03-classifying_damage location.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data2a - 120416_0229\n",
    "* re-run, trying regularization\n",
    "\n",
    "### data2a - 120116_1200\n",
    "* run with cleaned dataset, just on damage location, 256x256, ft 8 batch size, 88s per epoch, theano backend\n",
    "* saved history, created metrics plots\n",
    "* noted that rmsprop results in higher top layer accuracy for initial fit - closer to 70%\n",
    "* loss: 0.6640 - acc: 0.7273 - val_loss: 0.5893 - val_acc: 0.7836\n",
    "\n",
    "### data2 - 112916_1200\n",
    "* run without discards, just on damage location 256x256, 16 batch size, 80s per epoch, theano backend\n",
    "* loss: 0.6776 - acc: 0.7151 - val_loss: 0.7998 - val_acc: 0.6928"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import urllib\n",
    "from IPython.display import Image, display, clear_output\n",
    "from collections import Counter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "%matplotlib inline\n",
    "\n",
    "import json\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n",
      "Using gpu device 0: GRID K520 (CNMeM is enabled)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import h5py\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "from keras.regularizers import l2, activity_l2, l1, activity_l1\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
    "from keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras import optimizers\n",
    "from keras.callbacks import ModelCheckpoint, History\n",
    "\n",
    "# only import if using tf backend\n",
    "# import tensorflow as tf\n",
    "# tf.python.control_flow_ops = tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_vgg16(crop=True, weights_path='../vgg16_weights.h5'):\n",
    "    model = Sequential()\n",
    "    model.add(ZeroPadding2D((1,1),input_shape=(3, img_width, img_height)))\n",
    "    model.add(Convolution2D(64, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(64, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(128, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(128, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(256, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(256, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(256, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "      \n",
    "    # assert os.path.exists(weights_path), 'Model weights not found (see \"weights_path\")'\n",
    "    \n",
    "    if weights_path:\n",
    "    # note: this chops off the last layers of VGG16 \n",
    "\n",
    "        f = h5py.File(weights_path)\n",
    "        for k in range(f.attrs['nb_layers']):\n",
    "            if k >= len(model.layers): \n",
    "                # we don't look at the last (fully-connected) layers in the savefile\n",
    "                break\n",
    "            g = f['layer_{}'.format(k)]\n",
    "            weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]\n",
    "            model.layers[k].set_weights(weights)\n",
    "        f.close()\n",
    "        print('VGG16 Model with partial weights loaded.')\n",
    "    else:\n",
    "        print('VGG16 Model with no weights Loaded.')\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def save_bottleneck_features(location):\n",
    "    datagen = ImageDataGenerator(rescale=1./255)  \n",
    "    \n",
    "    model = load_vgg16(crop=True)\n",
    "    \n",
    "    generator = datagen.flow_from_directory(train_data_dir,\n",
    "                                            target_size=(img_width, img_height),\n",
    "                                            batch_size=16, # reduced batch size to 16\n",
    "                                            class_mode=None, \n",
    "                                            shuffle=False) \n",
    "    bottleneck_features_train = model.predict_generator(generator, nb_train_samples)\n",
    "    np.save(open(location+'/bottleneck_features_train.npy', 'w'), bottleneck_features_train)\n",
    "    \n",
    "    # repeat with the validation data\n",
    "    generator = datagen.flow_from_directory(validation_data_dir,\n",
    "                                           target_size=(img_width, img_height),\n",
    "                                           batch_size=16,\n",
    "                                           class_mode=None,\n",
    "                                           shuffle=False)\n",
    "    bottleneck_features_validation = model.predict_generator(generator, nb_validation_samples)\n",
    "    np.save(open(location+'/bottleneck_features_validation.npy', 'w'), bottleneck_features_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_best_model_results(model_hist):\n",
    "    best_epoch = np.argmax(model_hist['val_acc'])\n",
    "    print 'epoch:', best_epoch+1, \\\n",
    "    ', val_acc:', model_hist['val_acc'][best_epoch], \\\n",
    "    ', val_loss:', model_hist['val_loss'][best_epoch]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_metrics(hist, stop=50):\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10,4))\n",
    "                            \n",
    "    axes = axes.flatten()\n",
    "\n",
    "    axes[0].plot(range(stop), hist['acc'], label='Training')\n",
    "    axes[0].plot(range(stop), hist['val_acc'], label='Validation')\n",
    "    axes[0].set_title('Accuracy')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "    axes[0].set_xlabel('Epoch')\n",
    "    axes[0].legend(loc='lower right')\n",
    "                             \n",
    "    axes[1].plot(range(stop), hist['loss'], label='Training')\n",
    "    axes[1].plot(range(stop), hist['val_loss'], label='Validation')\n",
    "    axes[1].set_title('Loss')\n",
    "    axes[1].set_ylabel('Loss')\n",
    "    axes[1].set_xlabel('Epoch')\n",
    "    axes[1].legend(loc='upper right')\n",
    "                             \n",
    "    plt.tight_layout();\n",
    "           \n",
    "    print \"Best Model:\" \n",
    "    print_best_model_results(hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load saved data and train a small, fully-connected model\n",
    "def train_categorical_model():\n",
    "    # the features were saved in order, so recreating the labels is not hard\n",
    "    train_data = np.load(open(location+'/bottleneck_features_train.npy'))\n",
    "    train_labels = np.array([0] * train_samples[0] + \n",
    "                            [1] * train_samples[1] + \n",
    "                            [2] * train_samples[2])\n",
    "    train_labels = to_categorical(train_labels)\n",
    "\n",
    "    validation_data = np.load(open(location+'/bottleneck_features_validation.npy'))\n",
    "    validation_labels = np.array([0] * validation_samples[0] + \n",
    "                                 [1] * validation_samples[1] +\n",
    "                                 [2] * validation_samples[2])\n",
    "    validation_labels = to_categorical(validation_labels)\n",
    "    \n",
    "    model = Sequential()\n",
    "    model.add(Flatten(input_shape=train_data.shape[1:])) # 512, 4, 4\n",
    "    model.add(Dense(256, activation = 'relu', W_regularizer=l2(0.01)))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(3, activation = 'softmax')) # upped to 3 and activation softmax\n",
    "\n",
    "    model.compile(optimizers.SGD(lr=0.0001, momentum=0.9),\n",
    "              loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    # optimizer='rmsprop'\n",
    "    # optimizers.SGD(lr=0.0001, momentum=0.9)\n",
    "    \n",
    "    checkpoint = ModelCheckpoint(top_model_weights_path, monitor='val_acc', \n",
    "                                 verbose=1, save_best_only=True, save_weights_only=True, mode='auto')\n",
    "\n",
    "\n",
    "    fit = model.fit(train_data, train_labels,\n",
    "              nb_epoch=nb_epoch, batch_size=16,\n",
    "              validation_data=(validation_data, validation_labels),\n",
    "              callbacks=[checkpoint])\n",
    "    \n",
    "    with open(location+'/top_history.txt', 'wb') as f:\n",
    "        json.dump(fit.history, f)\n",
    "    \n",
    "    return model, fit.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def finetune_categorical_model():\n",
    "    # Build the VGG16 Network (again)\n",
    "    model = load_vgg16(crop=True)\n",
    "\n",
    "    # build a classifier model to put on top of the convolutional model\n",
    "    top_model = Sequential()\n",
    "    top_model.add(Flatten(input_shape=model.output_shape[1:]))\n",
    "    top_model.add(Dense(256, activation='relu', W_regularizer=l2(0.01)))\n",
    "    top_model.add(Dropout(0.5))\n",
    "    top_model.add(Dense(3, activation='softmax'))\n",
    "\n",
    "    top_model.load_weights(top_model_weights_path) # load weights_path\n",
    "\n",
    "    # add the model on top of the convolutional base\n",
    "    model.add(top_model)\n",
    "    \n",
    "    # set the first 25 layers (up to the last conv block)\n",
    "    # to non-trainable - weights will not be updated\n",
    "    for layer in model.layers[:25]:\n",
    "        layer.trainable=False\n",
    "\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                 optimizer = optimizers.SGD(lr=0.00001, momentum=0.9), # reduced learning rate by 1/10\n",
    "                  metrics=['accuracy'])\n",
    "    \n",
    "    # prepare data augmentation configuration\n",
    "    train_datagen = ImageDataGenerator(rescale=1./255,\n",
    "                                       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",
    "\n",
    "    test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "    train_generator= train_datagen.flow_from_directory(train_data_dir,\n",
    "                                                     target_size=(img_height, img_width),\n",
    "                                                     batch_size=8,\n",
    "                                                     class_mode='categorical')\n",
    "\n",
    "    validation_generator = test_datagen.flow_from_directory(validation_data_dir,\n",
    "                                                           target_size=(img_height, img_width),\n",
    "                                                           batch_size=8,\n",
    "                                                           class_mode='categorical')\n",
    "    \n",
    "    \n",
    "    checkpoint = ModelCheckpoint(fine_tuned_model_path, monitor='val_acc', \n",
    "                                 verbose=1, save_best_only=True, \n",
    "                                 save_weights_only=False, mode='auto')\n",
    "    # fine-tune the model\n",
    "    fit = model.fit_generator(train_generator,\n",
    "                        samples_per_epoch=nb_train_samples,\n",
    "                        nb_epoch=nb_epoch,\n",
    "                        validation_data=validation_generator,\n",
    "                        nb_val_samples=nb_validation_samples,\n",
    "                        verbose=1,\n",
    "                        callbacks=[checkpoint])\n",
    "    \n",
    "    with open(location+'/history.txt', 'wb') as f:\n",
    "        json.dump(fit.history, f)\n",
    "    \n",
    "    return model, fit.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluate_categorical_model(model, directory, labels):\n",
    "    datagen = ImageDataGenerator(rescale=1./255)  \n",
    "\n",
    "    generator = datagen.flow_from_directory(directory,\n",
    "                                target_size=(img_height, img_width),\n",
    "                                batch_size=8,\n",
    "                                class_mode='categorical',\n",
    "                                shuffle=False)\n",
    "    \n",
    "    predictions = model.predict_generator(generator, len(labels))\n",
    "    pred_labels = np.argmax(predictions, axis=1)\n",
    "    \n",
    "    print ''\n",
    "    print classification_report(validation_labels, pred_labels)\n",
    "    print ''\n",
    "    cm = confusion_matrix(validation_labels, pred_labels)\n",
    "    sns.heatmap(cm, annot=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def view_images(img_dir, img_list):\n",
    "    for img in img_list:\n",
    "        clear_output()\n",
    "        display(Image(img_dir+img))\n",
    "        num = raw_input(\"c to continue, q to quit\")\n",
    "        if num == 'c':\n",
    "            pass\n",
    "        else:\n",
    "            return 'Finished for now.'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# path to the model weights file\n",
    "location = 'data2a'\n",
    "top_model_weights_path=location+'/top_model_weights.h5' # will be saved into when we create our model\n",
    "# model_path = location + '/initial_data2_model.h5'\n",
    "fine_tuned_model_path = location+'/ft_model.h5'\n",
    "\n",
    "# dimensions of our images\n",
    "img_width, img_height = 256, 256\n",
    "\n",
    "train_data_dir = location+'/training'\n",
    "validation_data_dir = location+'/validation'\n",
    "\n",
    "train_samples = [len(os.listdir(train_data_dir+'/'+i)) for i in sorted(os.listdir(train_data_dir))]\n",
    "nb_train_samples = sum(train_samples)\n",
    "validation_samples = [len(os.listdir(validation_data_dir+'/'+i)) for i in sorted(os.listdir(validation_data_dir))]\n",
    "nb_validation_samples = sum(validation_samples)\n",
    "\n",
    "nb_epoch = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 979 samples, validate on 171 samples\n",
      "Epoch 1/50\n",
      "960/979 [============================>.] - ETA: 0s - loss: 8.3374 - acc: 0.3740Epoch 00000: val_acc improved from -inf to 0.47368, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 8.2361 - acc: 0.3790 - val_loss: 3.2459 - val_acc: 0.4737\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.0553 - acc: 0.5051Epoch 00001: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 4.0510 - acc: 0.5036 - val_loss: 2.0990 - val_acc: 0.3801\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0763 - acc: 0.6055Epoch 00002: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0761 - acc: 0.6057 - val_loss: 2.4129 - val_acc: 0.3743\n",
      "Epoch 4/50\n",
      "960/979 [============================>.] - ETA: 0s - loss: 1.1021 - acc: 0.6021Epoch 00003: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0999 - acc: 0.6006 - val_loss: 2.6318 - val_acc: 0.3216\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8134 - acc: 0.6824Epoch 00004: val_acc improved from 0.47368 to 0.59649, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.8163 - acc: 0.6813 - val_loss: 0.8916 - val_acc: 0.5965\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6986 - acc: 0.7152Epoch 00005: val_acc improved from 0.59649 to 0.64327, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.6970 - acc: 0.7160 - val_loss: 0.9802 - val_acc: 0.6433\n",
      "Epoch 7/50\n",
      "960/979 [============================>.] - ETA: 0s - loss: 0.5830 - acc: 0.7823Epoch 00006: val_acc improved from 0.64327 to 0.66082, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.5789 - acc: 0.7824 - val_loss: 1.0198 - val_acc: 0.6608\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5673 - acc: 0.7982Epoch 00007: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5670 - acc: 0.7978 - val_loss: 1.3998 - val_acc: 0.6023\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5716 - acc: 0.8176Epoch 00008: val_acc improved from 0.66082 to 0.71930, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.5703 - acc: 0.8182 - val_loss: 0.9770 - val_acc: 0.7193\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.4819 - acc: 0.8268Epoch 00009: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.4812 - acc: 0.8274 - val_loss: 1.0916 - val_acc: 0.6374\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.3869 - acc: 0.8678Epoch 00010: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.3859 - acc: 0.8682 - val_loss: 1.2071 - val_acc: 0.6725\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.3739 - acc: 0.8719Epoch 00011: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.3740 - acc: 0.8713 - val_loss: 1.0489 - val_acc: 0.6842\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.3780 - acc: 0.8842Epoch 00012: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.3769 - acc: 0.8846 - val_loss: 1.2573 - val_acc: 0.6901\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2742 - acc: 0.9027Epoch 00013: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2743 - acc: 0.9019 - val_loss: 1.1125 - val_acc: 0.7076\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2977 - acc: 0.9006Epoch 00014: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2968 - acc: 0.9009 - val_loss: 1.7322 - val_acc: 0.6608\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2868 - acc: 0.9078Epoch 00015: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2878 - acc: 0.9070 - val_loss: 1.2278 - val_acc: 0.6959\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2905 - acc: 0.9150Epoch 00016: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2915 - acc: 0.9142 - val_loss: 1.6896 - val_acc: 0.6199\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2493 - acc: 0.9160Epoch 00017: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2495 - acc: 0.9162 - val_loss: 1.9398 - val_acc: 0.6082\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1866 - acc: 0.9365Epoch 00018: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1861 - acc: 0.9367 - val_loss: 1.3942 - val_acc: 0.7135\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2541 - acc: 0.9191Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2533 - acc: 0.9193 - val_loss: 1.4075 - val_acc: 0.7193\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1481 - acc: 0.9498Epoch 00020: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1478 - acc: 0.9499 - val_loss: 1.6065 - val_acc: 0.7076\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2098 - acc: 0.9324Epoch 00021: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2092 - acc: 0.9326 - val_loss: 1.6659 - val_acc: 0.7018\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1745 - acc: 0.9549Epoch 00022: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1740 - acc: 0.9551 - val_loss: 1.7866 - val_acc: 0.6784\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1657 - acc: 0.9426Epoch 00023: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1652 - acc: 0.9428 - val_loss: 1.5683 - val_acc: 0.7018\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1710 - acc: 0.9426Epoch 00024: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1706 - acc: 0.9428 - val_loss: 1.6870 - val_acc: 0.6725\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1405 - acc: 0.9549Epoch 00025: val_acc improved from 0.71930 to 0.73684, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.1401 - acc: 0.9551 - val_loss: 2.0188 - val_acc: 0.7368\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2396 - acc: 0.9447Epoch 00026: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2388 - acc: 0.9448 - val_loss: 1.8264 - val_acc: 0.7193\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2054 - acc: 0.9498Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2048 - acc: 0.9499 - val_loss: 1.9463 - val_acc: 0.6667\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1021 - acc: 0.9682Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1023 - acc: 0.9683 - val_loss: 2.0340 - val_acc: 0.6784\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1302 - acc: 0.9611Epoch 00029: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1298 - acc: 0.9612 - val_loss: 2.4922 - val_acc: 0.7076\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1722 - acc: 0.9498Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1716 - acc: 0.9499 - val_loss: 1.9858 - val_acc: 0.7018\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2108 - acc: 0.9477Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2102 - acc: 0.9479 - val_loss: 1.8077 - val_acc: 0.7135\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1161 - acc: 0.9652Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1158 - acc: 0.9653 - val_loss: 2.1381 - val_acc: 0.7251\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1204 - acc: 0.9580Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1201 - acc: 0.9581 - val_loss: 2.3122 - val_acc: 0.6784\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1525 - acc: 0.9611Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1521 - acc: 0.9612 - val_loss: 2.2488 - val_acc: 0.7193\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1418 - acc: 0.9621Epoch 00035: val_acc improved from 0.73684 to 0.74269, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.1413 - acc: 0.9622 - val_loss: 2.2213 - val_acc: 0.7427\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1031 - acc: 0.9652Epoch 00036: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1059 - acc: 0.9642 - val_loss: 2.0718 - val_acc: 0.6725\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1327 - acc: 0.9621Epoch 00037: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1323 - acc: 0.9622 - val_loss: 4.1564 - val_acc: 0.6140\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1296 - acc: 0.9662Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1292 - acc: 0.9663 - val_loss: 2.5679 - val_acc: 0.7135\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1670 - acc: 0.9621Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1665 - acc: 0.9622 - val_loss: 2.5487 - val_acc: 0.7193\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1235 - acc: 0.9652Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1232 - acc: 0.9653 - val_loss: 2.1954 - val_acc: 0.6550\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1115 - acc: 0.9693Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1111 - acc: 0.9694 - val_loss: 2.2315 - val_acc: 0.7076\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1496 - acc: 0.9662Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1492 - acc: 0.9663 - val_loss: 2.7191 - val_acc: 0.7018\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.0861 - acc: 0.9775Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.0858 - acc: 0.9775 - val_loss: 2.5501 - val_acc: 0.7135\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.0909 - acc: 0.9764Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.0906 - acc: 0.9765 - val_loss: 2.9805 - val_acc: 0.7193\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.0973 - acc: 0.9754Epoch 00045: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.0970 - acc: 0.9755 - val_loss: 2.7327 - val_acc: 0.7076\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.0984 - acc: 0.9764Epoch 00046: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.0981 - acc: 0.9765 - val_loss: 2.3730 - val_acc: 0.7018\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.0856 - acc: 0.9775Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.0853 - acc: 0.9775 - val_loss: 2.9630 - val_acc: 0.6901\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.1323 - acc: 0.9703Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.1319 - acc: 0.9704 - val_loss: 2.8257 - val_acc: 0.6959\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.0899 - acc: 0.9775Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.0896 - acc: 0.9775 - val_loss: 2.7115 - val_acc: 0.7076\n"
     ]
    }
   ],
   "source": [
    "d2_model, d2_history = train_categorical_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model with partial VGG16 weights loaded.\n",
      "Found 979 images belonging to 3 classes.\n",
      "Found 171 images belonging to 3 classes.\n",
      "Epoch 1/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.7503 - acc: 0.5502Epoch 00000: val_acc improved from -inf to 0.69006, saving model to data2a/ft_d2_model.h5\n",
      "979/979 [==============================] - 89s - loss: 1.7468 - acc: 0.5506 - val_loss: 0.9886 - val_acc: 0.6901\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.1718 - acc: 0.5348Epoch 00001: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 1.1734 - acc: 0.5352 - val_loss: 0.7967 - val_acc: 0.6433\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0215 - acc: 0.5779Epoch 00002: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 1.0206 - acc: 0.5781 - val_loss: 0.8533 - val_acc: 0.6491\n",
      "Epoch 4/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.9218 - acc: 0.6107Epoch 00003: val_acc improved from 0.69006 to 0.69591, saving model to data2a/ft_d2_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.9206 - acc: 0.6108 - val_loss: 0.8348 - val_acc: 0.6959\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.9428 - acc: 0.5871Epoch 00004: val_acc improved from 0.69591 to 0.72515, saving model to data2a/ft_d2_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.9411 - acc: 0.5873 - val_loss: 0.8414 - val_acc: 0.7251\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.9180 - acc: 0.5943Epoch 00005: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.9164 - acc: 0.5955 - val_loss: 0.8988 - val_acc: 0.6316\n",
      "Epoch 7/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8677 - acc: 0.6250Epoch 00006: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8667 - acc: 0.6251 - val_loss: 0.8640 - val_acc: 0.6257\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8276 - acc: 0.6352Epoch 00007: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8260 - acc: 0.6364 - val_loss: 0.7964 - val_acc: 0.6842\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8722 - acc: 0.6158Epoch 00008: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8712 - acc: 0.6159 - val_loss: 0.7630 - val_acc: 0.6491\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8585 - acc: 0.6117Epoch 00009: val_acc improved from 0.72515 to 0.73099, saving model to data2a/ft_d2_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.8586 - acc: 0.6118 - val_loss: 0.7469 - val_acc: 0.7310\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8158 - acc: 0.6373Epoch 00010: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8159 - acc: 0.6374 - val_loss: 0.9235 - val_acc: 0.7076\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8174 - acc: 0.6311Epoch 00011: val_acc improved from 0.73099 to 0.73099, saving model to data2a/ft_d2_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.8174 - acc: 0.6302 - val_loss: 0.7895 - val_acc: 0.7310\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8229 - acc: 0.6363Epoch 00012: val_acc improved from 0.73099 to 0.74854, saving model to data2a/ft_d2_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.8229 - acc: 0.6364 - val_loss: 0.7036 - val_acc: 0.7485\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8216 - acc: 0.6383Epoch 00013: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8199 - acc: 0.6394 - val_loss: 0.7877 - val_acc: 0.7368\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7961 - acc: 0.6568Epoch 00014: val_acc improved from 0.74854 to 0.76608, saving model to data2a/ft_d2_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.7976 - acc: 0.6558 - val_loss: 0.7714 - val_acc: 0.7661\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8147 - acc: 0.6393Epoch 00015: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8135 - acc: 0.6404 - val_loss: 0.8762 - val_acc: 0.7310\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7602 - acc: 0.6557Epoch 00016: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7595 - acc: 0.6558 - val_loss: 0.8364 - val_acc: 0.7135\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8199 - acc: 0.6250Epoch 00017: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8189 - acc: 0.6261 - val_loss: 0.7903 - val_acc: 0.7310\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8056 - acc: 0.6363Epoch 00018: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8045 - acc: 0.6374 - val_loss: 0.7148 - val_acc: 0.7661\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8003 - acc: 0.6568Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8014 - acc: 0.6568 - val_loss: 0.7534 - val_acc: 0.7076\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7503 - acc: 0.6732Epoch 00020: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7513 - acc: 0.6721 - val_loss: 0.8295 - val_acc: 0.7076\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7893 - acc: 0.6609Epoch 00021: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7897 - acc: 0.6609 - val_loss: 0.8922 - val_acc: 0.7544\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8019 - acc: 0.6516Epoch 00022: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8032 - acc: 0.6507 - val_loss: 0.7805 - val_acc: 0.7076\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7858 - acc: 0.6680Epoch 00023: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7857 - acc: 0.6691 - val_loss: 0.7566 - val_acc: 0.7251\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7696 - acc: 0.6701Epoch 00024: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7724 - acc: 0.6691 - val_loss: 0.7463 - val_acc: 0.7485\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7819 - acc: 0.6639Epoch 00025: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7815 - acc: 0.6639 - val_loss: 0.6777 - val_acc: 0.7427\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7406 - acc: 0.6762Epoch 00026: val_acc improved from 0.76608 to 0.77193, saving model to data2a/ft_d2_model.h5\n",
      "979/979 [==============================] - 88s - loss: 0.7405 - acc: 0.6762 - val_loss: 0.7312 - val_acc: 0.7719\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7524 - acc: 0.6609Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7514 - acc: 0.6619 - val_loss: 0.7705 - val_acc: 0.7719\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7370 - acc: 0.7018Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7374 - acc: 0.7007 - val_loss: 0.7053 - val_acc: 0.7602\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7763 - acc: 0.6691Epoch 00029: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7779 - acc: 0.6691 - val_loss: 0.7931 - val_acc: 0.7485\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7526 - acc: 0.6844Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7531 - acc: 0.6844 - val_loss: 0.8044 - val_acc: 0.7310\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7626 - acc: 0.6793Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7625 - acc: 0.6782 - val_loss: 0.7533 - val_acc: 0.7310\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7335 - acc: 0.6957Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7335 - acc: 0.6956 - val_loss: 0.8671 - val_acc: 0.6901\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7163 - acc: 0.6967Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7161 - acc: 0.6966 - val_loss: 0.7712 - val_acc: 0.7251\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7072 - acc: 0.6936Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7050 - acc: 0.6946 - val_loss: 0.7745 - val_acc: 0.7427\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7173 - acc: 0.6875Epoch 00035: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7200 - acc: 0.6874 - val_loss: 0.8270 - val_acc: 0.7251\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7290 - acc: 0.6650Epoch 00036: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7290 - acc: 0.6650 - val_loss: 0.7780 - val_acc: 0.7251\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7085 - acc: 0.7008Epoch 00037: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7097 - acc: 0.6997 - val_loss: 0.7259 - val_acc: 0.7661\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7289 - acc: 0.6875Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7282 - acc: 0.6885 - val_loss: 0.7080 - val_acc: 0.7310\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7211 - acc: 0.6885Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7244 - acc: 0.6864 - val_loss: 0.8215 - val_acc: 0.6901\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7314 - acc: 0.6844Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7327 - acc: 0.6823 - val_loss: 0.7638 - val_acc: 0.7310\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7430 - acc: 0.6875Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7430 - acc: 0.6874 - val_loss: 0.7556 - val_acc: 0.7368\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7300 - acc: 0.6895Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7293 - acc: 0.6905 - val_loss: 0.7875 - val_acc: 0.7193\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6795 - acc: 0.7008Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.6794 - acc: 0.7007 - val_loss: 0.7741 - val_acc: 0.7251\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6955 - acc: 0.7070Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.6937 - acc: 0.7079 - val_loss: 0.7927 - val_acc: 0.7310\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6636 - acc: 0.7275Epoch 00045: val_acc improved from 0.77193 to 0.78363, saving model to data2a/ft_d2_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.6640 - acc: 0.7273 - val_loss: 0.5893 - val_acc: 0.7836\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7160 - acc: 0.6998Epoch 00046: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7166 - acc: 0.6987 - val_loss: 0.7002 - val_acc: 0.7368\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7158 - acc: 0.7049Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7165 - acc: 0.7038 - val_loss: 0.7786 - val_acc: 0.7310\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7035 - acc: 0.7182Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7022 - acc: 0.7191 - val_loss: 0.7345 - val_acc: 0.7251\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6967 - acc: 0.7285Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.6963 - acc: 0.7283 - val_loss: 0.7811 - val_acc: 0.7368\n"
     ]
    }
   ],
   "source": [
    "ft_d2_model, ft_d2_history = finetune_v2_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try:\n",
    "* more aggresive data augmentation\n",
    "* more aggressive dropout\n",
    "* use of L1 and L2 regularization (also known as \"weight decay\")\n",
    "* fine-tuning one more convolutional block (alongside greater regularization)\n",
    "* use  LR reduce callback\n",
    "ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=0, mode='auto', epsilon=0.0001, cooldown=0, min_lr=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second Run - With Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG16 Model with partial weights loaded.\n",
      "Found 979 images belonging to 3 classes.\n",
      "Found 171 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "# do not rerun!!\n",
    "save_bottleneck_features(location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 979 samples, validate on 171 samples\n",
      "Epoch 1/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 6.2937 - acc: 0.3965Epoch 00000: val_acc improved from -inf to 0.40936, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 6.2939 - acc: 0.3953 - val_loss: 1.0766 - val_acc: 0.4094\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 6.1099 - acc: 0.4631Epoch 00001: val_acc improved from 0.40936 to 0.50292, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 6.1112 - acc: 0.4617 - val_loss: 1.0198 - val_acc: 0.5029\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 6.0442 - acc: 0.4867Epoch 00002: val_acc improved from 0.50292 to 0.52632, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 6.0435 - acc: 0.4872 - val_loss: 0.9771 - val_acc: 0.5263\n",
      "Epoch 4/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.9947 - acc: 0.5410Epoch 00003: val_acc improved from 0.52632 to 0.58480, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.9937 - acc: 0.5424 - val_loss: 0.9365 - val_acc: 0.5848\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.9484 - acc: 0.5512Epoch 00004: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.9480 - acc: 0.5516 - val_loss: 0.9359 - val_acc: 0.5614\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.8964 - acc: 0.5799Epoch 00005: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.8981 - acc: 0.5781 - val_loss: 0.8882 - val_acc: 0.5848\n",
      "Epoch 7/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.8382 - acc: 0.6404Epoch 00006: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.8375 - acc: 0.6415 - val_loss: 0.9010 - val_acc: 0.5614\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.8152 - acc: 0.6414Epoch 00007: val_acc improved from 0.58480 to 0.60234, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.8162 - acc: 0.6394 - val_loss: 0.8717 - val_acc: 0.6023\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.7642 - acc: 0.6619Epoch 00008: val_acc improved from 0.60234 to 0.62573, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.7639 - acc: 0.6619 - val_loss: 0.8940 - val_acc: 0.6257\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.7199 - acc: 0.6752Epoch 00009: val_acc improved from 0.62573 to 0.66667, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.7216 - acc: 0.6742 - val_loss: 0.8479 - val_acc: 0.6667\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.6923 - acc: 0.6906Epoch 00010: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.6933 - acc: 0.6905 - val_loss: 0.8209 - val_acc: 0.6608\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.6377 - acc: 0.7039Epoch 00011: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.6392 - acc: 0.7028 - val_loss: 0.8156 - val_acc: 0.5965\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.6006 - acc: 0.7285Epoch 00012: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.5992 - acc: 0.7293 - val_loss: 0.8351 - val_acc: 0.6316\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.5848 - acc: 0.7172Epoch 00013: val_acc improved from 0.66667 to 0.67251, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.5849 - acc: 0.7160 - val_loss: 0.8191 - val_acc: 0.6725\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.5708 - acc: 0.7203Epoch 00014: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.5697 - acc: 0.7211 - val_loss: 0.8886 - val_acc: 0.5673\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.5290 - acc: 0.7418Epoch 00015: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.5285 - acc: 0.7426 - val_loss: 0.7975 - val_acc: 0.6374\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4872 - acc: 0.7592Epoch 00016: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.4866 - acc: 0.7600 - val_loss: 0.7994 - val_acc: 0.6725\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4341 - acc: 0.7848Epoch 00017: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.4329 - acc: 0.7855 - val_loss: 0.7681 - val_acc: 0.6667\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3994 - acc: 0.8023Epoch 00018: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.4019 - acc: 0.8008 - val_loss: 0.7848 - val_acc: 0.6491\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4030 - acc: 0.7941Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.4022 - acc: 0.7947 - val_loss: 0.7963 - val_acc: 0.6491\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3593 - acc: 0.7961Epoch 00020: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.3601 - acc: 0.7957 - val_loss: 0.7800 - val_acc: 0.6667\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3281 - acc: 0.8043Epoch 00021: val_acc improved from 0.67251 to 0.67836, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.3272 - acc: 0.8049 - val_loss: 0.7643 - val_acc: 0.6784\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3355 - acc: 0.8186Epoch 00022: val_acc improved from 0.67836 to 0.70760, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.3368 - acc: 0.8182 - val_loss: 0.7583 - val_acc: 0.7076\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2793 - acc: 0.8350Epoch 00023: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.2788 - acc: 0.8355 - val_loss: 0.7665 - val_acc: 0.6784\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2502 - acc: 0.8361Epoch 00024: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.2489 - acc: 0.8366 - val_loss: 0.7495 - val_acc: 0.6959\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2168 - acc: 0.8494Epoch 00025: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.2169 - acc: 0.8488 - val_loss: 0.7714 - val_acc: 0.6842\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2081 - acc: 0.8576Epoch 00026: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.2074 - acc: 0.8580 - val_loss: 0.8149 - val_acc: 0.6316\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1709 - acc: 0.8730Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.1713 - acc: 0.8723 - val_loss: 0.8383 - val_acc: 0.6140\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1612 - acc: 0.8566Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.1621 - acc: 0.8570 - val_loss: 0.7693 - val_acc: 0.6784\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1248 - acc: 0.8791Epoch 00029: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.1254 - acc: 0.8784 - val_loss: 0.7672 - val_acc: 0.7018\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0990 - acc: 0.8873Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0984 - acc: 0.8876 - val_loss: 0.7478 - val_acc: 0.6901\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0804 - acc: 0.8852Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0809 - acc: 0.8846 - val_loss: 0.7698 - val_acc: 0.6959\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0830 - acc: 0.8678Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0840 - acc: 0.8662 - val_loss: 0.7524 - val_acc: 0.6959\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0333 - acc: 0.9016Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0332 - acc: 0.9019 - val_loss: 0.7635 - val_acc: 0.6959\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0127 - acc: 0.9006Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0121 - acc: 0.9009 - val_loss: 0.7807 - val_acc: 0.6959\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.9767 - acc: 0.9201Epoch 00035: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.9767 - acc: 0.9203 - val_loss: 0.7545 - val_acc: 0.6842\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.9625 - acc: 0.9078Epoch 00036: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.9623 - acc: 0.9081 - val_loss: 0.7949 - val_acc: 0.6725\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.9622 - acc: 0.9129Epoch 00037: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.9620 - acc: 0.9132 - val_loss: 0.7736 - val_acc: 0.6842\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.9179 - acc: 0.9283Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.9179 - acc: 0.9285 - val_loss: 0.7936 - val_acc: 0.6784\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.8951 - acc: 0.9293Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.8953 - acc: 0.9295 - val_loss: 0.8238 - val_acc: 0.6842\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.8976 - acc: 0.9180Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.8975 - acc: 0.9183 - val_loss: 0.7482 - val_acc: 0.7076\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.8647 - acc: 0.9221Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.8648 - acc: 0.9224 - val_loss: 0.7624 - val_acc: 0.6959\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.8648 - acc: 0.9211Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.8649 - acc: 0.9213 - val_loss: 0.7540 - val_acc: 0.6901\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.8260 - acc: 0.9518Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.8258 - acc: 0.9520 - val_loss: 0.7989 - val_acc: 0.6901\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.8070 - acc: 0.9426Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.8068 - acc: 0.9428 - val_loss: 0.7839 - val_acc: 0.6667\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.7817 - acc: 0.9559Epoch 00045: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.7813 - acc: 0.9561 - val_loss: 0.7585 - val_acc: 0.7076\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.7810 - acc: 0.9477Epoch 00046: val_acc improved from 0.70760 to 0.71930, saving model to data2a/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 4.7815 - acc: 0.9479 - val_loss: 0.7495 - val_acc: 0.7193\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.7680 - acc: 0.9457Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.7677 - acc: 0.9459 - val_loss: 0.7678 - val_acc: 0.6959\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.7259 - acc: 0.9611Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.7256 - acc: 0.9612 - val_loss: 0.7669 - val_acc: 0.6901\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.7199 - acc: 0.9580Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.7195 - acc: 0.9581 - val_loss: 0.7542 - val_acc: 0.7018\n"
     ]
    }
   ],
   "source": [
    "d2_model, d2_history = train_categorical_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG16 Model with partial weights loaded.\n",
      "Found 979 images belonging to 3 classes.\n",
      "Found 171 images belonging to 3 classes.\n",
      "Epoch 1/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4258 - acc: 0.5922Epoch 00000: val_acc improved from -inf to 0.71345, saving model to data2a/ft_model.h5\n",
      "979/979 [==============================] - 91s - loss: 5.4254 - acc: 0.5924 - val_loss: 0.7412 - val_acc: 0.7135\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3986 - acc: 0.5994Epoch 00001: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.4000 - acc: 0.5996 - val_loss: 0.7712 - val_acc: 0.6725\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4055 - acc: 0.5943Epoch 00002: val_acc improved from 0.71345 to 0.73684, saving model to data2a/ft_model.h5\n",
      "979/979 [==============================] - 91s - loss: 5.4052 - acc: 0.5945 - val_loss: 0.7053 - val_acc: 0.7368\n",
      "Epoch 4/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3893 - acc: 0.6096Epoch 00003: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.3884 - acc: 0.6098 - val_loss: 0.7445 - val_acc: 0.6901\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3761 - acc: 0.6281Epoch 00004: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.3741 - acc: 0.6292 - val_loss: 0.7265 - val_acc: 0.7193\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3743 - acc: 0.6168Epoch 00005: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.3745 - acc: 0.6170 - val_loss: 0.7773 - val_acc: 0.6667\n",
      "Epoch 7/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3733 - acc: 0.6025Epoch 00006: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.3720 - acc: 0.6037 - val_loss: 0.7027 - val_acc: 0.7251\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3287 - acc: 0.6434Epoch 00007: val_acc did not improve\n",
      "979/979 [==============================] - 91s - loss: 5.3286 - acc: 0.6435 - val_loss: 0.7288 - val_acc: 0.7076\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3319 - acc: 0.6373Epoch 00008: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.3301 - acc: 0.6384 - val_loss: 0.7020 - val_acc: 0.6959\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3351 - acc: 0.6373Epoch 00009: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.3372 - acc: 0.6364 - val_loss: 0.7059 - val_acc: 0.7076\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3321 - acc: 0.6373Epoch 00010: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.3308 - acc: 0.6384 - val_loss: 0.7308 - val_acc: 0.7135\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3272 - acc: 0.6311Epoch 00011: val_acc improved from 0.73684 to 0.74854, saving model to data2a/ft_model.h5\n",
      "979/979 [==============================] - 91s - loss: 5.3278 - acc: 0.6313 - val_loss: 0.6920 - val_acc: 0.7485\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3304 - acc: 0.6301Epoch 00012: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.3311 - acc: 0.6302 - val_loss: 0.6940 - val_acc: 0.6784\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3496 - acc: 0.6281Epoch 00013: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.3493 - acc: 0.6282 - val_loss: 0.6783 - val_acc: 0.7368\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3002 - acc: 0.6424Epoch 00014: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.3001 - acc: 0.6425 - val_loss: 0.7001 - val_acc: 0.7251\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2884 - acc: 0.6527Epoch 00015: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2899 - acc: 0.6517 - val_loss: 0.6581 - val_acc: 0.7135\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2907 - acc: 0.6465Epoch 00016: val_acc improved from 0.74854 to 0.76023, saving model to data2a/ft_model.h5\n",
      "979/979 [==============================] - 91s - loss: 5.2922 - acc: 0.6445 - val_loss: 0.6835 - val_acc: 0.7602\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2867 - acc: 0.6619Epoch 00017: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2864 - acc: 0.6619 - val_loss: 0.6828 - val_acc: 0.7018\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2558 - acc: 0.6732Epoch 00018: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2561 - acc: 0.6731 - val_loss: 0.6828 - val_acc: 0.7135\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2603 - acc: 0.6598Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2602 - acc: 0.6599 - val_loss: 0.7022 - val_acc: 0.7193\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2672 - acc: 0.6691Epoch 00020: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2662 - acc: 0.6691 - val_loss: 0.6687 - val_acc: 0.7310\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2803 - acc: 0.6670Epoch 00021: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2813 - acc: 0.6660 - val_loss: 0.6395 - val_acc: 0.7544\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2550 - acc: 0.6660Epoch 00022: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2544 - acc: 0.6670 - val_loss: 0.6760 - val_acc: 0.7485\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2606 - acc: 0.6465Epoch 00023: val_acc improved from 0.76023 to 0.78363, saving model to data2a/ft_model.h5\n",
      "979/979 [==============================] - 91s - loss: 5.2611 - acc: 0.6456 - val_loss: 0.5805 - val_acc: 0.7836\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2434 - acc: 0.6639Epoch 00024: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2432 - acc: 0.6639 - val_loss: 0.6759 - val_acc: 0.7602\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2423 - acc: 0.6537Epoch 00025: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2454 - acc: 0.6527 - val_loss: 0.6786 - val_acc: 0.7251\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2515 - acc: 0.6619Epoch 00026: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2513 - acc: 0.6619 - val_loss: 0.6883 - val_acc: 0.7076\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2459 - acc: 0.6537Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2461 - acc: 0.6527 - val_loss: 0.6735 - val_acc: 0.7368\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2036 - acc: 0.6895Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2024 - acc: 0.6905 - val_loss: 0.6829 - val_acc: 0.7544\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2490 - acc: 0.6547Epoch 00029: val_acc improved from 0.78363 to 0.78947, saving model to data2a/ft_model.h5\n",
      "979/979 [==============================] - 91s - loss: 5.2492 - acc: 0.6547 - val_loss: 0.6357 - val_acc: 0.7895\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2109 - acc: 0.6650Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2117 - acc: 0.6650 - val_loss: 0.7035 - val_acc: 0.7368\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2197 - acc: 0.6711Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2183 - acc: 0.6721 - val_loss: 0.6819 - val_acc: 0.7251\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1991 - acc: 0.6916Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2006 - acc: 0.6905 - val_loss: 0.7322 - val_acc: 0.7018\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2431 - acc: 0.6537Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2426 - acc: 0.6547 - val_loss: 0.6600 - val_acc: 0.7251\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1934 - acc: 0.6793Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1936 - acc: 0.6782 - val_loss: 0.7685 - val_acc: 0.6959\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1837 - acc: 0.6824Epoch 00035: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1855 - acc: 0.6823 - val_loss: 0.6627 - val_acc: 0.7251\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1936 - acc: 0.6885Epoch 00036: val_acc did not improve\n",
      "979/979 [==============================] - 91s - loss: 5.1931 - acc: 0.6885 - val_loss: 0.6218 - val_acc: 0.7310\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1877 - acc: 0.6865Epoch 00037: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1881 - acc: 0.6864 - val_loss: 0.6706 - val_acc: 0.7485\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2084 - acc: 0.6814Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2077 - acc: 0.6813 - val_loss: 0.6628 - val_acc: 0.7778\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1633 - acc: 0.7111Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1620 - acc: 0.7120 - val_loss: 0.6775 - val_acc: 0.7368\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2038 - acc: 0.6516Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.2035 - acc: 0.6527 - val_loss: 0.6709 - val_acc: 0.7193\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1887 - acc: 0.6803Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1875 - acc: 0.6813 - val_loss: 0.6405 - val_acc: 0.7602\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1998 - acc: 0.6711Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1998 - acc: 0.6711 - val_loss: 0.6540 - val_acc: 0.7485\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1765 - acc: 0.6895Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1777 - acc: 0.6885 - val_loss: 0.6916 - val_acc: 0.7544\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1588 - acc: 0.6998Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1610 - acc: 0.6997 - val_loss: 0.6684 - val_acc: 0.7485\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1451 - acc: 0.6865Epoch 00045: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1471 - acc: 0.6854 - val_loss: 0.6615 - val_acc: 0.7544\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1578 - acc: 0.6977Epoch 00046: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1570 - acc: 0.6987 - val_loss: 0.6892 - val_acc: 0.7368\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1562 - acc: 0.6936Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1557 - acc: 0.6936 - val_loss: 0.6811 - val_acc: 0.7076\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1568 - acc: 0.7121Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1563 - acc: 0.7120 - val_loss: 0.7043 - val_acc: 0.7135\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1381 - acc: 0.6977Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 90s - loss: 5.1379 - acc: 0.6977 - val_loss: 0.5958 - val_acc: 0.7485\n"
     ]
    }
   ],
   "source": [
    "ft_d2_model, ft_d2_history = finetune_categorical_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Run - Without Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mEEL4MwmMxzCXy8XbW4oB+PnXlxAfFiCbcQghRkVvKoUExkIIP2Yc7Q6Ik9NH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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0d8992650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d2_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8TjZlrkOn05Eam8KdG27jC4vPoWugm4a+JrLjMkmJTZ70ucNxQvYmEkxWPmr4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Rc6k8XVk2dT/w9uZpdWJZidxdPVk5mxU/nw6DJWnljH7erGuteWHvuUUxVFXBs1oVWr9wF4uXkx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f229f69d2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(ft_d2_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second Run - Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 47 , val_acc: 0.719298247008 , val_loss: 0.749537965708\n"
     ]
    },
    {
     "data": {
      "image/png": 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tZL98IURekhgL8ZL+unSVVeu30bd3R2pWr1xguaysLIaN/IYzQRdp1/o9li2Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fee4491e510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d2_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 30 , val_acc: 0.789473684211 , val_loss: 0.635747588692\n"
     ]
    },
    {
     "data": {
      "image/png": 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HLokZY0EQ+oBEIiHA38csx1YorAkK9G02+9weqVTK5InjmDyx+znknTX3uunM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fee3f63c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(ft_d2_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can then use keras.models.load_model(filepath) to reinstantiate your model. load_model will also take care of compiling the model using the saved training configuration (unless the model was never compiled in the first place)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ft_d2_model2 = load_model('data2a/ft_d2_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 171 images belonging to 3 classes.\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.77      0.86      0.81        73\n",
      "          1       0.73      0.66      0.69        50\n",
      "          2       0.75      0.69      0.72        48\n",
      "\n",
      "avg / total       0.75      0.75      0.75       171\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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QUtjQKmhFJCxRNn1Jq6AVkaBoRisi0gmpoxWRoKSwoVXQikhY0jg6UNCKSFBSmLMKWhEJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f228f677590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(ft_d2_model, validation_data_dir, validation_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Model Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_metrics(hist, stop=50):\n",
    "    fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(4.25,6))\n",
    "                            \n",
    "    axes = axes.flatten()\n",
    "\n",
    "    axes[0].plot(range(stop), hist['acc'], label='Training', color='#FF533D')\n",
    "    axes[0].plot(range(stop), hist['val_acc'], label='Validation', color='#03507E')\n",
    "    axes[0].set_title('Accuracy')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "    \n",
    "    axes[0].set_xlabel('Epoch')\n",
    "    axes[0].legend(loc='lower right')\n",
    "                             \n",
    "    axes[1].plot(range(stop), hist['loss'], label='Training', color='#FF533D')\n",
    "    axes[1].plot(range(stop), hist['val_loss'], label='Validation', color='#03507E')\n",
    "    axes[1].set_title('Loss')\n",
    "    axes[1].set_ylabel('Loss')\n",
    "    axes[1].set_xlabel('Epoch')\n",
    "    axes[1].legend(loc='upper right')\n",
    "                             \n",
    "    plt.tight_layout();\n",
    "           \n",
    "    print \"Best Model:\" \n",
    "    print_best_model_results(hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluate_categorical_model(model, directory, labels):\n",
    "    datagen = ImageDataGenerator(rescale=1./255)  \n",
    "\n",
    "    generator = datagen.flow_from_directory(directory,\n",
    "                                target_size=(img_height, img_width),\n",
    "                                batch_size=8,\n",
    "                                class_mode='categorical',\n",
    "                                shuffle=False)\n",
    "    \n",
    "    predictions = model.predict_generator(generator, len(labels))\n",
    "    pred_labels = np.argmax(predictions, axis=1)\n",
    "    \n",
    "    print ''\n",
    "    print classification_report(validation_labels, pred_labels)\n",
    "    print ''\n",
    "    cm = confusion_matrix(validation_labels, pred_labels)\n",
    "    return cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ft_model = load_model('data2a/ft_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('data2a/top_history.txt') as f:    \n",
    "    top_history = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('data2a/ft_history.txt') as f:    \n",
    "    ft_history = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "validation_labels = np.array([0] * validation_samples[0] + \n",
    "                             [1] * validation_samples[1] +\n",
    "                             [2] * validation_samples[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 171 images belonging to 3 classes.\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.77      0.79      0.78        73\n",
      "          1       0.74      0.70      0.72        50\n",
      "          2       0.76      0.77      0.76        48\n",
      "\n",
      "avg / total       0.76      0.76      0.76       171\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm = evaluate_categorical_model(ft_model, validation_data_dir, validation_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('data3_1/top_history.txt') as f:    \n",
    "    top_history = json.load(f)\n",
    "\n",
    "def plot_acc_metrics(hist1, hist2, stop=50):\n",
    "    fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(4.25,6))\n",
    "                            \n",
    "    axes = axes.flatten()\n",
    "\n",
    "    axes[0].plot(range(stop), hist1['acc'], label='Training', color='#FF533D')\n",
    "    axes[0].plot(range(stop), hist1['val_acc'], label='Validation', color='#03507E')\n",
    "    axes[0].set_title('Training')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "    axes[0].set_xlabel('Epoch')\n",
    "    axes[0].legend(loc='lower right')\n",
    "                             \n",
    "    axes[1].plot(range(stop), hist2['acc'], label='Training', color='#FF533D')\n",
    "    axes[1].plot(range(stop), hist2['val_acc'], label='Validation', color='#03507E')\n",
    "    axes[1].set_title('Fine-tuning')\n",
    "    axes[1].set_ylabel('Accuracy')\n",
    "    axes[1].set_xlabel('Epoch')\n",
    "    axes[1].legend(loc='lower right')\n",
    "                             \n",
    "    plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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raXJy6tYXfvyDNcm2hL+IZVb/thz7egyz+7elYWVbTly9z7w9AajeuET8Lkt2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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d97e7f0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_metrics(top_history, ft_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 47 , val_acc: 0.719298247008 , val_loss: 0.749537965708\n"
     ]
    },
    {
     "data": {
      "image/png": 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JArfAryvlXSNHToROPUUiEgpFJCih+KWnyQUEjh6Q9+ieNFveTVIQCknjM8mDg4Pp2rUr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\nWxJQQgjdkoASQuiWBJQQQrf+P11jam289Ju2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d981d1ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(top_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 30 , val_acc: 0.789473684211 , val_loss: 0.635747588692\n"
     ]
    },
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ve2ej1/rTlOH09GnPVz+e5FBULDbWVvoPS1U6t3fmRsYto4HMguJSZn30PR9u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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d9863b510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(ft_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "heatmap_labels = ['Front', 'Rear', 'Side']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iudneHD8TLRE5Fla0RERyY0VLRCQrcy6JtTYmWiJyMEy0RETyYkVLRCQ3Jloi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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc69d3ec910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, annot_kws={\"size\": 14}, \n",
    "            fmt='g', cmap='OrRd', xticklabels=heatmap_labels, yticklabels=heatmap_labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PDELd4S/124WqALqr501dos27957u6Ht/kLnLMAurXUe7detWJCYmIj8/H4GBgTh16hSC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\nMWiJiGTGoCUiktn/Acl/odVLlFGCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f33a4fd7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, annot_kws={\"size\": 16}, \n",
    "            fmt='g', cmap='OrRd', xticklabels=heatmap_labels, yticklabels=heatmap_labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BoxpNtMrQhwGFE2qO7rZCdBZkb6WDW4qLi1FWVgZvb28AQEVFBYqLiyUNzFr2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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f33a4ca7950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, annot_kws={\"size\": 18}, \n",
    "            fmt='g', cmap='OrRd', xticklabels=heatmap_labels, yticklabels=heatmap_labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5cgQRERE4duwYpk2b1nhFDV3rwBp4rQPp8VoH0uO1DmzDGtc6uHrD8psStHez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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc69d115c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, annot_kws={\"size\": 14},\n",
    "            fmt='g', cmap='Blues', xticklabels=heatmap_labels, yticklabels=heatmap_labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FCwD+/+ywrKwsZGVloWPHjtJXaCIVFRXYl/QtevTuCxdXV3OXU28IgsCzwx7D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\nGIOWiEhi/wdXK6Zs3gs4cQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f33a4b0f690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, annot_kws={\"size\": 16},\n",
    "            fmt='g', cmap='Blues', xticklabels=heatmap_labels, yticklabels=heatmap_labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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T2RbujmqcyCqErqQKBqOIzh6OGB7ghTDf1ljyVQaKKgy124sisOnkHRRVVNfZz/X8UnOX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f33a49cc090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, annot_kws={\"size\": 18},\n",
    "            fmt='g', cmap='Blues', xticklabels=heatmap_labels, yticklabels=heatmap_labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking at Edge Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_edge_cases(model, directory, exp_result, false_result):\n",
    "    img_list = os.listdir(directory)\n",
    "    edge_list = []\n",
    "    for name in img_list:\n",
    "        img = load_img(directory+name, target_size=(256, 256)) # this is a PIL image \n",
    "        x = img_to_array(img) # this is a Numpy array with shape (3, 256, 256)\n",
    "        x = x.reshape((1,) + x.shape)/255 # this is a Numpy array with shape (1, 3, 256, 256)\n",
    "        pred = model.predict(x)\n",
    "        pred_label = np.argmax(pred, axis=1)\n",
    "        if pred_label[0] == false_result:\n",
    "            edge_list.append(name)\n",
    "    return edge_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "front1 = get_edge_cases(ft_model, 'data2a/validation/00-front/', 0, 1) # true front, predicted rear - 8\n",
    "front2 = get_edge_cases(ft_model, 'data2a/validation/00-front/', 0, 2) # true front, predicted side - 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rear1 = get_edge_cases(ft_model, 'data2a/validation/01-rear/', 1, 0) # true rear, predicted front - 10\n",
    "rear2 = get_edge_cases(ft_model, 'data2a/validation/01-rear/', 1, 2) # true rear, predicted side - 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "side1 = get_edge_cases(ft_model, 'data2a/validation/02-side/', 2, 0) # true side, predicted front - 7\n",
    "side2 = get_edge_cases(ft_model, 'data2a/validation/02-side/', 2, 1) # true side, predicted rear - 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True, True, True, True, True)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(front1) == 8, len(front2) == 7, len(rear1) == 10, len(rear2) == 5, len(side1) == 7, len(side2) == 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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y1/DmLeURcdttS2CXSxff9axRpng3VWB7WPWjONI5HjZbjHlRSVEhSTmSpJKS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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c to continue, q to quitc\n"
     ]
    }
   ],
   "source": [
    "view_images('data2a/validation/00-front/', front1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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pqgpejWY86mL2o7uM1zMByJEfOmjOTJ5MUI+5Ol8SxXr923vYzj9hpPwYA/Co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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c to continue, q to quitc\n"
     ]
    }
   ],
   "source": [
    "view_images('data2a/validation/00-front/', front2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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X+aWz3FbCKFaDZ498ebT96Sdm/tj0KWxuKwhIIVqdmftt9Ci/NX9430KextUk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\nua3t7uMnGP/Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c to continue, q to quitc\n"
     ]
    }
   ],
   "source": [
    "view_images('data2a/validation/01-rear/', rear1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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EZUmd4k+aPIP8LL20VFmqMpE9XV60uHbp9Rfa6PWDahxIHDTMKXTplwlrAAccS1gjTsU4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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c to continue, q to quitc\n"
     ]
    }
   ],
   "source": [
    "view_images('data2a/validation/01-rear/', rear2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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ztH1QAwONu4HhJB6Toou1Np/oNMVCzNWrGGsmIYDM8dTlVjhMFTpOD6j4y3cTZoaLmZ1F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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c to continue, q to quitc\n"
     ]
    }
   ],
   "source": [
    "view_images('data2a/validation/02-side/', side1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c to continue, q to quitc\n"
     ]
    }
   ],
   "source": [
    "view_images('data2a/validation/02-side/', side2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://keras.io/visualization/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldn't import dot_parser, loading of dot files will not be possible.\n"
     ]
    },
    {
     "data": {
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       "<g class=\"edge\" id=\"edge31\"><title>139828761311888-&gt;139828761367056</title>\n",
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       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"109\" y=\"-14.8\">sequential_2 (Sequential)</text>\n",
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import SVG\n",
    "from keras.utils.visualize_util import model_to_dot\n",
    "\n",
    "SVG(model_to_dot(ft_d2_model2).create(prog='dot', format='svg'))"
   ]
  }
 ],
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