/D+rbq8pFKq1TQBBEDf5CjGZCcAMGFACgCpMWGkxoSy7HD9AI2sKqrk995r+W/ij62Vxd66CmrK2i1mpVT3owkgQK47vhcicPrg1IayYZlxLMtvQQ0AWFVYBgjP+S6E8/8OG6fDixNg5+p2ilgp1d1oAgiQ84al8/0DExjcI6ahbHhmLHm7Kimt9hzmSMuq7dZf+5t3V+IfeT3cMA1qSq0ksGxqu8WtlOo+NAEEiIjQIy58v7JhmVY/wIqC5msBVg0Aajx+dpTVQPaP4KffQfoIeO8WmHYn1FW1feBKqW5DE0AnMiwzFoAZa3c2PCjWlDqvnw07yxlhdxxv2lVpbYhOgxs+tBacX/S6NgkppQ5LE0AnEhcRwpjsBF78bhMnPPk1T3y6mvKag5uDNuyswOMznJtjzceXV58AAJwuOO031vQRlcXwwskw/yV9XkApdRBNAJ3M6zeP4dlrRpGTEceLM/P45TvLDlpasr7555SByYS7nWwqrjz4RP1Og9t+gF4nwsf3wVtXQ3lRR1yCUqqL0ATQyYS5nZydk85LN+Tyy7MG8umKHfx77tb99lm1vYwwt4PeSVH0Topk066Kpk8WnQrXvANnPgEbvoJ/5MKcZ8Hn7YArUUp1dpoAOrFbx/Vh/DHJ/P6jVQ2jfgBWFZYyMC0Gp0PonRy5rw+gKQ4HHH873D4HMo+Fzx6EF06CwqUdcAVKqc5ME0An5nAIT10xnLhwNz97cxFVdV6MMazaXsagdGv4aJ+kSLaVVO+/mExTEvtu7YMNAAAbW0lEQVRa/QJXvAGVu6wO4m//pLUBpYKYJoBOLikqlL9OHMGmXZX83yer2V5aQ1mNt+H5gd5Jkfj8hm0lLRjyKQKDL4DbZ8Pgi2D6Y/DyaVAUNOvyKKUa0QTQBZzQL4kf/6g3/56zlX9N3wDA4PR9CQBouiP4UCIS4LKX4fLXYO82eP4kmPGkziekVJDRBNBF3HfGAAamRTN57lZEYGBaNNAoARyuH+BQhlwMd8yDIRfBjCfgX2Nhzcc6ZFSpIKEJoIsIczt5euIIQpwOshMjiQx1AdazAwmRIfs/C9AakYlw6UvWaCGH0xou+voFsPl7TQRKdXOuQAegWm5Qegx/v2okIvuX1w8FNcbwly/WsWJ7KS9en4vb2Yr83v90a/3hBa/Ct3+wFqHPGA0/+jkMPI+DPlQp1eVpDaCLOWtoGmcOSduvrHdSJHnFlTz56Rr+MX0DM9YW88qsTYc4w2E43XDcrXDPcjj3L1C1B6ZcCy+fAVvntNEVKKU6C00A3UDvpEh2ltfy/Mw8rhvbi9MGpfDXr9azfW/1kZ0wJAKO/THcuRAueAb2boVXzoT/TIRt89s2eKVUwGgC6AaOSbU6hK8a05PfXTCE35w/BIPh0Q9XHbTv9r3VTPph80HTSzTJ4YRR18Ndi2DCr2HbXGvY6KTzYcsPbX0ZSqkOpgmgGzhlQDL/ueU4Hr9oKA6HkJUQwZ0T+vPZyh18s2bf/D9+v+Hutxbzm2krWdrChWcACImE8ffDPSvgjMeheB28ejb8+1IoWNQOV6SU6giaALoBl9PBCX2TcDj2ddTeMq4P/VOiuG/q0oYhopPnbWX+5hIAPluxo/UfFBoFJ/wM7loMp/8eChbCi6fAcz+C7/8O5UdwTqVUwGgC6KZCXA5evD4XEeGmV+excnspf/h0DSf2S2Rc/yQ+W1HYsmagJk8eASfeBXcvg7P+AM4Q+PIR+GsOfHg37DmCDmilVIfTBNCNZSdF8uL1uWwvreHCf3yP1+/niYuHcdbQNDbvrmJtUfnRfUBYDIz9KdzyDfxsAYy4Bpb8B54ZBVOug7wZ+iyBUp2YJoBubnSveP46cQR+Y7j/zIH0TIzgjMFpiMCny9uwySapP5z/V6tWcPzPYPN38PqF8Mxoa9I5rRUo1enIETcDdIDc3FyzYMGCQIfRLZRWe4gNdze8v+K52ZTVePjsnvHt84GeGlj1ASx8DbbaI4Z6Hm8NLx10AbhC2udzlVKIyEJjTG5z+2kNIEg0vvmD9UDZmh3lRzaHUEu4w2D4RPifT60Hy079DVQUwbs3W30FX/3OWpOgE/8BolR3pwkgSJ051Hqa+NMVhfuV13p9fLe++LCL0rdaXE8Ydy/8bKE151D6MPj+b/D8eKu/4Ns/6QgipQJA5wIKUhlx4QzPiuOFmXnER4RwRW4WKwpK+cXbS1m/s4LTB6fy9ytHEh7ixBjDjHXFRLidjOmdgBzpvEAOhzXnUP/ToXI3rPkIVrxjrUvw7ZNwzFnWWsa9x0NCn1bPPzRj7U5ysxOICtX/rJVqCe0DCGIbdlbw8HvLmbd5D32SItmyp4rkqFDOG5bOy99vYkRWHHdN6M/fvl7Pkm17AatT+c4J/TjpmOQjTwQH2r3R6itY/g6Ub7fK4ntDzmWQcwUkH9PsKTbvquTkP8/g3tOP4a5T+7dNXEp1US3tA9AEEOSMMUxbup2nvlzHcb0T+NW5g4kNd/PZih3c/dZiar1+UmNCue/0AdR6fTw7YyPbS2u48YRsfnP+4LZLAlYwVjLYNANWfwibZoLxQ9owGHopDL3Eak5qwpvztvLQe8s5vk8ib946tu1iUqoL0gSgjtqSbXtZtKWEK8dkERFiNavUef088elqXv1+MzedmM3/ntfGSaCx8h2w4l3rVbDQKkvNgWPOgH6nW9NV26OJ7nlrMf9dsp1Ql4OlvzmDMLezfWJSqgtoswQgIlnA60AqYIAXjDF/E5EEYAqQDWwGrjDGlIh1N/gbcA5QBdxojFlkn+sG4Nf2qR8zxkw63GdrAuicjDE8+tEqXv1+M5eMyiAzLpziijpG9ozjitysFp+nzuvn5VmbOCcnjV6JkYffec8ma1jpus+tSemMD1zhkHUspu+pXDozhU3eJEqqPEy5dSzH9Uk8yqtse3VeP1+s2sG5OentlzSVom0TQDqQboxZJCLRwELgIuBGYI8x5kkReRCIN8Y8ICLnAHdiJYDjgL8ZY46zE8YCIBcrkSwERhtjSg712ZoAOi9jDI99vJqXZ21CBKJCXJTXenn2mlGcnZPeonP85oMVTJq9hcz4cN677QRSYsJa9uHVJbDpO9jyPWyeBUUrANgRPYRXS4aRNnYiN513ypFeWrv5YEkBd7+1hHdvO4HRveIDHY7qxlqaAJodLmGMKQQK7Z/LRWQ1kAFcCJxs7zYJmAE8YJe/bqzMMkdE4uwkcjLwpTFmjx3gl8BZwJutujLVKYgIj5w3mLtP60+E24nPGCY+P4f731nGMWnR9E2O2m//Go+P0moPqfZN/v3F+UyavYVzctKYsbaYG16dz9SfjCU6zN3Ux+0vPB4GX2C9gI9nzmbZ55P4edhKHnK9CQvehPwcyB4HPcdCrxMhMqnNfwettc6eemN9UbkmANUptOo5ABHJBkYCc4FUOzkA7MBqIgIrOWxrdFi+XXao8gM/41YRWSAiC4qLi1sTngqAmDA3LqeDUJeTf10zihCXg5++sZDSKg8APr/h7QXbOPlPMzju/77miudm89J3eTz03nKO653A364cybPXjmZ9UTk/eWMh5TWeVsfw9Y5w3g67lNDbZ/JMzrv8wXcN/pAYWPAKTL0e/tTXeubg60ehvKj5E7aT9UUVgDX6SqnOoMUJQESigHeBe4wxZY232X/tt0lvsjHmBWNMrjEmNzk5uS1OqTpIj7hwnrlqJBuLKxj+6Bec8MTXTPjLDO5/ZxmpMaHcc1p/dpbX8NjHq4kLD+EfV4/C7XRw0jHJ/PGyYczJ2835z8xiRcHBaxWsLyrnxlfnNXnznJu3hzHZ1vMJAwfl8KznXOaf/AY8uA1u/tJazMYdCbP+Cv+9rSN+FU3aUFyx379KBVqLnpgRETfWzX+yMeY9u7hIRNKNMYV2E89Ou7wAaNwTmGmXFbCvyai+fMaRh646oxP7JfH2T09gTt5u1heVU1xRy31nDOC8nHQcDuHuU/uzYEsJqdFhJEeHNhx3yahMMuLCueutxVzy7A88cu4grh3bCxGhpLKOmyctYOueKqrqljPl1rENnaj5JVUU7K3mlnG9AexEAHPy9lgdwVljrNf4+2Hmn+Gb38OOFZA2tEN/L7VeH1t2VwFaA1CdR7M1AHtUz8vAamPMU402TQNusH++AfigUfn1YhkLlNpNRZ8DZ4hIvIjEA2fYZaqbGd0rnjtO6cdfrxzJ5B+P5YLhPRoWqxERjs1OoGdixEHHHdcnkU/uGscJfRN55IOV/PTfC9lVUcttkxeyo6yG68b2Yt6mPby/uKDhmLl5exqOBYiNcDM4PYY5ebsb9vH7DRt2VvBhyFn43REw+x/teflN2ryrCp/f0C8lioK91VTX+To8BqUO1JIawInAdcByEVlilz0MPAlMFZGbgS3AFfa2T7BGAG3AGgZ6E4AxZo+I/B6oX1X80foOYaXqJUaF8soNx/LyrE384bM1zFj7DbVeP09dMZyLRmSwrKCU//tkNacOSiUyxMk3a3cSF+FmgL0uMsDYPom8MWcLP31jIcUVtWwsrmCv3ScRk342Jy1/GyY8ArEHdUG1m/U7rQ7gs4em8cw3G9hYXMHQjNgO+3ylmtKSUUCzgEMNWj61if0NcMchzvUK8EprAlTBx+EQbhnfhzG9E3jg3WWcOSSNS0ZlAvD4RUO54B+zuPm1+WwrqaKorJYrcjP3Ww7znJx0PlleyMbiCpKjQzlzcBqje8Xz6YpC/lJwGuPN+8jc5+CM33fYNa0vqsAhcPrgVE0AqtPQWbNUpzU8K+6g9QqGZsRywwnZTPphMycPSOHRC7OYMDBlv31G94pn9kMH/W2CzxgeWltMxdDziF74Gpx4d4cND91QXEHPhAgGpEXjEO0HUJ2DJgDV5Txy7mDuOe2Yg9Y4aM7xdj/BtynXcF7eJ/DP46xawPCrWj3zaGttKKqgX0oUoS4nvRIjNQGoTkHXA1BdjsMhrb75A/RKjCA9NoxPi1OsdYwTelvDQl+cAD/8w5qIrh14fX7ydlXQL8Xqp+ibHKUJQHUKmgBU0BARxvZJZE7ebkzaMPifL+CCZ8BTDV/8ylqc5q/D4L1brYfICpeBr/UPph1oy54qPD5D/xTr6eh+KVFs3l2J1+c/6nMrdTS0CUgFleP7JPL+4gLWFVUwIC0aRl1vvUq2WBPNbf4ONk6HZVOsA5yh1jMDacOslczSR0BaDjhbXgOp/2u/f+q+BODxGbbsqTpoygylOpImABVUju9r9QPMydttJYB68b34LPJ8QoZfyIQrUqBkE2xfbL+WwIr3YOGr1r7uCGsq6tShkNgXEvtB8kCITmvoS/h4WSE+Y7hgeI+GBFB/s+9n1wQ27KzQBKACShOACipZCRFkxIUze+Nubjghu6F86oJt/PKdZTgEnr8ul9MH97GWpRx6qbWDMbB3CxQsgm3zrCmpF70Onsp9Jw+LhZTBbAvpy3erw1nl70lVxemsL6ohIy6cSHupyr7J1tTXG3ZWcOaQjrpypQ6mCUAFneP7JvLV6iL8foPDIXy4dDsPvruMcf2TKKv28LP/LGLyj49jSI9Ypi0tYN6mEs4amsaEgb1wxmdbK5OBlRTKC2HXeiheCztXUZW/nIQt7/Cku8ba58tH+BEplIX2gPfegpgeRKcMYWx0JZuKUg8Zo1IdQVcEU0HnnYX5/OLtpRzXO4EQl4PZG3czqmc8k/5nDFV1Xi5/bjbF5bUgUF7jJdztpNrjo2dCBKcPTsXpEPx+Q3xkCD0TIugRF06t10dplYfHPl6N1+vlo+uyiC1fzwdffEVYyVpGxFSS5Sq11jz2ewHY7kinxx0fW81ISrUhXRJSqUMoqazjzjcXU1HrRQR6JkTw2EVDG9Yi2Lanip+8sZC+KVFcN7YXI3vG8cXKIl77YRPL8ktxiCACVU3M5xMR4uStW8cyLDMOgKo6L3/4dA2X52ZZT/76PLBrHV9+9Rmj1z1FaGgYkT/+CFIGdejvQHVvmgCUameVtV62lVRRuLeGULeD2HA3mXERxEY0P0LI6/Pz5KT3uWXLvcSGQNjFz0C/0yDEmiSvvkaRHB2KiODzGz5dUciU+dvolxLFLeP60CMuvMlzl1TWMX3tTubk7SY6zE1WfDjpceGEuhyEOB1kJ0Ue8ljVPWgCUKqT8/j8/HbSR/xk8730dBRTJ6GsjxzFYl8fZpWnsdqXQV1EOoOzktlYXMHm3VVkxIVTVGb1L5w6KIXyGi8biysoqfQQGeokIsRFYWk1fgNxEW5qPD5qPPs/b+AQODsnnVvG9WF4ZuxB6xN7fX4qa32U13owBtxOB06HUFJVR1FZDSVVHurvG8lRoYzsGU94iLNjfmmqRTQBKNUF1Hn9PD5tMd5N3zO6Zg653kX0bFhoz7JH4tjtTCE6tRcpmf0ocyUyI98wvUCojulNTGofEmPCqar1UV7jISshglMHpTIsIxYRKK6oZWdZLbVeP3VeP9+uK2by3C2U13gJcTmIC3cTFeqiss5LeY23yaatw3E7heGZcfSICyfM7cDpcFBW46Gkso7KOh8hTsHtdOB2Ogh1OXC7HFTX+Sir9lDt8REZ4iIm3EWY24nLITgc0lAWGeqixuOnosZLrddnncPtwBirea3G48dvDA4RHAJOhwO3U3A6BIdY/9Z5/dR4fNR5/USFuYiLCCE61IXXb/D5/Xj9Br+xpg1vrD4vOkQID3ESEeLE6zNUe3xU1/nw+Px4fKbh850O6yl1p1if7fUbPD4/Pr8hxK59OR1Wbc5nDE4Rq9zlwG8MPp/B6zfWylrGkBYbztXH9Tyi/640ASjVVdVWQPEa61VaAGX51r+l+dar8dBTsB5Wi04DcVh3rahUSDoGkgdAfDbEZkFMDwiJBFcYiFBR6+WDJQVs3V1FabWH8lovkSFOYsLcRIW5iA5zExXqxOlwUOf14/X7iY8IISU6lMSoEEQEY2BbSRVz8nYzb9Me9lTWUevx4/H5iQ13Ex8ZQmSoC6/PSjwen99KQj7/vpu+y0lFrZV4qj0+6+boN1TVeSmr8eKzb8pRoS5CXA48XuscDgeEu52Eu50NNRif37qBev1+fPaN2WcMbqeDcLcTt9NBRa2XshqrZnMgh9Bwrsb3RX8T+4pAiJ3UHGLtU39jN8ZKKE6H4LYTmsf+HfgNdqKyEkFT5643smcc799+Yqv+09kXnyYApbqnukqoLIayQti9AXattdc6NmD8VnnxGqg+xHIbKUNg3L0w5GJwdN6mG2MMNR4/oS7HftN9H636BONyWH+RuxxWp/6BTWH1/H5DjddHVZ0Pl8OqDYQ4HYfc/3CMMfsd5/VZCdEhVhxOhxzReQ+kCUCpYFe5y3p4be82KN8BnirrtfpDK0EkDYABZ1m1hLieEBYPYTEQkQjh8e0+Q6pqPy1NAPogmFLdVWSS9coYvX/5yQ/Dqv/CD3+HOc+Cr+7gY0OiraQQGgV+n1WziEyC6HSrickdBs4Q6+eM0dZT05owuhxNAEoFG4fDepp56CXWzb280Kol1JRCbRlU7ITSbdYEeZ4qu5lIrFrE9sVW89OBwuOthBGeYNUgIpOthBEeb82dVJ8wHC7rfO4Iq0/CHWkNfXWHW0nHqbekjqS/baWCmcMJsZnWq6X8fvB7wFtrJYr8BbB9EZRth6o91kR6VXusZNIa4oCoNGut5tAYO2E4rSen62sp9QnGFWIlL78XHG5whVod3O5wK6G4wu2yUGu7w7kv8bjthIPQ0G9SX8txhVrbnSFW/NV7wVcLodFWTK4wayZYh9M6pn668NBo65wi1hQh3lrr3ADitOLthDQBKKVax+EAh31zDRsCqUNg9A0H7+epgZq91noLnmrrJm584PPu64+oq7R/roaq3VYSKc23jvPVWTdZh8u6IWNgj51cfHX7ahN+H3hrrKQUSA6XdbP31R68zRUOEQnW76zOvnaRRskIKwE17pNNy4ErJ7dryJoAlFLtwx0G7rSO+zy/z04qVeCtBm+dnRi8dm3B02h7o5u0iJVIxGEdU1dh/WUfFms1YTld1tDc2jLrfD6vXfNw7RtFVVcBNWVWgnOFW3/xi73elt9r1SSqS6zjQyKtG7/x70t+iD2Mt9EaXQm92/1XpglAKdU9OJx2U0108/sqQJeEVEqpoKUJQCmlgpQmAKWUClKaAJRSKkhpAlBKqSClCUAppYKUJgCllApSmgCUUipIderpoEWkGNhyFKdIAna1UTidiV5X19Ndr02vq3PqZYxJbm6nTp0AjpaILGjJnNhdjV5X19Ndr02vq2vTJiCllApSmgCUUipIdfcE8EKgA2gnel1dT3e9Nr2uLqxb9wEopZQ6tO5eA1BKKXUImgCUUipIdcsEICJnichaEdkgIg8GOp4jJSJZIjJdRFaJyEoRudsuTxCRL0Vkvf1vfKBjPVIi4hSRxSLykf2+t4jMtb+7KSLSORdTPQwRiRORd0RkjYisFpHju8N3JiI/t/87XCEib4pIWFf9vkTkFRHZKSIrGpU1+R2J5e/2NS4TkVGBi7xtdbsEICJO4J/A2cBg4CoRGRzYqI6YF7jPGDMYGAvcYV/Lg8DXxpj+wNf2+67qbmB1o/d/AJ42xvQDSoCbAxLV0fkb8JkxZiAwHOv6uvR3JiIZwF1ArjFmKOAErqTrfl+vAWcdUHao7+hsoL/9uhV4toNibHfdLgEAY4ANxpg8Y0wd8BZwYYBjOiLGmEJjzCL753KsG0kG1vVMsnebBFwUmAiPjohkAucCL9nvBZgAvGPv0uWuTURigfHAywDGmDpjzF66x3fmAsJFxAVEAIV00e/LGDMT2HNA8aG+owuB141lDhAnIukdE2n76o4JIAPY1uh9vl3WpYlINjASmAukGmMK7U07gNQAhXW0/gr8EvDb7xOBvcYYr/2+K353vYFi4FW7aeslEYmki39nxpgC4M/AVqwbfymwkK7/fTV2qO+oW95ToHsmgG5HRKKAd4F7jDFljbcZaxxvlxvLKyLnATuNMQsDHUsbcwGjgGeNMSOBSg5o7umK35ndHn4hVoLrAURycBNKt9EVv6Mj0R0TQAGQ1eh9pl3WJYmIG+vmP9kY855dXFRfBbX/3Rmo+I7CicAFIrIZq5luAlbbeZzdxABd87vLB/KNMXPt9+9gJYSu/p2dBmwyxhQbYzzAe1jfYVf/vho71HfUre4pjXXHBDAf6G+PTgjB6qiaFuCYjojdJv4ysNoY81SjTdOAG+yfbwA+6OjYjpYx5iFjTKYxJhvrO/rGGHMNMB24zN6ty12bMWYHsE1EBthFpwKr6Prf2VZgrIhE2P9d1l9Xl/6+DnCo72gacL09GmgsUNqoqahrM8Z0uxdwDrAO2Aj8KtDxHMV1/AirGroMWGK/zsFqK/8aWA98BSQEOtajvM6TgY/sn/sA84ANwNtAaKDjO4LrGQEssL+3/wLx3eE7A34HrAFWAG8AoV31+wLexOrL8GDV2m4+1HcECNbIwo3AcqyRUAG/hrZ46VQQSikVpLpjE5BSSqkW0ASglFJBShOAUkoFKU0ASikVpDQBKKVUkNIEoFQ7EZGT62c5Vaoz0gSglFJBShOACnoicq2IzBORJSLyvL1GQYWIPG3Pf/+1iCTb+44QkTn2vPDvN5ozvp+IfCUiS0VkkYj0tU8f1WhtgMn2U7RKdQqaAFRQE5FBwETgRGPMCMAHXIM12dkCY8wQ4FvgN/YhrwMPGGOGYT0VWl8+GfinMWY4cALWU6ZgzeB6D9baFH2w5s9RqlNwNb+LUt3aqcBoYL79x3k41iRgfmCKvc+/gffsuf7jjDHf2uWTgLdFJBrIMMa8D2CMqQGwzzfPGJNvv18CZAOz2v+ylGqeJgAV7ASYZIx5aL9CkUcO2O9I50ypbfSzD/1/TnUi2gSkgt3XwGUikgIN68L2wvp/o36Wy6uBWcaYUqBERMbZ5dcB3xprtbZ8EbnIPkeoiER06FUodQT0rxEV1Iwxq0Tk18AXIuLAmh3yDqyFXMbY23Zi9ROANU3wc/YNPg+4yS6/DnheRB61z3F5B16GUkdEZwNVqgkiUmGMiQp0HEq1J20CUkqpIKU1AKWUClJaA1BKqSClCUAppYKUJgCllApSmgCUUipIaQJQSqkg9f/r6iM0/XsuYgAAAABJRU5ErkJggg==\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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr   :  [3.90625e-06, 3.90625e-06, 3.90625e-06, 3.90625e-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": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_acc_pred   :  [0.9309274835586547, 0.9309108416239421, 0.9309331912994385, 0.9309227172533671]\n",
      "acc_pred   :  [0.934169254234859, 0.9341148709569659, 0.9341702643121992, 0.9341682755606515]\n"
     ]
    },
    {
     "data": {
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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": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Model with Test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x3600 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 3600x3600 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if load_tfrecords:\n",
    "    x_test = gen_test.get_numpy_batch()\n",
    "\n",
    "x_rec = model.model.predict(x_test)\n",
    "PlotDataAE(np.argmax(x_test[:,:,:,:],axis=-1),np.argmax(x_rec[:,:,:,:],axis=-1),digit_size=(100,100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate random sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x3600 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_gen=model.generate(binary=True)\n",
    "PlotDataAE([],x_gen[:,:,:],Only_Result=False,digit_size=(100,100))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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