02 - Model building and selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Histology Tissue Classification Project - Selection of models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import needed packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import itertools\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pickle\n",
    "import shutil\n",
    "from glob import glob\n",
    "from skimage.io import imread\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras_preprocessing.image import ImageDataGenerator\n",
    "from skimage import exposure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.12.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data from pickle files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=pickle.load(open('./data/x_train.pkl', 'rb'))\n",
    "x_test=pickle.load(open('./data/x_test.pkl', 'rb'))\n",
    "x_val=pickle.load(open('./data/x_val.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=pickle.load(open('./data/y_train.pkl', 'rb'))\n",
    "y_test=pickle.load(open('./data/y_test.pkl', 'rb'))\n",
    "y_val=pickle.load(open('./data/y_val.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basics checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Training set: (3000, 150, 150, 3) (3000, 8)\n",
      "2- Validation set: (1000, 150, 150, 3) (1000, 8)\n",
      "3- Testing set: (1000, 150, 150, 3) (1000, 8)\n"
     ]
    }
   ],
   "source": [
    "print('1- Training set:', x_train.shape, y_train.shape)\n",
    "print('2- Validation set:', x_val.shape, y_val.shape)\n",
    "print('3- Testing set:', x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0, 0.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.amin(x_train), np.amin(x_test), np.amin(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 1.0, 1.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.amax(x_train), np.amax(x_test), np.amax(x_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the input shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 150, 3)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_train.shape[1],x_train.shape[2],x_train.shape[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shape=(x_train.shape[1],x_train.shape[2],x_train.shape[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 150, 3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define plot style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['seaborn-dark', 'seaborn-darkgrid', 'seaborn-ticks', 'fivethirtyeight', 'seaborn-whitegrid', 'classic', '_classic_test', 'fast', 'seaborn-talk', 'seaborn-dark-palette', 'seaborn-bright', 'seaborn-pastel', 'grayscale', 'seaborn-notebook', 'ggplot', 'seaborn-colorblind', 'seaborn-muted', 'seaborn', 'Solarize_Light2', 'seaborn-paper', 'bmh', 'tableau-colorblind10', 'seaborn-white', 'dark_background', 'seaborn-poster', 'seaborn-deep']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn-ticks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_acc_loss(history):\n",
    "    # plot the training loss and accuracy\n",
    "    fig = plt.figure(figsize=(9, 3), dpi=100)\n",
    "    plt.subplots_adjust(wspace = 0.6 )\n",
    "    ax1 = plt.subplot(121)\n",
    "    ax2 = plt.subplot(122)\n",
    "    ax1.plot(np.arange(0, EPOCHS), history.history['acc'], 'b', label='training accuracy')\n",
    "    ax1.plot(np.arange(0, EPOCHS), history.history['val_acc'], 'r', label='validation accuracy');\n",
    "    ax1.set_title('Accuracy')\n",
    "    ax1.set_xlabel(\"Number of epoch \")\n",
    "    ax1.set_ylabel(\"Accuracy\")\n",
    "    ax1.legend(loc=\"best\")\n",
    "\n",
    "\n",
    "    ax2.plot(np.arange(0, EPOCHS), history.history[\"loss\"], label=\"training loss\")\n",
    "    ax2.plot(np.arange(0, EPOCHS), history.history[\"val_loss\"], label=\"validation loss\")\n",
    "    ax2.set_title(\"Loss\")\n",
    "    ax2.set_xlabel(\"Number of epoch \")\n",
    "    ax2.set_ylabel(\"Loss\")\n",
    "    ax2.legend(loc=\"best\");\n",
    "    \n",
    "    print('Loss:')\n",
    "    print('  - loss [training dataset]: {0:.3f}'.format(history.history['loss'][-1]))\n",
    "    print('  - loss [validation dataset: {0:.3f}'.format(history.history['val_loss'][-1]))\n",
    "    print('')\n",
    "    print('Accuracy:')\n",
    "    print('  - accuracy [training dataset]: {:.2f}%'.format(100*history.history['acc'][-1]))\n",
    "    print('  - accuracy [validation dataset: {:.2f}%'.format(100*history.history['val_acc'][-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN model v1"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Loss:\n",
    "  - loss [training dataset]: 0.541\n",
    "  - loss [validation dataset: 0.610\n",
    "\n",
    "Accuracy:\n",
    "  - accuracy [training dataset]: 78.23%\n",
    "  - accuracy [validation dataset: 76.60%\n",
    "  \n",
    "100 epochs\n",
    "batch size 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 50, 50, 32)        896       \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 50, 50, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 25, 25, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 8, 8, 64)          18496     \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                65600     \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 8)                 520       \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 8)                 0         \n",
      "=================================================================\n",
      "Total params: 85,512\n",
      "Trainable params: 85,512\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def model_cnn_v1():\n",
    "    \n",
    "    # must define the input shape in the first layer of the neural network\n",
    "    model = tf.keras.models.Sequential()\n",
    "    model.add(tf.keras.layers.Conv2D(32, 3, 3, input_shape=input_shape))\n",
    "    model.add(tf.keras.layers.Activation('relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "    #model.add(tf.keras.layers.Conv2D(32, 3, 3))\n",
    "    #model.add(tf.keras.layers.Activation('relu'))\n",
    "    #model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "    model.add(tf.keras.layers.Conv2D(64, 3, 3))\n",
    "    model.add(tf.keras.layers.Activation('relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "    model.add(tf.keras.layers.Dense(64))\n",
    "    model.add(tf.keras.layers.Activation('relu'))\n",
    "    model.add(tf.keras.layers.Dropout(0.5))\n",
    "    model.add(tf.keras.layers.Dense(8))\n",
    "    model.add(tf.keras.layers.Activation('sigmoid'))\n",
    "    \n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "# reset the model\n",
    "tf.keras.backend.clear_session()\n",
    "\n",
    "# create the model\n",
    "model_cnn_v1=model_cnn_v1()\n",
    "\n",
    "# take a look at the model summary\n",
    "model_cnn_v1.summary()\n",
    "\n",
    "# save weight before training\n",
    "initial_weights = model_cnn_v1.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3000 samples, validate on 1000 samples\n",
      "Epoch 1/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 2.0357 - acc: 0.1772\n",
      "Epoch 00001: val_loss improved from inf to 1.92090, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 20s 7ms/step - loss: 2.0298 - acc: 0.1817 - val_loss: 1.9209 - val_acc: 0.3640\n",
      "Epoch 2/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.8440 - acc: 0.2688\n",
      "Epoch 00002: val_loss improved from 1.92090 to 1.69111, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 1.8381 - acc: 0.2697 - val_loss: 1.6911 - val_acc: 0.3690\n",
      "Epoch 3/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.7066 - acc: 0.2873\n",
      "Epoch 00003: val_loss improved from 1.69111 to 1.60795, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 1.6998 - acc: 0.2877 - val_loss: 1.6079 - val_acc: 0.3110\n",
      "Epoch 4/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.6341 - acc: 0.3072\n",
      "Epoch 00004: val_loss improved from 1.60795 to 1.52343, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 1.6340 - acc: 0.3050 - val_loss: 1.5234 - val_acc: 0.3200\n",
      "Epoch 5/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.5720 - acc: 0.3299\n",
      "Epoch 00005: val_loss improved from 1.52343 to 1.42441, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 1.5694 - acc: 0.3307 - val_loss: 1.4244 - val_acc: 0.4160\n",
      "Epoch 6/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.4745 - acc: 0.3690\n",
      "Epoch 00006: val_loss improved from 1.42441 to 1.28868, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 1.4689 - acc: 0.3697 - val_loss: 1.2887 - val_acc: 0.4540\n",
      "Epoch 7/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.3744 - acc: 0.3782\n",
      "Epoch 00007: val_loss improved from 1.28868 to 1.16740, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 1.3720 - acc: 0.3790 - val_loss: 1.1674 - val_acc: 0.4680\n",
      "Epoch 8/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.2939 - acc: 0.3896\n",
      "Epoch 00008: val_loss improved from 1.16740 to 1.11846, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 1.2918 - acc: 0.3920 - val_loss: 1.1185 - val_acc: 0.4480\n",
      "Epoch 9/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.2251 - acc: 0.4290\n",
      "Epoch 00009: val_loss improved from 1.11846 to 1.07381, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 1.2241 - acc: 0.4250 - val_loss: 1.0738 - val_acc: 0.4890\n",
      "Epoch 10/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.1687 - acc: 0.4521\n",
      "Epoch 00010: val_loss improved from 1.07381 to 1.05996, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 8s 3ms/step - loss: 1.1720 - acc: 0.4547 - val_loss: 1.0600 - val_acc: 0.4830\n",
      "Epoch 11/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.1581 - acc: 0.4510\n",
      "Epoch 00011: val_loss improved from 1.05996 to 1.05557, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 1.1521 - acc: 0.4523 - val_loss: 1.0556 - val_acc: 0.4620\n",
      "Epoch 12/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.1206 - acc: 0.4627\n",
      "Epoch 00012: val_loss improved from 1.05557 to 1.02727, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 1.1194 - acc: 0.4593 - val_loss: 1.0273 - val_acc: 0.5030\n",
      "Epoch 13/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.1168 - acc: 0.4609\n",
      "Epoch 00013: val_loss improved from 1.02727 to 1.00618, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 1.1126 - acc: 0.4600 - val_loss: 1.0062 - val_acc: 0.5170\n",
      "Epoch 14/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.0965 - acc: 0.4684\n",
      "Epoch 00014: val_loss improved from 1.00618 to 0.99005, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 1.0955 - acc: 0.4720 - val_loss: 0.9900 - val_acc: 0.5060\n",
      "Epoch 15/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.0636 - acc: 0.4904\n",
      "Epoch 00015: val_loss improved from 0.99005 to 0.93979, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 1.0620 - acc: 0.4923 - val_loss: 0.9398 - val_acc: 0.5120\n",
      "Epoch 16/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.0294 - acc: 0.4986\n",
      "Epoch 00016: val_loss did not improve from 0.93979\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 1.0281 - acc: 0.4983 - val_loss: 0.9518 - val_acc: 0.5200\n",
      "Epoch 17/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.0260 - acc: 0.4982\n",
      "Epoch 00017: val_loss improved from 0.93979 to 0.92840, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 1.0248 - acc: 0.4997 - val_loss: 0.9284 - val_acc: 0.5330\n",
      "Epoch 18/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 1.0243 - acc: 0.4993\n",
      "Epoch 00018: val_loss improved from 0.92840 to 0.91924, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 1.0203 - acc: 0.5000 - val_loss: 0.9192 - val_acc: 0.5440\n",
      "Epoch 19/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9913 - acc: 0.5142\n",
      "Epoch 00019: val_loss did not improve from 0.91924\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.9904 - acc: 0.5143 - val_loss: 0.9403 - val_acc: 0.5340\n",
      "Epoch 20/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9878 - acc: 0.5206\n",
      "Epoch 00020: val_loss improved from 0.91924 to 0.87557, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.9894 - acc: 0.5237 - val_loss: 0.8756 - val_acc: 0.5420\n",
      "Epoch 21/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9618 - acc: 0.5380\n",
      "Epoch 00021: val_loss improved from 0.87557 to 0.87316, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.9632 - acc: 0.5353 - val_loss: 0.8732 - val_acc: 0.5430\n",
      "Epoch 22/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9711 - acc: 0.5213\n",
      "Epoch 00022: val_loss did not improve from 0.87316\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.9722 - acc: 0.5250 - val_loss: 0.8844 - val_acc: 0.5660\n",
      "Epoch 23/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9500 - acc: 0.5156\n",
      "Epoch 00023: val_loss did not improve from 0.87316\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.9579 - acc: 0.5130 - val_loss: 0.8763 - val_acc: 0.5630\n",
      "Epoch 24/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9194 - acc: 0.5384\n",
      "Epoch 00024: val_loss improved from 0.87316 to 0.84819, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.9231 - acc: 0.5380 - val_loss: 0.8482 - val_acc: 0.5560\n",
      "Epoch 25/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9449 - acc: 0.5465\n",
      "Epoch 00025: val_loss did not improve from 0.84819\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.9484 - acc: 0.5450 - val_loss: 0.8546 - val_acc: 0.5680\n",
      "Epoch 26/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9205 - acc: 0.5636\n",
      "Epoch 00026: val_loss did not improve from 0.84819\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.9188 - acc: 0.5617 - val_loss: 0.8540 - val_acc: 0.5700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9240 - acc: 0.5419\n",
      "Epoch 00027: val_loss improved from 0.84819 to 0.84573, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.9242 - acc: 0.5453 - val_loss: 0.8457 - val_acc: 0.5540\n",
      "Epoch 28/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9216 - acc: 0.5479\n",
      "Epoch 00028: val_loss improved from 0.84573 to 0.83846, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.9199 - acc: 0.5507 - val_loss: 0.8385 - val_acc: 0.5570\n",
      "Epoch 29/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9242 - acc: 0.5476\n",
      "Epoch 00029: val_loss improved from 0.83846 to 0.83020, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.9201 - acc: 0.5470 - val_loss: 0.8302 - val_acc: 0.5810\n",
      "Epoch 30/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9323 - acc: 0.5284\n",
      "Epoch 00030: val_loss did not improve from 0.83020\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.9333 - acc: 0.5303 - val_loss: 0.8585 - val_acc: 0.5620\n",
      "Epoch 31/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.9324 - acc: 0.5426\n",
      "Epoch 00031: val_loss did not improve from 0.83020\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.9295 - acc: 0.5440 - val_loss: 0.8337 - val_acc: 0.5710\n",
      "Epoch 32/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8904 - acc: 0.5579\n",
      "Epoch 00032: val_loss improved from 0.83020 to 0.81649, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.8912 - acc: 0.5593 - val_loss: 0.8165 - val_acc: 0.5810\n",
      "Epoch 33/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8770 - acc: 0.5661\n",
      "Epoch 00033: val_loss did not improve from 0.81649\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.8798 - acc: 0.5650 - val_loss: 0.8288 - val_acc: 0.5720\n",
      "Epoch 34/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8756 - acc: 0.5650\n",
      "Epoch 00034: val_loss did not improve from 0.81649\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.8783 - acc: 0.5607 - val_loss: 0.8251 - val_acc: 0.5570\n",
      "Epoch 35/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8747 - acc: 0.5664\n",
      "Epoch 00035: val_loss did not improve from 0.81649\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.8732 - acc: 0.5653 - val_loss: 0.8519 - val_acc: 0.5620\n",
      "Epoch 36/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8639 - acc: 0.5806\n",
      "Epoch 00036: val_loss improved from 0.81649 to 0.77616, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.8596 - acc: 0.5830 - val_loss: 0.7762 - val_acc: 0.6050\n",
      "Epoch 37/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8296 - acc: 0.5909\n",
      "Epoch 00037: val_loss did not improve from 0.77616\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.8233 - acc: 0.5967 - val_loss: 0.7816 - val_acc: 0.5810\n",
      "Epoch 38/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8231 - acc: 0.5959\n",
      "Epoch 00038: val_loss improved from 0.77616 to 0.76927, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.8274 - acc: 0.5933 - val_loss: 0.7693 - val_acc: 0.6040\n",
      "Epoch 39/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8111 - acc: 0.6158\n",
      "Epoch 00039: val_loss did not improve from 0.76927\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.8139 - acc: 0.6113 - val_loss: 0.7853 - val_acc: 0.6090\n",
      "Epoch 40/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8257 - acc: 0.5987\n",
      "Epoch 00040: val_loss improved from 0.76927 to 0.75325, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.8201 - acc: 0.6020 - val_loss: 0.7532 - val_acc: 0.6040\n",
      "Epoch 41/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8013 - acc: 0.6122\n",
      "Epoch 00041: val_loss did not improve from 0.75325\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.7993 - acc: 0.6113 - val_loss: 0.7830 - val_acc: 0.6340\n",
      "Epoch 42/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8420 - acc: 0.6055\n",
      "Epoch 00042: val_loss did not improve from 0.75325\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.8373 - acc: 0.6070 - val_loss: 0.7624 - val_acc: 0.6450\n",
      "Epoch 43/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8067 - acc: 0.6229\n",
      "Epoch 00043: val_loss improved from 0.75325 to 0.73288, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.8035 - acc: 0.6270 - val_loss: 0.7329 - val_acc: 0.6520\n",
      "Epoch 44/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7878 - acc: 0.6204\n",
      "Epoch 00044: val_loss did not improve from 0.73288\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.7854 - acc: 0.6153 - val_loss: 0.7848 - val_acc: 0.6070\n",
      "Epoch 45/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7636 - acc: 0.6420\n",
      "Epoch 00045: val_loss did not improve from 0.73288\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.7703 - acc: 0.6367 - val_loss: 0.7344 - val_acc: 0.6430\n",
      "Epoch 46/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7706 - acc: 0.6385\n",
      "Epoch 00046: val_loss improved from 0.73288 to 0.70634, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.7707 - acc: 0.6383 - val_loss: 0.7063 - val_acc: 0.6680\n",
      "Epoch 47/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7598 - acc: 0.6417\n",
      "Epoch 00047: val_loss improved from 0.70634 to 0.69964, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.7534 - acc: 0.6463 - val_loss: 0.6996 - val_acc: 0.6650\n",
      "Epoch 48/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7571 - acc: 0.6410\n",
      "Epoch 00048: val_loss did not improve from 0.69964\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.7586 - acc: 0.6403 - val_loss: 0.8129 - val_acc: 0.6440\n",
      "Epoch 49/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.8372 - acc: 0.6204\n",
      "Epoch 00049: val_loss did not improve from 0.69964\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.8332 - acc: 0.6223 - val_loss: 0.7738 - val_acc: 0.6380\n",
      "Epoch 50/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7974 - acc: 0.6310\n",
      "Epoch 00050: val_loss did not improve from 0.69964\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.7975 - acc: 0.6317 - val_loss: 0.7170 - val_acc: 0.6620\n",
      "Epoch 51/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7604 - acc: 0.6442\n",
      "Epoch 00051: val_loss did not improve from 0.69964\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.7576 - acc: 0.6440 - val_loss: 0.7223 - val_acc: 0.6490\n",
      "Epoch 52/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7474 - acc: 0.6445\n",
      "Epoch 00052: val_loss did not improve from 0.69964\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.7476 - acc: 0.6463 - val_loss: 0.7116 - val_acc: 0.6580\n",
      "Epoch 53/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7663 - acc: 0.6491\n",
      "Epoch 00053: val_loss did not improve from 0.69964\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.7630 - acc: 0.6520 - val_loss: 0.7346 - val_acc: 0.6420\n",
      "Epoch 54/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7688 - acc: 0.6403\n",
      "Epoch 00054: val_loss improved from 0.69964 to 0.68849, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.7698 - acc: 0.6380 - val_loss: 0.6885 - val_acc: 0.6690\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 55/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7103 - acc: 0.6609\n",
      "Epoch 00055: val_loss did not improve from 0.68849\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.7089 - acc: 0.6620 - val_loss: 0.6887 - val_acc: 0.6650\n",
      "Epoch 56/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6986 - acc: 0.6669\n",
      "Epoch 00056: val_loss did not improve from 0.68849\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.7006 - acc: 0.6657 - val_loss: 0.6966 - val_acc: 0.6710\n",
      "Epoch 57/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7068 - acc: 0.6587\n",
      "Epoch 00057: val_loss improved from 0.68849 to 0.68450, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.7021 - acc: 0.6620 - val_loss: 0.6845 - val_acc: 0.6730\n",
      "Epoch 58/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6959 - acc: 0.6584\n",
      "Epoch 00058: val_loss improved from 0.68450 to 0.68231, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6995 - acc: 0.6557 - val_loss: 0.6823 - val_acc: 0.6680\n",
      "Epoch 59/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7280 - acc: 0.6495\n",
      "Epoch 00059: val_loss did not improve from 0.68231\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.7251 - acc: 0.6543 - val_loss: 0.7169 - val_acc: 0.6610\n",
      "Epoch 60/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7155 - acc: 0.6523\n",
      "Epoch 00060: val_loss did not improve from 0.68231\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.7208 - acc: 0.6500 - val_loss: 0.6990 - val_acc: 0.6710\n",
      "Epoch 61/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.7032 - acc: 0.6626\n",
      "Epoch 00061: val_loss did not improve from 0.68231\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.7005 - acc: 0.6623 - val_loss: 0.6942 - val_acc: 0.6700\n",
      "Epoch 62/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6997 - acc: 0.6648\n",
      "Epoch 00062: val_loss did not improve from 0.68231\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.6982 - acc: 0.6633 - val_loss: 0.7027 - val_acc: 0.6610\n",
      "Epoch 63/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6861 - acc: 0.6683\n",
      "Epoch 00063: val_loss improved from 0.68231 to 0.67281, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.6850 - acc: 0.6687 - val_loss: 0.6728 - val_acc: 0.6640\n",
      "Epoch 64/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6690 - acc: 0.6641\n",
      "Epoch 00064: val_loss did not improve from 0.67281\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.6663 - acc: 0.6643 - val_loss: 0.7124 - val_acc: 0.6590\n",
      "Epoch 65/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6786 - acc: 0.6822\n",
      "Epoch 00065: val_loss did not improve from 0.67281\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.6818 - acc: 0.6777 - val_loss: 0.6756 - val_acc: 0.6670\n",
      "Epoch 66/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6809 - acc: 0.6605\n",
      "Epoch 00066: val_loss did not improve from 0.67281\n",
      "3000/3000 [==============================] - 6s 2ms/step - loss: 0.6785 - acc: 0.6610 - val_loss: 0.7404 - val_acc: 0.6400\n",
      "Epoch 67/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6985 - acc: 0.6673\n",
      "Epoch 00067: val_loss did not improve from 0.67281\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.7001 - acc: 0.6667 - val_loss: 0.6772 - val_acc: 0.6700\n",
      "Epoch 68/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6750 - acc: 0.6580\n",
      "Epoch 00068: val_loss did not improve from 0.67281\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6746 - acc: 0.6597 - val_loss: 0.6842 - val_acc: 0.6690\n",
      "Epoch 69/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6611 - acc: 0.6783\n",
      "Epoch 00069: val_loss did not improve from 0.67281\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6613 - acc: 0.6800 - val_loss: 0.6924 - val_acc: 0.6720\n",
      "Epoch 70/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6805 - acc: 0.6612\n",
      "Epoch 00070: val_loss did not improve from 0.67281\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6793 - acc: 0.6597 - val_loss: 0.6784 - val_acc: 0.6810\n",
      "Epoch 71/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6487 - acc: 0.6783\n",
      "Epoch 00071: val_loss did not improve from 0.67281\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6507 - acc: 0.6770 - val_loss: 0.6738 - val_acc: 0.6730\n",
      "Epoch 72/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6686 - acc: 0.6779\n",
      "Epoch 00072: val_loss did not improve from 0.67281\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6711 - acc: 0.6767 - val_loss: 0.6866 - val_acc: 0.6710\n",
      "Epoch 73/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6481 - acc: 0.6907\n",
      "Epoch 00073: val_loss improved from 0.67281 to 0.66071, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6498 - acc: 0.6913 - val_loss: 0.6607 - val_acc: 0.6770\n",
      "Epoch 74/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6372 - acc: 0.6815\n",
      "Epoch 00074: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6386 - acc: 0.6793 - val_loss: 0.6984 - val_acc: 0.6710\n",
      "Epoch 75/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6472 - acc: 0.6861\n",
      "Epoch 00075: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6449 - acc: 0.6853 - val_loss: 0.6821 - val_acc: 0.6680\n",
      "Epoch 76/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6368 - acc: 0.6776\n",
      "Epoch 00076: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6378 - acc: 0.6780 - val_loss: 0.6828 - val_acc: 0.6690\n",
      "Epoch 77/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6403 - acc: 0.6839\n",
      "Epoch 00077: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6381 - acc: 0.6843 - val_loss: 0.6768 - val_acc: 0.6660\n",
      "Epoch 78/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6256 - acc: 0.6882\n",
      "Epoch 00078: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6290 - acc: 0.6880 - val_loss: 0.6975 - val_acc: 0.6640\n",
      "Epoch 79/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6402 - acc: 0.6864\n",
      "Epoch 00079: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6365 - acc: 0.6813 - val_loss: 0.6722 - val_acc: 0.6580\n",
      "Epoch 80/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6231 - acc: 0.6843\n",
      "Epoch 00080: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6250 - acc: 0.6837 - val_loss: 0.6825 - val_acc: 0.6840\n",
      "Epoch 81/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6335 - acc: 0.6939\n",
      "Epoch 00081: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6343 - acc: 0.6940 - val_loss: 0.6920 - val_acc: 0.6700\n",
      "Epoch 82/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6459 - acc: 0.6964\n",
      "Epoch 00082: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6452 - acc: 0.6960 - val_loss: 0.6644 - val_acc: 0.6840\n",
      "Epoch 83/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6244 - acc: 0.6989\n",
      "Epoch 00083: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6178 - acc: 0.7020 - val_loss: 0.6780 - val_acc: 0.6750\n",
      "Epoch 84/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6220 - acc: 0.6928\n",
      "Epoch 00084: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6222 - acc: 0.6907 - val_loss: 0.6714 - val_acc: 0.6730\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 85/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6085 - acc: 0.6985\n",
      "Epoch 00085: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6094 - acc: 0.6977 - val_loss: 0.6649 - val_acc: 0.6720\n",
      "Epoch 86/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6447 - acc: 0.6889\n",
      "Epoch 00086: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6559 - acc: 0.6887 - val_loss: 0.7398 - val_acc: 0.6700\n",
      "Epoch 87/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6641 - acc: 0.6719\n",
      "Epoch 00087: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6572 - acc: 0.6760 - val_loss: 0.6940 - val_acc: 0.6590\n",
      "Epoch 88/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6131 - acc: 0.6850\n",
      "Epoch 00088: val_loss did not improve from 0.66071\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6077 - acc: 0.6870 - val_loss: 0.6729 - val_acc: 0.6770\n",
      "Epoch 89/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6064 - acc: 0.6960\n",
      "Epoch 00089: val_loss improved from 0.66071 to 0.65922, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6126 - acc: 0.6920 - val_loss: 0.6592 - val_acc: 0.6870\n",
      "Epoch 90/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.5925 - acc: 0.7067\n",
      "Epoch 00090: val_loss did not improve from 0.65922\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.5975 - acc: 0.7037 - val_loss: 0.6722 - val_acc: 0.6630\n",
      "Epoch 91/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6218 - acc: 0.6886\n",
      "Epoch 00091: val_loss did not improve from 0.65922\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6208 - acc: 0.6883 - val_loss: 0.7412 - val_acc: 0.6620\n",
      "Epoch 92/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6495 - acc: 0.6960\n",
      "Epoch 00092: val_loss did not improve from 0.65922\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6485 - acc: 0.6937 - val_loss: 0.7213 - val_acc: 0.6540\n",
      "Epoch 93/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6251 - acc: 0.6854\n",
      "Epoch 00093: val_loss did not improve from 0.65922\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6266 - acc: 0.6857 - val_loss: 0.6710 - val_acc: 0.6770\n",
      "Epoch 94/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6151 - acc: 0.7010\n",
      "Epoch 00094: val_loss did not improve from 0.65922\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6091 - acc: 0.7033 - val_loss: 0.6795 - val_acc: 0.6680\n",
      "Epoch 95/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6055 - acc: 0.6903\n",
      "Epoch 00095: val_loss did not improve from 0.65922\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.5991 - acc: 0.6950 - val_loss: 0.6849 - val_acc: 0.6680\n",
      "Epoch 96/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.5693 - acc: 0.7042\n",
      "Epoch 00096: val_loss did not improve from 0.65922\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.5683 - acc: 0.7073 - val_loss: 0.6890 - val_acc: 0.6620\n",
      "Epoch 97/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.6092 - acc: 0.6946\n",
      "Epoch 00097: val_loss did not improve from 0.65922\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.6083 - acc: 0.6957 - val_loss: 0.7187 - val_acc: 0.6570\n",
      "Epoch 98/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.5961 - acc: 0.6918\n",
      "Epoch 00098: val_loss improved from 0.65922 to 0.65094, saving model to ./results/model_cnn_v1.weights.best.hdf5\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.5901 - acc: 0.6940 - val_loss: 0.6509 - val_acc: 0.6740\n",
      "Epoch 99/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.5740 - acc: 0.7088\n",
      "Epoch 00099: val_loss did not improve from 0.65094\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.5710 - acc: 0.7077 - val_loss: 0.6646 - val_acc: 0.6770\n",
      "Epoch 100/100\n",
      "2816/3000 [===========================>..] - ETA: 0s - loss: 0.5795 - acc: 0.6953\n",
      "Epoch 00100: val_loss did not improve from 0.65094\n",
      "3000/3000 [==============================] - 5s 2ms/step - loss: 0.5826 - acc: 0.6967 - val_loss: 0.6672 - val_acc: 0.6780\n",
      "CPU times: user 10min 19s, sys: 53.7 s, total: 11min 12s\n",
      "Wall time: 9min 18s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# set to the original weights for testing other pipelines\n",
    "model_cnn_v1.set_weights(initial_weights)\n",
    "\n",
    "# define path to save the mnodel\n",
    "path_model='./results/model_cnn_v1.weights.best.hdf5'\n",
    "shutil.rmtree(path_model, ignore_errors=True)\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath=path_model, \n",
    "                               verbose = 1, \n",
    "                               save_best_only=True)\n",
    "EPOCHS=100\n",
    "BATCH_SIZE=256\n",
    "#BATCH_SIZE=128\n",
    "#BATCH_SIZE=64\n",
    "\n",
    "history=model_cnn_v1.fit(x_train,\n",
    "                         y_train,\n",
    "                         batch_size=BATCH_SIZE,\n",
    "                         epochs=EPOCHS,\n",
    "                         validation_data=(x_test, y_test),\n",
    "                         callbacks=[checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss:\n",
      "  - loss [training dataset]: 0.541\n",
      "  - loss [validation dataset: 0.610\n",
      "\n",
      "Accuracy:\n",
      "  - accuracy [training dataset]: 78.23%\n",
      "  - accuracy [validation dataset: 76.60%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_loss(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN model v2"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Loss:\n",
    "  - loss [training dataset]: 0.046\n",
    "  - loss [validation dataset: 0.951\n",
    "\n",
    "Accuracy:\n",
    "  - accuracy [training dataset]: 98.87%\n",
    "  - accuracy [validation dataset: 78.70%\n",
    "  \n",
    "50 epochs\n",
    "batch size 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 150, 150, 128)     9728      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 75, 75, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 75, 75, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 75, 75, 64)        73792     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 37, 37, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 37, 37, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 37, 37, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 18, 18, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 18, 18, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 20736)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               5308672   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                16448     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 8)                 264       \n",
      "=================================================================\n",
      "Total params: 5,447,912\n",
      "Trainable params: 5,447,912\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def model_cnn_v2():\n",
    "    \n",
    "    # must define the input shape in the first layer of the neural network\n",
    "    model = tf.keras.models.Sequential()\n",
    "\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 128, kernel_size = (5,5),padding = 'same',activation ='relu', input_shape = input_shape))\n",
    "    model.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))\n",
    "    model.add(tf.keras.layers.Dropout(0.25))\n",
    "\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = (3,3),padding = 'same',activation ='relu'))\n",
    "    model.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))\n",
    "    model.add(tf.keras.layers.Dropout(0.25))\n",
    "\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = (3,3),padding = 'same',activation ='relu'))\n",
    "    model.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))\n",
    "    model.add(tf.keras.layers.Dropout(0.25))\n",
    "\n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "\n",
    "    model.add(tf.keras.layers.Dense(256,activation = \"relu\"))          \n",
    "    model.add(tf.keras.layers.Dense(64,activation = \"relu\"))\n",
    "    model.add(tf.keras.layers.Dense(32,activation = \"relu\"))\n",
    "\n",
    "    model.add(tf.keras.layers.Dense(8, activation = \"softmax\"))\n",
    "    \n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "# reset the model\n",
    "tf.keras.backend.clear_session()\n",
    "\n",
    "# create the model\n",
    "model_cnn_v2=model_cnn_v2()\n",
    "\n",
    "# take a look at the model summary\n",
    "model_cnn_v2.summary()\n",
    "\n",
    "# save weight before training\n",
    "initial_weights = model_cnn_v2.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3000 samples, validate on 1000 samples\n",
      "Epoch 1/50\n",
      "2944/3000 [============================>.] - ETA: 6s - loss: 1.7105 - acc: 0.2812 \n",
      "Epoch 00001: val_loss improved from inf to 1.60882, saving model to ./results/model_cnn_v2.weights.best.hdf5\n",
      "3000/3000 [==============================] - 383s 128ms/step - loss: 1.6989 - acc: 0.2833 - val_loss: 1.6088 - val_acc: 0.3690\n",
      "Epoch 2/50\n",
      "2944/3000 [============================>.] - ETA: 6s - loss: 1.1224 - acc: 0.4786 \n",
      "Epoch 00002: val_loss improved from 1.60882 to 0.93550, saving model to ./results/model_cnn_v2.weights.best.hdf5\n",
      "3000/3000 [==============================] - 380s 127ms/step - loss: 1.1177 - acc: 0.4817 - val_loss: 0.9355 - val_acc: 0.5940\n",
      "Epoch 3/50\n",
      "2944/3000 [============================>.] - ETA: 6s - loss: 0.9435 - acc: 0.5730 \n",
      "Epoch 00003: val_loss did not improve from 0.93550\n",
      "3000/3000 [==============================] - 377s 126ms/step - loss: 0.9406 - acc: 0.5750 - val_loss: 0.9471 - val_acc: 0.5950\n",
      "Epoch 4/50\n",
      "2944/3000 [============================>.] - ETA: 6s - loss: 0.8281 - acc: 0.6447 \n",
      "Epoch 00004: val_loss improved from 0.93550 to 0.85400, saving model to ./results/model_cnn_v2.weights.best.hdf5\n",
      "3000/3000 [==============================] - 383s 128ms/step - loss: 0.8274 - acc: 0.6443 - val_loss: 0.8540 - val_acc: 0.5920\n",
      "Epoch 5/50\n",
      "2944/3000 [============================>.] - ETA: 6s - loss: 0.8870 - acc: 0.6067 \n",
      "Epoch 00005: val_loss improved from 0.85400 to 0.80145, saving model to ./results/model_cnn_v2.weights.best.hdf5\n",
      "3000/3000 [==============================] - 371s 124ms/step - loss: 0.8835 - acc: 0.6083 - val_loss: 0.8015 - val_acc: 0.7100\n",
      "Epoch 6/50\n",
      "2944/3000 [============================>.] - ETA: 6s - loss: 0.7021 - acc: 0.7021 \n",
      "Epoch 00006: val_loss improved from 0.80145 to 0.66935, saving model to ./results/model_cnn_v2.weights.best.hdf5\n",
      "3000/3000 [==============================] - 379s 126ms/step - loss: 0.7007 - acc: 0.7037 - val_loss: 0.6693 - val_acc: 0.7570\n",
      "Epoch 7/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.6479 - acc: 0.7347 \n",
      "Epoch 00007: val_loss did not improve from 0.66935\n",
      "3000/3000 [==============================] - 359s 120ms/step - loss: 0.6487 - acc: 0.7343 - val_loss: 0.6949 - val_acc: 0.7090\n",
      "Epoch 8/50\n",
      "2944/3000 [============================>.] - ETA: 6s - loss: 0.6058 - acc: 0.7480 \n",
      "Epoch 00008: val_loss improved from 0.66935 to 0.62846, saving model to ./results/model_cnn_v2.weights.best.hdf5\n",
      "3000/3000 [==============================] - 403s 134ms/step - loss: 0.6003 - acc: 0.7513 - val_loss: 0.6285 - val_acc: 0.7620\n",
      "Epoch 9/50\n",
      "2944/3000 [============================>.] - ETA: 6s - loss: 0.6317 - acc: 0.7323 \n",
      "Epoch 00009: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 390s 130ms/step - loss: 0.6322 - acc: 0.7323 - val_loss: 0.6750 - val_acc: 0.7690\n",
      "Epoch 10/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.4844 - acc: 0.8098 \n",
      "Epoch 00010: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 355s 118ms/step - loss: 0.4837 - acc: 0.8100 - val_loss: 0.6511 - val_acc: 0.7480\n",
      "Epoch 11/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.5468 - acc: 0.7758 \n",
      "Epoch 00011: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 360s 120ms/step - loss: 0.5483 - acc: 0.7760 - val_loss: 0.6424 - val_acc: 0.7410\n",
      "Epoch 12/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.5061 - acc: 0.7948 \n",
      "Epoch 00012: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 343s 114ms/step - loss: 0.5052 - acc: 0.7947 - val_loss: 0.7059 - val_acc: 0.7380\n",
      "Epoch 13/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.4158 - acc: 0.8353 \n",
      "Epoch 00013: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 335s 112ms/step - loss: 0.4165 - acc: 0.8350 - val_loss: 0.7761 - val_acc: 0.7640\n",
      "Epoch 14/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.3422 - acc: 0.8750 \n",
      "Epoch 00014: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 343s 114ms/step - loss: 0.3396 - acc: 0.8767 - val_loss: 0.6441 - val_acc: 0.7820\n",
      "Epoch 15/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.7671 - acc: 0.7137 \n",
      "Epoch 00015: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 339s 113ms/step - loss: 0.7648 - acc: 0.7140 - val_loss: 0.8236 - val_acc: 0.6340\n",
      "Epoch 16/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.4341 - acc: 0.8152 \n",
      "Epoch 00016: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 338s 113ms/step - loss: 0.4328 - acc: 0.8163 - val_loss: 0.7948 - val_acc: 0.7350\n",
      "Epoch 17/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.3668 - acc: 0.8539 \n",
      "Epoch 00017: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 337s 112ms/step - loss: 0.3683 - acc: 0.8530 - val_loss: 0.8372 - val_acc: 0.6820\n",
      "Epoch 18/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.3600 - acc: 0.8580 \n",
      "Epoch 00018: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 337s 112ms/step - loss: 0.3595 - acc: 0.8580 - val_loss: 0.7687 - val_acc: 0.7460\n",
      "Epoch 19/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.2778 - acc: 0.9001 \n",
      "Epoch 00019: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 338s 113ms/step - loss: 0.2795 - acc: 0.8993 - val_loss: 0.7712 - val_acc: 0.7630\n",
      "Epoch 20/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.2507 - acc: 0.9012 \n",
      "Epoch 00020: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.2479 - acc: 0.9027 - val_loss: 0.7851 - val_acc: 0.7870\n",
      "Epoch 21/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.3082 - acc: 0.8879 \n",
      "Epoch 00021: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 338s 113ms/step - loss: 0.3107 - acc: 0.8860 - val_loss: 1.3236 - val_acc: 0.6230\n",
      "Epoch 22/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.3243 - acc: 0.8753 \n",
      "Epoch 00022: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 337s 112ms/step - loss: 0.3245 - acc: 0.8747 - val_loss: 0.8791 - val_acc: 0.7350\n",
      "Epoch 23/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.1738 - acc: 0.9395 \n",
      "Epoch 00023: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 337s 112ms/step - loss: 0.1729 - acc: 0.9400 - val_loss: 1.0516 - val_acc: 0.7310\n",
      "Epoch 24/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.2009 - acc: 0.9273 \n",
      "Epoch 00024: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 337s 112ms/step - loss: 0.2017 - acc: 0.9273 - val_loss: 0.8957 - val_acc: 0.7850\n",
      "Epoch 25/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.1489 - acc: 0.9538 \n",
      "Epoch 00025: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 335s 112ms/step - loss: 0.1503 - acc: 0.9530 - val_loss: 1.0953 - val_acc: 0.7380\n",
      "Epoch 26/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.1524 - acc: 0.9416 \n",
      "Epoch 00026: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.1525 - acc: 0.9410 - val_loss: 0.9927 - val_acc: 0.7480\n",
      "Epoch 27/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.1794 - acc: 0.9365 \n",
      "Epoch 00027: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 338s 113ms/step - loss: 0.1794 - acc: 0.9363 - val_loss: 1.1106 - val_acc: 0.7630\n",
      "Epoch 28/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0777 - acc: 0.9738 \n",
      "Epoch 00028: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 338s 113ms/step - loss: 0.0797 - acc: 0.9737 - val_loss: 1.0830 - val_acc: 0.7730\n",
      "Epoch 29/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0906 - acc: 0.9759 \n",
      "Epoch 00029: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.0924 - acc: 0.9750 - val_loss: 1.0885 - val_acc: 0.7470\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0902 - acc: 0.9708 \n",
      "Epoch 00030: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 338s 113ms/step - loss: 0.0887 - acc: 0.9713 - val_loss: 1.1311 - val_acc: 0.7690\n",
      "Epoch 31/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0495 - acc: 0.9830 \n",
      "Epoch 00031: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.0488 - acc: 0.9833 - val_loss: 1.1020 - val_acc: 0.7720\n",
      "Epoch 32/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0683 - acc: 0.9786 \n",
      "Epoch 00032: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.0677 - acc: 0.9787 - val_loss: 1.1498 - val_acc: 0.7790\n",
      "Epoch 33/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0762 - acc: 0.9738 \n",
      "Epoch 00033: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 337s 112ms/step - loss: 0.0769 - acc: 0.9737 - val_loss: 1.1899 - val_acc: 0.7660\n",
      "Epoch 34/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0756 - acc: 0.9779 \n",
      "Epoch 00034: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.0755 - acc: 0.9777 - val_loss: 1.2249 - val_acc: 0.7390\n",
      "Epoch 35/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0954 - acc: 0.9708 \n",
      "Epoch 00035: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.0955 - acc: 0.9710 - val_loss: 1.2747 - val_acc: 0.7250\n",
      "Epoch 36/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.1486 - acc: 0.9518 \n",
      "Epoch 00036: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 339s 113ms/step - loss: 0.1474 - acc: 0.9523 - val_loss: 1.0850 - val_acc: 0.7760\n",
      "Epoch 37/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.1020 - acc: 0.9704 \n",
      "Epoch 00037: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.1026 - acc: 0.9700 - val_loss: 1.1061 - val_acc: 0.7530\n",
      "Epoch 38/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.2309 - acc: 0.9270 \n",
      "Epoch 00038: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.2308 - acc: 0.9263 - val_loss: 1.0079 - val_acc: 0.7070\n",
      "Epoch 39/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.1418 - acc: 0.9507 \n",
      "Epoch 00039: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.1469 - acc: 0.9480 - val_loss: 1.1078 - val_acc: 0.6830\n",
      "Epoch 40/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.1079 - acc: 0.9643 \n",
      "Epoch 00040: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.1078 - acc: 0.9640 - val_loss: 1.2584 - val_acc: 0.7640\n",
      "Epoch 41/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.1092 - acc: 0.9620 \n",
      "Epoch 00041: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 337s 112ms/step - loss: 0.1104 - acc: 0.9617 - val_loss: 1.0653 - val_acc: 0.7610\n",
      "Epoch 42/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0594 - acc: 0.9837 \n",
      "Epoch 00042: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.0585 - acc: 0.9840 - val_loss: 1.1320 - val_acc: 0.7840\n",
      "Epoch 43/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0462 - acc: 0.9881 \n",
      "Epoch 00043: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.0470 - acc: 0.9877 - val_loss: 1.0730 - val_acc: 0.7660\n",
      "Epoch 44/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0549 - acc: 0.9851 \n",
      "Epoch 00044: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.0674 - acc: 0.9813 - val_loss: 1.3605 - val_acc: 0.7270\n",
      "Epoch 45/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.1580 - acc: 0.9443 \n",
      "Epoch 00045: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.1559 - acc: 0.9453 - val_loss: 1.1837 - val_acc: 0.7840\n",
      "Epoch 46/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0439 - acc: 0.9891 \n",
      "Epoch 00046: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 335s 112ms/step - loss: 0.0432 - acc: 0.9893 - val_loss: 1.1741 - val_acc: 0.7940\n",
      "Epoch 47/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.1075 - acc: 0.9640 \n",
      "Epoch 00047: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.1064 - acc: 0.9640 - val_loss: 1.2257 - val_acc: 0.7460\n",
      "Epoch 48/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0604 - acc: 0.9817 \n",
      "Epoch 00048: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 337s 112ms/step - loss: 0.0606 - acc: 0.9817 - val_loss: 1.1091 - val_acc: 0.7750\n",
      "Epoch 49/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.1494 - acc: 0.9579 \n",
      "Epoch 00049: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 336s 112ms/step - loss: 0.1478 - acc: 0.9583 - val_loss: 0.8877 - val_acc: 0.7690\n",
      "Epoch 50/50\n",
      "2944/3000 [============================>.] - ETA: 5s - loss: 0.0438 - acc: 0.9891 \n",
      "Epoch 00050: val_loss did not improve from 0.62846\n",
      "3000/3000 [==============================] - 337s 112ms/step - loss: 0.0456 - acc: 0.9887 - val_loss: 0.9506 - val_acc: 0.7870\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# set to the original weights for testing other pipelines\n",
    "model_cnn_v2.set_weights(initial_weights)\n",
    "\n",
    "# define path to save the mnodel\n",
    "path_model='./results/model_cnn_v2.weights.best.hdf5'\n",
    "shutil.rmtree(path_model, ignore_errors=True)\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath=path_model, \n",
    "                               verbose = 1, \n",
    "                               save_best_only=True)\n",
    "EPOCHS=50\n",
    "#BATCH_SIZE=256\n",
    "#BATCH_SIZE=128\n",
    "BATCH_SIZE=64\n",
    "\n",
    "history=model_cnn_v2.fit(x_train,\n",
    "                         y_train,\n",
    "                         batch_size=BATCH_SIZE,\n",
    "                         epochs=EPOCHS,\n",
    "                         validation_data=(x_test, y_test),\n",
    "                         callbacks=[checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss:\n",
      "  - loss [training dataset]: 0.046\n",
      "  - loss [validation dataset: 0.951\n",
      "\n",
      "Accuracy:\n",
      "  - accuracy [training dataset]: 98.87%\n",
      "  - accuracy [validation dataset: 78.70%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_loss(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN model v3"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Loss:\n",
    "  - loss [training dataset]: 0.132\n",
    "  - loss [validation dataset: 0.633\n",
    "\n",
    "Accuracy:\n",
    "  - accuracy [training dataset]: 95.90%\n",
    "  - accuracy [validation dataset: 79.00%\n",
    "  \n",
    "   \n",
    "50 epochs\n",
    "batch size 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 150, 150, 16)      448       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 150, 150, 16)      2320      \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 150, 150, 16)      2320      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 150, 150, 16)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 50, 50, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 50, 50, 32)        4640      \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 50, 50, 32)        9248      \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 50, 50, 32)        9248      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 50, 50, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 5, 5, 128)         73856     \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 5, 5, 128)         147584    \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 5, 5, 256)         295168    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 5, 5, 256)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 1, 1, 256)         0         \n",
      "_________________________________________________________________\n",
      "global_max_pooling2d (Global (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 8)                 2056      \n",
      "=================================================================\n",
      "Total params: 639,240\n",
      "Trainable params: 639,240\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def model_cnn_v3():\n",
    "    \n",
    "    # must define the input shape in the first layer of the neural network\n",
    "    model = tf.keras.models.Sequential()\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 16, kernel_size = 3, padding = 'same', activation = 'relu', input_shape = input_shape))\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 16, kernel_size = 3, padding = 'same', activation = 'relu'))\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 16, kernel_size = 3, padding = 'same', activation = 'relu'))\n",
    "    model.add(tf.keras.layers.Dropout(0.3))\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size = 3)) \n",
    "\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = 3, padding = 'same', activation = 'relu')) \n",
    "    model.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = 3, padding = 'same', activation = 'relu')) \n",
    "    model.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = 3, padding = 'same', activation = 'relu'))\n",
    "    model.add(tf.keras.layers.Dropout(0.3))\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size = 3)) \n",
    "\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = 3, padding = 'same', activation = 'relu'))\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = 3, padding = 'same', activation = 'relu'))\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = 3, padding = 'same', activation = 'relu'))\n",
    "    model.add(tf.keras.layers.Dropout(0.3))\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size = 3))\n",
    "\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 128, kernel_size = 3, padding = 'same', activation = 'relu'))\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 128, kernel_size = 3, padding = 'same', activation = 'relu'))\n",
    "    model.add(tf.keras.layers.Conv2D(filters = 256, kernel_size = 3, padding = 'same', activation = 'relu'))\n",
    "    model.add(tf.keras.layers.Dropout(0.3))\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size = 3))\n",
    "    model.add(tf.keras.layers.GlobalMaxPooling2D())\n",
    "    model.add(tf.keras.layers.Dense(8, activation = 'softmax'))\n",
    "    \n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "# reset the model\n",
    "tf.keras.backend.clear_session()\n",
    "\n",
    "# create the model\n",
    "model_cnn_v3=model_cnn_v3()\n",
    "\n",
    "# take a look at the model summary\n",
    "model_cnn_v3.summary()\n",
    "\n",
    "# save weight before training\n",
    "initial_weights = model_cnn_v3.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3000 samples, validate on 1000 samples\n",
      "Epoch 1/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 1.6463 - acc: 0.2833\n",
      "Epoch 00001: val_loss improved from inf to 1.69766, saving model to ./results/model_cnn_v3.weights.best.hdf5\n",
      "3000/3000 [==============================] - 144s 48ms/step - loss: 1.6401 - acc: 0.2840 - val_loss: 1.6977 - val_acc: 0.3070\n",
      "Epoch 2/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 1.2633 - acc: 0.4079\n",
      "Epoch 00002: val_loss improved from 1.69766 to 1.55507, saving model to ./results/model_cnn_v3.weights.best.hdf5\n",
      "3000/3000 [==============================] - 118s 39ms/step - loss: 1.2652 - acc: 0.4077 - val_loss: 1.5551 - val_acc: 0.2830\n",
      "Epoch 3/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 1.1490 - acc: 0.4494\n",
      "Epoch 00003: val_loss improved from 1.55507 to 1.30854, saving model to ./results/model_cnn_v3.weights.best.hdf5\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 1.1478 - acc: 0.4510 - val_loss: 1.3085 - val_acc: 0.4540\n",
      "Epoch 4/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 1.0781 - acc: 0.5245\n",
      "Epoch 00004: val_loss did not improve from 1.30854\n",
      "3000/3000 [==============================] - 118s 39ms/step - loss: 1.0747 - acc: 0.5227 - val_loss: 1.3561 - val_acc: 0.3750\n",
      "Epoch 5/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.9261 - acc: 0.5907\n",
      "Epoch 00005: val_loss did not improve from 1.30854\n",
      "3000/3000 [==============================] - 118s 39ms/step - loss: 0.9217 - acc: 0.5933 - val_loss: 1.5102 - val_acc: 0.4760\n",
      "Epoch 6/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.8709 - acc: 0.6257\n",
      "Epoch 00006: val_loss improved from 1.30854 to 1.28912, saving model to ./results/model_cnn_v3.weights.best.hdf5\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.8679 - acc: 0.6273 - val_loss: 1.2891 - val_acc: 0.5450\n",
      "Epoch 7/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.9832 - acc: 0.5740\n",
      "Epoch 00007: val_loss improved from 1.28912 to 1.07345, saving model to ./results/model_cnn_v3.weights.best.hdf5\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.9836 - acc: 0.5747 - val_loss: 1.0735 - val_acc: 0.6000\n",
      "Epoch 8/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.8209 - acc: 0.6610\n",
      "Epoch 00008: val_loss did not improve from 1.07345\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.8239 - acc: 0.6617 - val_loss: 1.1820 - val_acc: 0.5260\n",
      "Epoch 9/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.7376 - acc: 0.7058\n",
      "Epoch 00009: val_loss did not improve from 1.07345\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.7443 - acc: 0.7040 - val_loss: 1.0781 - val_acc: 0.6430\n",
      "Epoch 10/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.6905 - acc: 0.7368\n",
      "Epoch 00010: val_loss improved from 1.07345 to 0.99203, saving model to ./results/model_cnn_v3.weights.best.hdf5\n",
      "3000/3000 [==============================] - 118s 39ms/step - loss: 0.6912 - acc: 0.7367 - val_loss: 0.9920 - val_acc: 0.6500\n",
      "Epoch 11/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.5943 - acc: 0.7809\n",
      "Epoch 00011: val_loss did not improve from 0.99203\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.5939 - acc: 0.7813 - val_loss: 1.6350 - val_acc: 0.3460\n",
      "Epoch 12/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.5902 - acc: 0.7768\n",
      "Epoch 00012: val_loss improved from 0.99203 to 0.79248, saving model to ./results/model_cnn_v3.weights.best.hdf5\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.5894 - acc: 0.7767 - val_loss: 0.7925 - val_acc: 0.7860\n",
      "Epoch 13/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.5805 - acc: 0.7646\n",
      "Epoch 00013: val_loss did not improve from 0.79248\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.5803 - acc: 0.7663 - val_loss: 1.0087 - val_acc: 0.6990\n",
      "Epoch 14/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.5359 - acc: 0.7914\n",
      "Epoch 00014: val_loss did not improve from 0.79248\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.5320 - acc: 0.7920 - val_loss: 0.9100 - val_acc: 0.6770\n",
      "Epoch 15/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.5047 - acc: 0.8091\n",
      "Epoch 00015: val_loss did not improve from 0.79248\n",
      "3000/3000 [==============================] - 118s 39ms/step - loss: 0.5029 - acc: 0.8100 - val_loss: 0.8279 - val_acc: 0.7700\n",
      "Epoch 16/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.4555 - acc: 0.8240\n",
      "Epoch 00016: val_loss improved from 0.79248 to 0.69427, saving model to ./results/model_cnn_v3.weights.best.hdf5\n",
      "3000/3000 [==============================] - 118s 39ms/step - loss: 0.4564 - acc: 0.8233 - val_loss: 0.6943 - val_acc: 0.7950\n",
      "Epoch 17/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.4189 - acc: 0.8438\n",
      "Epoch 00017: val_loss did not improve from 0.69427\n",
      "3000/3000 [==============================] - 118s 39ms/step - loss: 0.4186 - acc: 0.8447 - val_loss: 0.7068 - val_acc: 0.7710\n",
      "Epoch 18/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.5444 - acc: 0.8054\n",
      "Epoch 00018: val_loss did not improve from 0.69427\n",
      "3000/3000 [==============================] - 122s 41ms/step - loss: 0.5449 - acc: 0.8043 - val_loss: 0.7841 - val_acc: 0.7570\n",
      "Epoch 19/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.4617 - acc: 0.8268\n",
      "Epoch 00019: val_loss did not improve from 0.69427\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.4598 - acc: 0.8280 - val_loss: 0.7664 - val_acc: 0.7700\n",
      "Epoch 20/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.4031 - acc: 0.8438\n",
      "Epoch 00020: val_loss did not improve from 0.69427\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.4038 - acc: 0.8440 - val_loss: 0.7569 - val_acc: 0.6880\n",
      "Epoch 21/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.3872 - acc: 0.8505\n",
      "Epoch 00021: val_loss did not improve from 0.69427\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.3857 - acc: 0.8513 - val_loss: 0.8996 - val_acc: 0.6530\n",
      "Epoch 22/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.3755 - acc: 0.8587\n",
      "Epoch 00022: val_loss improved from 0.69427 to 0.63561, saving model to ./results/model_cnn_v3.weights.best.hdf5\n",
      "3000/3000 [==============================] - 116s 39ms/step - loss: 0.3724 - acc: 0.8597 - val_loss: 0.6356 - val_acc: 0.7990\n",
      "Epoch 23/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.3674 - acc: 0.8597\n",
      "Epoch 00023: val_loss did not improve from 0.63561\n",
      "3000/3000 [==============================] - 116s 39ms/step - loss: 0.3689 - acc: 0.8593 - val_loss: 0.7534 - val_acc: 0.7690\n",
      "Epoch 24/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.3442 - acc: 0.8672\n",
      "Epoch 00024: val_loss did not improve from 0.63561\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.3432 - acc: 0.8670 - val_loss: 0.6683 - val_acc: 0.7430\n",
      "Epoch 25/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.3241 - acc: 0.8804\n",
      "Epoch 00025: val_loss did not improve from 0.63561\n",
      "3000/3000 [==============================] - 116s 39ms/step - loss: 0.3243 - acc: 0.8800 - val_loss: 0.7415 - val_acc: 0.7680\n",
      "Epoch 26/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.3111 - acc: 0.8794\n",
      "Epoch 00026: val_loss did not improve from 0.63561\n",
      "3000/3000 [==============================] - 114s 38ms/step - loss: 0.3090 - acc: 0.8807 - val_loss: 0.6877 - val_acc: 0.7410\n",
      "Epoch 27/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.2871 - acc: 0.8913\n",
      "Epoch 00027: val_loss improved from 0.63561 to 0.58502, saving model to ./results/model_cnn_v3.weights.best.hdf5\n",
      "3000/3000 [==============================] - 114s 38ms/step - loss: 0.2870 - acc: 0.8913 - val_loss: 0.5850 - val_acc: 0.8230\n",
      "Epoch 28/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.2734 - acc: 0.8944\n",
      "Epoch 00028: val_loss did not improve from 0.58502\n",
      "3000/3000 [==============================] - 114s 38ms/step - loss: 0.2730 - acc: 0.8943 - val_loss: 0.5895 - val_acc: 0.8110\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.2848 - acc: 0.8971\n",
      "Epoch 00029: val_loss did not improve from 0.58502\n",
      "3000/3000 [==============================] - 114s 38ms/step - loss: 0.2852 - acc: 0.8967 - val_loss: 1.3567 - val_acc: 0.5830\n",
      "Epoch 30/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.2405 - acc: 0.9168\n",
      "Epoch 00030: val_loss did not improve from 0.58502\n",
      "3000/3000 [==============================] - 114s 38ms/step - loss: 0.2416 - acc: 0.9153 - val_loss: 0.6695 - val_acc: 0.7730\n",
      "Epoch 31/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.2038 - acc: 0.9273\n",
      "Epoch 00031: val_loss improved from 0.58502 to 0.56030, saving model to ./results/model_cnn_v3.weights.best.hdf5\n",
      "3000/3000 [==============================] - 114s 38ms/step - loss: 0.2038 - acc: 0.9270 - val_loss: 0.5603 - val_acc: 0.8050\n",
      "Epoch 32/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.2166 - acc: 0.9161\n",
      "Epoch 00032: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 114s 38ms/step - loss: 0.2151 - acc: 0.9170 - val_loss: 0.7693 - val_acc: 0.7440\n",
      "Epoch 33/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.2212 - acc: 0.9239\n",
      "Epoch 00033: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 114s 38ms/step - loss: 0.2235 - acc: 0.9227 - val_loss: 0.6269 - val_acc: 0.7750\n",
      "Epoch 34/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.1983 - acc: 0.9317\n",
      "Epoch 00034: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 114s 38ms/step - loss: 0.1954 - acc: 0.9327 - val_loss: 0.5874 - val_acc: 0.7920\n",
      "Epoch 35/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.1736 - acc: 0.9378\n",
      "Epoch 00035: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 114s 38ms/step - loss: 0.1879 - acc: 0.9347 - val_loss: 0.6518 - val_acc: 0.7670\n",
      "Epoch 36/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.2973 - acc: 0.8961\n",
      "Epoch 00036: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 114s 38ms/step - loss: 0.2973 - acc: 0.8953 - val_loss: 0.6776 - val_acc: 0.7780\n",
      "Epoch 37/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.5526 - acc: 0.8026\n",
      "Epoch 00037: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 122s 41ms/step - loss: 0.5507 - acc: 0.8033 - val_loss: 0.7808 - val_acc: 0.7150\n",
      "Epoch 38/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.3741 - acc: 0.8543\n",
      "Epoch 00038: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 128s 43ms/step - loss: 0.3737 - acc: 0.8547 - val_loss: 0.6632 - val_acc: 0.7590\n",
      "Epoch 39/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.2128 - acc: 0.9202\n",
      "Epoch 00039: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 116s 39ms/step - loss: 0.2121 - acc: 0.9197 - val_loss: 0.6038 - val_acc: 0.8080\n",
      "Epoch 40/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.1414 - acc: 0.9467\n",
      "Epoch 00040: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 116s 39ms/step - loss: 0.1419 - acc: 0.9463 - val_loss: 0.6591 - val_acc: 0.7520\n",
      "Epoch 41/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.1433 - acc: 0.9494\n",
      "Epoch 00041: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.1424 - acc: 0.9493 - val_loss: 0.6543 - val_acc: 0.7840\n",
      "Epoch 42/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.1573 - acc: 0.9389\n",
      "Epoch 00042: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 116s 39ms/step - loss: 0.1569 - acc: 0.9390 - val_loss: 0.6040 - val_acc: 0.7910\n",
      "Epoch 43/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.1393 - acc: 0.9470\n",
      "Epoch 00043: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 116s 39ms/step - loss: 0.1380 - acc: 0.9477 - val_loss: 0.5936 - val_acc: 0.8000\n",
      "Epoch 44/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.1878 - acc: 0.9331\n",
      "Epoch 00044: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 116s 39ms/step - loss: 0.1881 - acc: 0.9333 - val_loss: 0.6558 - val_acc: 0.7860\n",
      "Epoch 45/50\n",
      "2944/3000 [============================>.] - ETA: 1s - loss: 0.2092 - acc: 0.9283\n",
      "Epoch 00045: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 116s 39ms/step - loss: 0.2062 - acc: 0.9297 - val_loss: 0.6751 - val_acc: 0.7720\n",
      "Epoch 46/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.1152 - acc: 0.9603\n",
      "Epoch 00046: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 117s 39ms/step - loss: 0.1158 - acc: 0.9600 - val_loss: 0.6489 - val_acc: 0.7880\n",
      "Epoch 47/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.0905 - acc: 0.9698\n",
      "Epoch 00047: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 125s 42ms/step - loss: 0.0934 - acc: 0.9690 - val_loss: 0.6591 - val_acc: 0.7790\n",
      "Epoch 48/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.0818 - acc: 0.9745\n",
      "Epoch 00048: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 127s 42ms/step - loss: 0.0842 - acc: 0.9730 - val_loss: 0.6630 - val_acc: 0.7780\n",
      "Epoch 49/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.0847 - acc: 0.9718\n",
      "Epoch 00049: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 125s 42ms/step - loss: 0.0850 - acc: 0.9713 - val_loss: 0.6432 - val_acc: 0.7910\n",
      "Epoch 50/50\n",
      "2944/3000 [============================>.] - ETA: 2s - loss: 0.1313 - acc: 0.9586\n",
      "Epoch 00050: val_loss did not improve from 0.56030\n",
      "3000/3000 [==============================] - 127s 42ms/step - loss: 0.1318 - acc: 0.9590 - val_loss: 0.6334 - val_acc: 0.7900\n",
      "CPU times: user 8 µs, sys: 6 µs, total: 14 µs\n",
      "Wall time: 1.01 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# set to the original weights for testing other pipelines\n",
    "model_cnn_v3.set_weights(initial_weights)\n",
    "\n",
    "# define path to save the mnodel\n",
    "path_model='./results/model_cnn_v3.weights.best.hdf5'\n",
    "shutil.rmtree(path_model, ignore_errors=True)\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath=path_model, \n",
    "                               verbose = 1, \n",
    "                               save_best_only=True)\n",
    "EPOCHS=50\n",
    "#BATCH_SIZE=256\n",
    "#BATCH_SIZE=128\n",
    "BATCH_SIZE=64\n",
    "\n",
    "history=model_cnn_v3.fit(x_train,\n",
    "                         y_train,\n",
    "                         batch_size=BATCH_SIZE,\n",
    "                         epochs=EPOCHS,\n",
    "                         validation_data=(x_test, y_test),\n",
    "                         callbacks=[checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss:\n",
      "  - loss [training dataset]: 0.132\n",
      "  - loss [validation dataset: 0.633\n",
      "\n",
      "Accuracy:\n",
      "  - accuracy [training dataset]: 95.90%\n",
      "  - accuracy [validation dataset: 79.00%\n",
      "CPU times: user 33.9 ms, sys: 8.61 ms, total: 42.5 ms\n",
      "Wall time: 45 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_loss(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VGG16 model v4 and retraining only the last 4 layers"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Loss:\n",
    "  - loss [training dataset]: 0.005\n",
    "  - loss [validation dataset: 0.624\n",
    "\n",
    "Accuracy:\n",
    "  - accuracy [training dataset]: 99.90%\n",
    "  - accuracy [validation dataset: 90.10%\n",
    "  \n",
    "   \n",
    "30 epochs\n",
    "batch size 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tensorflow.python.keras.engine.input_layer.InputLayer object at 0x147394320> False\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x1c88d26d8> False\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x1c88d2438> False\n",
      "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x1c88d2a58> False\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x1c8d84ac8> False\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x1ca293828> False\n",
      "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x1ca281ef0> False\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x1c89e29b0> False\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x1c89bcc50> False\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x1c8c2a748> False\n",
      "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x1c8bfbe10> False\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x1c8c2bfd0> False\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x194b23780> False\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x194afeef0> False\n",
      "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x194ab9f28> False\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x194ab97b8> True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x194a5b080> True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x194a2db70> True\n",
      "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x194a0f7b8> True\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Model)                (None, 4, 4, 512)         14714688  \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              8389632   \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 8)                 8200      \n",
      "=================================================================\n",
      "Total params: 23,112,520\n",
      "Trainable params: 15,477,256\n",
      "Non-trainable params: 7,635,264\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def model_vgg16_v4():\n",
    "    \n",
    "    # load the VGG model\n",
    "    vgg_conv = tf.keras.applications.VGG16(weights='imagenet', include_top=False, input_shape = input_shape)\n",
    "    \n",
    "    # freeze the layers except the last 4 layers\n",
    "    for layer in vgg_conv.layers[:-4]:\n",
    "        layer.trainable = False\n",
    " \n",
    "    # Check the trainable status of the individual layers\n",
    "    for layer in vgg_conv.layers:\n",
    "        print(layer, layer.trainable)\n",
    "        \n",
    "    # create the model\n",
    "    model = tf.keras.models.Sequential()\n",
    " \n",
    "    # add the vgg convolutional base model\n",
    "    model.add(vgg_conv)\n",
    " \n",
    "    # add new layers\n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "    model.add(tf.keras.layers.Dense(1024, activation='relu'))\n",
    "    model.add(tf.keras.layers.Dropout(0.5))\n",
    "    model.add(tf.keras.layers.Dense(8, activation='softmax'))\n",
    "    \n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    \n",
    "    return model\n",
    "\n",
    "# reset the model\n",
    "tf.keras.backend.clear_session()\n",
    "\n",
    "# create the model\n",
    "model_vgg16_v4=model_vgg16_v4()\n",
    "\n",
    "# take a look at the model summary\n",
    "model_vgg16_v4.summary()\n",
    "\n",
    "# save weight before training\n",
    "initial_weights = model_vgg16_v4.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3000 samples, validate on 1000 samples\n",
      "Epoch 1/30\n",
      "2944/3000 [============================>.] - ETA: 12s - loss: 2.0486 - acc: 0.3662\n",
      "Epoch 00001: val_loss improved from inf to 1.27946, saving model to ./results/model_vgg16_v4.weights.best.hdf5\n",
      "3000/3000 [==============================] - 865s 288ms/step - loss: 2.0349 - acc: 0.3687 - val_loss: 1.2795 - val_acc: 0.5090\n",
      "Epoch 2/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 1.2152 - acc: 0.4898\n",
      "Epoch 00002: val_loss improved from 1.27946 to 1.00935, saving model to ./results/model_vgg16_v4.weights.best.hdf5\n",
      "3000/3000 [==============================] - 820s 273ms/step - loss: 1.2121 - acc: 0.4897 - val_loss: 1.0094 - val_acc: 0.5760\n",
      "Epoch 3/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.9811 - acc: 0.5815\n",
      "Epoch 00003: val_loss improved from 1.00935 to 0.81545, saving model to ./results/model_vgg16_v4.weights.best.hdf5\n",
      "3000/3000 [==============================] - 818s 273ms/step - loss: 0.9788 - acc: 0.5817 - val_loss: 0.8154 - val_acc: 0.6740\n",
      "Epoch 4/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.7811 - acc: 0.6967\n",
      "Epoch 00004: val_loss improved from 0.81545 to 0.68271, saving model to ./results/model_vgg16_v4.weights.best.hdf5\n",
      "3000/3000 [==============================] - 808s 269ms/step - loss: 0.7784 - acc: 0.6980 - val_loss: 0.6827 - val_acc: 0.7260\n",
      "Epoch 5/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.5197 - acc: 0.8023\n",
      "Epoch 00005: val_loss improved from 0.68271 to 0.53102, saving model to ./results/model_vgg16_v4.weights.best.hdf5\n",
      "3000/3000 [==============================] - 813s 271ms/step - loss: 0.5168 - acc: 0.8037 - val_loss: 0.5310 - val_acc: 0.8060\n",
      "Epoch 6/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.3922 - acc: 0.8635\n",
      "Epoch 00006: val_loss improved from 0.53102 to 0.42525, saving model to ./results/model_vgg16_v4.weights.best.hdf5\n",
      "3000/3000 [==============================] - 787s 262ms/step - loss: 0.3887 - acc: 0.8647 - val_loss: 0.4252 - val_acc: 0.8590\n",
      "Epoch 7/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.2704 - acc: 0.9069\n",
      "Epoch 00007: val_loss improved from 0.42525 to 0.41251, saving model to ./results/model_vgg16_v4.weights.best.hdf5\n",
      "3000/3000 [==============================] - 773s 258ms/step - loss: 0.2697 - acc: 0.9067 - val_loss: 0.4125 - val_acc: 0.8700\n",
      "Epoch 8/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.2204 - acc: 0.9263\n",
      "Epoch 00008: val_loss improved from 0.41251 to 0.38088, saving model to ./results/model_vgg16_v4.weights.best.hdf5\n",
      "3000/3000 [==============================] - 776s 259ms/step - loss: 0.2248 - acc: 0.9260 - val_loss: 0.3809 - val_acc: 0.8870\n",
      "Epoch 9/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.1967 - acc: 0.9338\n",
      "Epoch 00009: val_loss improved from 0.38088 to 0.36182, saving model to ./results/model_vgg16_v4.weights.best.hdf5\n",
      "3000/3000 [==============================] - 772s 257ms/step - loss: 0.2006 - acc: 0.9320 - val_loss: 0.3618 - val_acc: 0.8880\n",
      "Epoch 10/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.1553 - acc: 0.9467\n",
      "Epoch 00010: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 776s 259ms/step - loss: 0.1564 - acc: 0.9463 - val_loss: 0.4788 - val_acc: 0.8600\n",
      "Epoch 11/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.1913 - acc: 0.9361\n",
      "Epoch 00011: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 775s 258ms/step - loss: 0.1893 - acc: 0.9367 - val_loss: 0.3913 - val_acc: 0.8830\n",
      "Epoch 12/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.1141 - acc: 0.9620\n",
      "Epoch 00012: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 775s 258ms/step - loss: 0.1124 - acc: 0.9627 - val_loss: 0.4175 - val_acc: 0.8940\n",
      "Epoch 13/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0720 - acc: 0.9766\n",
      "Epoch 00013: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 772s 257ms/step - loss: 0.0716 - acc: 0.9767 - val_loss: 0.4759 - val_acc: 0.8900\n",
      "Epoch 14/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0691 - acc: 0.9796\n",
      "Epoch 00014: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 771s 257ms/step - loss: 0.0687 - acc: 0.9797 - val_loss: 0.4571 - val_acc: 0.8980\n",
      "Epoch 15/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0542 - acc: 0.9854\n",
      "Epoch 00015: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 773s 258ms/step - loss: 0.0532 - acc: 0.9857 - val_loss: 0.5096 - val_acc: 0.8820\n",
      "Epoch 16/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0496 - acc: 0.9830\n",
      "Epoch 00016: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 773s 258ms/step - loss: 0.0490 - acc: 0.9833 - val_loss: 0.5538 - val_acc: 0.8670\n",
      "Epoch 17/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0385 - acc: 0.9885\n",
      "Epoch 00017: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 772s 257ms/step - loss: 0.0379 - acc: 0.9887 - val_loss: 0.4581 - val_acc: 0.8980\n",
      "Epoch 18/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0248 - acc: 0.9942\n",
      "Epoch 00018: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 773s 258ms/step - loss: 0.0244 - acc: 0.9943 - val_loss: 0.5328 - val_acc: 0.8990\n",
      "Epoch 19/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0218 - acc: 0.9939\n",
      "Epoch 00019: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 772s 257ms/step - loss: 0.0216 - acc: 0.9940 - val_loss: 0.4246 - val_acc: 0.9120\n",
      "Epoch 20/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0170 - acc: 0.9949\n",
      "Epoch 00020: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 772s 257ms/step - loss: 0.0176 - acc: 0.9947 - val_loss: 0.6941 - val_acc: 0.8840\n",
      "Epoch 21/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0609 - acc: 0.9803\n",
      "Epoch 00021: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 774s 258ms/step - loss: 0.0608 - acc: 0.9803 - val_loss: 0.6856 - val_acc: 0.8780\n",
      "Epoch 22/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.1090 - acc: 0.9667\n",
      "Epoch 00022: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 772s 257ms/step - loss: 0.1120 - acc: 0.9663 - val_loss: 0.6106 - val_acc: 0.8740\n",
      "Epoch 23/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0716 - acc: 0.9806\n",
      "Epoch 00023: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 772s 257ms/step - loss: 0.0705 - acc: 0.9810 - val_loss: 0.4787 - val_acc: 0.8900\n",
      "Epoch 24/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0201 - acc: 0.9959\n",
      "Epoch 00024: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 773s 258ms/step - loss: 0.0198 - acc: 0.9960 - val_loss: 0.5291 - val_acc: 0.8930\n",
      "Epoch 25/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0157 - acc: 0.9966\n",
      "Epoch 00025: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 773s 258ms/step - loss: 0.0155 - acc: 0.9967 - val_loss: 0.5480 - val_acc: 0.9020\n",
      "Epoch 26/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0108 - acc: 0.9969\n",
      "Epoch 00026: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 773s 258ms/step - loss: 0.0107 - acc: 0.9970 - val_loss: 0.6252 - val_acc: 0.8840\n",
      "Epoch 27/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0093 - acc: 0.9983\n",
      "Epoch 00027: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 772s 257ms/step - loss: 0.0092 - acc: 0.9983 - val_loss: 0.6026 - val_acc: 0.8970\n",
      "Epoch 28/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0115 - acc: 0.9980\n",
      "Epoch 00028: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 771s 257ms/step - loss: 0.0113 - acc: 0.9980 - val_loss: 0.6176 - val_acc: 0.9010\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0068 - acc: 0.9990\n",
      "Epoch 00029: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 773s 258ms/step - loss: 0.0067 - acc: 0.9990 - val_loss: 0.6518 - val_acc: 0.9010\n",
      "Epoch 30/30\n",
      "2944/3000 [============================>.] - ETA: 11s - loss: 0.0055 - acc: 0.9990\n",
      "Epoch 00030: val_loss did not improve from 0.36182\n",
      "3000/3000 [==============================] - 773s 258ms/step - loss: 0.0055 - acc: 0.9990 - val_loss: 0.6236 - val_acc: 0.9010\n",
      "CPU times: user 7h 4min 55s, sys: 2min 6s, total: 7h 7min 1s\n",
      "Wall time: 6h 31min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# set to the original weights for testing other pipelines\n",
    "model_vgg16_v4.set_weights(initial_weights)\n",
    "\n",
    "# define path to save the mnodel\n",
    "path_model='./results/model_vgg16_v4.weights.best.hdf5'\n",
    "shutil.rmtree(path_model, ignore_errors=True)\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath=path_model, \n",
    "                               verbose = 1, \n",
    "                               save_best_only=True)\n",
    "EPOCHS=30\n",
    "#BATCH_SIZE=256\n",
    "BATCH_SIZE=128\n",
    "#BATCH_SIZE=64\n",
    "\n",
    "history=model_vgg16_v4.fit(x_train,\n",
    "                           y_train,\n",
    "                           batch_size=BATCH_SIZE,\n",
    "                           epochs=EPOCHS,\n",
    "                           validation_data=(x_test, y_test),\n",
    "                           callbacks=[checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss:\n",
      "  - loss [training dataset]: 0.005\n",
      "  - loss [validation dataset: 0.624\n",
      "\n",
      "Accuracy:\n",
      "  - accuracy [training dataset]: 99.90%\n",
      "  - accuracy [validation dataset: 90.10%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_loss(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN model v1 with data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct the image generator for data augmentation\n",
    "datagen = ImageDataGenerator(rotation_range=40, \n",
    "                             width_shift_range=0.2,\n",
    "                             height_shift_range=0.2, \n",
    "                             shear_range=0.2, \n",
    "                             zoom_range=0.2,\n",
    "                             horizontal_flip=True, \n",
    "                             fill_mode=\"nearest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- rotation_range is a value in degrees (0-180), a range within which to randomly rotate pictures\n",
    "- width_shift and \n",
    "- height_shift are ranges (as a fraction of total width or height) within which to randomly translate pictures vertically or horizontally\n",
    "- rescale is a value by which we will multiply the data before any other processing. Our original images consist in RGB coefficients in the 0-255, but such values would be too high for our models to process (given a typical learning rate), so we target values between 0 and 1 instead by scaling with a 1/255. factor.\n",
    "- shear_range is for randomly applying shearing transformations\n",
    "- zoom_range is for randomly zooming inside pictures\n",
    "- horizontal_flip is for randomly flipping half of the images horizontally --relevant when there are no assumptions of horizontal assymetry (e.g. real-world pictures).\n",
    "- fill_mode is the strategy used for filling in newly created pixels, which can appear after a rotation or a width/height shift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 2.0493 - acc: 0.1453\n",
      "Epoch 00001: val_loss improved from inf to 1.96368, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 19s 2s/step - loss: 2.0432 - acc: 0.1537 - val_loss: 1.9637 - val_acc: 0.2760\n",
      "Epoch 2/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.8954 - acc: 0.2647\n",
      "Epoch 00002: val_loss improved from 1.96368 to 1.69389, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 16s 1s/step - loss: 1.8866 - acc: 0.2676 - val_loss: 1.6939 - val_acc: 0.2740\n",
      "Epoch 3/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.7036 - acc: 0.3053\n",
      "Epoch 00003: val_loss improved from 1.69389 to 1.49983, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 15s 1s/step - loss: 1.6964 - acc: 0.3073 - val_loss: 1.4998 - val_acc: 0.2940\n",
      "Epoch 4/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.5512 - acc: 0.3453\n",
      "Epoch 00004: val_loss improved from 1.49983 to 1.36323, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 17s 2s/step - loss: 1.5448 - acc: 0.3487 - val_loss: 1.3632 - val_acc: 0.4330\n",
      "Epoch 5/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.4550 - acc: 0.3739\n",
      "Epoch 00005: val_loss improved from 1.36323 to 1.23510, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 17s 2s/step - loss: 1.4487 - acc: 0.3760 - val_loss: 1.2351 - val_acc: 0.5050\n",
      "Epoch 6/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.3666 - acc: 0.4141\n",
      "Epoch 00006: val_loss improved from 1.23510 to 1.17362, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 21s 2s/step - loss: 1.3624 - acc: 0.4120 - val_loss: 1.1736 - val_acc: 0.5090\n",
      "Epoch 7/100\n",
      "10/11 [==========================>...] - ETA: 2s - loss: 1.2835 - acc: 0.4421\n",
      "Epoch 00007: val_loss improved from 1.17362 to 1.13845, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 24s 2s/step - loss: 1.2822 - acc: 0.4441 - val_loss: 1.1385 - val_acc: 0.4930\n",
      "Epoch 8/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.2465 - acc: 0.4289\n",
      "Epoch 00008: val_loss improved from 1.13845 to 1.08966, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 15s 1s/step - loss: 1.2467 - acc: 0.4283 - val_loss: 1.0897 - val_acc: 0.5320\n",
      "Epoch 9/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.2188 - acc: 0.4565\n",
      "Epoch 00009: val_loss improved from 1.08966 to 1.06419, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.2115 - acc: 0.4558 - val_loss: 1.0642 - val_acc: 0.5330\n",
      "Epoch 10/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.1962 - acc: 0.4633\n",
      "Epoch 00010: val_loss did not improve from 1.06419\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.1867 - acc: 0.4649 - val_loss: 1.0731 - val_acc: 0.5140\n",
      "Epoch 11/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.1493 - acc: 0.4713\n",
      "Epoch 00011: val_loss improved from 1.06419 to 1.03031, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.1425 - acc: 0.4750 - val_loss: 1.0303 - val_acc: 0.5230\n",
      "Epoch 12/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.1512 - acc: 0.4791\n",
      "Epoch 00012: val_loss improved from 1.03031 to 1.01172, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.1589 - acc: 0.4749 - val_loss: 1.0117 - val_acc: 0.5270\n",
      "Epoch 13/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.1375 - acc: 0.4736\n",
      "Epoch 00013: val_loss did not improve from 1.01172\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.1381 - acc: 0.4689 - val_loss: 1.0246 - val_acc: 0.4960\n",
      "Epoch 14/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.1108 - acc: 0.4813\n",
      "Epoch 00014: val_loss improved from 1.01172 to 0.96474, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.1137 - acc: 0.4847 - val_loss: 0.9647 - val_acc: 0.5210\n",
      "Epoch 15/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.0847 - acc: 0.4913\n",
      "Epoch 00015: val_loss improved from 0.96474 to 0.94152, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.0869 - acc: 0.4941 - val_loss: 0.9415 - val_acc: 0.5490\n",
      "Epoch 16/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.0658 - acc: 0.4908\n",
      "Epoch 00016: val_loss improved from 0.94152 to 0.93909, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.0628 - acc: 0.4927 - val_loss: 0.9391 - val_acc: 0.5480\n",
      "Epoch 17/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.0726 - acc: 0.5047\n",
      "Epoch 00017: val_loss improved from 0.93909 to 0.90660, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.0642 - acc: 0.5067 - val_loss: 0.9066 - val_acc: 0.5340\n",
      "Epoch 18/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.0686 - acc: 0.5040\n",
      "Epoch 00018: val_loss did not improve from 0.90660\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.0705 - acc: 0.5026 - val_loss: 0.9297 - val_acc: 0.5540\n",
      "Epoch 19/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.0286 - acc: 0.4980\n",
      "Epoch 00019: val_loss did not improve from 0.90660\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.0261 - acc: 0.4977 - val_loss: 0.9086 - val_acc: 0.5430\n",
      "Epoch 20/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.0565 - acc: 0.4857\n",
      "Epoch 00020: val_loss did not improve from 0.90660\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.0515 - acc: 0.4892 - val_loss: 0.9178 - val_acc: 0.5220\n",
      "Epoch 21/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9964 - acc: 0.5191\n",
      "Epoch 00021: val_loss improved from 0.90660 to 0.87617, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.9963 - acc: 0.5217 - val_loss: 0.8762 - val_acc: 0.5400\n",
      "Epoch 22/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9906 - acc: 0.5104\n",
      "Epoch 00022: val_loss improved from 0.87617 to 0.86577, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.9980 - acc: 0.5087 - val_loss: 0.8658 - val_acc: 0.5500\n",
      "Epoch 23/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9977 - acc: 0.5211\n",
      "Epoch 00023: val_loss did not improve from 0.86577\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.9918 - acc: 0.5297 - val_loss: 0.8852 - val_acc: 0.5430\n",
      "Epoch 24/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 1.0025 - acc: 0.5197\n",
      "Epoch 00024: val_loss did not improve from 0.86577\n",
      "11/11 [==============================] - 14s 1s/step - loss: 1.0035 - acc: 0.5154 - val_loss: 0.8802 - val_acc: 0.5430\n",
      "Epoch 25/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9958 - acc: 0.5094\n",
      "Epoch 00025: val_loss improved from 0.86577 to 0.83586, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.9905 - acc: 0.5128 - val_loss: 0.8359 - val_acc: 0.5590\n",
      "Epoch 26/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9681 - acc: 0.5212\n",
      "Epoch 00026: val_loss did not improve from 0.83586\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.9635 - acc: 0.5207 - val_loss: 0.8523 - val_acc: 0.5490\n",
      "Epoch 27/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9751 - acc: 0.5284\n",
      "Epoch 00027: val_loss did not improve from 0.83586\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.9700 - acc: 0.5261 - val_loss: 0.8503 - val_acc: 0.5550\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9497 - acc: 0.5340\n",
      "Epoch 00028: val_loss improved from 0.83586 to 0.81288, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.9472 - acc: 0.5352 - val_loss: 0.8129 - val_acc: 0.5760\n",
      "Epoch 29/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9656 - acc: 0.5257\n",
      "Epoch 00029: val_loss did not improve from 0.81288\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.9671 - acc: 0.5247 - val_loss: 0.8560 - val_acc: 0.5430\n",
      "Epoch 30/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9723 - acc: 0.5332\n",
      "Epoch 00030: val_loss did not improve from 0.81288\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.9696 - acc: 0.5307 - val_loss: 0.8433 - val_acc: 0.5580\n",
      "Epoch 31/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9601 - acc: 0.5270\n",
      "Epoch 00031: val_loss did not improve from 0.81288\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.9652 - acc: 0.5266 - val_loss: 0.8589 - val_acc: 0.5510\n",
      "Epoch 32/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9304 - acc: 0.5521\n",
      "Epoch 00032: val_loss did not improve from 0.81288\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.9299 - acc: 0.5502 - val_loss: 0.8160 - val_acc: 0.5550\n",
      "Epoch 33/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9083 - acc: 0.5349\n",
      "Epoch 00033: val_loss improved from 0.81288 to 0.79305, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.9106 - acc: 0.5352 - val_loss: 0.7930 - val_acc: 0.5560\n",
      "Epoch 34/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9210 - acc: 0.5238\n",
      "Epoch 00034: val_loss did not improve from 0.79305\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.9143 - acc: 0.5281 - val_loss: 0.7985 - val_acc: 0.5580\n",
      "Epoch 35/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9145 - acc: 0.5464\n",
      "Epoch 00035: val_loss improved from 0.79305 to 0.79290, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.9147 - acc: 0.5453 - val_loss: 0.7929 - val_acc: 0.5570\n",
      "Epoch 36/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9401 - acc: 0.5295\n",
      "Epoch 00036: val_loss did not improve from 0.79290\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.9334 - acc: 0.5342 - val_loss: 0.8400 - val_acc: 0.5510\n",
      "Epoch 37/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9149 - acc: 0.5402\n",
      "Epoch 00037: val_loss did not improve from 0.79290\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.9160 - acc: 0.5391 - val_loss: 0.8112 - val_acc: 0.5570\n",
      "Epoch 38/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9000 - acc: 0.5416\n",
      "Epoch 00038: val_loss improved from 0.79290 to 0.77098, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.9008 - acc: 0.5453 - val_loss: 0.7710 - val_acc: 0.5610\n",
      "Epoch 39/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8840 - acc: 0.5507\n",
      "Epoch 00039: val_loss did not improve from 0.77098\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.8834 - acc: 0.5507 - val_loss: 0.7891 - val_acc: 0.5630\n",
      "Epoch 40/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8790 - acc: 0.5547\n",
      "Epoch 00040: val_loss did not improve from 0.77098\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8818 - acc: 0.5540 - val_loss: 0.8128 - val_acc: 0.5530\n",
      "Epoch 41/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8777 - acc: 0.5472\n",
      "Epoch 00041: val_loss did not improve from 0.77098\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8851 - acc: 0.5443 - val_loss: 0.7726 - val_acc: 0.5730\n",
      "Epoch 42/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8773 - acc: 0.5568\n",
      "Epoch 00042: val_loss did not improve from 0.77098\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.8766 - acc: 0.5540 - val_loss: 0.8153 - val_acc: 0.5640\n",
      "Epoch 43/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.9304 - acc: 0.5571\n",
      "Epoch 00043: val_loss improved from 0.77098 to 0.76016, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.9377 - acc: 0.5540 - val_loss: 0.7602 - val_acc: 0.5660\n",
      "Epoch 44/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8675 - acc: 0.5512\n",
      "Epoch 00044: val_loss did not improve from 0.76016\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.8634 - acc: 0.5537 - val_loss: 0.7659 - val_acc: 0.5720\n",
      "Epoch 45/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8483 - acc: 0.5570\n",
      "Epoch 00045: val_loss did not improve from 0.76016\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8412 - acc: 0.5586 - val_loss: 0.7627 - val_acc: 0.5840\n",
      "Epoch 46/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8867 - acc: 0.5520\n",
      "Epoch 00046: val_loss did not improve from 0.76016\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.8909 - acc: 0.5520 - val_loss: 0.7874 - val_acc: 0.5710\n",
      "Epoch 47/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8664 - acc: 0.5625\n",
      "Epoch 00047: val_loss did not improve from 0.76016\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8650 - acc: 0.5625 - val_loss: 0.8149 - val_acc: 0.5490\n",
      "Epoch 48/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8777 - acc: 0.5542\n",
      "Epoch 00048: val_loss did not improve from 0.76016\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.8720 - acc: 0.5529 - val_loss: 0.7779 - val_acc: 0.5700\n",
      "Epoch 49/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8445 - acc: 0.5563\n",
      "Epoch 00049: val_loss did not improve from 0.76016\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.8483 - acc: 0.5594 - val_loss: 0.7873 - val_acc: 0.5670\n",
      "Epoch 50/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8387 - acc: 0.5672\n",
      "Epoch 00050: val_loss improved from 0.76016 to 0.75830, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8381 - acc: 0.5650 - val_loss: 0.7583 - val_acc: 0.5770\n",
      "Epoch 51/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8417 - acc: 0.5657\n",
      "Epoch 00051: val_loss did not improve from 0.75830\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.8366 - acc: 0.5675 - val_loss: 0.7777 - val_acc: 0.5670\n",
      "Epoch 52/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8558 - acc: 0.5648\n",
      "Epoch 00052: val_loss improved from 0.75830 to 0.74183, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8568 - acc: 0.5636 - val_loss: 0.7418 - val_acc: 0.5850\n",
      "Epoch 53/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8604 - acc: 0.5520\n",
      "Epoch 00053: val_loss did not improve from 0.74183\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.8573 - acc: 0.5547 - val_loss: 0.7802 - val_acc: 0.5630\n",
      "Epoch 54/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8351 - acc: 0.5754\n",
      "Epoch 00054: val_loss did not improve from 0.74183\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8394 - acc: 0.5710 - val_loss: 0.7854 - val_acc: 0.5740\n",
      "Epoch 55/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8587 - acc: 0.5715\n",
      "Epoch 00055: val_loss did not improve from 0.74183\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8567 - acc: 0.5711 - val_loss: 0.8483 - val_acc: 0.5490\n",
      "Epoch 56/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8663 - acc: 0.5492\n",
      "Epoch 00056: val_loss did not improve from 0.74183\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8575 - acc: 0.5555 - val_loss: 0.7804 - val_acc: 0.5850\n",
      "Epoch 57/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8304 - acc: 0.5902\n",
      "Epoch 00057: val_loss improved from 0.74183 to 0.72090, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8315 - acc: 0.5852 - val_loss: 0.7209 - val_acc: 0.6020\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 58/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8616 - acc: 0.5650\n",
      "Epoch 00058: val_loss did not improve from 0.72090\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8561 - acc: 0.5677 - val_loss: 0.7565 - val_acc: 0.5980\n",
      "Epoch 59/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8412 - acc: 0.5860\n",
      "Epoch 00059: val_loss did not improve from 0.72090\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.8304 - acc: 0.5928 - val_loss: 0.7244 - val_acc: 0.5940\n",
      "Epoch 60/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8188 - acc: 0.5896\n",
      "Epoch 00060: val_loss did not improve from 0.72090\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8176 - acc: 0.5895 - val_loss: 0.7307 - val_acc: 0.5960\n",
      "Epoch 61/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8338 - acc: 0.5816\n",
      "Epoch 00061: val_loss did not improve from 0.72090\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.8290 - acc: 0.5838 - val_loss: 0.7483 - val_acc: 0.5950\n",
      "Epoch 62/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8216 - acc: 0.5919\n",
      "Epoch 00062: val_loss improved from 0.72090 to 0.70993, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8232 - acc: 0.5873 - val_loss: 0.7099 - val_acc: 0.6140\n",
      "Epoch 63/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8068 - acc: 0.5934\n",
      "Epoch 00063: val_loss did not improve from 0.70993\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8100 - acc: 0.5984 - val_loss: 0.7551 - val_acc: 0.6030\n",
      "Epoch 64/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8062 - acc: 0.6054\n",
      "Epoch 00064: val_loss improved from 0.70993 to 0.70033, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.8111 - acc: 0.5983 - val_loss: 0.7003 - val_acc: 0.6280\n",
      "Epoch 65/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8020 - acc: 0.6090\n",
      "Epoch 00065: val_loss did not improve from 0.70033\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.8006 - acc: 0.6085 - val_loss: 0.7106 - val_acc: 0.6370\n",
      "Epoch 66/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7990 - acc: 0.6112\n",
      "Epoch 00066: val_loss did not improve from 0.70033\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8092 - acc: 0.6117 - val_loss: 0.7490 - val_acc: 0.6230\n",
      "Epoch 67/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7921 - acc: 0.6124\n",
      "Epoch 00067: val_loss improved from 0.70033 to 0.67839, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8012 - acc: 0.6085 - val_loss: 0.6784 - val_acc: 0.6240\n",
      "Epoch 68/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.8143 - acc: 0.6033\n",
      "Epoch 00068: val_loss did not improve from 0.67839\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.8103 - acc: 0.6053 - val_loss: 0.7105 - val_acc: 0.6190\n",
      "Epoch 69/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7824 - acc: 0.6301\n",
      "Epoch 00069: val_loss did not improve from 0.67839\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7761 - acc: 0.6303 - val_loss: 0.7278 - val_acc: 0.6390\n",
      "Epoch 70/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7778 - acc: 0.6257\n",
      "Epoch 00070: val_loss did not improve from 0.67839\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.7761 - acc: 0.6280 - val_loss: 0.7269 - val_acc: 0.6410\n",
      "Epoch 71/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7638 - acc: 0.6280\n",
      "Epoch 00071: val_loss improved from 0.67839 to 0.67177, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 13s 1s/step - loss: 0.7664 - acc: 0.6273 - val_loss: 0.6718 - val_acc: 0.6620\n",
      "Epoch 72/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7593 - acc: 0.6377\n",
      "Epoch 00072: val_loss did not improve from 0.67177\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.7651 - acc: 0.6358 - val_loss: 0.6982 - val_acc: 0.6450\n",
      "Epoch 73/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7910 - acc: 0.6247\n",
      "Epoch 00073: val_loss did not improve from 0.67177\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7927 - acc: 0.6289 - val_loss: 0.7737 - val_acc: 0.6120\n",
      "Epoch 74/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7948 - acc: 0.6272\n",
      "Epoch 00074: val_loss improved from 0.67177 to 0.66181, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7884 - acc: 0.6301 - val_loss: 0.6618 - val_acc: 0.6610\n",
      "Epoch 75/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7608 - acc: 0.6504\n",
      "Epoch 00075: val_loss did not improve from 0.66181\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7712 - acc: 0.6519 - val_loss: 0.6807 - val_acc: 0.6500\n",
      "Epoch 76/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7289 - acc: 0.6512\n",
      "Epoch 00076: val_loss did not improve from 0.66181\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7262 - acc: 0.6509 - val_loss: 0.7209 - val_acc: 0.6370\n",
      "Epoch 77/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7417 - acc: 0.6473\n",
      "Epoch 00077: val_loss improved from 0.66181 to 0.65434, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7456 - acc: 0.6453 - val_loss: 0.6543 - val_acc: 0.6650\n",
      "Epoch 78/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7667 - acc: 0.6303\n",
      "Epoch 00078: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.7666 - acc: 0.6296 - val_loss: 0.7751 - val_acc: 0.6070\n",
      "Epoch 79/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7486 - acc: 0.6324\n",
      "Epoch 00079: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7455 - acc: 0.6360 - val_loss: 0.6795 - val_acc: 0.6610\n",
      "Epoch 80/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7283 - acc: 0.6680\n",
      "Epoch 00080: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7292 - acc: 0.6676 - val_loss: 0.7029 - val_acc: 0.6340\n",
      "Epoch 81/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7861 - acc: 0.6435\n",
      "Epoch 00081: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7836 - acc: 0.6442 - val_loss: 0.7216 - val_acc: 0.6400\n",
      "Epoch 82/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7836 - acc: 0.6515\n",
      "Epoch 00082: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7768 - acc: 0.6522 - val_loss: 0.7180 - val_acc: 0.6320\n",
      "Epoch 83/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7740 - acc: 0.6387\n",
      "Epoch 00083: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7685 - acc: 0.6364 - val_loss: 0.7007 - val_acc: 0.6460\n",
      "Epoch 84/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7437 - acc: 0.6708\n",
      "Epoch 00084: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7473 - acc: 0.6670 - val_loss: 0.6644 - val_acc: 0.6550\n",
      "Epoch 85/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7622 - acc: 0.6442\n",
      "Epoch 00085: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.7559 - acc: 0.6442 - val_loss: 0.6883 - val_acc: 0.6560\n",
      "Epoch 86/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7366 - acc: 0.6438\n",
      "Epoch 00086: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7285 - acc: 0.6467 - val_loss: 0.6861 - val_acc: 0.6560\n",
      "Epoch 87/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7168 - acc: 0.6672\n",
      "Epoch 00087: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7108 - acc: 0.6692 - val_loss: 0.6650 - val_acc: 0.6630\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 88/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7420 - acc: 0.6738\n",
      "Epoch 00088: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7340 - acc: 0.6758 - val_loss: 0.6984 - val_acc: 0.6490\n",
      "Epoch 89/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7575 - acc: 0.6494\n",
      "Epoch 00089: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7561 - acc: 0.6503 - val_loss: 0.6897 - val_acc: 0.6510\n",
      "Epoch 90/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7148 - acc: 0.6617\n",
      "Epoch 00090: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.7107 - acc: 0.6641 - val_loss: 0.7511 - val_acc: 0.6240\n",
      "Epoch 91/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7388 - acc: 0.6691\n",
      "Epoch 00091: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.7400 - acc: 0.6710 - val_loss: 0.6920 - val_acc: 0.6560\n",
      "Epoch 92/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7218 - acc: 0.6746\n",
      "Epoch 00092: val_loss did not improve from 0.65434\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.7171 - acc: 0.6811 - val_loss: 0.6672 - val_acc: 0.6570\n",
      "Epoch 93/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7008 - acc: 0.6802\n",
      "Epoch 00093: val_loss improved from 0.65434 to 0.64999, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7077 - acc: 0.6826 - val_loss: 0.6500 - val_acc: 0.6510\n",
      "Epoch 94/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7407 - acc: 0.6742\n",
      "Epoch 00094: val_loss did not improve from 0.64999\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7275 - acc: 0.6794 - val_loss: 0.7188 - val_acc: 0.6440\n",
      "Epoch 95/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7107 - acc: 0.6891\n",
      "Epoch 00095: val_loss did not improve from 0.64999\n",
      "11/11 [==============================] - 16s 1s/step - loss: 0.7115 - acc: 0.6871 - val_loss: 0.6597 - val_acc: 0.7870\n",
      "Epoch 96/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7019 - acc: 0.6961\n",
      "Epoch 00096: val_loss did not improve from 0.64999\n",
      "11/11 [==============================] - 15s 1s/step - loss: 0.7055 - acc: 0.6950 - val_loss: 0.7129 - val_acc: 0.7050\n",
      "Epoch 97/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7357 - acc: 0.6960\n",
      "Epoch 00097: val_loss did not improve from 0.64999\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7358 - acc: 0.6949 - val_loss: 0.6700 - val_acc: 0.7580\n",
      "Epoch 98/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.7169 - acc: 0.6895\n",
      "Epoch 00098: val_loss did not improve from 0.64999\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.7124 - acc: 0.6921 - val_loss: 0.6917 - val_acc: 0.7720\n",
      "Epoch 99/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.6888 - acc: 0.7070\n",
      "Epoch 00099: val_loss improved from 0.64999 to 0.64301, saving model to ./results/model_cnn_v1_aug.weights.best.hdf5\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.6990 - acc: 0.7035 - val_loss: 0.6430 - val_acc: 0.7790\n",
      "Epoch 100/100\n",
      "10/11 [==========================>...] - ETA: 1s - loss: 0.6929 - acc: 0.7045\n",
      "Epoch 00100: val_loss did not improve from 0.64301\n",
      "11/11 [==============================] - 14s 1s/step - loss: 0.6957 - acc: 0.7022 - val_loss: 0.6542 - val_acc: 0.7790\n",
      "CPU times: user 32min 42s, sys: 55 s, total: 33min 37s\n",
      "Wall time: 23min 38s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# set to the original weights for testing other pipelines\n",
    "model_cnn_v1.set_weights(initial_weights)\n",
    "\n",
    "# define path to save the mnodel\n",
    "path_model='./results/model_cnn_v1_aug.weights.best.hdf5'\n",
    "shutil.rmtree(path_model, ignore_errors=True)\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath=path_model, \n",
    "                               verbose = 1, \n",
    "                               save_best_only=True)\n",
    "EPOCHS=100\n",
    "BATCH_SIZE=256\n",
    "#BATCH_SIZE=128\n",
    "#BATCH_SIZE=64\n",
    "\n",
    "\n",
    "history=model_cnn_v1.fit_generator(datagen.flow(x_train, y_train, batch_size=BATCH_SIZE),\n",
    "                                   validation_data=(x_test, y_test),\n",
    "                                   steps_per_epoch=len(x_train) // BATCH_SIZE,\n",
    "                                   epochs=EPOCHS, \n",
    "                                   callbacks=[checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss:\n",
      "  - loss [training dataset]: 0.695\n",
      "  - loss [validation dataset: 0.654\n",
      "\n",
      "Accuracy:\n",
      "  - accuracy [training dataset]: 70.23%\n",
      "  - accuracy [validation dataset: 77.90%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_loss(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN model v1 with data augmentation and histogram equalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "def AHE(img):\n",
    "    img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)\n",
    "    return img_adapteq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct the image generator for data augmentation\n",
    "datagen_hist_train = ImageDataGenerator(rotation_range=40, \n",
    "                                        width_shift_range=0.2,\n",
    "                                        height_shift_range=0.2, \n",
    "                                        shear_range=0.2, \n",
    "                                        zoom_range=0.2,\n",
    "                                        horizontal_flip=True, \n",
    "                                        fill_mode=\"nearest\",\n",
    "                                        preprocessing_function=AHE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen_hist_test = ImageDataGenerator(preprocessing_function=AHE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen_hist_train.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen_hist_test.fit(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 2.0403 - acc: 0.1629 \n",
      "Epoch 00001: val_loss improved from inf to 1.92117, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 106s 10s/step - loss: 2.0342 - acc: 0.1663 - val_loss: 1.9212 - val_acc: 0.2340\n",
      "Epoch 2/100\n",
      "10/11 [==========================>...] - ETA: 8s - loss: 1.8339 - acc: 0.2499 \n",
      "Epoch 00002: val_loss improved from 1.92117 to 1.66884, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 138s 13s/step - loss: 1.8269 - acc: 0.2469 - val_loss: 1.6688 - val_acc: 0.2500\n",
      "Epoch 3/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 1.7067 - acc: 0.2473 \n",
      "Epoch 00003: val_loss improved from 1.66884 to 1.60032, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 125s 11s/step - loss: 1.7031 - acc: 0.2476 - val_loss: 1.6003 - val_acc: 0.2890\n",
      "Epoch 4/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.6370 - acc: 0.2673 \n",
      "Epoch 00004: val_loss improved from 1.60032 to 1.52217, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 121s 11s/step - loss: 1.6385 - acc: 0.2672 - val_loss: 1.5222 - val_acc: 0.4050\n",
      "Epoch 5/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.5767 - acc: 0.2938 \n",
      "Epoch 00005: val_loss improved from 1.52217 to 1.49477, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 120s 11s/step - loss: 1.5813 - acc: 0.2915 - val_loss: 1.4948 - val_acc: 0.3400\n",
      "Epoch 6/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.5939 - acc: 0.2842 \n",
      "Epoch 00006: val_loss improved from 1.49477 to 1.48451, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 121s 11s/step - loss: 1.5908 - acc: 0.2884 - val_loss: 1.4845 - val_acc: 0.3120\n",
      "Epoch 7/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.5469 - acc: 0.2940 \n",
      "Epoch 00007: val_loss improved from 1.48451 to 1.47882, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 118s 11s/step - loss: 1.5438 - acc: 0.2971 - val_loss: 1.4788 - val_acc: 0.3450\n",
      "Epoch 8/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.5138 - acc: 0.3201 \n",
      "Epoch 00008: val_loss improved from 1.47882 to 1.40796, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 125s 11s/step - loss: 1.5131 - acc: 0.3186 - val_loss: 1.4080 - val_acc: 0.3560\n",
      "Epoch 9/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 1.4534 - acc: 0.3296 \n",
      "Epoch 00009: val_loss improved from 1.40796 to 1.33368, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 134s 12s/step - loss: 1.4475 - acc: 0.3331 - val_loss: 1.3337 - val_acc: 0.3560\n",
      "Epoch 10/100\n",
      "10/11 [==========================>...] - ETA: 8s - loss: 1.4046 - acc: 0.3602 \n",
      "Epoch 00010: val_loss improved from 1.33368 to 1.26088, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 150s 14s/step - loss: 1.4058 - acc: 0.3572 - val_loss: 1.2609 - val_acc: 0.4350\n",
      "Epoch 11/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 1.3475 - acc: 0.3789 \n",
      "Epoch 00011: val_loss improved from 1.26088 to 1.23720, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 133s 12s/step - loss: 1.3480 - acc: 0.3783 - val_loss: 1.2372 - val_acc: 0.4770\n",
      "Epoch 12/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.3078 - acc: 0.3749 \n",
      "Epoch 00012: val_loss improved from 1.23720 to 1.17567, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 126s 11s/step - loss: 1.3013 - acc: 0.3771 - val_loss: 1.1757 - val_acc: 0.4680\n",
      "Epoch 13/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 1.2539 - acc: 0.4102 \n",
      "Epoch 00013: val_loss improved from 1.17567 to 1.13533, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 111s 10s/step - loss: 1.2548 - acc: 0.4127 - val_loss: 1.1353 - val_acc: 0.4570\n",
      "Epoch 14/100\n",
      "10/11 [==========================>...] - ETA: 9s - loss: 1.2303 - acc: 0.3977 \n",
      "Epoch 00014: val_loss improved from 1.13533 to 1.12513, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 136s 12s/step - loss: 1.2321 - acc: 0.3967 - val_loss: 1.1251 - val_acc: 0.5020\n",
      "Epoch 15/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 1.2054 - acc: 0.4223 \n",
      "Epoch 00015: val_loss improved from 1.12513 to 1.09311, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 134s 12s/step - loss: 1.2031 - acc: 0.4243 - val_loss: 1.0931 - val_acc: 0.4980\n",
      "Epoch 16/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 1.1746 - acc: 0.4427 \n",
      "Epoch 00016: val_loss improved from 1.09311 to 1.06619, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 127s 12s/step - loss: 1.1765 - acc: 0.4451 - val_loss: 1.0662 - val_acc: 0.5060\n",
      "Epoch 17/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 1.1507 - acc: 0.4461 \n",
      "Epoch 00017: val_loss improved from 1.06619 to 1.04543, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 125s 11s/step - loss: 1.1469 - acc: 0.4510 - val_loss: 1.0454 - val_acc: 0.5320\n",
      "Epoch 18/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 1.1478 - acc: 0.4534 \n",
      "Epoch 00018: val_loss did not improve from 1.04543\n",
      "11/11 [==============================] - 132s 12s/step - loss: 1.1459 - acc: 0.4508 - val_loss: 1.0756 - val_acc: 0.5070\n",
      "Epoch 19/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 1.1831 - acc: 0.4378 \n",
      "Epoch 00019: val_loss did not improve from 1.04543\n",
      "11/11 [==============================] - 129s 12s/step - loss: 1.1881 - acc: 0.4360 - val_loss: 1.1525 - val_acc: 0.4570\n",
      "Epoch 20/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 1.1506 - acc: 0.4616 \n",
      "Epoch 00020: val_loss improved from 1.04543 to 1.01589, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 129s 12s/step - loss: 1.1470 - acc: 0.4556 - val_loss: 1.0159 - val_acc: 0.5240\n",
      "Epoch 21/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.0937 - acc: 0.4832 \n",
      "Epoch 00021: val_loss did not improve from 1.01589\n",
      "11/11 [==============================] - 123s 11s/step - loss: 1.0981 - acc: 0.4815 - val_loss: 1.0301 - val_acc: 0.5280\n",
      "Epoch 22/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.1216 - acc: 0.4834 \n",
      "Epoch 00022: val_loss improved from 1.01589 to 0.99402, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 118s 11s/step - loss: 1.1186 - acc: 0.4859 - val_loss: 0.9940 - val_acc: 0.5260\n",
      "Epoch 23/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.0734 - acc: 0.4793 \n",
      "Epoch 00023: val_loss improved from 0.99402 to 0.95881, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 121s 11s/step - loss: 1.0750 - acc: 0.4801 - val_loss: 0.9588 - val_acc: 0.5230\n",
      "Epoch 24/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.0863 - acc: 0.4782 \n",
      "Epoch 00024: val_loss did not improve from 0.95881\n",
      "11/11 [==============================] - 120s 11s/step - loss: 1.0835 - acc: 0.4822 - val_loss: 0.9589 - val_acc: 0.5430\n",
      "Epoch 25/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.0701 - acc: 0.4871 \n",
      "Epoch 00025: val_loss improved from 0.95881 to 0.94774, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 100s 9s/step - loss: 1.0682 - acc: 0.4851 - val_loss: 0.9477 - val_acc: 0.5440\n",
      "Epoch 26/100\n",
      "10/11 [==========================>...] - ETA: 9s - loss: 1.0607 - acc: 0.4878 \n",
      "Epoch 00026: val_loss did not improve from 0.94774\n",
      "11/11 [==============================] - 137s 12s/step - loss: 1.0562 - acc: 0.4911 - val_loss: 0.9784 - val_acc: 0.5120\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 1.0378 - acc: 0.4914 \n",
      "Epoch 00027: val_loss improved from 0.94774 to 0.94219, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 124s 11s/step - loss: 1.0357 - acc: 0.4940 - val_loss: 0.9422 - val_acc: 0.5220\n",
      "Epoch 28/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.0361 - acc: 0.4982 \n",
      "Epoch 00028: val_loss improved from 0.94219 to 0.92740, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 122s 11s/step - loss: 1.0394 - acc: 0.4947 - val_loss: 0.9274 - val_acc: 0.5260\n",
      "Epoch 29/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.0239 - acc: 0.5031 \n",
      "Epoch 00029: val_loss improved from 0.92740 to 0.91430, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 118s 11s/step - loss: 1.0203 - acc: 0.5032 - val_loss: 0.9143 - val_acc: 0.5300\n",
      "Epoch 30/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.0525 - acc: 0.5055 \n",
      "Epoch 00030: val_loss did not improve from 0.91430\n",
      "11/11 [==============================] - 117s 11s/step - loss: 1.0440 - acc: 0.5098 - val_loss: 0.9212 - val_acc: 0.5230\n",
      "Epoch 31/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.0039 - acc: 0.5082 \n",
      "Epoch 00031: val_loss improved from 0.91430 to 0.88366, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 120s 11s/step - loss: 1.0038 - acc: 0.5075 - val_loss: 0.8837 - val_acc: 0.5270\n",
      "Epoch 32/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.0191 - acc: 0.5065 \n",
      "Epoch 00032: val_loss did not improve from 0.88366\n",
      "11/11 [==============================] - 118s 11s/step - loss: 1.0125 - acc: 0.5105 - val_loss: 0.9039 - val_acc: 0.5210\n",
      "Epoch 33/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.0078 - acc: 0.5023 \n",
      "Epoch 00033: val_loss did not improve from 0.88366\n",
      "11/11 [==============================] - 117s 11s/step - loss: 1.0078 - acc: 0.5036 - val_loss: 0.9013 - val_acc: 0.5290\n",
      "Epoch 34/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9989 - acc: 0.5289 \n",
      "Epoch 00034: val_loss did not improve from 0.88366\n",
      "11/11 [==============================] - 119s 11s/step - loss: 0.9977 - acc: 0.5302 - val_loss: 0.8963 - val_acc: 0.5190\n",
      "Epoch 35/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 1.0027 - acc: 0.5188 \n",
      "Epoch 00035: val_loss did not improve from 0.88366\n",
      "11/11 [==============================] - 117s 11s/step - loss: 1.0050 - acc: 0.5170 - val_loss: 0.9135 - val_acc: 0.5240\n",
      "Epoch 36/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9929 - acc: 0.5087 \n",
      "Epoch 00036: val_loss improved from 0.88366 to 0.88235, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 119s 11s/step - loss: 0.9907 - acc: 0.5133 - val_loss: 0.8823 - val_acc: 0.5300\n",
      "Epoch 37/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9741 - acc: 0.5306 \n",
      "Epoch 00037: val_loss improved from 0.88235 to 0.87108, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 97s 9s/step - loss: 0.9781 - acc: 0.5286 - val_loss: 0.8711 - val_acc: 0.5300\n",
      "Epoch 38/100\n",
      "10/11 [==========================>...] - ETA: 9s - loss: 0.9564 - acc: 0.5311 \n",
      "Epoch 00038: val_loss improved from 0.87108 to 0.86062, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 136s 12s/step - loss: 0.9622 - acc: 0.5268 - val_loss: 0.8606 - val_acc: 0.5670\n",
      "Epoch 39/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 0.9575 - acc: 0.5369 \n",
      "Epoch 00039: val_loss improved from 0.86062 to 0.85039, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 123s 11s/step - loss: 0.9545 - acc: 0.5393 - val_loss: 0.8504 - val_acc: 0.5670\n",
      "Epoch 40/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9795 - acc: 0.5293 \n",
      "Epoch 00040: val_loss did not improve from 0.85039\n",
      "11/11 [==============================] - 122s 11s/step - loss: 0.9810 - acc: 0.5277 - val_loss: 0.8677 - val_acc: 0.5500\n",
      "Epoch 41/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9717 - acc: 0.5151 \n",
      "Epoch 00041: val_loss did not improve from 0.85039\n",
      "11/11 [==============================] - 115s 10s/step - loss: 0.9704 - acc: 0.5134 - val_loss: 0.8766 - val_acc: 0.5530\n",
      "Epoch 42/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9946 - acc: 0.5242 \n",
      "Epoch 00042: val_loss did not improve from 0.85039\n",
      "11/11 [==============================] - 119s 11s/step - loss: 0.9973 - acc: 0.5224 - val_loss: 0.8988 - val_acc: 0.5390\n",
      "Epoch 43/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9925 - acc: 0.5162 \n",
      "Epoch 00043: val_loss did not improve from 0.85039\n",
      "11/11 [==============================] - 118s 11s/step - loss: 0.9909 - acc: 0.5180 - val_loss: 0.9060 - val_acc: 0.5480\n",
      "Epoch 44/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9415 - acc: 0.5406 \n",
      "Epoch 00044: val_loss did not improve from 0.85039\n",
      "11/11 [==============================] - 119s 11s/step - loss: 0.9420 - acc: 0.5426 - val_loss: 0.8518 - val_acc: 0.5520\n",
      "Epoch 45/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9383 - acc: 0.5414 \n",
      "Epoch 00045: val_loss improved from 0.85039 to 0.83299, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 119s 11s/step - loss: 0.9399 - acc: 0.5408 - val_loss: 0.8330 - val_acc: 0.5830\n",
      "Epoch 46/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9096 - acc: 0.5566 \n",
      "Epoch 00046: val_loss improved from 0.83299 to 0.83292, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 118s 11s/step - loss: 0.9071 - acc: 0.5588 - val_loss: 0.8329 - val_acc: 0.5530\n",
      "Epoch 47/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9272 - acc: 0.5540 \n",
      "Epoch 00047: val_loss improved from 0.83292 to 0.81608, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.9230 - acc: 0.5544 - val_loss: 0.8161 - val_acc: 0.5480\n",
      "Epoch 48/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9127 - acc: 0.5547 \n",
      "Epoch 00048: val_loss did not improve from 0.81608\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.9141 - acc: 0.5547 - val_loss: 0.8370 - val_acc: 0.5480\n",
      "Epoch 49/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9169 - acc: 0.5426 \n",
      "Epoch 00049: val_loss did not improve from 0.81608\n",
      "11/11 [==============================] - 99s 9s/step - loss: 0.9226 - acc: 0.5433 - val_loss: 0.8861 - val_acc: 0.5250\n",
      "Epoch 50/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 0.9246 - acc: 0.5527 \n",
      "Epoch 00050: val_loss did not improve from 0.81608\n",
      "11/11 [==============================] - 120s 11s/step - loss: 0.9182 - acc: 0.5558 - val_loss: 0.8244 - val_acc: 0.5830\n",
      "Epoch 51/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 0.9119 - acc: 0.5514 \n",
      "Epoch 00051: val_loss did not improve from 0.81608\n",
      "11/11 [==============================] - 122s 11s/step - loss: 0.9146 - acc: 0.5492 - val_loss: 0.8501 - val_acc: 0.5690\n",
      "Epoch 52/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9367 - acc: 0.5312 \n",
      "Epoch 00052: val_loss did not improve from 0.81608\n",
      "11/11 [==============================] - 119s 11s/step - loss: 0.9324 - acc: 0.5354 - val_loss: 0.8334 - val_acc: 0.5520\n",
      "Epoch 53/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8925 - acc: 0.5688 \n",
      "Epoch 00053: val_loss did not improve from 0.81608\n",
      "11/11 [==============================] - 118s 11s/step - loss: 0.8944 - acc: 0.5671 - val_loss: 0.8167 - val_acc: 0.5600\n",
      "Epoch 54/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8843 - acc: 0.5508 \n",
      "Epoch 00054: val_loss did not improve from 0.81608\n",
      "11/11 [==============================] - 131s 12s/step - loss: 0.8926 - acc: 0.5463 - val_loss: 0.8427 - val_acc: 0.5700\n",
      "Epoch 55/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8951 - acc: 0.5656 \n",
      "Epoch 00055: val_loss did not improve from 0.81608\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.8908 - acc: 0.5657 - val_loss: 0.8828 - val_acc: 0.5390\n",
      "Epoch 56/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8916 - acc: 0.5508 \n",
      "Epoch 00056: val_loss did not improve from 0.81608\n",
      "11/11 [==============================] - 120s 11s/step - loss: 0.9011 - acc: 0.5487 - val_loss: 0.8701 - val_acc: 0.5460\n",
      "Epoch 57/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.9219 - acc: 0.5461 \n",
      "Epoch 00057: val_loss did not improve from 0.81608\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.9185 - acc: 0.5491 - val_loss: 0.8271 - val_acc: 0.5530\n",
      "Epoch 58/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8865 - acc: 0.5721 \n",
      "Epoch 00058: val_loss improved from 0.81608 to 0.79594, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 118s 11s/step - loss: 0.8846 - acc: 0.5709 - val_loss: 0.7959 - val_acc: 0.5580\n",
      "Epoch 59/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8754 - acc: 0.5661 \n",
      "Epoch 00059: val_loss did not improve from 0.79594\n",
      "11/11 [==============================] - 132s 12s/step - loss: 0.8696 - acc: 0.5690 - val_loss: 0.7994 - val_acc: 0.5810\n",
      "Epoch 60/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8827 - acc: 0.5705 \n",
      "Epoch 00060: val_loss improved from 0.79594 to 0.78099, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 118s 11s/step - loss: 0.8730 - acc: 0.5712 - val_loss: 0.7810 - val_acc: 0.5660\n",
      "Epoch 61/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8881 - acc: 0.5669 \n",
      "Epoch 00061: val_loss did not improve from 0.78099\n",
      "11/11 [==============================] - 98s 9s/step - loss: 0.8766 - acc: 0.5684 - val_loss: 0.8502 - val_acc: 0.5560\n",
      "Epoch 62/100\n",
      "10/11 [==========================>...] - ETA: 8s - loss: 0.8764 - acc: 0.5648 \n",
      "Epoch 00062: val_loss did not improve from 0.78099\n",
      "11/11 [==============================] - 120s 11s/step - loss: 0.8744 - acc: 0.5657 - val_loss: 0.8107 - val_acc: 0.5850\n",
      "Epoch 63/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 0.8773 - acc: 0.5810 \n",
      "Epoch 00063: val_loss did not improve from 0.78099\n",
      "11/11 [==============================] - 122s 11s/step - loss: 0.8803 - acc: 0.5789 - val_loss: 0.7825 - val_acc: 0.5700\n",
      "Epoch 64/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8684 - acc: 0.5582 \n",
      "Epoch 00064: val_loss improved from 0.78099 to 0.76566, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 122s 11s/step - loss: 0.8638 - acc: 0.5579 - val_loss: 0.7657 - val_acc: 0.5930\n",
      "Epoch 65/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8711 - acc: 0.5694 \n",
      "Epoch 00065: val_loss did not improve from 0.76566\n",
      "11/11 [==============================] - 118s 11s/step - loss: 0.8745 - acc: 0.5684 - val_loss: 0.7864 - val_acc: 0.5610\n",
      "Epoch 66/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8684 - acc: 0.5575 \n",
      "Epoch 00066: val_loss did not improve from 0.76566\n",
      "11/11 [==============================] - 115s 10s/step - loss: 0.8736 - acc: 0.5577 - val_loss: 0.8629 - val_acc: 0.5590\n",
      "Epoch 67/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8736 - acc: 0.5590 \n",
      "Epoch 00067: val_loss did not improve from 0.76566\n",
      "11/11 [==============================] - 119s 11s/step - loss: 0.8758 - acc: 0.5604 - val_loss: 0.7919 - val_acc: 0.5650\n",
      "Epoch 68/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8507 - acc: 0.5858 \n",
      "Epoch 00068: val_loss did not improve from 0.76566\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.8515 - acc: 0.5811 - val_loss: 0.7909 - val_acc: 0.5920\n",
      "Epoch 69/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8655 - acc: 0.5780 \n",
      "Epoch 00069: val_loss improved from 0.76566 to 0.76245, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.8670 - acc: 0.5797 - val_loss: 0.7625 - val_acc: 0.5760\n",
      "Epoch 70/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8558 - acc: 0.5746 \n",
      "Epoch 00070: val_loss did not improve from 0.76245\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.8583 - acc: 0.5756 - val_loss: 0.7806 - val_acc: 0.5890\n",
      "Epoch 71/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8803 - acc: 0.5692 \n",
      "Epoch 00071: val_loss did not improve from 0.76245\n",
      "11/11 [==============================] - 118s 11s/step - loss: 0.8865 - acc: 0.5721 - val_loss: 0.8327 - val_acc: 0.5590\n",
      "Epoch 72/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8777 - acc: 0.5637 \n",
      "Epoch 00072: val_loss did not improve from 0.76245\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.8720 - acc: 0.5661 - val_loss: 0.7688 - val_acc: 0.5890\n",
      "Epoch 73/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8534 - acc: 0.5785 \n",
      "Epoch 00073: val_loss did not improve from 0.76245\n",
      "11/11 [==============================] - 99s 9s/step - loss: 0.8456 - acc: 0.5836 - val_loss: 0.7962 - val_acc: 0.5660\n",
      "Epoch 74/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 0.8471 - acc: 0.5774 \n",
      "Epoch 00074: val_loss did not improve from 0.76245\n",
      "11/11 [==============================] - 120s 11s/step - loss: 0.8474 - acc: 0.5791 - val_loss: 0.8472 - val_acc: 0.5280\n",
      "Epoch 75/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 0.8303 - acc: 0.5996 \n",
      "Epoch 00075: val_loss improved from 0.76245 to 0.76186, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 122s 11s/step - loss: 0.8298 - acc: 0.5973 - val_loss: 0.7619 - val_acc: 0.5850\n",
      "Epoch 76/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8377 - acc: 0.5766 \n",
      "Epoch 00076: val_loss did not improve from 0.76186\n",
      "11/11 [==============================] - 119s 11s/step - loss: 0.8424 - acc: 0.5758 - val_loss: 0.7670 - val_acc: 0.6110\n",
      "Epoch 77/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8330 - acc: 0.5906 \n",
      "Epoch 00077: val_loss did not improve from 0.76186\n",
      "11/11 [==============================] - 118s 11s/step - loss: 0.8285 - acc: 0.5913 - val_loss: 0.8148 - val_acc: 0.5630\n",
      "Epoch 78/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8526 - acc: 0.5849 \n",
      "Epoch 00078: val_loss did not improve from 0.76186\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.8513 - acc: 0.5857 - val_loss: 0.7898 - val_acc: 0.5510\n",
      "Epoch 79/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8443 - acc: 0.5846 \n",
      "Epoch 00079: val_loss did not improve from 0.76186\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.8432 - acc: 0.5823 - val_loss: 0.8099 - val_acc: 0.5910\n",
      "Epoch 80/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8280 - acc: 0.5915 \n",
      "Epoch 00080: val_loss did not improve from 0.76186\n",
      "11/11 [==============================] - 118s 11s/step - loss: 0.8391 - acc: 0.5881 - val_loss: 0.7820 - val_acc: 0.5870\n",
      "Epoch 81/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8239 - acc: 0.5999 \n",
      "Epoch 00081: val_loss did not improve from 0.76186\n",
      "11/11 [==============================] - 132s 12s/step - loss: 0.8202 - acc: 0.6064 - val_loss: 0.8097 - val_acc: 0.5620\n",
      "Epoch 82/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8327 - acc: 0.5972 \n",
      "Epoch 00082: val_loss did not improve from 0.76186\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.8318 - acc: 0.5979 - val_loss: 0.8297 - val_acc: 0.5800\n",
      "Epoch 83/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8391 - acc: 0.5797 \n",
      "Epoch 00083: val_loss improved from 0.76186 to 0.74087, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.8390 - acc: 0.5787 - val_loss: 0.7409 - val_acc: 0.5880\n",
      "Epoch 84/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8487 - acc: 0.5885 \n",
      "Epoch 00084: val_loss did not improve from 0.74087\n",
      "11/11 [==============================] - 119s 11s/step - loss: 0.8426 - acc: 0.5879 - val_loss: 0.8231 - val_acc: 0.5810\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 85/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8227 - acc: 0.5828 \n",
      "Epoch 00085: val_loss did not improve from 0.74087\n",
      "11/11 [==============================] - 98s 9s/step - loss: 0.8200 - acc: 0.5845 - val_loss: 0.7988 - val_acc: 0.5900\n",
      "Epoch 86/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 0.8286 - acc: 0.5914 \n",
      "Epoch 00086: val_loss did not improve from 0.74087\n",
      "11/11 [==============================] - 120s 11s/step - loss: 0.8227 - acc: 0.5931 - val_loss: 0.7546 - val_acc: 0.5880\n",
      "Epoch 87/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 0.8349 - acc: 0.5848 \n",
      "Epoch 00087: val_loss did not improve from 0.74087\n",
      "11/11 [==============================] - 122s 11s/step - loss: 0.8354 - acc: 0.5866 - val_loss: 0.7730 - val_acc: 0.5960\n",
      "Epoch 88/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8185 - acc: 0.6102 \n",
      "Epoch 00088: val_loss did not improve from 0.74087\n",
      "11/11 [==============================] - 119s 11s/step - loss: 0.8101 - acc: 0.6110 - val_loss: 0.8180 - val_acc: 0.6010\n",
      "Epoch 89/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8253 - acc: 0.5977 \n",
      "Epoch 00089: val_loss did not improve from 0.74087\n",
      "11/11 [==============================] - 120s 11s/step - loss: 0.8287 - acc: 0.5952 - val_loss: 0.7419 - val_acc: 0.6140\n",
      "Epoch 90/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8186 - acc: 0.6072 \n",
      "Epoch 00090: val_loss improved from 0.74087 to 0.73243, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 115s 10s/step - loss: 0.8251 - acc: 0.6050 - val_loss: 0.7324 - val_acc: 0.5990\n",
      "Epoch 91/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8468 - acc: 0.5918 \n",
      "Epoch 00091: val_loss did not improve from 0.73243\n",
      "11/11 [==============================] - 120s 11s/step - loss: 0.8504 - acc: 0.5927 - val_loss: 0.8389 - val_acc: 0.5760\n",
      "Epoch 92/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8235 - acc: 0.5957 \n",
      "Epoch 00092: val_loss did not improve from 0.73243\n",
      "11/11 [==============================] - 118s 11s/step - loss: 0.8143 - acc: 0.5999 - val_loss: 0.8003 - val_acc: 0.5780\n",
      "Epoch 93/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8222 - acc: 0.6007 \n",
      "Epoch 00093: val_loss did not improve from 0.73243\n",
      "11/11 [==============================] - 117s 11s/step - loss: 0.8257 - acc: 0.6004 - val_loss: 0.7760 - val_acc: 0.5670\n",
      "Epoch 94/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8012 - acc: 0.6067 \n",
      "Epoch 00094: val_loss did not improve from 0.73243\n",
      "11/11 [==============================] - 132s 12s/step - loss: 0.7986 - acc: 0.6088 - val_loss: 0.7968 - val_acc: 0.6030\n",
      "Epoch 95/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8209 - acc: 0.6012 \n",
      "Epoch 00095: val_loss improved from 0.73243 to 0.73105, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 118s 11s/step - loss: 0.8141 - acc: 0.6013 - val_loss: 0.7311 - val_acc: 0.6040\n",
      "Epoch 96/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8080 - acc: 0.6014 \n",
      "Epoch 00096: val_loss did not improve from 0.73105\n",
      "11/11 [==============================] - 118s 11s/step - loss: 0.8129 - acc: 0.5987 - val_loss: 0.7814 - val_acc: 0.5890\n",
      "Epoch 97/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.8134 - acc: 0.6031 \n",
      "Epoch 00097: val_loss did not improve from 0.73105\n",
      "11/11 [==============================] - 97s 9s/step - loss: 0.8158 - acc: 0.6051 - val_loss: 0.7405 - val_acc: 0.6130\n",
      "Epoch 98/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 0.8119 - acc: 0.6120 \n",
      "Epoch 00098: val_loss improved from 0.73105 to 0.72263, saving model to ./results/model_cnn_v1_aug_hist.weights.best.hdf5\n",
      "11/11 [==============================] - 120s 11s/step - loss: 0.8129 - acc: 0.6106 - val_loss: 0.7226 - val_acc: 0.6230\n",
      "Epoch 99/100\n",
      "10/11 [==========================>...] - ETA: 7s - loss: 0.8034 - acc: 0.6080 \n",
      "Epoch 00099: val_loss did not improve from 0.72263\n",
      "11/11 [==============================] - 122s 11s/step - loss: 0.8002 - acc: 0.6044 - val_loss: 0.7944 - val_acc: 0.5920\n",
      "Epoch 100/100\n",
      "10/11 [==========================>...] - ETA: 6s - loss: 0.7939 - acc: 0.6238 \n",
      "Epoch 00100: val_loss did not improve from 0.72263\n",
      "11/11 [==============================] - 119s 11s/step - loss: 0.7911 - acc: 0.6237 - val_loss: 0.7450 - val_acc: 0.6150\n",
      "CPU times: user 3h 43min 47s, sys: 42min 21s, total: 4h 26min 8s\n",
      "Wall time: 3h 19min 54s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# set to the original weights for testing other pipelines\n",
    "model_cnn_v1.set_weights(initial_weights)\n",
    "\n",
    "# define path to save the mnodel\n",
    "path_model='./results/model_cnn_v1_aug_hist.weights.best.hdf5'\n",
    "shutil.rmtree(path_model, ignore_errors=True)\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath=path_model, \n",
    "                               verbose = 1, \n",
    "                               save_best_only=True)\n",
    "EPOCHS=100\n",
    "BATCH_SIZE=256\n",
    "#BATCH_SIZE=128\n",
    "#BATCH_SIZE=64\n",
    "\n",
    "\n",
    "history=model_cnn_v1.fit_generator(datagen_hist_train.flow(x_train, y_train, batch_size=BATCH_SIZE),\n",
    "                                   validation_data=datagen_hist_test.flow(x_test, y_test, batch_size=len(x_test)),\n",
    "                                   steps_per_epoch=len(x_train) // BATCH_SIZE,\n",
    "                                   epochs=EPOCHS, \n",
    "                                   callbacks=[checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss:\n",
      "  - loss [training dataset]: 0.788\n",
      "  - loss [validation dataset: 0.745\n",
      "\n",
      "Accuracy:\n",
      "  - accuracy [training dataset]: 62.43%\n",
      "  - accuracy [validation dataset: 61.50%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_loss(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VGG16 model v4 with data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "23/24 [===========================>..] - ETA: 27s - loss: 2.0188 - acc: 0.2694\n",
      "Epoch 00001: val_loss improved from inf to 1.67101, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 835s 35s/step - loss: 2.0126 - acc: 0.2705 - val_loss: 1.6710 - val_acc: 0.3620\n",
      "Epoch 2/50\n",
      "23/24 [===========================>..] - ETA: 25s - loss: 1.5906 - acc: 0.3606\n",
      "Epoch 00002: val_loss improved from 1.67101 to 1.41722, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 789s 33s/step - loss: 1.5849 - acc: 0.3609 - val_loss: 1.4172 - val_acc: 0.4090\n",
      "Epoch 3/50\n",
      "23/24 [===========================>..] - ETA: 25s - loss: 1.4246 - acc: 0.3858\n",
      "Epoch 00003: val_loss improved from 1.41722 to 1.35437, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 784s 33s/step - loss: 1.4264 - acc: 0.3824 - val_loss: 1.3544 - val_acc: 0.4720\n",
      "Epoch 4/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 1.3600 - acc: 0.4297\n",
      "Epoch 00004: val_loss improved from 1.35437 to 1.15975, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 776s 32s/step - loss: 1.3560 - acc: 0.4315 - val_loss: 1.1597 - val_acc: 0.5580\n",
      "Epoch 5/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 1.1688 - acc: 0.5119\n",
      "Epoch 00005: val_loss improved from 1.15975 to 0.87845, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 780s 33s/step - loss: 1.1608 - acc: 0.5173 - val_loss: 0.8784 - val_acc: 0.6570\n",
      "Epoch 6/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.9137 - acc: 0.6403\n",
      "Epoch 00006: val_loss improved from 0.87845 to 0.75162, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 776s 32s/step - loss: 0.9094 - acc: 0.6425 - val_loss: 0.7516 - val_acc: 0.7080\n",
      "Epoch 7/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.7700 - acc: 0.6879\n",
      "Epoch 00007: val_loss improved from 0.75162 to 0.67145, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 776s 32s/step - loss: 0.7745 - acc: 0.6868 - val_loss: 0.6715 - val_acc: 0.7300\n",
      "Epoch 8/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.6871 - acc: 0.7307\n",
      "Epoch 00008: val_loss improved from 0.67145 to 0.60172, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 776s 32s/step - loss: 0.6856 - acc: 0.7328 - val_loss: 0.6017 - val_acc: 0.7710\n",
      "Epoch 9/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.6022 - acc: 0.7767\n",
      "Epoch 00009: val_loss improved from 0.60172 to 0.46900, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 780s 33s/step - loss: 0.6067 - acc: 0.7762 - val_loss: 0.4690 - val_acc: 0.8380\n",
      "Epoch 10/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.5174 - acc: 0.8132\n",
      "Epoch 00010: val_loss improved from 0.46900 to 0.40907, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 774s 32s/step - loss: 0.5108 - acc: 0.8148 - val_loss: 0.4091 - val_acc: 0.8480\n",
      "Epoch 11/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.4116 - acc: 0.8437\n",
      "Epoch 00011: val_loss did not improve from 0.40907\n",
      "24/24 [==============================] - 773s 32s/step - loss: 0.4162 - acc: 0.8433 - val_loss: 0.4396 - val_acc: 0.8650\n",
      "Epoch 12/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.4076 - acc: 0.8527\n",
      "Epoch 00012: val_loss improved from 0.40907 to 0.36928, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 775s 32s/step - loss: 0.4085 - acc: 0.8534 - val_loss: 0.3693 - val_acc: 0.8780\n",
      "Epoch 13/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.3800 - acc: 0.8597\n",
      "Epoch 00013: val_loss did not improve from 0.36928\n",
      "24/24 [==============================] - 774s 32s/step - loss: 0.3803 - acc: 0.8600 - val_loss: 0.3976 - val_acc: 0.8560\n",
      "Epoch 14/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.3845 - acc: 0.8594\n",
      "Epoch 00014: val_loss did not improve from 0.36928\n",
      "24/24 [==============================] - 773s 32s/step - loss: 0.3859 - acc: 0.8590 - val_loss: 0.4399 - val_acc: 0.8450\n",
      "Epoch 15/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.3488 - acc: 0.8704\n",
      "Epoch 00015: val_loss improved from 0.36928 to 0.30423, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 773s 32s/step - loss: 0.3507 - acc: 0.8700 - val_loss: 0.3042 - val_acc: 0.8890\n",
      "Epoch 16/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.3451 - acc: 0.8706\n",
      "Epoch 00016: val_loss did not improve from 0.30423\n",
      "24/24 [==============================] - 772s 32s/step - loss: 0.3460 - acc: 0.8714 - val_loss: 0.3215 - val_acc: 0.8950\n",
      "Epoch 17/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.3017 - acc: 0.8941\n",
      "Epoch 00017: val_loss did not improve from 0.30423\n",
      "24/24 [==============================] - 774s 32s/step - loss: 0.3089 - acc: 0.8922 - val_loss: 0.3506 - val_acc: 0.8780\n",
      "Epoch 18/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.3186 - acc: 0.8900\n",
      "Epoch 00018: val_loss did not improve from 0.30423\n",
      "24/24 [==============================] - 773s 32s/step - loss: 0.3179 - acc: 0.8910 - val_loss: 0.3972 - val_acc: 0.8730\n",
      "Epoch 19/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.3274 - acc: 0.8831\n",
      "Epoch 00019: val_loss did not improve from 0.30423\n",
      "24/24 [==============================] - 775s 32s/step - loss: 0.3268 - acc: 0.8825 - val_loss: 0.4272 - val_acc: 0.8610\n",
      "Epoch 20/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.3015 - acc: 0.8919\n",
      "Epoch 00020: val_loss did not improve from 0.30423\n",
      "24/24 [==============================] - 772s 32s/step - loss: 0.3058 - acc: 0.8915 - val_loss: 0.3763 - val_acc: 0.8690\n",
      "Epoch 21/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2843 - acc: 0.8959\n",
      "Epoch 00021: val_loss did not improve from 0.30423\n",
      "24/24 [==============================] - 775s 32s/step - loss: 0.2870 - acc: 0.8954 - val_loss: 0.3532 - val_acc: 0.8910\n",
      "Epoch 22/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2627 - acc: 0.9116\n",
      "Epoch 00022: val_loss did not improve from 0.30423\n",
      "24/24 [==============================] - 773s 32s/step - loss: 0.2641 - acc: 0.9113 - val_loss: 0.3579 - val_acc: 0.8820\n",
      "Epoch 23/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2510 - acc: 0.9102\n",
      "Epoch 00023: val_loss did not improve from 0.30423\n",
      "24/24 [==============================] - 774s 32s/step - loss: 0.2524 - acc: 0.9103 - val_loss: 0.3124 - val_acc: 0.9010\n",
      "Epoch 24/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2804 - acc: 0.9020\n",
      "Epoch 00024: val_loss improved from 0.30423 to 0.27504, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 773s 32s/step - loss: 0.2765 - acc: 0.9038 - val_loss: 0.2750 - val_acc: 0.9070\n",
      "Epoch 25/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2971 - acc: 0.8990\n",
      "Epoch 00025: val_loss did not improve from 0.27504\n",
      "24/24 [==============================] - 773s 32s/step - loss: 0.2950 - acc: 0.9002 - val_loss: 0.4491 - val_acc: 0.8740\n",
      "Epoch 26/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2496 - acc: 0.9096\n",
      "Epoch 00026: val_loss improved from 0.27504 to 0.26304, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 773s 32s/step - loss: 0.2442 - acc: 0.9114 - val_loss: 0.2630 - val_acc: 0.9150\n",
      "Epoch 27/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2535 - acc: 0.9117\n",
      "Epoch 00027: val_loss did not improve from 0.26304\n",
      "24/24 [==============================] - 777s 32s/step - loss: 0.2539 - acc: 0.9108 - val_loss: 0.3001 - val_acc: 0.9030\n",
      "Epoch 28/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2646 - acc: 0.9088\n",
      "Epoch 00028: val_loss did not improve from 0.26304\n",
      "24/24 [==============================] - 774s 32s/step - loss: 0.2650 - acc: 0.9090 - val_loss: 0.3718 - val_acc: 0.8810\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2594 - acc: 0.9116\n",
      "Epoch 00029: val_loss did not improve from 0.26304\n",
      "24/24 [==============================] - 776s 32s/step - loss: 0.2643 - acc: 0.9100 - val_loss: 0.2724 - val_acc: 0.9130\n",
      "Epoch 30/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2263 - acc: 0.9257\n",
      "Epoch 00030: val_loss did not improve from 0.26304\n",
      "24/24 [==============================] - 773s 32s/step - loss: 0.2295 - acc: 0.9232 - val_loss: 0.2666 - val_acc: 0.9160\n",
      "Epoch 31/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2342 - acc: 0.9216\n",
      "Epoch 00031: val_loss did not improve from 0.26304\n",
      "24/24 [==============================] - 775s 32s/step - loss: 0.2360 - acc: 0.9216 - val_loss: 0.2724 - val_acc: 0.9120\n",
      "Epoch 32/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2320 - acc: 0.9230\n",
      "Epoch 00032: val_loss did not improve from 0.26304\n",
      "24/24 [==============================] - 774s 32s/step - loss: 0.2320 - acc: 0.9226 - val_loss: 0.3527 - val_acc: 0.8900\n",
      "Epoch 33/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2377 - acc: 0.9178\n",
      "Epoch 00033: val_loss did not improve from 0.26304\n",
      "24/24 [==============================] - 775s 32s/step - loss: 0.2412 - acc: 0.9164 - val_loss: 0.3756 - val_acc: 0.8860\n",
      "Epoch 34/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2378 - acc: 0.9196\n",
      "Epoch 00034: val_loss did not improve from 0.26304\n",
      "24/24 [==============================] - 781s 33s/step - loss: 0.2367 - acc: 0.9211 - val_loss: 0.3139 - val_acc: 0.9040\n",
      "Epoch 35/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2192 - acc: 0.9258\n",
      "Epoch 00035: val_loss improved from 0.26304 to 0.25500, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 774s 32s/step - loss: 0.2187 - acc: 0.9244 - val_loss: 0.2550 - val_acc: 0.9120\n",
      "Epoch 36/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2175 - acc: 0.9252\n",
      "Epoch 00036: val_loss did not improve from 0.25500\n",
      "24/24 [==============================] - 774s 32s/step - loss: 0.2196 - acc: 0.9244 - val_loss: 0.3073 - val_acc: 0.9050\n",
      "Epoch 37/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2114 - acc: 0.9272\n",
      "Epoch 00037: val_loss did not improve from 0.25500\n",
      "24/24 [==============================] - 807s 34s/step - loss: 0.2135 - acc: 0.9273 - val_loss: 0.3748 - val_acc: 0.9020\n",
      "Epoch 38/50\n",
      "23/24 [===========================>..] - ETA: 7:46 - loss: 0.2073 - acc: 0.9288 \n",
      "Epoch 00038: val_loss did not improve from 0.25500\n",
      "24/24 [==============================] - 10951s 456s/step - loss: 0.2102 - acc: 0.9279 - val_loss: 0.3143 - val_acc: 0.9020\n",
      "Epoch 39/50\n",
      "23/24 [===========================>..] - ETA: 25s - loss: 0.2441 - acc: 0.9138\n",
      "Epoch 00039: val_loss did not improve from 0.25500\n",
      "24/24 [==============================] - 797s 33s/step - loss: 0.2444 - acc: 0.9134 - val_loss: 0.2667 - val_acc: 0.9190\n",
      "Epoch 40/50\n",
      "23/24 [===========================>..] - ETA: 25s - loss: 0.1873 - acc: 0.9330\n",
      "Epoch 00040: val_loss did not improve from 0.25500\n",
      "24/24 [==============================] - 791s 33s/step - loss: 0.1846 - acc: 0.9349 - val_loss: 0.2824 - val_acc: 0.9100\n",
      "Epoch 41/50\n",
      "23/24 [===========================>..] - ETA: 25s - loss: 0.2092 - acc: 0.9319\n",
      "Epoch 00041: val_loss did not improve from 0.25500\n",
      "24/24 [==============================] - 827s 34s/step - loss: 0.2078 - acc: 0.9314 - val_loss: 0.3146 - val_acc: 0.9020\n",
      "Epoch 42/50\n",
      "23/24 [===========================>..] - ETA: 26s - loss: 0.1949 - acc: 0.9293\n",
      "Epoch 00042: val_loss did not improve from 0.25500\n",
      "24/24 [==============================] - 820s 34s/step - loss: 0.1922 - acc: 0.9300 - val_loss: 0.2966 - val_acc: 0.9150\n",
      "Epoch 43/50\n",
      "23/24 [===========================>..] - ETA: 25s - loss: 0.1820 - acc: 0.9409\n",
      "Epoch 00043: val_loss did not improve from 0.25500\n",
      "24/24 [==============================] - 791s 33s/step - loss: 0.1814 - acc: 0.9418 - val_loss: 0.3282 - val_acc: 0.9150\n",
      "Epoch 44/50\n",
      "23/24 [===========================>..] - ETA: 25s - loss: 0.1858 - acc: 0.9345\n",
      "Epoch 00044: val_loss did not improve from 0.25500\n",
      "24/24 [==============================] - 783s 33s/step - loss: 0.1878 - acc: 0.9336 - val_loss: 0.2642 - val_acc: 0.9270\n",
      "Epoch 45/50\n",
      "23/24 [===========================>..] - ETA: 25s - loss: 0.2139 - acc: 0.9323\n",
      "Epoch 00045: val_loss improved from 0.25500 to 0.24045, saving model to ./results/model_vgg16_v4_aug.weights.best.hdf5\n",
      "24/24 [==============================] - 796s 33s/step - loss: 0.2125 - acc: 0.9332 - val_loss: 0.2404 - val_acc: 0.9260\n",
      "Epoch 46/50\n",
      "23/24 [===========================>..] - ETA: 25s - loss: 0.1759 - acc: 0.9380\n",
      "Epoch 00046: val_loss did not improve from 0.24045\n",
      "24/24 [==============================] - 784s 33s/step - loss: 0.1754 - acc: 0.9379 - val_loss: 0.3404 - val_acc: 0.9060\n",
      "Epoch 47/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.2107 - acc: 0.9253\n",
      "Epoch 00047: val_loss did not improve from 0.24045\n",
      "24/24 [==============================] - 776s 32s/step - loss: 0.2088 - acc: 0.9252 - val_loss: 0.3758 - val_acc: 0.8890\n",
      "Epoch 48/50\n",
      "23/24 [===========================>..] - ETA: 24s - loss: 0.1818 - acc: 0.9410\n",
      "Epoch 00048: val_loss did not improve from 0.24045\n",
      "24/24 [==============================] - 782s 33s/step - loss: 0.1816 - acc: 0.9405 - val_loss: 0.2612 - val_acc: 0.9190\n",
      "Epoch 49/50\n",
      "23/24 [===========================>..] - ETA: 25s - loss: 0.1810 - acc: 0.9349\n",
      "Epoch 00049: val_loss did not improve from 0.24045\n",
      "24/24 [==============================] - 807s 34s/step - loss: 0.1792 - acc: 0.9357 - val_loss: 0.2919 - val_acc: 0.9220\n",
      "Epoch 50/50\n",
      "23/24 [===========================>..] - ETA: 25s - loss: 0.1717 - acc: 0.9331\n",
      "Epoch 00050: val_loss did not improve from 0.24045\n",
      "24/24 [==============================] - 794s 33s/step - loss: 0.1743 - acc: 0.9327 - val_loss: 0.2605 - val_acc: 0.9180\n",
      "CPU times: user 12h 2min 52s, sys: 5min 32s, total: 12h 8min 25s\n",
      "Wall time: 13h 41min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# set to the original weights for testing other pipelines\n",
    "model_vgg16_v4.set_weights(initial_weights)\n",
    "\n",
    "# define path to save the mnodel\n",
    "path_model='./results/model_vgg16_v4_aug.weights.best.hdf5'\n",
    "shutil.rmtree(path_model, ignore_errors=True)\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath=path_model, \n",
    "                               verbose = 1, \n",
    "                               save_best_only=True)\n",
    "EPOCHS=50\n",
    "#BATCH_SIZE=256\n",
    "BATCH_SIZE=128\n",
    "#BATCH_SIZE=64\n",
    "\n",
    "history=model_vgg16_v4.fit_generator(datagen.flow(x_train, y_train, batch_size=BATCH_SIZE),\n",
    "                                     validation_data=(x_test, y_test),\n",
    "                                     #steps_per_epoch=len(x_train) // BATCH_SIZE,\n",
    "                                     epochs=EPOCHS, \n",
    "                                     callbacks=[checkpointer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss:\n",
      "  - loss [training dataset]: 0.176\n",
      "  - loss [validation dataset: 0.260\n",
      "\n",
      "Accuracy:\n",
      "  - accuracy [training dataset]: 93.23%\n",
      "  - accuracy [validation dataset: 91.80%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_loss(history)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:env_ml_dl]",
   "language": "python",
   "name": "conda-env-env_ml_dl-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}