04-classifying_damage_severity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 120316_0855\n",
    "* repeat of first run without regularization; added history and metrics tracking\n",
    "\n",
    "### 120316_0000\n",
    "* change to run with regularization\n",
    "\n",
    "### 120116_1223\n",
    "* run with cleaned dataset, just on damage severity, 256x256, 8 batch size, theano backend\n",
    "* loss: 0.5195 - acc: 0.7763 - val_loss: 0.5586 - val_acc: 0.7544"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "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",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "sns.set_palette(\"cubehelix\")\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def load_vgg16(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",
    "        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": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def save_bottleneck_features(location):\n",
    "    datagen = ImageDataGenerator(rescale=1./255)  \n",
    "    \n",
    "    model = load_vgg16()\n",
    "    \n",
    "    generator = datagen.flow_from_directory(train_data_dir,\n",
    "                                            target_size=(img_width, img_height),\n",
    "                                            batch_size=16,\n",
    "                                            class_mode=None, \n",
    "                                            shuffle=False) \n",
    "\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": 6,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "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": 7,
   "metadata": {
    "collapsed": true,
    "scrolled": 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": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def train_categorical_model():\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'))\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), # reduced learning rate\n",
    "              loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "    # model.compile(optimizer='rmsprop', \n",
    "    #               loss='categorical_crossentropy', metrics=['accuracy']) \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",
    "    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": 9,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def finetune_categorical_model():\n",
    "    model = load_vgg16()\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'))\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",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                 optimizer = optimizers.SGD(lr=0.000001, 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",
    "        \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+'/ft_history.txt', 'wb') as f:\n",
    "        json.dump(fit.history, f)\n",
    "    \n",
    "    return model, fit.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "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, fmt='g');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "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 Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# path to the model weights file\n",
    "location = 'data3_1'\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": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": 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": "markdown",
   "metadata": {},
   "source": [
    "## Model Parameter Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
      "976/979 [============================>.] - ETA: 0s - loss: 1.1788 - acc: 0.4037Epoch 00000: val_acc improved from -inf to 0.54386, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 1.1776 - acc: 0.4045 - val_loss: 1.0011 - val_acc: 0.5439\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0127 - acc: 0.4918Epoch 00001: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0117 - acc: 0.4934 - val_loss: 0.9500 - val_acc: 0.5439\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8934 - acc: 0.5973Epoch 00002: val_acc improved from 0.54386 to 0.54971, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.8940 - acc: 0.5965 - val_loss: 0.9063 - val_acc: 0.5497\n",
      "Epoch 4/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8534 - acc: 0.5984Epoch 00003: val_acc improved from 0.54971 to 0.64912, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.8550 - acc: 0.5975 - val_loss: 0.8550 - val_acc: 0.6491\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8267 - acc: 0.6086Epoch 00004: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.8274 - acc: 0.6088 - val_loss: 0.8612 - val_acc: 0.5789\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8144 - acc: 0.6363Epoch 00005: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.8135 - acc: 0.6364 - val_loss: 0.8547 - val_acc: 0.5848\n",
      "Epoch 7/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7841 - acc: 0.6568Epoch 00006: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.7859 - acc: 0.6558 - val_loss: 0.8250 - val_acc: 0.5965\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7318 - acc: 0.6721Epoch 00007: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.7317 - acc: 0.6721 - val_loss: 0.8483 - val_acc: 0.6140\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7115 - acc: 0.6619Epoch 00008: val_acc improved from 0.64912 to 0.64912, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.7108 - acc: 0.6619 - val_loss: 0.7820 - val_acc: 0.6491\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6914 - acc: 0.7049Epoch 00009: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.6918 - acc: 0.7048 - val_loss: 0.8274 - val_acc: 0.6374\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6697 - acc: 0.7223Epoch 00010: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.6692 - acc: 0.7232 - val_loss: 0.7612 - val_acc: 0.6374\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6290 - acc: 0.7367Epoch 00011: val_acc improved from 0.64912 to 0.65497, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.6294 - acc: 0.7365 - val_loss: 0.7593 - val_acc: 0.6550\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6170 - acc: 0.7408Epoch 00012: val_acc improved from 0.65497 to 0.66667, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.6170 - acc: 0.7406 - val_loss: 0.7424 - val_acc: 0.6667\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6306 - acc: 0.7254Epoch 00013: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.6304 - acc: 0.7252 - val_loss: 0.7552 - val_acc: 0.6550\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6051 - acc: 0.7449Epoch 00014: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.6039 - acc: 0.7457 - val_loss: 0.7986 - val_acc: 0.6082\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5932 - acc: 0.7510Epoch 00015: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5923 - acc: 0.7518 - val_loss: 0.7451 - val_acc: 0.6667\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5550 - acc: 0.7859Epoch 00016: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5568 - acc: 0.7855 - val_loss: 0.7897 - val_acc: 0.6082\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5575 - acc: 0.7715Epoch 00017: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5586 - acc: 0.7712 - val_loss: 0.7434 - val_acc: 0.6667\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5393 - acc: 0.7674Epoch 00018: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5392 - acc: 0.7671 - val_loss: 0.7581 - val_acc: 0.6257\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5141 - acc: 0.8023Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5137 - acc: 0.8029 - val_loss: 0.7387 - val_acc: 0.6491\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5091 - acc: 0.8105Epoch 00020: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5093 - acc: 0.8100 - val_loss: 0.7425 - val_acc: 0.6316\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.4790 - acc: 0.8135Epoch 00021: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.4784 - acc: 0.8141 - val_loss: 0.7793 - val_acc: 0.6257\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.4851 - acc: 0.8115Epoch 00022: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.4859 - acc: 0.8110 - val_loss: 0.7366 - val_acc: 0.6433\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.4561 - acc: 0.8227Epoch 00023: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.4564 - acc: 0.8233 - val_loss: 0.7796 - val_acc: 0.6608\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.4411 - acc: 0.8320Epoch 00024: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.4418 - acc: 0.8325 - val_loss: 0.7428 - val_acc: 0.6199\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.4318 - acc: 0.8494Epoch 00025: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.4307 - acc: 0.8498 - val_loss: 0.7514 - val_acc: 0.6257\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.4154 - acc: 0.8566Epoch 00026: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.4159 - acc: 0.8570 - val_loss: 0.7411 - val_acc: 0.6199\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.4043 - acc: 0.8607Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.4043 - acc: 0.8611 - val_loss: 0.7407 - val_acc: 0.6374\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.3772 - acc: 0.8648Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.3778 - acc: 0.8652 - val_loss: 0.7631 - val_acc: 0.6491\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.4055 - acc: 0.8514Epoch 00029: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.4049 - acc: 0.8519 - val_loss: 0.7509 - val_acc: 0.6491\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.3598 - acc: 0.8842Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.3591 - acc: 0.8846 - val_loss: 0.7371 - val_acc: 0.6550\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.3484 - acc: 0.8873Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.3487 - acc: 0.8876 - val_loss: 0.7627 - val_acc: 0.6608\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.3404 - acc: 0.8832Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.3411 - acc: 0.8825 - val_loss: 0.7692 - val_acc: 0.6433\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.3555 - acc: 0.8863Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.3551 - acc: 0.8866 - val_loss: 0.7585 - val_acc: 0.6433\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.3274 - acc: 0.8975Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.3275 - acc: 0.8979 - val_loss: 0.7499 - val_acc: 0.6433\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.3245 - acc: 0.9078Epoch 00035: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.3239 - acc: 0.9081 - val_loss: 0.7566 - val_acc: 0.6491\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.3153 - acc: 0.8975Epoch 00036: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.3145 - acc: 0.8979 - val_loss: 0.7492 - val_acc: 0.6433\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2928 - acc: 0.9252Epoch 00037: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2926 - acc: 0.9254 - val_loss: 0.7627 - val_acc: 0.6550\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2776 - acc: 0.9191Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2771 - acc: 0.9193 - val_loss: 0.7676 - val_acc: 0.6550\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2785 - acc: 0.9232Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2781 - acc: 0.9234 - val_loss: 0.7615 - val_acc: 0.6491\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2694 - acc: 0.9314Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2689 - acc: 0.9316 - val_loss: 0.7774 - val_acc: 0.6433\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2583 - acc: 0.9447Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2584 - acc: 0.9448 - val_loss: 0.7883 - val_acc: 0.6257\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2616 - acc: 0.9252Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2609 - acc: 0.9254 - val_loss: 0.7706 - val_acc: 0.6199\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2304 - acc: 0.9611Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2304 - acc: 0.9612 - val_loss: 0.7791 - val_acc: 0.6374\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2509 - acc: 0.9395Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2506 - acc: 0.9397 - val_loss: 0.8506 - val_acc: 0.6316\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2406 - acc: 0.9303Epoch 00045: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2406 - acc: 0.9305 - val_loss: 0.8026 - val_acc: 0.6316\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2272 - acc: 0.9518Epoch 00046: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2270 - acc: 0.9520 - val_loss: 0.8035 - val_acc: 0.6491\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2409 - acc: 0.9385Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2411 - acc: 0.9387 - val_loss: 0.8103 - val_acc: 0.6257\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2247 - acc: 0.9436Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2245 - acc: 0.9438 - val_loss: 0.7927 - val_acc: 0.6140\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.2158 - acc: 0.9447Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.2156 - acc: 0.9448 - val_loss: 0.7911 - val_acc: 0.6140\n"
     ]
    }
   ],
   "source": [
    "# WINNER\n",
    "d3_model2, d3_hist2 = train_categorical_model() # no regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
      "976/979 [============================>.] - ETA: 0s - loss: 6.2912 - acc: 0.4109Epoch 00000: val_acc improved from -inf to 0.61988, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 6.2923 - acc: 0.4096 - val_loss: 0.9706 - val_acc: 0.6199\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 6.0353 - acc: 0.5266Epoch 00001: val_acc improved from 0.61988 to 0.64912, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 6.0341 - acc: 0.5281 - val_loss: 0.9172 - val_acc: 0.6491\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.9593 - acc: 0.5635Epoch 00002: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.9608 - acc: 0.5628 - val_loss: 0.8912 - val_acc: 0.5439\n",
      "Epoch 4/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.8936 - acc: 0.6045Epoch 00003: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.8929 - acc: 0.6047 - val_loss: 0.8752 - val_acc: 0.5556\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.8609 - acc: 0.6189Epoch 00004: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.8612 - acc: 0.6180 - val_loss: 0.8530 - val_acc: 0.6316\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.7841 - acc: 0.6527Epoch 00005: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.7856 - acc: 0.6517 - val_loss: 0.8293 - val_acc: 0.6374\n",
      "Epoch 7/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.7503 - acc: 0.6803Epoch 00006: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.7495 - acc: 0.6813 - val_loss: 0.8339 - val_acc: 0.5965\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.7445 - acc: 0.6619Epoch 00007: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.7437 - acc: 0.6619 - val_loss: 0.8108 - val_acc: 0.5906\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.6984 - acc: 0.6834Epoch 00008: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.6976 - acc: 0.6834 - val_loss: 0.8544 - val_acc: 0.5789\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.6649 - acc: 0.6998Epoch 00009: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.6642 - acc: 0.6997 - val_loss: 0.8173 - val_acc: 0.6023\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.6049 - acc: 0.7377Epoch 00010: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.6047 - acc: 0.7375 - val_loss: 0.7889 - val_acc: 0.6316\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.5751 - acc: 0.7459Epoch 00011: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.5745 - acc: 0.7467 - val_loss: 0.7733 - val_acc: 0.6316\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.5440 - acc: 0.7418Epoch 00012: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.5441 - acc: 0.7416 - val_loss: 0.7724 - val_acc: 0.6140\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.5275 - acc: 0.7500Epoch 00013: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.5276 - acc: 0.7497 - val_loss: 0.7728 - val_acc: 0.6374\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.5163 - acc: 0.7633Epoch 00014: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.5179 - acc: 0.7620 - val_loss: 0.7498 - val_acc: 0.6491\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4945 - acc: 0.7592Epoch 00015: val_acc improved from 0.64912 to 0.66082, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.4933 - acc: 0.7600 - val_loss: 0.7820 - val_acc: 0.6608\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4294 - acc: 0.7756Epoch 00016: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.4311 - acc: 0.7743 - val_loss: 0.7845 - val_acc: 0.6550\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4004 - acc: 0.7838Epoch 00017: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.4011 - acc: 0.7835 - val_loss: 0.7752 - val_acc: 0.6491\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4050 - acc: 0.7828Epoch 00018: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.4047 - acc: 0.7824 - val_loss: 0.7570 - val_acc: 0.6257\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3585 - acc: 0.8135Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.3588 - acc: 0.8121 - val_loss: 0.7604 - val_acc: 0.6316\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3331 - acc: 0.8197Epoch 00020: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.3331 - acc: 0.8192 - val_loss: 0.7601 - val_acc: 0.6491\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3008 - acc: 0.8033Epoch 00021: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.3012 - acc: 0.8029 - val_loss: 0.7502 - val_acc: 0.6257\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2669 - acc: 0.8361Epoch 00022: val_acc improved from 0.66082 to 0.66667, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.2669 - acc: 0.8366 - val_loss: 0.7530 - val_acc: 0.6667\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2512 - acc: 0.8309Epoch 00023: val_acc improved from 0.66667 to 0.67251, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.2506 - acc: 0.8315 - val_loss: 0.7822 - val_acc: 0.6725\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2358 - acc: 0.8350Epoch 00024: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.2349 - acc: 0.8355 - val_loss: 0.8355 - val_acc: 0.6667\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2113 - acc: 0.8453Epoch 00025: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.2106 - acc: 0.8458 - val_loss: 0.7681 - val_acc: 0.6199\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1720 - acc: 0.8525Epoch 00026: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.1720 - acc: 0.8529 - val_loss: 0.7561 - val_acc: 0.6257\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1657 - acc: 0.8607Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.1659 - acc: 0.8611 - val_loss: 0.7643 - val_acc: 0.6023\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1234 - acc: 0.8586Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.1238 - acc: 0.8590 - val_loss: 0.7899 - val_acc: 0.5906\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0989 - acc: 0.8945Epoch 00029: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0992 - acc: 0.8938 - val_loss: 0.7714 - val_acc: 0.6491\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0859 - acc: 0.8801Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0855 - acc: 0.8805 - val_loss: 0.7646 - val_acc: 0.6023\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0599 - acc: 0.8996Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0593 - acc: 0.8999 - val_loss: 0.7896 - val_acc: 0.6023\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0279 - acc: 0.8924Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0278 - acc: 0.8917 - val_loss: 0.7576 - val_acc: 0.6140\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0342 - acc: 0.8822Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0349 - acc: 0.8815 - val_loss: 0.7700 - val_acc: 0.6316\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0020 - acc: 0.9078Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0015 - acc: 0.9081 - val_loss: 0.7659 - val_acc: 0.6140\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.9701 - acc: 0.9170Epoch 00035: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.9697 - acc: 0.9173 - val_loss: 0.7750 - val_acc: 0.6023\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.9419 - acc: 0.9252Epoch 00036: val_acc improved from 0.67251 to 0.67836, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 4.9424 - acc: 0.9244 - val_loss: 0.8095 - val_acc: 0.6784\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.9441 - acc: 0.9119Epoch 00037: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.9444 - acc: 0.9111 - val_loss: 0.8265 - val_acc: 0.6550\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.9078 - acc: 0.9303Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.9093 - acc: 0.9295 - val_loss: 0.7830 - val_acc: 0.6199\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.9179 - acc: 0.9027Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.9173 - acc: 0.9030 - val_loss: 0.8324 - val_acc: 0.5965\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.8849 - acc: 0.9221Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.8842 - acc: 0.9224 - val_loss: 0.8007 - val_acc: 0.6550\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.8480 - acc: 0.9365Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.8488 - acc: 0.9356 - val_loss: 0.7985 - val_acc: 0.6374\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.8484 - acc: 0.9303Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.8477 - acc: 0.9305 - val_loss: 0.7999 - val_acc: 0.6082\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.8282 - acc: 0.9303Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.8279 - acc: 0.9305 - val_loss: 0.7926 - val_acc: 0.6433\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.8009 - acc: 0.9395Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.8007 - acc: 0.9397 - val_loss: 0.8083 - val_acc: 0.6374\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.7785 - acc: 0.9395Epoch 00045: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.7781 - acc: 0.9397 - val_loss: 0.8068 - val_acc: 0.6316\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.7520 - acc: 0.9529Epoch 00046: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.7529 - acc: 0.9530 - val_loss: 0.8097 - val_acc: 0.6316\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.7558 - acc: 0.9549Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.7567 - acc: 0.9540 - val_loss: 0.8239 - val_acc: 0.6257\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.7538 - acc: 0.9395Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.7540 - acc: 0.9397 - val_loss: 0.8072 - val_acc: 0.6374\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.7163 - acc: 0.9549Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.7158 - acc: 0.9551 - val_loss: 0.8105 - val_acc: 0.6433\n"
     ]
    }
   ],
   "source": [
    "d3_model, d3_hist = train_categorical_model() # l2 regularization, sgd lr = 0.0001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
      "976/979 [============================>.] - ETA: 0s - loss: 547.8632 - acc: 0.4119Epoch 00000: val_acc improved from -inf to 0.50877, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 547.7880 - acc: 0.4127 - val_loss: 0.9907 - val_acc: 0.5088\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 498.9137 - acc: 0.5154Epoch 00001: val_acc improved from 0.50877 to 0.51462, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 498.8393 - acc: 0.5169 - val_loss: 0.9264 - val_acc: 0.5146\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 451.2671 - acc: 0.5564Epoch 00002: val_acc improved from 0.51462 to 0.57895, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 451.1974 - acc: 0.5557 - val_loss: 0.8945 - val_acc: 0.5789\n",
      "Epoch 4/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 406.0301 - acc: 0.5984Epoch 00003: val_acc improved from 0.57895 to 0.60234, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 405.9638 - acc: 0.5975 - val_loss: 0.8707 - val_acc: 0.6023\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 363.2027 - acc: 0.6117Epoch 00004: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 363.1416 - acc: 0.6108 - val_loss: 0.8534 - val_acc: 0.5731\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 322.7412 - acc: 0.6424Epoch 00005: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 322.6832 - acc: 0.6415 - val_loss: 0.8444 - val_acc: 0.5906\n",
      "Epoch 7/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 284.7540 - acc: 0.6322Epoch 00006: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 284.6990 - acc: 0.6313 - val_loss: 0.8649 - val_acc: 0.5673\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 249.0881 - acc: 0.6639Epoch 00007: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 249.0346 - acc: 0.6650 - val_loss: 0.8432 - val_acc: 0.5965\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 215.8344 - acc: 0.6814Epoch 00008: val_acc improved from 0.60234 to 0.64327, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 215.7858 - acc: 0.6813 - val_loss: 0.8272 - val_acc: 0.6433\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 185.0067 - acc: 0.6701Epoch 00009: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 184.9617 - acc: 0.6701 - val_loss: 0.8088 - val_acc: 0.6374\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 156.5707 - acc: 0.6793Epoch 00010: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 156.5284 - acc: 0.6803 - val_loss: 0.8331 - val_acc: 0.6082\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 130.5208 - acc: 0.6783Epoch 00011: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 130.4825 - acc: 0.6793 - val_loss: 0.8075 - val_acc: 0.6140\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 106.8870 - acc: 0.6742Epoch 00012: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 106.8534 - acc: 0.6731 - val_loss: 0.8252 - val_acc: 0.6023\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 85.6181 - acc: 0.6752Epoch 00013: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 85.5880 - acc: 0.6752 - val_loss: 0.8217 - val_acc: 0.6023\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 66.7761 - acc: 0.6762Epoch 00014: val_acc improved from 0.64327 to 0.65497, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 66.7493 - acc: 0.6772 - val_loss: 0.8309 - val_acc: 0.6550\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 50.2987 - acc: 0.6711Epoch 00015: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 50.2778 - acc: 0.6691 - val_loss: 0.8272 - val_acc: 0.6140\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 36.2783 - acc: 0.6527Epoch 00016: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 36.2584 - acc: 0.6537 - val_loss: 0.8843 - val_acc: 0.5614\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 24.6377 - acc: 0.6301Epoch 00017: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 24.6217 - acc: 0.6302 - val_loss: 0.9609 - val_acc: 0.5088\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 15.4158 - acc: 0.5881Epoch 00018: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 15.4035 - acc: 0.5884 - val_loss: 0.8850 - val_acc: 0.6199\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.5337 - acc: 0.6096Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.5257 - acc: 0.6088 - val_loss: 0.9433 - val_acc: 0.5731\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 4.1146 - acc: 0.5010Epoch 00020: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 4.1105 - acc: 0.5005 - val_loss: 0.9909 - val_acc: 0.5731\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.9398 - acc: 0.4631Epoch 00021: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.9386 - acc: 0.4637 - val_loss: 1.0493 - val_acc: 0.3626\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.5121 - acc: 0.4293Epoch 00022: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.5121 - acc: 0.4290 - val_loss: 1.0417 - val_acc: 0.3977\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4416 - acc: 0.4068Epoch 00023: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4410 - acc: 0.4076 - val_loss: 1.0542 - val_acc: 0.4854\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4172 - acc: 0.4098Epoch 00024: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4175 - acc: 0.4096 - val_loss: 1.1015 - val_acc: 0.3216\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4502 - acc: 0.3709Epoch 00025: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4511 - acc: 0.3698 - val_loss: 1.0654 - val_acc: 0.4035\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4087 - acc: 0.3924Epoch 00026: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4083 - acc: 0.3933 - val_loss: 1.0772 - val_acc: 0.5205\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3634 - acc: 0.3893Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3629 - acc: 0.3892 - val_loss: 1.1565 - val_acc: 0.5029\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4119 - acc: 0.3863Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4118 - acc: 0.3861 - val_loss: 1.0626 - val_acc: 0.3977\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3707 - acc: 0.4057Epoch 00029: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3705 - acc: 0.4065 - val_loss: 1.0720 - val_acc: 0.3977\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3587 - acc: 0.3934Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3574 - acc: 0.3943 - val_loss: 1.0637 - val_acc: 0.5029\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3932 - acc: 0.3822Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3932 - acc: 0.3820 - val_loss: 1.1238 - val_acc: 0.2865\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3742 - acc: 0.3596Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3739 - acc: 0.3585 - val_loss: 1.1355 - val_acc: 0.2807\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3965 - acc: 0.3617Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3962 - acc: 0.3626 - val_loss: 1.0979 - val_acc: 0.3216\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3632 - acc: 0.3678Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3638 - acc: 0.3677 - val_loss: 1.0945 - val_acc: 0.3977\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3463 - acc: 0.4057Epoch 00035: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3460 - acc: 0.4065 - val_loss: 1.1052 - val_acc: 0.3977\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3550 - acc: 0.3525Epoch 00036: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3555 - acc: 0.3514 - val_loss: 1.0935 - val_acc: 0.4035\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3518 - acc: 0.3709Epoch 00037: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3522 - acc: 0.3708 - val_loss: 1.1383 - val_acc: 0.3977\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3729 - acc: 0.3996Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3730 - acc: 0.3994 - val_loss: 1.0860 - val_acc: 0.3977\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3685 - acc: 0.3586Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3679 - acc: 0.3596 - val_loss: 1.0756 - val_acc: 0.5322\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3624 - acc: 0.3852Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3620 - acc: 0.3861 - val_loss: 1.0646 - val_acc: 0.3977\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3671 - acc: 0.3750Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3668 - acc: 0.3749 - val_loss: 1.0963 - val_acc: 0.2807\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3562 - acc: 0.3719Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3558 - acc: 0.3718 - val_loss: 1.0855 - val_acc: 0.3977\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3606 - acc: 0.3791Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3599 - acc: 0.3800 - val_loss: 1.0682 - val_acc: 0.3977\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3831 - acc: 0.3945Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3826 - acc: 0.3953 - val_loss: 1.0842 - val_acc: 0.3977\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3727 - acc: 0.3781Epoch 00045: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3722 - acc: 0.3779 - val_loss: 1.0801 - val_acc: 0.5263\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3591 - acc: 0.3893Epoch 00046: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3590 - acc: 0.3882 - val_loss: 1.0867 - val_acc: 0.2865\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3688 - acc: 0.3883Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3695 - acc: 0.3871 - val_loss: 1.0786 - val_acc: 0.3392\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3628 - acc: 0.3822Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3625 - acc: 0.3820 - val_loss: 1.0775 - val_acc: 0.3977\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3745 - acc: 0.3525Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3749 - acc: 0.3524 - val_loss: 1.0790 - val_acc: 0.3977\n"
     ]
    }
   ],
   "source": [
    "d3_model3, d3_hist3 = train_categorical_model() # l1 regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
      "976/979 [============================>.] - ETA: 0s - loss: 1.2657 - acc: 0.3596Epoch 00000: val_acc improved from -inf to 0.42690, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 1.2650 - acc: 0.3585 - val_loss: 1.0652 - val_acc: 0.4269\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.1009 - acc: 0.3873Epoch 00001: val_acc improved from 0.42690 to 0.46784, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 1.0999 - acc: 0.3892 - val_loss: 1.0423 - val_acc: 0.4678\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0640 - acc: 0.4314Epoch 00002: val_acc improved from 0.46784 to 0.47368, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 1.0637 - acc: 0.4321 - val_loss: 1.0414 - val_acc: 0.4737\n",
      "Epoch 4/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0379 - acc: 0.4549Epoch 00003: val_acc improved from 0.47368 to 0.53801, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 1.0381 - acc: 0.4556 - val_loss: 1.0045 - val_acc: 0.5380\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0252 - acc: 0.4672Epoch 00004: val_acc improved from 0.53801 to 0.59064, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 1.0255 - acc: 0.4668 - val_loss: 0.9681 - val_acc: 0.5906\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.9962 - acc: 0.5092Epoch 00005: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.9955 - acc: 0.5107 - val_loss: 0.9555 - val_acc: 0.5731\n",
      "Epoch 7/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.9942 - acc: 0.4990Epoch 00006: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.9937 - acc: 0.5005 - val_loss: 0.9399 - val_acc: 0.5906\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.9842 - acc: 0.5031Epoch 00007: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.9850 - acc: 0.5036 - val_loss: 0.9518 - val_acc: 0.5906\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.9492 - acc: 0.5389Epoch 00008: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.9501 - acc: 0.5393 - val_loss: 0.9166 - val_acc: 0.5789\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.9364 - acc: 0.5492Epoch 00009: val_acc improved from 0.59064 to 0.63158, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.9368 - acc: 0.5495 - val_loss: 0.8918 - val_acc: 0.6316\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.9083 - acc: 0.5707Epoch 00010: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.9081 - acc: 0.5700 - val_loss: 0.8930 - val_acc: 0.5906\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8904 - acc: 0.5840Epoch 00011: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.8882 - acc: 0.5853 - val_loss: 0.8798 - val_acc: 0.5731\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8750 - acc: 0.5758Epoch 00012: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.8751 - acc: 0.5761 - val_loss: 0.8500 - val_acc: 0.5848\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8698 - acc: 0.5850Epoch 00013: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.8715 - acc: 0.5832 - val_loss: 0.8582 - val_acc: 0.6023\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8513 - acc: 0.5953Epoch 00014: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.8506 - acc: 0.5955 - val_loss: 0.8385 - val_acc: 0.6023\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8316 - acc: 0.6066Epoch 00015: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.8307 - acc: 0.6067 - val_loss: 0.8166 - val_acc: 0.6199\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8301 - acc: 0.6291Epoch 00016: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.8287 - acc: 0.6302 - val_loss: 0.8272 - val_acc: 0.6257\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8249 - acc: 0.6301Epoch 00017: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.8255 - acc: 0.6292 - val_loss: 0.8404 - val_acc: 0.5848\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8122 - acc: 0.6363Epoch 00018: val_acc improved from 0.63158 to 0.64327, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.8127 - acc: 0.6353 - val_loss: 0.8064 - val_acc: 0.6433\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7897 - acc: 0.6373Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.7893 - acc: 0.6374 - val_loss: 0.8244 - val_acc: 0.6316\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7986 - acc: 0.6363Epoch 00020: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.7981 - acc: 0.6364 - val_loss: 0.8193 - val_acc: 0.5789\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7620 - acc: 0.6486Epoch 00021: val_acc improved from 0.64327 to 0.66082, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.7608 - acc: 0.6496 - val_loss: 0.7783 - val_acc: 0.6608\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7597 - acc: 0.6434Epoch 00022: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.7599 - acc: 0.6435 - val_loss: 0.7870 - val_acc: 0.6374\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7448 - acc: 0.6783Epoch 00023: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.7449 - acc: 0.6793 - val_loss: 0.7715 - val_acc: 0.6374\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7382 - acc: 0.6855Epoch 00024: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.7365 - acc: 0.6864 - val_loss: 0.7836 - val_acc: 0.6374\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7315 - acc: 0.6711Epoch 00025: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.7307 - acc: 0.6711 - val_loss: 0.7725 - val_acc: 0.6491\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6967 - acc: 0.6834Epoch 00026: val_acc improved from 0.66082 to 0.66667, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.6967 - acc: 0.6844 - val_loss: 0.7609 - val_acc: 0.6667\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6934 - acc: 0.6875Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.6926 - acc: 0.6874 - val_loss: 0.7523 - val_acc: 0.6491\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7120 - acc: 0.6803Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.7131 - acc: 0.6793 - val_loss: 0.7722 - val_acc: 0.6433\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7040 - acc: 0.6783Epoch 00029: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.7035 - acc: 0.6782 - val_loss: 0.7854 - val_acc: 0.6491\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6776 - acc: 0.7275Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.6774 - acc: 0.7273 - val_loss: 0.7777 - val_acc: 0.6316\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6541 - acc: 0.7254Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.6536 - acc: 0.7252 - val_loss: 0.7746 - val_acc: 0.6550\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6516 - acc: 0.7254Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.6502 - acc: 0.7263 - val_loss: 0.8105 - val_acc: 0.6257\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6440 - acc: 0.7398Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.6448 - acc: 0.7385 - val_loss: 0.7660 - val_acc: 0.6667\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6386 - acc: 0.7080Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.6379 - acc: 0.7079 - val_loss: 0.7778 - val_acc: 0.6199\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6286 - acc: 0.7162Epoch 00035: val_acc improved from 0.66667 to 0.67251, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 0.6276 - acc: 0.7171 - val_loss: 0.7482 - val_acc: 0.6725\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5980 - acc: 0.7510Epoch 00036: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5983 - acc: 0.7508 - val_loss: 0.7395 - val_acc: 0.6491\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5888 - acc: 0.7623Epoch 00037: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5886 - acc: 0.7620 - val_loss: 0.7549 - val_acc: 0.6725\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6117 - acc: 0.7377Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.6125 - acc: 0.7365 - val_loss: 0.7650 - val_acc: 0.6433\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.6108 - acc: 0.7346Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.6110 - acc: 0.7344 - val_loss: 0.7333 - val_acc: 0.6550\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5827 - acc: 0.7766Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5818 - acc: 0.7773 - val_loss: 0.7355 - val_acc: 0.6433\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5501 - acc: 0.7879Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5504 - acc: 0.7875 - val_loss: 0.7368 - val_acc: 0.6608\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5315 - acc: 0.7992Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5315 - acc: 0.7998 - val_loss: 0.7775 - val_acc: 0.6608\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5205 - acc: 0.7807Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5207 - acc: 0.7814 - val_loss: 0.7829 - val_acc: 0.6374\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5109 - acc: 0.8115Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5109 - acc: 0.8110 - val_loss: 0.7463 - val_acc: 0.6550\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5011 - acc: 0.8033Epoch 00045: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5003 - acc: 0.8039 - val_loss: 0.7408 - val_acc: 0.6491\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5066 - acc: 0.7971Epoch 00046: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5053 - acc: 0.7978 - val_loss: 0.7413 - val_acc: 0.6608\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.5153 - acc: 0.8156Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.5165 - acc: 0.8151 - val_loss: 0.7366 - val_acc: 0.6491\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.4983 - acc: 0.8064Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.4983 - acc: 0.8069 - val_loss: 0.7437 - val_acc: 0.6491\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.4484 - acc: 0.8340Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 0.4477 - acc: 0.8345 - val_loss: 0.7536 - val_acc: 0.6257\n"
     ]
    }
   ],
   "source": [
    "d3_model4, d3_hist4 = train_categorical_model() # double dense and dropout layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
      "976/979 [============================>.] - ETA: 0s - loss: 8.8480 - acc: 0.3514Epoch 00000: val_acc improved from -inf to 0.32164, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 8.8472 - acc: 0.3514 - val_loss: 1.0879 - val_acc: 0.3216\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.6976 - acc: 0.3904Epoch 00001: val_acc improved from 0.32164 to 0.45029, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 8.6967 - acc: 0.3922 - val_loss: 1.0286 - val_acc: 0.4503\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.6254 - acc: 0.4426Epoch 00002: val_acc improved from 0.45029 to 0.53801, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 8.6266 - acc: 0.4423 - val_loss: 0.9922 - val_acc: 0.5380\n",
      "Epoch 4/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.5943 - acc: 0.4652Epoch 00003: val_acc improved from 0.53801 to 0.61404, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 8.5941 - acc: 0.4668 - val_loss: 1.0023 - val_acc: 0.6140\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.5418 - acc: 0.4816Epoch 00004: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.5421 - acc: 0.4821 - val_loss: 0.9478 - val_acc: 0.5614\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.5041 - acc: 0.5174Epoch 00005: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.5030 - acc: 0.5179 - val_loss: 0.9388 - val_acc: 0.5965\n",
      "Epoch 7/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.4710 - acc: 0.5195Epoch 00006: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.4722 - acc: 0.5189 - val_loss: 0.9287 - val_acc: 0.6082\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.4365 - acc: 0.5502Epoch 00007: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.4357 - acc: 0.5506 - val_loss: 0.9060 - val_acc: 0.5848\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.3953 - acc: 0.5348Epoch 00008: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.3964 - acc: 0.5332 - val_loss: 0.9026 - val_acc: 0.5965\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.3559 - acc: 0.5645Epoch 00009: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.3561 - acc: 0.5638 - val_loss: 0.8733 - val_acc: 0.5731\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.3417 - acc: 0.5584Epoch 00010: val_acc improved from 0.61404 to 0.64327, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 8.3414 - acc: 0.5587 - val_loss: 0.8613 - val_acc: 0.6433\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.2877 - acc: 0.5881Epoch 00011: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.2880 - acc: 0.5873 - val_loss: 0.8340 - val_acc: 0.6023\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.2663 - acc: 0.5850Epoch 00012: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.2657 - acc: 0.5853 - val_loss: 0.8405 - val_acc: 0.6023\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.2154 - acc: 0.6096Epoch 00013: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.2141 - acc: 0.6108 - val_loss: 0.8405 - val_acc: 0.5965\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.1980 - acc: 0.6096Epoch 00014: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.1973 - acc: 0.6098 - val_loss: 0.8136 - val_acc: 0.5965\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.1589 - acc: 0.6086Epoch 00015: val_acc improved from 0.64327 to 0.64327, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 8.1583 - acc: 0.6088 - val_loss: 0.8089 - val_acc: 0.6433\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.1346 - acc: 0.6445Epoch 00016: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.1342 - acc: 0.6445 - val_loss: 0.7871 - val_acc: 0.6316\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.1110 - acc: 0.6393Epoch 00017: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.1108 - acc: 0.6394 - val_loss: 0.8125 - val_acc: 0.6257\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.0735 - acc: 0.6547Epoch 00018: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.0748 - acc: 0.6537 - val_loss: 0.7939 - val_acc: 0.6199\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.0399 - acc: 0.6578Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 8.0396 - acc: 0.6578 - val_loss: 0.7795 - val_acc: 0.6316\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 8.0200 - acc: 0.6732Epoch 00020: val_acc improved from 0.64327 to 0.66082, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 8.0190 - acc: 0.6742 - val_loss: 0.7778 - val_acc: 0.6608\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.9956 - acc: 0.6434Epoch 00021: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.9962 - acc: 0.6425 - val_loss: 0.7776 - val_acc: 0.6316\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.9479 - acc: 0.6578Epoch 00022: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.9461 - acc: 0.6588 - val_loss: 0.7776 - val_acc: 0.6140\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.9283 - acc: 0.6906Epoch 00023: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.9275 - acc: 0.6915 - val_loss: 0.7573 - val_acc: 0.6608\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.8867 - acc: 0.6916Epoch 00024: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.8854 - acc: 0.6925 - val_loss: 0.7678 - val_acc: 0.6257\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.8705 - acc: 0.6834Epoch 00025: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.8695 - acc: 0.6844 - val_loss: 0.7835 - val_acc: 0.6082\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.8447 - acc: 0.6967Epoch 00026: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.8444 - acc: 0.6966 - val_loss: 0.7470 - val_acc: 0.6140\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.8177 - acc: 0.7162Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.8187 - acc: 0.7150 - val_loss: 0.7554 - val_acc: 0.6374\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.8128 - acc: 0.6895Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.8135 - acc: 0.6895 - val_loss: 0.7780 - val_acc: 0.6199\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.7816 - acc: 0.7080Epoch 00029: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.7815 - acc: 0.7079 - val_loss: 0.7646 - val_acc: 0.6257\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.7582 - acc: 0.7131Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.7583 - acc: 0.7130 - val_loss: 0.7650 - val_acc: 0.6140\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.7185 - acc: 0.7316Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.7174 - acc: 0.7324 - val_loss: 0.7432 - val_acc: 0.6374\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.6506 - acc: 0.7592Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.6506 - acc: 0.7589 - val_loss: 0.7436 - val_acc: 0.6433\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.6706 - acc: 0.7295Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.6709 - acc: 0.7283 - val_loss: 0.7515 - val_acc: 0.6433\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.6242 - acc: 0.7469Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.6231 - acc: 0.7477 - val_loss: 0.7534 - val_acc: 0.6433\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.5971 - acc: 0.7439Epoch 00035: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.5960 - acc: 0.7446 - val_loss: 0.7701 - val_acc: 0.6082\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.5844 - acc: 0.7500Epoch 00036: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.5844 - acc: 0.7508 - val_loss: 0.7542 - val_acc: 0.6433\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.5338 - acc: 0.7756Epoch 00037: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.5337 - acc: 0.7763 - val_loss: 0.7490 - val_acc: 0.6433\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.5115 - acc: 0.7859Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.5103 - acc: 0.7865 - val_loss: 0.7696 - val_acc: 0.6374\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.5011 - acc: 0.7602Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.5003 - acc: 0.7610 - val_loss: 0.7961 - val_acc: 0.6199\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.4737 - acc: 0.7664Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.4746 - acc: 0.7651 - val_loss: 0.7628 - val_acc: 0.6374\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.4534 - acc: 0.7807Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.4533 - acc: 0.7804 - val_loss: 0.7370 - val_acc: 0.6550\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.4324 - acc: 0.7695Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.4332 - acc: 0.7692 - val_loss: 0.7441 - val_acc: 0.6199\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.3930 - acc: 0.8002Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.3930 - acc: 0.8008 - val_loss: 0.7757 - val_acc: 0.6316\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.3777 - acc: 0.7777Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.3775 - acc: 0.7783 - val_loss: 0.7405 - val_acc: 0.6199\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.3450 - acc: 0.7961Epoch 00045: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.3459 - acc: 0.7957 - val_loss: 0.7397 - val_acc: 0.6257\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.3346 - acc: 0.7930Epoch 00046: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.3354 - acc: 0.7916 - val_loss: 0.7391 - val_acc: 0.6257\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.2923 - acc: 0.8084Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.2934 - acc: 0.8069 - val_loss: 0.7566 - val_acc: 0.6257\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.2586 - acc: 0.8094Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.2587 - acc: 0.8090 - val_loss: 0.7448 - val_acc: 0.6491\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 7.2532 - acc: 0.8156Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 7.2523 - acc: 0.8161 - val_loss: 0.7485 - val_acc: 0.6316\n"
     ]
    }
   ],
   "source": [
    "d3_model5, d3_hist5 = train_categorical_model() # double dense and dropout layers with regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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",
      "976/979 [============================>.] - ETA: 0s - loss: 1.5143 - acc: 0.3391Epoch 00000: val_acc improved from -inf to 0.42105, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 0s - loss: 1.5132 - acc: 0.3391 - val_loss: 1.0902 - val_acc: 0.4211\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.1145 - acc: 0.3637Epoch 00001: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.1146 - acc: 0.3647 - val_loss: 1.0951 - val_acc: 0.4211\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.1011 - acc: 0.3668Epoch 00002: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.1009 - acc: 0.3677 - val_loss: 1.0934 - val_acc: 0.3977\n",
      "Epoch 4/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0998 - acc: 0.3832Epoch 00003: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0998 - acc: 0.3841 - val_loss: 1.0965 - val_acc: 0.4094\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0997 - acc: 0.3863Epoch 00004: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0997 - acc: 0.3851 - val_loss: 1.0966 - val_acc: 0.3918\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0980 - acc: 0.3832Epoch 00005: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0980 - acc: 0.3830 - val_loss: 1.0868 - val_acc: 0.4152\n",
      "Epoch 7/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0998 - acc: 0.4037Epoch 00006: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0998 - acc: 0.4035 - val_loss: 1.0955 - val_acc: 0.3918\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0957 - acc: 0.3791Epoch 00007: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0956 - acc: 0.3810 - val_loss: 1.0948 - val_acc: 0.3977\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0952 - acc: 0.3924Epoch 00008: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0953 - acc: 0.3912 - val_loss: 1.0945 - val_acc: 0.3801\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0940 - acc: 0.3852Epoch 00009: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0938 - acc: 0.3851 - val_loss: 1.0913 - val_acc: 0.3977\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0976 - acc: 0.3852Epoch 00010: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0976 - acc: 0.3851 - val_loss: 1.0928 - val_acc: 0.3977\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0955 - acc: 0.3934Epoch 00011: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0956 - acc: 0.3922 - val_loss: 1.0933 - val_acc: 0.3977\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0934 - acc: 0.3955Epoch 00012: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0934 - acc: 0.3953 - val_loss: 1.0928 - val_acc: 0.3977\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0925 - acc: 0.3965Epoch 00013: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0927 - acc: 0.3953 - val_loss: 1.0926 - val_acc: 0.3977\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0915 - acc: 0.3975Epoch 00014: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0916 - acc: 0.3973 - val_loss: 1.0916 - val_acc: 0.3977\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0916 - acc: 0.3842Epoch 00015: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0918 - acc: 0.3830 - val_loss: 1.0789 - val_acc: 0.3977\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0974 - acc: 0.3955Epoch 00016: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0975 - acc: 0.3943 - val_loss: 1.0921 - val_acc: 0.3977\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0920 - acc: 0.3965Epoch 00017: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0920 - acc: 0.3963 - val_loss: 1.0916 - val_acc: 0.3977\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0920 - acc: 0.3965Epoch 00018: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0920 - acc: 0.3963 - val_loss: 1.0908 - val_acc: 0.3977\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0922 - acc: 0.3924Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0923 - acc: 0.3912 - val_loss: 1.0904 - val_acc: 0.3977\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0920 - acc: 0.3883Epoch 00020: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0919 - acc: 0.3892 - val_loss: 1.0909 - val_acc: 0.3977\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0913 - acc: 0.3955Epoch 00021: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0912 - acc: 0.3963 - val_loss: 1.0908 - val_acc: 0.3977\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0894 - acc: 0.3863Epoch 00022: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0896 - acc: 0.3861 - val_loss: 1.0763 - val_acc: 0.3977\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0864 - acc: 0.3873Epoch 00023: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0865 - acc: 0.3871 - val_loss: 1.0896 - val_acc: 0.3977\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0913 - acc: 0.3945Epoch 00024: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0914 - acc: 0.3943 - val_loss: 1.0897 - val_acc: 0.3977\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0916 - acc: 0.3945Epoch 00025: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0915 - acc: 0.3943 - val_loss: 1.0900 - val_acc: 0.3977\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0899 - acc: 0.3955Epoch 00026: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0898 - acc: 0.3963 - val_loss: 1.0900 - val_acc: 0.3977\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0842 - acc: 0.3934Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0853 - acc: 0.3922 - val_loss: 1.0795 - val_acc: 0.3977\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0915 - acc: 0.3883Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0914 - acc: 0.3892 - val_loss: 1.0885 - val_acc: 0.3977\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0905 - acc: 0.3955Epoch 00029: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0905 - acc: 0.3953 - val_loss: 1.0892 - val_acc: 0.3977\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0900 - acc: 0.3934Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0901 - acc: 0.3933 - val_loss: 1.0895 - val_acc: 0.3977\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0909 - acc: 0.3924Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0906 - acc: 0.3943 - val_loss: 1.0893 - val_acc: 0.3977\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0896 - acc: 0.3955Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0896 - acc: 0.3953 - val_loss: 1.0889 - val_acc: 0.3977\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0900 - acc: 0.3955Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0898 - acc: 0.3963 - val_loss: 1.0892 - val_acc: 0.3977\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0900 - acc: 0.3945Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0899 - acc: 0.3953 - val_loss: 1.0889 - val_acc: 0.3977\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0898 - acc: 0.3965Epoch 00035: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0899 - acc: 0.3953 - val_loss: 1.0889 - val_acc: 0.3977\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0898 - acc: 0.3945Epoch 00036: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0900 - acc: 0.3933 - val_loss: 1.0880 - val_acc: 0.3977\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0895 - acc: 0.3975Epoch 00037: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0895 - acc: 0.3973 - val_loss: 1.0869 - val_acc: 0.3977\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0912 - acc: 0.3945Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0911 - acc: 0.3943 - val_loss: 1.0887 - val_acc: 0.3977\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0892 - acc: 0.3965Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0891 - acc: 0.3973 - val_loss: 1.0884 - val_acc: 0.3977\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0905 - acc: 0.3955Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0905 - acc: 0.3953 - val_loss: 1.0883 - val_acc: 0.3977\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0888 - acc: 0.3965Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0888 - acc: 0.3963 - val_loss: 1.0841 - val_acc: 0.3977\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0894 - acc: 0.4047Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0892 - acc: 0.4055 - val_loss: 1.0884 - val_acc: 0.3977\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0886 - acc: 0.3955Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0888 - acc: 0.3943 - val_loss: 1.0884 - val_acc: 0.3977\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0893 - acc: 0.3945Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0893 - acc: 0.3943 - val_loss: 1.0882 - val_acc: 0.3977\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0895 - acc: 0.3955Epoch 00045: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0896 - acc: 0.3943 - val_loss: 1.0883 - val_acc: 0.3977\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0887 - acc: 0.3945Epoch 00046: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0886 - acc: 0.3953 - val_loss: 1.0883 - val_acc: 0.3977\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0898 - acc: 0.3955Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0900 - acc: 0.3943 - val_loss: 1.0882 - val_acc: 0.3977\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0895 - acc: 0.3965Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0896 - acc: 0.3963 - val_loss: 1.0880 - val_acc: 0.3977\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.0888 - acc: 0.3955Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 0s - loss: 1.0888 - acc: 0.3953 - val_loss: 1.0881 - val_acc: 0.3977\n"
     ]
    }
   ],
   "source": [
    "d3_model6, d3_hist6 = train_categorical_model() # double dense with aggressive dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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",
      "976/979 [============================>.] - ETA: 0s - loss: 6.3249 - acc: 0.4006Epoch 00000: val_acc improved from -inf to 0.39766, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 6.3256 - acc: 0.3994 - val_loss: 1.0454 - val_acc: 0.3977\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 6.1304 - acc: 0.4365Epoch 00001: val_acc improved from 0.39766 to 0.53801, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 6.1301 - acc: 0.4372 - val_loss: 1.0185 - val_acc: 0.5380\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 6.0747 - acc: 0.4672Epoch 00002: val_acc improved from 0.53801 to 0.59649, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 6.0741 - acc: 0.4678 - val_loss: 0.9916 - val_acc: 0.5965\n",
      "Epoch 4/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 6.0512 - acc: 0.4846Epoch 00003: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 6.0515 - acc: 0.4842 - val_loss: 0.9564 - val_acc: 0.5673\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 6.0050 - acc: 0.5092Epoch 00004: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 6.0053 - acc: 0.5087 - val_loss: 0.9307 - val_acc: 0.5906\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.9722 - acc: 0.5195Epoch 00005: val_acc improved from 0.59649 to 0.60234, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.9727 - acc: 0.5189 - val_loss: 0.9168 - val_acc: 0.6023\n",
      "Epoch 7/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.9398 - acc: 0.5553Epoch 00006: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.9399 - acc: 0.5557 - val_loss: 0.9078 - val_acc: 0.5848\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.9143 - acc: 0.5574Epoch 00007: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.9146 - acc: 0.5577 - val_loss: 0.8931 - val_acc: 0.5965\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.8710 - acc: 0.5738Epoch 00008: val_acc improved from 0.60234 to 0.61404, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.8703 - acc: 0.5741 - val_loss: 0.9035 - val_acc: 0.6140\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.8428 - acc: 0.5697Epoch 00009: val_acc improved from 0.61404 to 0.61404, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.8437 - acc: 0.5689 - val_loss: 0.8533 - val_acc: 0.6140\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.8235 - acc: 0.5943Epoch 00010: val_acc improved from 0.61404 to 0.66667, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 5.8237 - acc: 0.5935 - val_loss: 0.8494 - val_acc: 0.6667\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.8014 - acc: 0.5902Epoch 00011: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.8009 - acc: 0.5894 - val_loss: 0.8354 - val_acc: 0.6374\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.7727 - acc: 0.6240Epoch 00012: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.7729 - acc: 0.6241 - val_loss: 0.8289 - val_acc: 0.6082\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.7472 - acc: 0.6363Epoch 00013: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.7475 - acc: 0.6353 - val_loss: 0.8156 - val_acc: 0.5848\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.7201 - acc: 0.6219Epoch 00014: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.7197 - acc: 0.6221 - val_loss: 0.8091 - val_acc: 0.6199\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.6869 - acc: 0.6516Epoch 00015: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.6854 - acc: 0.6527 - val_loss: 0.8123 - val_acc: 0.6199\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.6764 - acc: 0.6311Epoch 00016: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.6758 - acc: 0.6313 - val_loss: 0.8212 - val_acc: 0.6199\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.6573 - acc: 0.6465Epoch 00017: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.6567 - acc: 0.6476 - val_loss: 0.8106 - val_acc: 0.6257\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.6433 - acc: 0.6363Epoch 00018: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.6426 - acc: 0.6364 - val_loss: 0.8176 - val_acc: 0.6316\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.6014 - acc: 0.6701Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.6012 - acc: 0.6701 - val_loss: 0.8098 - val_acc: 0.5965\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.5851 - acc: 0.6639Epoch 00020: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.5860 - acc: 0.6629 - val_loss: 0.7896 - val_acc: 0.6491\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.5636 - acc: 0.6701Epoch 00021: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.5643 - acc: 0.6701 - val_loss: 0.7869 - val_acc: 0.5965\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.5537 - acc: 0.6773Epoch 00022: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.5539 - acc: 0.6772 - val_loss: 0.7942 - val_acc: 0.6433\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.5285 - acc: 0.6885Epoch 00023: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.5275 - acc: 0.6895 - val_loss: 0.8284 - val_acc: 0.6374\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.5154 - acc: 0.6824Epoch 00024: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.5137 - acc: 0.6834 - val_loss: 0.7758 - val_acc: 0.6199\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4630 - acc: 0.6885Epoch 00025: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.4630 - acc: 0.6885 - val_loss: 0.7692 - val_acc: 0.6082\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4627 - acc: 0.7059Epoch 00026: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.4624 - acc: 0.7068 - val_loss: 0.8249 - val_acc: 0.6433\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4723 - acc: 0.6865Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.4755 - acc: 0.6864 - val_loss: 0.7583 - val_acc: 0.6550\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4193 - acc: 0.7152Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.4192 - acc: 0.7150 - val_loss: 0.7751 - val_acc: 0.6374\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.4088 - acc: 0.7080Epoch 00029: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.4084 - acc: 0.7079 - val_loss: 0.7551 - val_acc: 0.5965\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3800 - acc: 0.7100Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.3797 - acc: 0.7109 - val_loss: 0.7643 - val_acc: 0.6550\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3498 - acc: 0.7182Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.3488 - acc: 0.7191 - val_loss: 0.7543 - val_acc: 0.6491\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3440 - acc: 0.7203Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.3446 - acc: 0.7191 - val_loss: 0.7647 - val_acc: 0.6257\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3321 - acc: 0.7162Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.3325 - acc: 0.7160 - val_loss: 0.7383 - val_acc: 0.6316\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.3012 - acc: 0.7357Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.3010 - acc: 0.7365 - val_loss: 0.7380 - val_acc: 0.6374\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2952 - acc: 0.7254Epoch 00035: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.2957 - acc: 0.7242 - val_loss: 0.7393 - val_acc: 0.6433\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2626 - acc: 0.7357Epoch 00036: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.2622 - acc: 0.7354 - val_loss: 0.7449 - val_acc: 0.6374\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2531 - acc: 0.7439Epoch 00037: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.2534 - acc: 0.7436 - val_loss: 0.7367 - val_acc: 0.6316\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2322 - acc: 0.7510Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.2340 - acc: 0.7497 - val_loss: 0.7420 - val_acc: 0.6199\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.2293 - acc: 0.7469Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.2293 - acc: 0.7477 - val_loss: 0.7409 - val_acc: 0.6199\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1794 - acc: 0.7715Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.1791 - acc: 0.7712 - val_loss: 0.7377 - val_acc: 0.6199\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1834 - acc: 0.7602Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.1828 - acc: 0.7600 - val_loss: 0.7391 - val_acc: 0.6550\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1687 - acc: 0.7654Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.1684 - acc: 0.7661 - val_loss: 0.7650 - val_acc: 0.6257\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.1270 - acc: 0.7684Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.1262 - acc: 0.7692 - val_loss: 0.7447 - val_acc: 0.6491\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0877 - acc: 0.7920Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0872 - acc: 0.7916 - val_loss: 0.7359 - val_acc: 0.6433\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0974 - acc: 0.8033Epoch 00045: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0990 - acc: 0.8029 - val_loss: 0.7464 - val_acc: 0.6491\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0829 - acc: 0.7879Epoch 00046: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0826 - acc: 0.7886 - val_loss: 0.7334 - val_acc: 0.6491\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0651 - acc: 0.7889Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0651 - acc: 0.7886 - val_loss: 0.7413 - val_acc: 0.6316\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0377 - acc: 0.7879Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0388 - acc: 0.7865 - val_loss: 0.7321 - val_acc: 0.6257\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 5.0426 - acc: 0.7838Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 5.0423 - acc: 0.7845 - val_loss: 0.7330 - val_acc: 0.6433\n"
     ]
    }
   ],
   "source": [
    "d3_model7, d3_hist7 = train_categorical_model() # l2 regularization and aggresive dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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",
      "976/979 [============================>.] - ETA: 0s - loss: 10.5105 - acc: 0.3873Epoch 00000: val_acc improved from -inf to 0.39766, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 10.5279 - acc: 0.3861 - val_loss: 9.7086 - val_acc: 0.3977\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 6.5000 - acc: 0.3791Epoch 00001: val_acc improved from 0.39766 to 0.49708, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 6.4850 - acc: 0.3790 - val_loss: 0.9769 - val_acc: 0.4971\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.9327 - acc: 0.4857Epoch 00002: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.9286 - acc: 0.4872 - val_loss: 1.7564 - val_acc: 0.4035\n",
      "Epoch 4/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.8165 - acc: 0.4693Epoch 00003: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.8148 - acc: 0.4699 - val_loss: 1.0865 - val_acc: 0.4620\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.9868 - acc: 0.5082Epoch 00004: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.9886 - acc: 0.5077 - val_loss: 1.4189 - val_acc: 0.4854\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.8388 - acc: 0.5102Epoch 00005: val_acc improved from 0.49708 to 0.56725, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 1.8371 - acc: 0.5107 - val_loss: 1.0879 - val_acc: 0.5673\n",
      "Epoch 7/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.8226 - acc: 0.5287Epoch 00006: val_acc improved from 0.56725 to 0.58480, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 1.8215 - acc: 0.5281 - val_loss: 0.8840 - val_acc: 0.5848\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.9552 - acc: 0.5082Epoch 00007: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.9564 - acc: 0.5077 - val_loss: 1.0928 - val_acc: 0.4269\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.7748 - acc: 0.5266Epoch 00008: val_acc improved from 0.58480 to 0.60234, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 1.7736 - acc: 0.5260 - val_loss: 0.7856 - val_acc: 0.6023\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.9256 - acc: 0.5359Epoch 00009: val_acc improved from 0.60234 to 0.61988, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 1.9297 - acc: 0.5352 - val_loss: 0.8668 - val_acc: 0.6199\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.7424 - acc: 0.5389Epoch 00010: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.7398 - acc: 0.5403 - val_loss: 0.7870 - val_acc: 0.6199\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4864 - acc: 0.5738Epoch 00011: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4840 - acc: 0.5751 - val_loss: 1.5445 - val_acc: 0.5965\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.9175 - acc: 0.5297Epoch 00012: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.9143 - acc: 0.5301 - val_loss: 0.9209 - val_acc: 0.5673\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.6721 - acc: 0.5768Epoch 00013: val_acc improved from 0.61988 to 0.64912, saving model to data3_1/top_model_weights.h5\n",
      "979/979 [==============================] - 1s - loss: 1.6684 - acc: 0.5781 - val_loss: 0.8399 - val_acc: 0.6491\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.7666 - acc: 0.5420Epoch 00014: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.7636 - acc: 0.5424 - val_loss: 1.7732 - val_acc: 0.4678\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.7133 - acc: 0.5564Epoch 00015: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.7157 - acc: 0.5557 - val_loss: 2.6118 - val_acc: 0.4386\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.8225 - acc: 0.5492Epoch 00016: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.8212 - acc: 0.5495 - val_loss: 1.2600 - val_acc: 0.4854\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.5589 - acc: 0.5943Epoch 00017: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.5584 - acc: 0.5945 - val_loss: 0.8756 - val_acc: 0.5673\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.6736 - acc: 0.5645Epoch 00018: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.6732 - acc: 0.5638 - val_loss: 0.8543 - val_acc: 0.5906\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4950 - acc: 0.5748Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4926 - acc: 0.5751 - val_loss: 2.1399 - val_acc: 0.5556\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.6261 - acc: 0.5809Epoch 00020: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.6234 - acc: 0.5812 - val_loss: 1.1625 - val_acc: 0.5965\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4880 - acc: 0.5984Epoch 00021: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4861 - acc: 0.5986 - val_loss: 2.2179 - val_acc: 0.3801\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.5700 - acc: 0.5748Epoch 00022: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.5661 - acc: 0.5761 - val_loss: 1.2643 - val_acc: 0.5380\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3478 - acc: 0.6250Epoch 00023: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3507 - acc: 0.6241 - val_loss: 0.7505 - val_acc: 0.6374\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4932 - acc: 0.5932Epoch 00024: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4948 - acc: 0.5924 - val_loss: 0.8855 - val_acc: 0.5673\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4566 - acc: 0.6055Epoch 00025: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4544 - acc: 0.6057 - val_loss: 1.0271 - val_acc: 0.5789\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.5413 - acc: 0.5779Epoch 00026: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.5399 - acc: 0.5771 - val_loss: 1.5795 - val_acc: 0.4503\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3472 - acc: 0.6209Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3453 - acc: 0.6210 - val_loss: 1.4918 - val_acc: 0.5380\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4030 - acc: 0.5768Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4029 - acc: 0.5761 - val_loss: 1.2716 - val_acc: 0.5556\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4971 - acc: 0.6014Epoch 00029: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4969 - acc: 0.5996 - val_loss: 0.8099 - val_acc: 0.6082\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4324 - acc: 0.6096Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4307 - acc: 0.6098 - val_loss: 1.1888 - val_acc: 0.5848\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.6227 - acc: 0.5902Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.6232 - acc: 0.5904 - val_loss: 1.7905 - val_acc: 0.5380\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.5262 - acc: 0.6066Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.5256 - acc: 0.6067 - val_loss: 1.5645 - val_acc: 0.5380\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.5686 - acc: 0.5850Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.5695 - acc: 0.5843 - val_loss: 0.8140 - val_acc: 0.6374\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3757 - acc: 0.6209Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3739 - acc: 0.6221 - val_loss: 1.3318 - val_acc: 0.5088\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.2709 - acc: 0.6486Epoch 00035: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.2679 - acc: 0.6496 - val_loss: 0.8452 - val_acc: 0.5789\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.5327 - acc: 0.6281Epoch 00036: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.5327 - acc: 0.6282 - val_loss: 1.1710 - val_acc: 0.5673\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4559 - acc: 0.6414Epoch 00037: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4609 - acc: 0.6404 - val_loss: 1.4873 - val_acc: 0.5789\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4034 - acc: 0.6014Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4009 - acc: 0.6027 - val_loss: 1.9142 - val_acc: 0.4561\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3261 - acc: 0.6199Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3237 - acc: 0.6210 - val_loss: 1.2241 - val_acc: 0.5731\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4340 - acc: 0.6127Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4471 - acc: 0.6129 - val_loss: 2.1749 - val_acc: 0.5497\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.6063 - acc: 0.5973Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.6047 - acc: 0.5975 - val_loss: 1.0718 - val_acc: 0.5731\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.2342 - acc: 0.6311Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.2364 - acc: 0.6302 - val_loss: 1.3822 - val_acc: 0.4561\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4367 - acc: 0.6281Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4346 - acc: 0.6292 - val_loss: 2.4386 - val_acc: 0.4327\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.2702 - acc: 0.6168Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.2769 - acc: 0.6159 - val_loss: 2.8843 - val_acc: 0.3392\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.4200 - acc: 0.5984Epoch 00045: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.4202 - acc: 0.5986 - val_loss: 1.0823 - val_acc: 0.6140\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.2741 - acc: 0.6475Epoch 00046: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.2720 - acc: 0.6486 - val_loss: 0.8713 - val_acc: 0.6316\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.2571 - acc: 0.6404Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.2618 - acc: 0.6384 - val_loss: 3.5736 - val_acc: 0.3333\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.3403 - acc: 0.6475Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.3407 - acc: 0.6476 - val_loss: 0.8962 - val_acc: 0.6433\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 1.2626 - acc: 0.6506Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 1s - loss: 1.2609 - acc: 0.6507 - val_loss: 0.9172 - val_acc: 0.6257\n"
     ]
    }
   ],
   "source": [
    "d3_model8, d3_hist8 = train_categorical_model() # l2 regularization, rmsprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 13 , val_acc: 0.666666668061 , val_loss: 0.742396930156\n"
     ]
    },
    {
     "data": {
      "image/png": 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3r0+l8qU+O/TB2MiADm0d6NDWIRsjFHmNmZlZio2VXrx4gbm5eYbOdT/qjUX+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\nAPD/bFNIBiOTbDsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17677e9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WINNER\n",
    "plot_metrics(d3_hist2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 27 , val_acc: 0.678362572751 , val_loss: 0.764172005375\n"
     ]
    },
    {
     "data": {
      "image/png": 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4eA1KpZKhA7vTvKkvzfzrl+gV3equVTmw68kLPQhR1liYm9L6pca0fqmxelt+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\n0LRGHZr51aFpjTr4V65uFgmzEMK8mXRi/GDiXYN6siKFEI9r7qyXit2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8eaxcuZLt27cT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\nIYQA4P8BtIAwuGw+KSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f947fa3aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d3_hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 15 , val_acc: 0.654970761977 , val_loss: 0.830861142504\n"
     ]
    },
    {
     "data": {
      "image/png": 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TQPPeiKF9sbay4PW3v2DsxA/o1qU9YfuOUtfFiWULvqRFs7ITVGtrS96ePJaXxg5h4W/r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\nIiIiqDAWE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9497f32f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d3_hist3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 36 , val_acc: 0.672514619534 , val_loss: 0.748187764346\n"
     ]
    },
    {
     "data": {
      "image/png": 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ujvPHEh8fzx/jFhD5ISqjQxJCiFSRxFgIwT734wz+YzZR0THMmzGSLRvmkcc0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\nEREREUCFsYiIiIgIAP8PiTVU6YJ5RrcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94a16361d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d3_hist4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 21 , val_acc: 0.660818713102 , val_loss: 0.777828329836\n"
     ]
    },
    {
     "data": {
      "image/png": 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L59KvEEK1aWlp4uhgg6ODzWPbKZVKomPiuHDpOhcuXefi5RtEx9wnOiaOkBvh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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f948b025310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d3_hist5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 1 , val_acc: 0.421052632276 , val_loss: 1.09023717412\n"
     ]
    },
    {
     "data": {
      "image/png": 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QQgghxDNs3rx5/PPPP7leW716NZ9++mmB57Rs2RKAuLg43nzzzXyPGTt2LBcvXiz03qtX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94893b76d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d3_hist6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 11 , val_acc: 0.666666668409 , val_loss: 0.849356167498\n"
     ]
    },
    {
     "data": {
      "image/png": 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vjWD4kP/x6fC+6OhoP3HMrZBwNm3bx9E/znLj5h0yMjPz7K9SqRyL50+SuYSF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\nFRrLnTzWZzhf7tzMJ1u/YVSXvkSePEqWPvuOs43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z589HoVDQvXt32rdvX64ALe2fNop7\ns2Pciq82oc/J4bFJoVIUCyGsxvXr13nppZcwGo0YjUYeeughunXrVtlhCSFEpSuxMFar1TzzzDOM\nGDGCjRs3snHjRjZv3syOHTvuRXxlsuvYb6iUSro2bm3xa2VmZrFqzfe4u7swalg/i19PCCEqygMP\nPMDGjRsrOwwhhKhy7lgY5+bmEhERwXfffceff/5Jbm4uy5cvp2XLlvcqvlJLSrvJ7+dO07ZBY1wd\nnCx+vW82bCcp6SbTnhqHTmdv8esJIYQQQgjLKna5tnnz5tGtWzfWrVvHoEGD2L17N66urlWyKAbY\nc/IoRpOR7k0t30ZhNBr5fMV3aNRqHhlXtTc7EUIIIYQQpVPsjPG6deto2bIlYWFhdOjQAeCe3NBW\nXvtO/QFAt3tQGP/086+cv3CZ0aED8PH2sPj1hBBCCCGE5RVbGO/Zs4ctW7bw7rvvkpKSwtChQzEY\nDPcytjK5GH8VgIZ+/ha/1qfLvwEgbHKoxa8lhBBCCCHujWJbKVxcXBg7diwbNmxg6dKl3Lx5k+zs\nbMaOHcvatWvvZYylEpsYj5eLGzqN1qLX+SPqNAd/O0aPoHY8cH89i15LCCGEEELcOyVuCQ3QqFEj\n/vOf/xAZGcm4ceOIiIiwdFxlYjQauZIQj5+Hj8Wv9ekX3wIQNnmExa8lhBBCCCHunTJtyaZWqxkw\nYAADBgywVDzlkpCaQnZuDrU8LVsYX469Rvi23QQ2qk/XTpZfEk4IIYQQQtw7pZoxrupiE+MB8PPw\ntuh1/rtyAwaDkccnj6zSNyIKIYQQQoiyqx6FcUJeYVzb09di10i5mcb/vgmnhq8nQ4J7WOw6Qggh\nhBCiclSLwvhK/oyxpVopjp34izGPzCI9PZNJ40PQaNQWuY4QQgghhKg8ZeoxrqpuzRhX9M13qanp\nLPjgS778ahNGo5FhQ3ozZeLwCr2GEEIIIYSoGqpHYZx4HQA/z4rpMTaZTIRvi+TVt5ZwLS6B+vVq\n8/brz9Glo9xwJ4QQQghRXVm8MI6MjGTevHmYTCaGDx9OWFhYge8fOnSIp556ijp16gDQp08fnnrq\nqTJd40pCPFo7NZ7Obncd7/Ubicx46V1+3n0IrUbN81Mf4amw0Wi1mrs+txBCCCGEqLosWhgbjUbm\nzp3LihUr8PHxITQ0lF69ehEQEFDguLZt27Js2bJyX+dyQhy1PH3ueqUIo9HI0zPeYt+vv9O1Uxvm\nvT6N+nVr39U5hRBCCCGEdbDozXdRUVH4+/vj5+eHWq0mODi4wjcHydRnk5CaUiH9xavWbGHfr7/T\nu0cH1qx4V4piIYQQQggbYtHCOC4ujpo1a5q/9vX1JT4+vtBxv//+O0OGDCEsLIwzZ86U6RpXzf3F\nd1cYX4y5wpvvfIqbqzPvvjlT1ikWQgghhLAxlX7zXZMmTdi1axc6nY7du3fz9NNPs3379lI/37wi\nxV0UxkajkRkvvUtmZhYL3pyJr49nuc8lhBBCCCGsk0ULY19fX65cuWL+Oi4uDh+fggWso6Oj+fNu\n3brx+uuvk5ycjJvbnW+kO3LkCAD7T/4GQE5qhvmxsvr+xz0cOBxFx3ZNqVPLtdznsbSqGpel2Fq+\nYHs521q+QgghqjaLFsbNmjUjJiaG2NhYvL29CQ8PZ9GiRQWOuXHjBl5eXkBeTzJQYlEM0KZNGwB2\nXToBQOfW7WjTuFWZYzx7/hJffbMdD3dXln30Ol6e7mU+x71w5MgRc862wNbyBdvL2dbyBXkhIIQQ\nVZ1FC2OVSsWcOXOYPHkyJpOJ0NBQAgICWLt2LQqFglGjRrF9+3bWrFmDnZ0d9vb2vP/++2W6xpX8\nHuPy7HpnMBiY/uI7ZGVl88G7L1bZolgIIYQQQliexXuMg4KCCAoKKvDY6NGjzZ+PHTuWsWPHlvv8\nt3qMa7p7lfm5n33xLUd+P8ng4B4MGtC93DEIIYQQQgjrZ9FVKe6FywlxeLm4odNoy/S86L/Ps+CD\nL/H2cuetV6daKDohhBBCCGEtKn1VirthNBq5mnidRrXrlel5Z89f4uFJL5Ctz+GduTPwcHe1UIRC\nCFH1XLt2jRdeeIGEhASUSiUjRoxgwoQJlR2WEEJUOqsujBNSU8jOzSlTf/GZczGMHD+TuPgEXp39\nJP16d7ZghEIIUfWoVCpefvllAgMDSU9PZ9iwYXTu3LnQrqRCCGFrrLqVIjYxr7+4dikL47/PXGTE\nuBnExSfw+n+eJmzSCEuGJ4QQVZK3tzeBgYFA3pKZAQEBRW6+JIQQtsaqZ4zNm3uUYjvov/6+wMgJ\nM7l+I4k3X3mWSeNDLB2eEEJUeZcvX+b06dM0b968skMRQohKZ9WF8ZX8GeOSWilO/3WekeNnkpCY\nzFuvTeORsUPuRXhCCFGlpaenM3XqVGbPnl1gsyUhhLBVVl0Yl2bGOPrv84wYN4PEpBTmvzGd8WMG\n3avwhBCiysrNzWXq1KkMGTKE3r17l+o5trZBia3lC7aXs63lC7aZc1lYd2Gcv7mHn6d3kd83mUzM\nfPk9EpNSePfNGYwdNfBehieEEFXW7NmzadCgARMnTiz1c2xpp0Jb3ZnRlnK2tXzB9nIuz4sA6775\n7kYcWjs1ns5FbyG9dccefv/zFMH9g6QoFkKIfEeOHGHLli0cOHCAoUOHEhISQmRkZGWHJYQQlc7K\nZ4zjqeXpg0KhKPS93FwDby/8LyqVkhdnTKmE6IQQompq06YNp06dquwwhBCiyrHaGeNMfTYJqSnF\n9hevXb+Vc+cv8/DIYALq1bnH0QkhhBBCCGtjtYXxVXN/ceHCOCMjk0WLV6LT2TP9GdnNSQghhBBC\nlMxqC2PzihRFFMb/XbmBuPgEwiaF4uvjea9DE0IIIYQQVsh6C+PEopdqS0xM4ePP1uLu7sKTj46q\njNCEEEIIIYQVst7CuJgZ44+WfU1qWjrPPTUeZ2dZsF4IIYQQQpSO1RbGV/J7jG/f9e7S5Wus/Goz\ndWrXkI08hBBCCCFEmVhtYXw5IQ6Amu5e5scWfPgl+pwcXnhuMlqtprJCE0IIIYQQVshqC+PYhHi8\nXNzQabQAnDh1lg2bd9K4UQBDB/Ws5OiEEEIIIYS1sdrC+Gri9QI33r2/eCUmk4nZsx5DqbTatIQQ\nQgghRCWx2goyOzfHfOPdxZgrbNu5jxbNHqB71wcrOTIhhBBCCGGNrLYwhn9WpPjyq02YTCYenTi8\nyO2hhRBCCCGEKIl1F8YePqSlZbD22634+ngycEC3yg5JCCGEEEJYKasujGt5+vDNhm2kpqUz4eHB\naDTqyg5JCCGEEEJYKasujGu6e7F85Qa0GjXjR8u6xUIIIYQQovysujA+eyqGCzFXGDakD56ebpUd\njhBCCCGEsGJWWxhr7dSsW7cNgCkTh1VyNEIIIYQQwtpZbWHs5ezO/l//oEun1gQ+UL+ywxFCCCGE\nEFbO4oVxZGQk/fv3p1+/fnz22WfFHhcVFUWTJk3YsWNHqc6bm54LwKMTh1dInEIIIYQQwrZZtDA2\nGo3MnTuX5cuX88MPPxAeHs7Zs2eLPG7hwoV06dKl1Oe+fjmRuv5+9OreviJDFkIIIYQQNsqihXFU\nVBT+/v74+fmhVqsJDg4mIiKi0HGrV6+mX79+eHh4lPrchkwDj04cJts/CyGEEEKICmHRqjIuFiPP\n5QAAGjxJREFULo6aNWuav/b19SU+Pr7QMTt37uThhx8u07l1Ci0jh/WvkDiFEEIIIYSo9OnWefPm\nMWvWLPPXJpOpVM/r1ak9jo46S4UlhBBCCCFsjJ0lT+7r68uVK1fMX8fFxeHj41PgmOPHjzN9+nRM\nJhNJSUlERkZiZ2dHr1697njuDk0DOXLkiEXirqok3+rP1nK2tXyFEEJUbRYtjJs1a0ZMTAyxsbF4\ne3sTHh7OokWLChxze8/xyy+/TI8ePUosigHGDx+OnUpV4TFXVUeOHKFNmzaVHcY9Y2v5gu3lbGv5\nQtV6ITB79mx27dqFp6cnW7ZsqexwhBCiSrBoK4VKpWLOnDlMnjyZgQMHEhwcTEBAAGvXrmXdunV3\ndW5bKoqFEKKiDRs2jOXLl1d2GEIIUaVYdMYYICgoiKCgoAKPjR49ushj3377bUuHI4SoQpKTk3nk\nkUdQKBRcv34dpVKJh4cHCoWCb7/9Fju7koeo2bNnExYWRt26dYs95uuvv8bV1ZWBAwdWYPTWrW3b\ntsTGxpbpOVMWv0Zd31rU9a5FXZ9a1PWtRS0Pb1RKmagQQlQPFi+MhRCiOG5ubmzatAmAJUuW4Ojo\nyKRJkwodZzKZUCgURZ5j3rx5JV5n7NixdxeoAGD77/sLPaZW2VHHq0ZeweyT98/fpxYNatShjpev\nLKkphLAqUhgLIaqcmJgYnnzySQIDAzl9+jRffPEFS5Ys4eTJk2RnZzNgwACeeuopAB5++GFeeeUV\n7r//fjp06MDo0aOJjIxEp9Px8ccf4+HhwQcffICHhwcTJkzg4Ycfpk2bNhw4cIC0tDTefvttWrZs\nSWZmJi+++CLnzp2jfv36xMbG8tZbb9GoUaNK/mlUHSsn/YerKQlcu5nA1eSE/M8TuZqSwLm4y4WO\nt1drqOtZk3peNfDP/3ifhy8OGvtKiL7sqlJP+L1iaznbWr5gmzmXhRTGQggA5s5fxg/bdlfoOQf2\n78acl54o13PPnz/PggULaNy4MQDPP/88Li4uGAwGJkyYQL9+/QgICCjwnNTUVNq3b8/MmTOZP38+\n3333HY899liR5//222/5+eefWbJkCf/9739ZvXo13t7efPTRR5w+fZrhw2W7+X/r1bVbsd9LyUjj\nYvwVLsRf4ULcFf66epFTl87x99VLnL52scCxHk6u3OddA3+fWvh71+A+75rU8vDG3ckFd0cX3Byd\ncbTXFfsuwb1gqzeH2lLOtpYv2F7O5XkRIIWxEKJKqlOnjrkoBtiyZQvfffcdubm5XL9+nbNnzxYq\njHU6nXlr+SZNmhQ7KPbp08d8zK0lJY8ePWouohs1akSDBg0qPKeqprTrxpeGq4MTzes2pHndhgUe\nz8rR8/eVi5yMOcfJS+c4c+0SMdevciLmLH+cjy72fGqVHW6Ozrg6OuGo1eFgr8NRa2/+3Mleh7uj\nCx7OLrg5ueDh5Iq7kwuOWnvSs7NIy8wgLSuDtMwMUrMyMJlMtG3QmIa1/Cu14BZCVG1SGAshAJjz\n0hPlnt21BAcHB/PnFy9eZNWqVXz33Xc4OTkxa9YssrOzCz1HrVabP1epVBgMhiLPrdFoSjymIovG\nqmjmzJkcPHiQ5ORkunfvzrPPPmuRWXJ7tYZm/vfTzP/+Ao8bjAauJSVw8fpVYq5f5WrSDZLTUklO\nTyUp/Wbex7SbJKXd5HJCPFn6wr/v8vBx9aBTYAs6N2pJl8atqONVo0LOK4SoHqQwFkJUSbcXpmlp\naTg5OeHo6Eh8fDx79+4ttNrNv59TVq1bt2br1q20adOG6Ohozp07V+5zWYOFCxdW6vVVShV+nj74\nefrQqVGLEo83GA1kZmeTnp1JenYWqRnpJKXfJDE1haS0mySm3SQ57SZpWZk42etw0jngrHPAyd4R\nJ50DWfpsDvwVxb6Tf7DpwC9sOvALADXcvXCy16FWqVGrVKjt1NipVGRnZlLn0DbcHJ1xd3LO/+iC\nq4MzOo0We40We40GrZ0Ge40Ge40WFwcn7NUaS//oqrRcgwGjyYjGTl3ywUJUQVIYCyGqpNvf7m7S\npAkBAQEMGDAAPz+/Aj1ytx9XmrfIiztm3LhxvPTSSwwcOJCAgAACAgJwdna+iwxERVIpVTjpHHDS\nOZR8cDHGdQ/GZDLx95UY9p76nX2nfufYxTMkpd0k12Agx5BLTm4uOYZcAP64dKbM17DXaPNaQByc\ncHN0xs3RCZ3WPr+Azium7e00aNUaTJjIMRgwGAzkGg3kGnLJNRjI1GeTkZ1JelYW6dmZZGZnkZGd\nhdrODmedY17Br3PA2d4RR3sdJkxkZWeTlaMnS59NVk42WXo9SqUSnUaLg9YencYenUaLTmuPu5ML\nNdw9qeHmSQ03L3zcPMpcyJpMJuKSEzl1+RynL5/n1OXznL58njNXL6FQKOjQsBlBTdoQ1LQNjfzq\nluq/TYPRwJ/n/+LnY4f5JeoQ128m0adFB4a070HbBo1lhRNxTyhMVvh+oa01j4Pt5Wxr+YLt5VzV\n8jXkFygajYaLFy8yZcoUduzYUaH/M65qOZeFNcdeViaTiYOHD1H/gftJSvunrSM5Pa/VI6/4zCtC\ns3NyyMrJJjM7m5uZaSSnpZKSkUZyeio3M9MrpCVHp9Gi09iTY8ghLSvTIm0+Xi5uaBQqHB0cUSqV\n2KlUqBRKc7tRVo6eTH0Wmfq8XLNysjEYjQXOYa/R8oBfXbL02UTHXjA/7uvqQdcmrWlR7wEcNPZo\n1f/MsmvVGq4l3eCXY4fZdew3EtNSgLxNvBztHUhJTwWgloc3g9p1Y0i77jTzvx+TyURKRhrXU5K4\nkZpMws1k0rMz8Xb1oKabJ75unng4uxZbkOtzczh4+DCd2rcv1Trc+twcsvR6nHUOVt2jXpb/jrNy\n9JiMRnRa61hFpijlGbdkxlgIIYCMjAwmTpxo7jmeO3euzFDZKIVCgVplh4+rBz6uHuU+j8FoIDUz\ng0x9trmYzjYX1HqU+YWnWmWHKr8YtVOq0Gntcci/0VCn1RYo3IxGI+nZmaRm/nNjoVKh+Ke9Q61F\np80rOI357Sd5M9B5RW2GPouE1GSuJSUQl5zAtaQbXMv/mJx6E326gVyDAYPRgMFoJNdgQKVU5s04\na7W4OjhRw80LnUaLp4sbgbXr0ah2PQLr1Mffu4Y51rjkBPacOMruE0fYc/Io6/fvZP3+nXf8efm6\nejC6a396Nm9H18at0Gns2Xfqd74/tJutR/by6bb1fLptPa4OTqRlZRQqzP9NY6fGx9UDDycXsnL0\neW04WXn/br0rwOd5Lzyc7B1wtNfhZO+ATqMlQ1/wBs7s3BwAHLU67vOuib9P3r+63rWo7eWLNr+F\n5lbRfKt0NuX/zkwmE0ZT3keD0UiOIRd9Tg7ZOXqyc/M+5uTmorazwyH/9++gsTf/LRhNJvS5Oehz\n9GTn5OR9npuD0WRCqVCgUChu+6jMe3GjVOX9TalUqJR5f2fRVy6Q46TGaDJhMhkxGvPiSkhNIeb6\nVXPP/8Xr17iWdAPIu7G2hnvei40abl7UcPfE09kt72/UXoeDxh4Hex0OWnuUCkXe32ZWBqmZGaTn\nfzQajbjkv4vi6uiU946Kkwv2ag1Zt/99ZmeSqc8m12jAQWNv/p3cupZOo8VkwvyzvPXfhIPWvsIK\neCmMhRACcHZ2ZsOGDZUdhqhGVEpVfjtFxbXkKJXK/HYKx1Id76DVlfrcFfmugK+bJ6Gd+xDauQ9G\no5FTl89z9tolsvT5Lw7y2z2ycvQ42uvo1qQNjevULzQb261pW7o1bcu88c+y6/hvbD74C8cvnuX+\nWvfh6eyGl4sbXi7ueLm4odNouXEziatJeYV+XHIC15ITiL5y0fxCw9fNAwetDketjsyMDDQ6e9Iz\nM0jLyiQtK4P4lEQy9dk4avP61N2dXKjjVQNnnQMaOw1XEq9z8foVTl2uvvcgKBVKanl406lRC9Qq\nO64l3+BaUgLRsRdLfnIlsddo2fv2Cmq4e971uaQwFkIIIYTFKJVKmtwXQJP7Ako+uBhatYZ+rTrR\nr1WnCourvC8ETCYTN24mc/H6FS7EXyU2IY5cgwHTbd/P/wSFUonyX7O5CoUCjZ0dGjsNWrUajVqN\n1k6D2s6OnNxcMvV5PeXmf/oslAolGjs1Wjs1WrUGjVqNWmWHUqnElD/7e2sm1WgyYcyf7c815rWI\n5eT3r1+7do1atWrlxZQfmwIFro5O+PvU4j7vmtT29Cmy5zwzO4ur+e8uJKXdJDM7rwc+IzuL9Ows\nMrOzMJqMONnn3/iqc8Q5fyZeqVRyMyONlPQ0UjJSSUpPJSU9jaycbHSaW7PjWvNHlVKVn39er31a\nVkb+rHJW/s8QIC8HBeDt4o6Hs0t5/xQKkMJYCCGEEKKUFAoF3q7ueLu607ZBk8oOp0zu5l0Bndae\n+r5+1Pf1q+CoqhZpoBNCCCGEEAIpjIUQQgghhACkMBZCVKIJEyawb9++Ao+tXLmS119/vdjntGrV\nCoD4+HimTZtW5DHjx4/nxIkTd7z2ypUrC+ye9/jjj5OWllba0IUQQlRDUhgLISrNoEGD+OGHHwo8\n9uOPPxIcHFzsc27dte7j48OHH35Y7muvXLmSzMxM89effvopTk5O5T6fEEII6yeFsRCi0vTt25fI\nyEhyc/PWFI2NjeX69es0btyYRx55hGHDhjF48GAiIiIKPTc2NpZBgwYBkJ2dzYwZMwgODuaZZ55B\nr9ebj3vttdcIDQ1l0KBBLFmyBIDVq1cTHx/PhAkTmDhxIgA9e/YkOTkZgC+//JJBgwYxaNAgVq5c\nab7eQw89xJw5cxg4cCBTpkwpcB0hhBDWT1alEEIAMPebzwg/vKdCzxn8YFfmjAwr9vuurq40a9aM\nyMhIevbsSXh4OP3798fe3p6lS5fi6OhIUlISo0aNolevXsWeZ82aNeh0OsLDw4mOjmbYsGHm782Y\nMQMXFxeMRiMTJ06kb9++jB8/nhUrVrB69WpcXV2Bf2aiT5w4wcaNG1m/fj0Gg4GRI0fSvn17nJ2d\niYmJ4f3332fu3Lk899xzbN++3VycCyGEsH4yYyyEqFTBwcGEh4cDeW0UAwcOxGg0snDhQgYPHsyk\nSZOIj48nISGh2HMcPnyYwYMHA/DAAw/wwAMPmL8XHh7OsGHDGDp0KGfPnuXMmTMA+Wt/Ft5a98iR\nI/Tp0wetVouDgwN9+vTht99+A8DPz8987iZNmhAbG1sxPwQhhBBVgswYCyEAmDMy7I6zu5bSq1cv\n5s+fz8mTJ8nKyqJx48Zs3LiR5ORkNm3ahFKppGfPngVulCuty5cv8+WXX7JhwwacnJx4+eWX76r9\nQaPRmD9XqVTlikkIIUTVJTPGQohK5eDgQLt27Zg9ezYDBw4EIDU1FQ8PD5RKJQcOHODKlSvm44ua\n5X3wwQfZsmULAH/99RfR0dEApKWl4eDggKOjIzdu3CAyMtL8HCcnpwKrUNw6b9u2bdm5cyfZ2dlk\nZGSwc+dO2rZtW/GJCyGEqHKkMBZCVLrg4GCio6PNq1EMGjSI48ePM3jwYL7//nsCAv7ZSvZWL/Dt\nxowZQ0ZGBsHBwSxZsoSmTZsC0KhRIwIDAxkwYACzZs0qsOPTyJEjefTRR8033906b+PGjQkJCSE0\nNJRRo0YxcuRIGjVqZLHchRBCVB0KU1HTL1Xc3WxpaK1sLWdbyxdsL2dbyxesO2drjr08bC1fsL2c\nbS1fsL2cy5OvzBgLIYQQQgiBFMZCCCGEEEIAUhgLIYQQQggBSGEshBBCCCEEIIWxEEIIIYQQwD0o\njCMjI+nfvz/9+vXjs88+K/T9iIgIBg8ezNChQwkNDeXIkSOWDkkIIWxeSWOzEELYIovufGc0Gpk7\ndy4rVqzAx8eH0NBQevXqVWBN0k6dOtGrVy8AoqOjee6559i6daslwxJCCJtWmrFZCCFskUVnjKOi\novD398fPzw+1Wk1wcDAREREFjtHpdObPMzIyUCqlu0MIISypNGOzEELYIovOGMfFxVGzZk3z176+\nvhw7dqzQcTt37mThwoUkJibKW3pCCGFhpR2bhRDC1lSJ6dnevXuzdetWli5dygcffFDZ4QghhBBC\nCBtk0RljX19frly5Yv46Li4OHx+fYo9v27Ytly5dIjk5GTc3tzue2xZv0rO1nG0tX7C9nG0t36qi\nrGPzLbb2+7K1fMH2cra1fME2cy4LixbGzZo1IyYmhtjYWLy9vQkPD2fRokUFjomJieG+++4D4MSJ\nE+Tk5JRYFNvSPt9CCFHRSjM2/5uMu0IIW2DRwlilUjFnzhwmT56MyWQiNDSUgIAA1q5di0KhYNSo\nUWzfvp3NmzejVqvRarXSSiGEEBZW3NgshBC2TmEymUyVHYQQQgghhBCVrUrcfCeEEEIIIURlk8JY\nCCGEEEIIpDAWQgghhBACsPDNd5YQGRnJvHnzMJlMDB8+nLCwsMoOqULNnj2bXbt24enpyZYtWwBI\nSUlh+vTpxMbGUrt2bT744AOcnZ0rOdKKc+3aNV544QUSEhJQKpWMGDGCCRMmVNu89Xo9Y8eOJScn\nB4PBQL9+/XjmmWeqbb63GI1Ghg8fjq+vL8uWLav2+fbs2RMnJyeUSiV2dnasX7/eKnOu7mMu2N64\na2tjLsi4K+Nu6XO2qhljo9HI3LlzWb58OT/88APh4eGcPXu2ssOqUMOGDWP58uUFHvvss8/o2LEj\n27dvp3379nz66aeVFJ1lqFQqXn75ZcLDw1m7di1ff/01Z8+erbZ5azQaVq1axaZNm9i0aRORkZFE\nRUVV23xvWbVqVYGVD6p7vgqFgtWrV7Np0ybWr18PWF/OtjDmgu2Nu7Y25oKMu7dU93wrYty1qsI4\nKioKf39//Pz8UKvVBAcHExERUdlhVai2bdvi4uJS4LGIiAhCQkIACAkJYefOnZURmsV4e3sTGBgI\ngKOjIwEBAcTFxVXrvHU6HZA3i5GbmwtU79/ztWvX2L17NyNGjDA/Vp3zBTCZTBiNxgKPWVvOtjDm\ngu2Nu7Y45oKMu1C984WKGXetqjCOi4ujZs2a5q99fX2Jj4+vxIjujcTERLy8vIC8AS0xMbGSI7Kc\ny5cvc/r0aVq0aEFCQkK1zdtoNDJ06FA6d+5M586dad68ebXOd968ebzwwgsoFArzY9U5X8ibuZg8\neTLDhw/n22+/BawvZ1sdc8F2xl1bGXNBxl2wvjGorCpi3LW6HmNBgT/y6iQ9PZ2pU6cye/ZsHB0d\nC+VZnfJWKpVs2rSJtLQ0nn76af7+++9qm++uXbvw8vIiMDCQgwcPFntcdcn3ljVr1uDj40NiYiKT\nJ0+mXr161fZ3bAuq4+/KlsZckHG3KNUl31sqYty1qsLY19eXK1eumL+Oi4vDx8enEiO6Nzw9Pblx\n4wZeXl5cv34dDw+Pyg6pwuXm5jJ16lSGDBlC7969AdvI28nJiXbt2rFnz55qm+/Ro0f5+eef2b17\nN9nZ2aSnpzNr1iy8vLyqZb633BqbPDw86N27N1FRUVb3O7bVMReq//hjq2MuyLhbHfO9pSLGXatq\npWjWrBkxMTHExsai1+sJDw+nV69elR1Whfv3ZoQ9e/Zkw4YNAGzcuLFa5jx79mwaNGjAxIkTzY9V\n17wTExNJTU0FICsri/379xMQEFBt850xYwa7du0iIiKCRYsW0b59exYsWECPHj2qZb4AmZmZpKen\nA5CRkcHevXtp2LCh1f2ObWXMBdsbd21pzAUZd2XcLX3OVrcldGRkJG+99RYmk4nQ0NBqt3TQzJkz\nOXjwIMnJyXh5efHss8/Su3dvpk2bxtWrV/Hz8+ODDz4odKOINTty5Ajjxo2jYcOGKBQKFAoF06dP\np3nz5jz33HPVLu/o6GheeukljEYjRqORhx56iCeffJLk5ORqme/tDh06xBdffMGyZcuqdb6XLl3i\nmWeeQaFQYDAYGDRoEGFhYVaZc3Ufc8H2xl1bG3NBxl0Zd0ufs9UVxkIIIYQQQliCVbVSCCGEEEII\nYSlSGAshhBBCCIEUxkIIIYQQQgBSGAshhBBCCAFIYSyEEEIIIQQghbEQQgghhBCAle18J0Rp9OzZ\nE3t7e7RaLSaTCYVCwdKlS6lVq1aFXSM2Npbhw4dz4MCBCjunEEJYIxlzRXUihbGolhYvXkxAQIBF\nr1Hd9pgXQojykjFXVBfSSiGqpaL2rWnUqBGLFy9m6NChDBgwgB07dpi/FxkZSUhICEOGDGHSpEnE\nxMSYv7d+/XqGDBnCkCFDGDFiBImJieZrvP/++4SEhDBgwACOHj1q+cSEEKIKkjFXVBcyYyyqpalT\np5rf1rOzs2P9+vUA2NnZsWnTJs6fP8/o0aNp27YtAC+++CJff/019evXZ/369Tz//PN88803HDx4\nkM8//5w1a9bg4eFBZmYmKpWKzMxMkpOTad26NdOnT2fLli0sWLCANWvWVGbaQghRKWTMFdWFFMai\nWirubb3Q0FAA6tWrR9OmTfnzzz8BCAwMpH79+gAMHz6cN954g4yMDHbv3s2QIUPw8PAAQKfTmc/l\n6OhIt27dAGjZsiXvvPOORXMSQoiqSsZcUV1IK4Wolop6W+/fjxf3eWlpNBrz50qlEoPBUOZzCCFE\ndSBjrqgupDAWNmXDhg0AXLhwgVOnTtGiRQtatGhBdHQ058+fNx/TuHFjHBwc6N69O5s3byYhIQGA\njIwM9Ho9UHhgL89AL4QQ1ZmMucLaSCuFqHYUCkWBfjeFQsHcuXMByM3NJSQkhKysLObOnWt+u+7d\nd99l5syZGAwGPDw8WLBgAQDt2rUjLCyMRx55BKVSiVarZdmyZebr/Pu6Qghha2TMFdWJwiQvuYSN\naNSoEX/88Qf29vaVHYoQQlR7MuYKayStFMJmKBQKeetNCCHuERlzhTWSGWMhhBBCCCGQGWMhhBBC\nCCEAKYyFEEIIIYQApDAWQgghhBACkMJYCCGEEEIIQApjIYQQQgghACmMhRBCCCGEAOD/ATRW+D2v\n4oNbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9488b30810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d3_hist7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 14 , val_acc: 0.649122808412 , val_loss: 0.839906761172\n"
     ]
    },
    {
     "data": {
      "image/png": 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iUxLRpkvIsmXcKINSCqVSxZ59x5+Jlfm5M8bxqQ8zxlqtlo1bdmFrY0X/Pr75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\nggCIwFgQBEEQBEEQAPg/SoIb2Qw9bUsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f948ac75150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d3_hist8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finetuning Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run 1 - With Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 44 , val_acc: 0.70760233953 , val_loss: 0.663656747132\n"
     ]
    },
    {
     "data": {
      "image/png": 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jCYyFEKI5qdfAOCkpCU9PT+Njd3d3kpNL/s88KiqKzMxM5syZw5Qp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5UaT5WaTp+EovMLJNgvAJ3Wn2C/QJRKJQ2WBhosFsyO7/WcKzdQcL6IcxVlLfq7UygU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\nxYpm25SXlxMSEgI09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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f947a55f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(ft_d3_hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run 2 - Without Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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: 0.8826 - acc: 0.5686Epoch 00000: val_acc improved from -inf to 0.64327, saving model to data3_1/ft_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.8841 - acc: 0.5689 - val_loss: 0.7774 - val_acc: 0.6433\n",
      "Epoch 2/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8661 - acc: 0.5984Epoch 00001: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8668 - acc: 0.5975 - val_loss: 0.7866 - val_acc: 0.6374\n",
      "Epoch 3/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8316 - acc: 0.6219Epoch 00002: val_acc improved from 0.64327 to 0.65497, saving model to data3_1/ft_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.8341 - acc: 0.6221 - val_loss: 0.7475 - val_acc: 0.6550\n",
      "Epoch 4/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8091 - acc: 0.6148Epoch 00003: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8081 - acc: 0.6149 - val_loss: 0.7331 - val_acc: 0.6199\n",
      "Epoch 5/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8269 - acc: 0.6055Epoch 00004: val_acc improved from 0.65497 to 0.66667, saving model to data3_1/ft_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.8252 - acc: 0.6067 - val_loss: 0.7337 - val_acc: 0.6667\n",
      "Epoch 6/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8134 - acc: 0.6219Epoch 00005: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8135 - acc: 0.6210 - val_loss: 0.7254 - val_acc: 0.6608\n",
      "Epoch 7/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8292 - acc: 0.6158Epoch 00006: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8285 - acc: 0.6159 - val_loss: 0.7241 - val_acc: 0.6550\n",
      "Epoch 8/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.8010 - acc: 0.6189Epoch 00007: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8012 - acc: 0.6190 - val_loss: 0.7319 - val_acc: 0.6550\n",
      "Epoch 9/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7792 - acc: 0.6506Epoch 00008: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7791 - acc: 0.6507 - val_loss: 0.7362 - val_acc: 0.6491\n",
      "Epoch 10/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7953 - acc: 0.6311Epoch 00009: val_acc improved from 0.66667 to 0.68421, saving model to data3_1/ft_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.7977 - acc: 0.6302 - val_loss: 0.7019 - val_acc: 0.6842\n",
      "Epoch 11/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7997 - acc: 0.6404Epoch 00010: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.8003 - acc: 0.6394 - val_loss: 0.7116 - val_acc: 0.6725\n",
      "Epoch 12/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7961 - acc: 0.6434Epoch 00011: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7987 - acc: 0.6425 - val_loss: 0.7248 - val_acc: 0.6433\n",
      "Epoch 13/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7979 - acc: 0.6301Epoch 00012: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7988 - acc: 0.6292 - val_loss: 0.7425 - val_acc: 0.6491\n",
      "Epoch 14/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7845 - acc: 0.6609Epoch 00013: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7854 - acc: 0.6599 - val_loss: 0.7469 - val_acc: 0.6491\n",
      "Epoch 15/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7894 - acc: 0.6393Epoch 00014: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7891 - acc: 0.6394 - val_loss: 0.7420 - val_acc: 0.6608\n",
      "Epoch 16/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7659 - acc: 0.6475Epoch 00015: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7676 - acc: 0.6466 - val_loss: 0.7011 - val_acc: 0.6842\n",
      "Epoch 17/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7702 - acc: 0.6363Epoch 00016: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7704 - acc: 0.6364 - val_loss: 0.7598 - val_acc: 0.6491\n",
      "Epoch 18/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7622 - acc: 0.6527Epoch 00017: val_acc improved from 0.68421 to 0.69006, saving model to data3_1/ft_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.7642 - acc: 0.6507 - val_loss: 0.6729 - val_acc: 0.6901\n",
      "Epoch 19/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7778 - acc: 0.6352Epoch 00018: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7768 - acc: 0.6364 - val_loss: 0.7243 - val_acc: 0.6374\n",
      "Epoch 20/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7541 - acc: 0.6629Epoch 00019: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7536 - acc: 0.6629 - val_loss: 0.7002 - val_acc: 0.6784\n",
      "Epoch 21/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7543 - acc: 0.6537Epoch 00020: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7549 - acc: 0.6537 - val_loss: 0.7078 - val_acc: 0.6608\n",
      "Epoch 22/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7800 - acc: 0.6629Epoch 00021: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7785 - acc: 0.6639 - val_loss: 0.7328 - val_acc: 0.6140\n",
      "Epoch 23/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7671 - acc: 0.6711Epoch 00022: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7663 - acc: 0.6711 - val_loss: 0.7151 - val_acc: 0.6433\n",
      "Epoch 24/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7486 - acc: 0.6701Epoch 00023: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7506 - acc: 0.6691 - val_loss: 0.7125 - val_acc: 0.6608\n",
      "Epoch 25/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7703 - acc: 0.6660Epoch 00024: val_acc improved from 0.69006 to 0.70760, saving model to data3_1/ft_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.7704 - acc: 0.6650 - val_loss: 0.6909 - val_acc: 0.7076\n",
      "Epoch 26/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7393 - acc: 0.6516Epoch 00025: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7407 - acc: 0.6507 - val_loss: 0.7128 - val_acc: 0.6725\n",
      "Epoch 27/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7521 - acc: 0.6680Epoch 00026: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7515 - acc: 0.6691 - val_loss: 0.7293 - val_acc: 0.6374\n",
      "Epoch 28/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7657 - acc: 0.6609Epoch 00027: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7658 - acc: 0.6609 - val_loss: 0.6778 - val_acc: 0.6433\n",
      "Epoch 29/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7584 - acc: 0.6475Epoch 00028: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7581 - acc: 0.6476 - val_loss: 0.6837 - val_acc: 0.6667\n",
      "Epoch 30/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7585 - acc: 0.6670Epoch 00029: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7586 - acc: 0.6670 - val_loss: 0.7198 - val_acc: 0.6608\n",
      "Epoch 31/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7506 - acc: 0.6629Epoch 00030: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7503 - acc: 0.6629 - val_loss: 0.7050 - val_acc: 0.6433\n",
      "Epoch 32/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7652 - acc: 0.6629Epoch 00031: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7650 - acc: 0.6629 - val_loss: 0.7050 - val_acc: 0.6959\n",
      "Epoch 33/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7418 - acc: 0.6752Epoch 00032: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7423 - acc: 0.6752 - val_loss: 0.7460 - val_acc: 0.6550\n",
      "Epoch 34/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7377 - acc: 0.6578Epoch 00033: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7364 - acc: 0.6588 - val_loss: 0.7053 - val_acc: 0.6667\n",
      "Epoch 35/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7423 - acc: 0.6537Epoch 00034: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7426 - acc: 0.6537 - val_loss: 0.7103 - val_acc: 0.7076\n",
      "Epoch 36/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7458 - acc: 0.6619Epoch 00035: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7466 - acc: 0.6619 - val_loss: 0.6967 - val_acc: 0.6725\n",
      "Epoch 37/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7547 - acc: 0.6506Epoch 00036: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7562 - acc: 0.6496 - val_loss: 0.6496 - val_acc: 0.7076\n",
      "Epoch 38/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7255 - acc: 0.6824Epoch 00037: val_acc improved from 0.70760 to 0.71345, saving model to data3_1/ft_model.h5\n",
      "979/979 [==============================] - 89s - loss: 0.7256 - acc: 0.6823 - val_loss: 0.7054 - val_acc: 0.7135\n",
      "Epoch 39/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7304 - acc: 0.6752Epoch 00038: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7302 - acc: 0.6752 - val_loss: 0.7113 - val_acc: 0.6725\n",
      "Epoch 40/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7410 - acc: 0.6732Epoch 00039: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7408 - acc: 0.6731 - val_loss: 0.7084 - val_acc: 0.6550\n",
      "Epoch 41/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7522 - acc: 0.6609Epoch 00040: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7517 - acc: 0.6619 - val_loss: 0.7127 - val_acc: 0.6608\n",
      "Epoch 42/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7258 - acc: 0.6885Epoch 00041: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7249 - acc: 0.6885 - val_loss: 0.6800 - val_acc: 0.6784\n",
      "Epoch 43/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7140 - acc: 0.6650Epoch 00042: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7126 - acc: 0.6660 - val_loss: 0.6919 - val_acc: 0.6725\n",
      "Epoch 44/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7392 - acc: 0.6732Epoch 00043: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7377 - acc: 0.6742 - val_loss: 0.7506 - val_acc: 0.6316\n",
      "Epoch 45/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7277 - acc: 0.6762Epoch 00044: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7273 - acc: 0.6762 - val_loss: 0.6878 - val_acc: 0.6784\n",
      "Epoch 46/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7183 - acc: 0.6793Epoch 00045: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7187 - acc: 0.6782 - val_loss: 0.7143 - val_acc: 0.6784\n",
      "Epoch 47/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7057 - acc: 0.6783Epoch 00046: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7060 - acc: 0.6793 - val_loss: 0.7015 - val_acc: 0.6842\n",
      "Epoch 48/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7184 - acc: 0.6885Epoch 00047: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7176 - acc: 0.6885 - val_loss: 0.7373 - val_acc: 0.6901\n",
      "Epoch 49/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7116 - acc: 0.6855Epoch 00048: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7118 - acc: 0.6854 - val_loss: 0.6677 - val_acc: 0.6608\n",
      "Epoch 50/50\n",
      "976/979 [============================>.] - ETA: 0s - loss: 0.7238 - acc: 0.6752Epoch 00049: val_acc did not improve\n",
      "979/979 [==============================] - 88s - loss: 0.7229 - acc: 0.6762 - val_loss: 0.7260 - val_acc: 0.6433\n"
     ]
    }
   ],
   "source": [
    "ft_d3_model, ft_d3_hist = finetune_categorical_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 38 , val_acc: 0.713450292572 , val_loss: 0.705431878218\n"
     ]
    },
    {
     "data": {
      "image/png": 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j4OBwF3dw90TGWGiSVv21jVV/bSOs9wAe69KroZdjMn+cOcb6Q7vwdfPkvadfrPH4qON/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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17890fbf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(ft_d3_hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ft_d3_model = load_model('data3_1/120116_1223/ft_d3_model.h5') # without regularization, first run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "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": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": 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       0.83      0.60      0.70        48\n",
      "          1       0.51      0.56      0.53        55\n",
      "          2       0.73      0.81      0.77        68\n",
      "\n",
      "avg / total       0.69      0.67      0.67       171\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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LHS1vWCAiUhg7WiISSrfbDCMi6m749C4iIoVpsKHljJaISGnsaIlILBpsaRm0RCQUboYR\nESlMgznLoCUisfDOMCIihbGjJSJSGGe0REQK02DOMmiJSCxa7Gh5wwIRkcLY0RKRUDTY0DJoiUgs\nkl57ScugJSKhcEZLROSD2NESkVA02NAyaIlILJ4cHdTU1GDVqlVoaGiATqfD7NmzMX/+fDQ2NmLZ\nsmWoqqpCZGQk0tLSEBgY2OU6HB0QkVAkyf3DFb1ejxdffBE7duzAli1b8M4776CsrAwZGRmIi4tD\nfn4+YmNjkZ6e7nQdBi0RicWDSWsymRAdHQ0AMBgMiIqKQm1tLQoLC2G1WgEAVqsVBQUFTtfh6ICI\nhKLU07sqKytx9OhRjB49Gg0NDTAajQAuhbHdbnf6vQxaIhKKEpthzc3NSElJQWpqKgwGwxVzYFdz\nYY4OiEgokiS5fbijra0NKSkpmDFjBiZPngwACA0NRX19PQDAZrMhJCTE6RoMWiISiic3wwAgNTUV\nQ4cOxYIFCzrOJSQkICcnBwCQm5sLs9nsdA0GLRFRF/bt24dt27ahpKQEDz30EKxWK4qKivDkk0+i\nuLgYFosFJSUlSE5OdroOZ7REJBYPDmnHjBmDI0eOXPVzmZmZbq/DoCUiofA9w4iIFKbFoOWMlohI\nYYp3tH0iIpV+CZ83/sHb1C5BeFOfWad2CT6h9PFHbngNPlSGiEhhWhwdMGiJSChafPA3g5aIxKK9\nnOVmGBGR0tjREpFQdDrt9Y8MWiISi/ZylkFLRGLR4maYBrOfiEgs7GiJSCha7GgZtEQkFu3lLIOW\niMTCO8OIiJTG0QERkbI0mLMMWiISCzfDiIiUxhktEZGytNjR8oYFIiKFsaMlIqHw8i4iIoUxaImI\nlKbBGS2DloiEws0wIiIfxI6WiMSivYaWQUtEYuFmGBGRwiQNvmeY9ioiIhIMO1oiEgtHB0REyuLl\nXURESpOu4XAhNTUV48ePR2JiYse5xsZGJCUlwWKxYNGiRWhqanK5DoOWiIQiSZLbhyszZ87Epk2b\nOp3LyMhAXFwc8vPzERsbi/T0dJfrMGiJiLowduxYBAUFdTpXWFgIq9UKALBarSgoKHC5Dme0P7Cn\nuASvbkiDLMuwPjgdixb8VO2ShPD6lndRcugQ+gYG4u1VLwAA/vjRhyg+eAA6SULfwECsmjsPIf/2\nA03X5qM9W3C2qRkOWUbbxTbMm/E0nv7ZAjw8dzrsDacBAG++9nv8Y9cXKleqLEmvbP9ot9thNBoB\nACaTCXa73eX3MGgvczgcWPv6erz91pswmUyYO38RJt03AbcMHqx2ad3elHGxsE6Ix3+98z8d5+Yk\nmPH4A1MBALlFRcjK/xuWzn5ErRKFIMsykh79GZrOnO10Puvt97H57b+oVJUKvLwZ5s4IgqODyw4c\nOoyBAwbg5v790bNHD0y534xPd+1WuywhxNwShUD/Pp3O+ffu3fHn860XNLlT3N1IkgTdVS7W97X/\ntZ6c0V5NaGgo6uvrAQA2mw0hISEuv8dl0JaVleGzzz5Dc3Nzp/NFRUXXVaRW1dls6Bce1vFxeFgY\n6mw2FSsS3x8+3IG5L/0Chfv3YeHl7paunyzLyHhnPd79azoenjO94/zcBTPxlw/fxi9fXYmAQIOK\nFXZPsix3+jghIQE5OTkAgNzcXJjNZpdrOA3arKwsPPvss9i8eTMSExM7DX3feOON66mZqEPS1Gl4\n7xcvwTxmLPJ2i/UPtxrmP7wYj057EosXrsKj8x/CnWNj8OfNeXjg3jl4ZOoTqLfZ8fzqxWqXqTyd\n5P7hwooVKzBnzhyUl5dj4sSJyM7ORnJyMoqLi2GxWFBSUoLk5GSX6zid0b7//vvIycmBwWBAZWUl\nUlJSUFVVhQULFlyR8t1dmMmEmprajo9r6+oQZjKpWJHvMN81Bqm/T8eCKQ+oXUq3Vl93aVPmlL0R\nn+Tvxsg7ovG/ew90fD77ve3YuGmdWuV5jSfHUOvXr7/q+czMzGtax2lH63A4YDBc+lUjMjISmzdv\nRlFREdatWydc0I4cEY2KykpUnzyJixcv4m9/L8Sk+AlqlyUMGZ1/Xqp+MJb5x4EDGBgW7u2ShOLn\n1xv+ffwBAP7+foiLvxvHj32LUNO/5ofmKfE4/nW5WiV6jyS5f3iJ0442NDQUR44cQXR0NADAYDAg\nPT0dqamp+Prrr71SoLfo9XqkrlyBp5Ysg0N2wPrgdNwyZLDaZQlhzeY/4avjx3GmuRlzX/oFFkx5\nACWHD6PSVgedpEN4SF8sncUrDm5EqKkv3kj/T8gAeuj12JH3MT7bvRdrNqRi+IihcDgcqK6swcup\nV+/QRKLFxyRKspPWtKamBnq9Hqar/Aq9b98+jBkzxuULtJ5puLEKyaW6Pf9UuwThTX1G/F+5taD0\nu103vEb93s/c/lrj2Lgbfj13OO1o+/Xr1+Xn3AlZIiJv0+KlgrxhgYjEwqAlIlKWFme0vDOMiEhh\n7GiJSCwcHRARKUuLb87IoCUisXBGS0Tke9jREpFQJEl7/SODlojEws0wIiJl8c4wIiKlaXAzjEFL\nREJhR0tEpDQGLRGRwnjVARGRsvhQGSIiH8SOlojEwhktEZGyJJ1e7RKuwKAlIqFwRktE5IPY0RKR\nWDijJSJSFu8MIyJSGm9YICJSmAY3wxi0RCQUjg6IiJTG0QERkbK02NFqL/qJiG6EpHP/cENRURGm\nTJkCi8WCjIyM6yqJQUtE1AWHw4FXXnkFmzZtwvbt27Fjxw6UlZVd8zoMWiISiqST3D5cKS0txaBB\ngxAREYGePXti2rRpKCwsvOaaGLREJBZJcv9woba2Fv379+/4ODw8HHV1dddcEjfDiEgoPvn0rl5B\noUq/hM+LnDpF7RKEV/od/x93F57MnPDwcFRXV3d8XFtbi7CwsGteh6MDIqIuxMTEoKKiAlVVVWht\nbcWOHTtgNpuveR2ODoiIuqDX67F69WokJSVBlmXMmjULUVFR17yOJMuyrEB9RER0GUcHREQKY9AS\nESmMQUtEpDAG7Q944p5mci41NRXjx49HYmKi2qUIq6amBvPnz8e0adOQmJiIrKwstUvyedwMu8zh\ncMBisSAzMxNhYWGYNWsWNmzYcF07jNS1vXv3wmAwYNWqVdi2bZva5QjJZrOhvr4e0dHRaG5uxsyZ\nM/HWW2/xZ1lF7Ggv89Q9zeTc2LFjERQUpHYZQjOZTIiOjgYAGAwGREVFXddto+Q5DNrLPHVPM5GW\nVFZW4ujRoxg1apTapfg0Bi2RoJqbm5GSkoLU1FQYDAa1y/FpDNrLPHVPM5EWtLW1ISUlBTNmzMDk\nyZPVLsfnMWgv89Q9zeQa91+Vl5qaiqFDh2LBggVql0LgVQedFBUVYc2aNR33NCcnJ6tdknBWrFiB\nzz//HKdPn4bRaMRzzz2Hhx9+WO2yhLJv3z785Cc/wbBhwyBJEiRJwrJlyxAfH692aT6LQUtEpDCO\nDoiIFMagJSJSGIOWiEhhDFoiIoUxaImIFMagJSJSGIOWiEhhDFoiIoX9H7ZTJ2jUkoj4AAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f176545f610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_categorical_model(ft_d3_model, validation_data_dir, validation_labels) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ft_d3_model2 = load_model('data3_1/120316_0000/ft_model.h5') # with regularization, first run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": 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       0.79      0.62      0.70        48\n",
      "          1       0.49      0.65      0.56        55\n",
      "          2       0.78      0.69      0.73        68\n",
      "\n",
      "avg / total       0.69      0.66      0.67       171\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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tXgwAXCqLjxsHAHu5saNlwwIA2IyOFoBRgu5iGAAEm6a8K1drI2gBGMWFDS0zWgCwGx0t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\nRtACgM0IWgCwGUELADb7v/2pF36svsSMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f176e311250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_categorical_model(ft_d3_model2, validation_data_dir, validation_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": 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       0.79      0.71      0.75        48\n",
      "          1       0.53      0.65      0.59        55\n",
      "          2       0.80      0.71      0.75        68\n",
      "\n",
      "avg / total       0.71      0.69      0.70       171\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f175a207150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_categorical_model(ft_d3_model3, validation_data_dir, validation_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Model Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_metrics(hist, stop=50):\n",
    "    fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(4,6))\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": 14,
   "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": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# WINNER\n",
    "ft_model = load_model('data3_1/ft_model.h5')  # without regularization, second run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('data3_1/ft_history.txt') as f:    \n",
    "    ft_history = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "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       0.79      0.71      0.75        48\n",
      "          1       0.53      0.65      0.59        55\n",
      "          2       0.80      0.71      0.75        68\n",
      "\n",
      "avg / total       0.71      0.69      0.70       171\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm = evaluate_categorical_model(ft_model, validation_data_dir, validation_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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=(4.25,7))\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": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UH/YtI3/YxYlrke/8M88LsQnPUauz//eb13JVg7pz5w4lSpTQ3i9ZsiQRERG5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9XCktvtv6vhnYrKgHL+GyhJEh5UajoEB3s/ftZcUb4SFAzjJgd+fSoqKh45//fPG+vB2AJjfewE\nGhQEP/8jGBohMiqwa1NRUemW899Alfmwg9eV9CzxT0VFZUA5/0O8Uh8T5CoqKoOOgBuo3s7F8/Vc\nr/TGg1JRURkUBDTE68tcPF/O9QnVg1JROWcJqAflOBcvODhYmYvniKe5eL6c6xNlZyAhSRT9qqio\nnFME1ED1ZS6eL+d6xdgC9bWq96Sico4y4Lt48ly81157DZPJxM0338zkyX7SH5WdFT/V/JOKyjlJ\nQA1UX+bi+XKuJ+SZYfEHd5ENnLFoqD0H5oidq7POztV1w7m79nN13T0mAOOvFPoyF8+Xc93hNHvr\njZfErLrD+/z91PzOhTTrbLBwrq79Qlp3QD0ox7l4kiSxfPlyZS6eRqPhpptucpqLp9Vqlbl4gNtz\ne4Qc4mUM8fMzU1FR6Q8CnoPq7Vw8T+f2iKoyMYFFr45AUlE5Fzl/leSSBFUVooOBRjPQq1FRUekF\n56+BaqyD9jbRxUBFReWc5Pw1UBVl4meKaqBUVM5Vzl8DVWWTKKgte1VUzlnOXwNVKXtQqoFSUTlX\nOX8NlOJBqSGeisq5yvlroCrLRHfMBN/U5yoqKoOP89dAVZVDUirodAO9EhUVlV5yfhooYws0N6kJ\nchWVc5wB72YQEKpsCXLVQKkMchobG7nrrrvQaDTU1NSg1WqJj49Ho9HwzjvvEBTk/SP6+OOPs2LF\nCoYNG+bxmLfeegu9Xs+CBQv8uPrAc34aqEpbglzVQKkMcmJjY5UeaKtWrSIyMpK7777b5ThJktB4\nqIh4+umnvT7Obbfd1reFDhDnp4FSPSiVc5yzZ89y7733MmbMGAoLC3nllVdYtWoVBQUFNDY2snTp\nUu677z4Abr31Vp566ilGjhzJjBkzuPnmm9m6dSvh4eG8+OKLxMfH89xzzxEfH8+dd97JrbfeSl5e\nHjt27KClpYVnnnmGSZMm0drays9+9jNOnTrF8OHDKSsr47e//S25ubkD9jqcnwZK8aBUA6XSA15/\nEbZ/6d9rzrwC7ryvV6cWFxfzhz/8gbFjxwLw05/+lJiYGHbv3s1zzz3HNddc49Lho7m5menTp/Pw\nww/zu9/9jnXr1vH973/f7fXfeecdtmzZwqpVq1i9ejVvvPEGSUlJPP/88xQWFrJs2bJerdufnKcG\nyuZBJad1f5yKyiAmKytLMU4g+vevW7eO5uZmWlpaOHnypIuBCg8PZ9asWQCMGzfOY2O7efPmKcfI\njSH37t2rGLPc3Fyl7dFAEnADtXXrVp5++mkkSWLZsmWsWLHC6f5du3Zx3333kZUlBmXOmzdPcV1f\nffVV/vvf/6LRaBg1ahTPPPMMISEh3h+0qgzikyA01O/PR+U85s77eu3tBIKICPugjzNnzvD666+z\nbt06ioqK+Pe//017e7vLOcHBwcr/dTodFovF7bXlz1F3x0iS1Jfl+4WAygzk0VFr1qzhgw8+YNOm\nTZw8edLluKlTp7JhwwY2bNigGKeqqireeOMN1q9fz8aN/7+9ew+Kqv7/OP7clRRh0UK85x9Ko4LX\nMcLp6x/WgiLCAisXK+9WOI2Ed1PSmYzRSia11AatSfk6DTPFVzDCopGCzRrwGk1SpKVjMCkKbYnL\npd09vz8Y9icJtijrnt3ej7/gnLPnvHYH3nM+Zz+XQmw2G0ePHnXuwvXXpAe58Hi3FojGxkZ0Oh3+\n/v78/vvvHD9+/B9f011Tpkzh008/BaC6uppffvnlrs/VU1x6B3Xr0lGAY+koZ2fGtNvtNDU1odVq\naW5udnpOchRFHpALj3frt3bjxo0jODiY6OhodDodjz76aKfHdfVNX1fnvdX8+fPZsGEDsbGxBAcH\nExwcTEBAwD28gx7Qk3MO/91nn32mbNq0yfF7QUGBkpmZ2eGYiooKJTw8XImLi1Oef/555fz58459\nOTk5yuTJk5XHH39cWbt2rVPXPHXqVNs85Hn/7Zk3cZ/8m+aZVgtPze6q3FarVWlpaVEURVEuXbqk\nREREKDabrcfOr7o5yZ0xbtw4SktL6du3L2VlZSxfvpzi4mL+/PNPSkpK+PLLLwkICCA9PZ3CwkIM\nBoNzJ5YmnhDdYrFYWLRokeOZVGZmJlqtewebuH3ZKX9/f8fP06dPZ8uWLZjNZsrLyxkxYgQPPvgg\n0Pbw/OzZs04XqB9+/xOLhy3N46lLCXlqbvDc7K7K/fLLL9+X6zjLpQVqwoQJXL58mdraWgYOHEhR\nURE7duzocMz169cJCgoC2p5ZQVvv2mHDhlFZWUlLSwu9e/emvLycCRMmOH3tkCciQefm9nM3nD59\nusNzBU/hqbnBc7N7cu7ucvuyU8XFxeTm5uLj44Ovry87d+4EYOLEiURFRZGQkICPjw+hoaGkpKQ4\nd2FdgEcVJyFE59y+7NS8efO6HCeUlpZGWlpa9y8qPciF8AreOd2KDBIWwit4Z4EaKndQwjMsXLiQ\nr7/+usO2nJwctmzZ0uVrli5dCkBdXR0rVqzo9JgFCxZw7ty5O147JyenQ2/0ZcuW0djY6Gz0+8I7\nC5Q08YSHMBgMfPLJJx22HT16lJiYmC5f097RctCgQbz11lt3fe2cnByampocv+/btw+dTnfX53MF\n7yxQ0gdKeIiZM2diMpmwWq0A1NbWcu3aNUJDQ1m8eDFz5swhLi6OkpKS215bW1vr6HbT0tLC6tWr\niYmJIS0tjdbWVsdxr7zyCklJSRgMBvbs2QPAoUOHqKurY+HChSxatAgAvV6P2WwG4MCBAxgMBgwG\nAzk5OY7rzZ49m82bNxMbG8uzzz7b4Tqu4PaOmi4hd1DiLqw/WMz/vqnq0XMm/ieU7Yujutzfv39/\nJkyYgMlkQq/XU1RUxKxZs/D19WXv3r2OsXdz584lIiKiy/Pk5ubSt29fioqKqK6uZs6cOY59q1ev\npl+/ftjtdhYtWsTMmTNZsGABBw8e5NChQ/Tv3x/4/zuzc+fOkZ+fT15eHjabjZSUFKZOnUpAQACX\nL19m586dZGZmsnLlSoqLi53vPH0XvPMO6qEB7k4ghNNiYmIoKioC2pp3sbGx2O123nzzTeLi4liy\nZAl1dXXU19d3eY6TJ08SFxcHwJgxYxgzZoxjX1FREXPmzCEhIYGff/6ZCxcuAG0Di5VOBhefPn2a\nGTNm0KdPH/z8/JgxYwanTp0CYPjw4Y5zjxs3jtra2p75ELrgnXdQbu6eLzzT9sVRd7zbcZWIiAhe\nf/11qqqqaG5uJjQ0lPz8fMxmMwUFBWi1WvR6fafTq/yTmpoaDhw4wOHDh9HpdGzcuPGemmW3TnfU\nq1evu8rUHfKfLISb+fn5ER4eTkZGhmNRgxs3bhAYGIhWq6W8vLzDkLHO7noee+wxCgsLAfjpp5+o\nrq4G2qZp8fPzw9/fn+vXr2MymRyv0el0Hb61az9vWFgYx44do6WlBYvFwrFjxwgLC+v5N+4EKVBC\nqEBMTAzV1dWOb+8MBgPff/89cXFxfPzxxx2mKOpsupSnn34ai8VCTEwMe/bsYfz48UDbzJghISFE\nR0ezbt26DkNkUlJSeO655xwPydvPGxoaitFoJCkpiblz55KSkuK2eck1Smfl2IN58jglyX1/eWr2\nf1NuuYMSQqiWFCghhGpJgRJCqJYUKCGEakmBEkKolssLlMlkYtasWURFRbF///7b9p84cYKwsDCM\nRiNGo5F33nnHse/GjRukp6cTHR1NTEwMlZWVro4rhFARl/Ykb18X7+DBgwwaNIikpCQiIiJuW3Yq\nLCyM7Ozs216/detWpk+fzttvv43VaqW5udmVcYUQKuPSO6hb18V74IEHHOviOaOxsZFTp0451of3\n8fFR3VQQQgjXcmmBunr1KkOHDnX8PnjwYOrq6m477uzZs8THx5OamuoYyFhTU8NDDz3Exo0bMRqN\nbN68We6ghPiXcftD8vZ18Y4cOcK8efNYvnw5AFarlaqqKp555hny8/Px9fXt9BmWEMJ7qXZdvCFD\nhjBkyBDHUlNRUVG89957Tl3X3Wt53S3Jff95anZPzd1dql0XD2Do0KFcvHiRkSNHUl5eftvD9c54\n4hglIUTnVLsuHsCmTZtYu3YtVquVESNG8Nprr7kyrhBCZbxuNgMhhPdw+0NyIYToihQoIYRqSYES\nQqiW1yyaYDKZ2LZtG4qikJiYSGpqqrsjdSkjI4PS0lIGDBjgmEf6jz/+YNWqVdTW1vLwww+za9cu\nAgIC3Jy0oytXrrB+/Xrq6+vRarUkJyezcOFC1WdvbW1l3rx5/PXXX9hsNqKiokhLS1N97nZ2u53E\nxEQGDx5Mdna2x+TW6/XodDq0Wi0+Pj7k5eV1P7viBWw2mxIZGanU1NQora2tSlxcnHLhwgV3x+rS\nyZMnlaqqKiU2Ntaxbfv27cr+/fsVRVGUffv2KVlZWe6K16W6ujqlqqpKURRFaWxsVGbOnKlcuHDB\nI7JbLBZFURTFarUqycnJSmVlpUfkVhRFOXDggLJmzRpl2bJliqJ4xt+KoiiKXq9XzGZzh23dze4V\nTbx7GfPnDmFhYfTr16/DtpKSEoxGIwBGo5Fjx465I9odDRw4kJCQEKCtg21wcDBXr171iOx9+/YF\n2u6m2lfx9YTcV65coaysjOTkZMc2T8gNbavE2O32Dtu6m90rCpSzY/7UrKGhwdFhdeDAgTQ0NLg5\n0Z3V1NTw448/MmnSJOrr61Wf3W63k5CQwLRp05g2bRoTJ070iNzbtm1j/fr1HVZy8YTc0LZKzNKl\nS0lMTOSjjz4Cup/da55BeZvOlhZSi5s3b5Kenk5GRgb+/v63ZVVjdq1WS0FBAY2NjSxfvpzz58+r\nPndpaSlBQUGEhIRQUVHR5XFqy90uNzeXQYMG0dDQwNKlSxk5cmS3P3OvKFDOjPlTuwEDBjiG/Vy7\ndo3AwEB3R+qU1WolPT2d+Ph4IiMjAc/JDm2LVYaHh/PVV1+pPveZM2f44osvKCsro6WlhZs3b7Ju\n3TqCgoJUnbtd+/9gYGAgkZGRfPfdd93+zL2iiXfrmL/W1laKioqIiIhwd6w7Uv7WgV+v13P48GEA\n8vPzVZs/IyODRx55xLHYI6g/e0NDAzdu3ACgubmZb775huDgYNXnXr16NaWlpZSUlLBjxw6mTp1K\nVlYWTz75pKpzAzQ1NXHz5k0ALBYLx48fZ/To0d3+zL1mqIvJZGLr1q2OMX9q7mawZs0aKioqMJvN\nBAUF8eKLLxIZGcmKFSv47bffGD58OLt27brtQbq7nT59mvnz5zN69Gg0Gg0ajYZVq1YxceJEVq5c\nqdrs1dXVbNiwAbvdjt1uZ/bs2bzwwguYzWZV577ViRMneP/998nOzvaI3L/++itpaWloNBpsNhsG\ng4HU1NRuZ/eaAiWE8D5e0cQTQngnKVBCCNWSAiWEUC0pUEII1ZICJYRQLSlQQgjV8oqe5EK99Ho9\nvr6+9OnTB0VR0Gg07N27l2HDhvXYNWpra0lMTKS8vLzHzinUQQqUcLndu3c7tSLPvVDreDRxb6SJ\nJ1yus77AY8eOZffu3SQkJBAdHc3nn3/u2GcymTAajcTHx7NkyRIuX77s2JeXl0d8fDzx8fEkJyc7\nRsMrisLOnTsxGo1ER0dz5swZ178x4XJyByVcLj093dHEa59ZEcDHx4eCggIuXrzIU089RVhYGAAv\nvfQSH3zwAaNGjSIvL4+1a9fy4YcfUlFRwbvvvktubi6BgYE0NTXRq1cvmpqaMJvNTJkyhVWrVlFY\nWEhWVha5ubnufNuiB0iBEi7XVRMvKSkJgJEjRzJ+/HgqKysBCAkJYdSoUQAkJiby6quvYrFYKCsr\nIz4+3jECvn0SOmibQG/69OkATJ48mTfeeMOl70ncH9LEEy7X1XDPW7d39bOzevfu7fhZq9Vis9m6\nfQ6hPlKghNu0T7tx6dIlfvjhByZNmsSkSZOorq7m4sWLjmNCQ0Px8/PjiSee4MiRI9TX1wNt03i0\ntrYCtxc1GQPvHaSJJ1xKo9F0eAal0WjIzMwE2ia/MxqNNDc3k5mZ6Wi6bd++nXHJR8wAAAB2SURB\nVDVr1mCz2QgMDCQrKwuA8PBwUlNTWbx4MVqtlj59+pCdne24zt+vKzyfTLci3GLs2LF8++23+Pr6\nujuKUDFp4gm30Gg00gwT/0juoIQQqiV3UEII1ZICJYRQLSlQQgjVkgIlhFAtKVBCCNWSAiWEUK3/\nA+NlGUCfTrbUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1634dafad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_metrics(top_history, ft_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "heatmap_labels = ['Minor', 'Moderate', 'Severe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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m0LlzZ27dugVAo0aNrGrYoLi4hBMnT9Ep4JkyyzsFtCc55TcTpbIueXdK0CoK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\nIYTKpNAKIYTK/g9m6tLN8RVzawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f163d2297d0>"
      ]
     },
     "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": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j7epE5wY1eK19IM6Of/7IZebmM//HM+w5e5O8wiIa+1ZlYrdGBFZ3MWP01mvw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\n2i9ug56IRAv3+mLnz5/PmjV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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f163c521d50>"
      ]
     },
     "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": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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enpVEK4SwMVLRCiGEyjSWN7OAJFohhG2RilYIIVRmgYnW8mpsIYSwMVLRCiFs\niwVWtJJohRC2RRKtEEKoTEYdCCGEyqSiFUIIlUlFK4QQarO8irZCqT8nJ0etOIQQonJU8ssZK4NR\nifbEiROEhITQp08fAM6fP8/s2bPVjEsIIe6PRmv8x0SMOtL8+fNZu3Ytbm5uADRt2pRjx46pGpgQ\nQtwfTQU+pmF0H62Pj0+J71qt5XU4CyEEGp25IyjFqETr4+PDiRMn0Gg0FBQUsGHDBnx9fdWOTQgh\nKs4Ch3cZVZbOnj2bTz75hMTERAIDAzl37hwzZ85UOzYhhKg4C7wZVm5FW1RURGRkJEuWLDFFPEII\n8YAsr6ItN9HqdDp27NjB888/b4JwzENRFD7e9Dmff/U18ddvUMPdnT69ejD+Xy/i5FTF3OFZlbjk\nLCJP/snBSzf5MzWbvEI99WpUJbhlbZ7t7IuTQ8kfudjkLJbsOcOxuBQKivQ0q+3GuCeb0sHX00xn\nYJ3sqlThld9+w61BA44uX86eCRNKrH+kY0f+MW0atdq0wblmTTKvXyf2hx/4af580uPizBO0Wqz1\ngYW2bdsyZ84cQkJCcHJyMixv3ry5aoGZ0jsL32fTZ1vo1bM7I599miuxcWz89EvOXbjI+jX/Z+7w\nrMrW43/w2eFYnmhWi7DWdbHXaTlyJYml351j7+nrbB4TiINd8c2KP1OzeWplNPZ2WkZ186eaox1f\n/hLHi+sPsub5znSUZGu0HnPn4lSzJoqilFrXODiYp3fuJPXSJY4uW8bt5GQ8mzen3ejRNBs4kBUt\nW5KVkGCGqFVigX20RiXac+fOAbB06VLDMo1Gw4YNG9SJyoQuXY7lk81fERz0BEsXvW1YXqe2D28v\n+De79nxHaJ8gM0ZoXYJb1GF0d3+qOtoblg1p34B6NauyJup3thz7g6c7NgLgvW9+IzuvkK9e7I5/\nreoA9GtTl7D39zE38ld2vdrTLOdgbXzatKHDhAl8N3kywe+9V2p9x1dfRV9YyH86dSI3Pd2wPOns\nWcLWrKF5RARHli0zZcgqs9JEu3HjRrXjMJsde74F4LnhQ0ssHzKoH0uWriRy115JtBXQvI7bXZf3\nafkIq6N+52JiJgA5+YXsP59A+0YehiQL4Oxgx+B2DVi+7xxnrqXR4hF3k8RtrTQaDWEffsjF3bs5\nt23bXROto4sLhbm5JZIsQNaNG2g0GvKzs00VrklotJU7vGv9+vVs2bIFjUaDv78/8+fPx8HBoUJt\nGNWZkZyDVGW+AAAPbUlEQVSczPTp0xk1ahQAly5d4ssvv6x4xBbozG/n0Wo1tGzerMRyBwcHmjbx\n4/Rv58wUmW1JuFX8+LZHNUcALiRkkF+kp1XdGqW2bVXPHQU4fS291DpRUqdJk6jp78/usWPvuc2l\nvXtxcHEhfMMGvFu2xMXHB99evei1eDE3f/uNM5s3mzBiU6i8BxYSExPZuHEjW7duZceOHRQVFbF7\n9+4KR2RUop06dSpdu3bl5s2bADRo0MAmug0AbiYl4+7mhr196eLe28uTtPRbFBYWmiEy26HXK6zc\nfwE7rZbQVo8AcDMjFwBv19I3G71di+8DJGbI3BplcWvQgO6zZxP91ltkXLt2z+1+mjePY6tW8ejg\nwbwcE8Oka9d4Zs8eUi9fZm2nThTcvm3CqE2gkh/B1ev15OTkUFhYSG5uLl5eXhUOyagjpaWlERIS\nYngazM7OzmaeDMvNzcXBwf6u6xwdHf7aJs+UIdmceTtPcerPVMYHNaOBRzUAcguKAHCwK/1z5PjX\nsjvbiLvru2oVqZcucejf/y5zO0WvJzM+nivffcfXI0eyOTycg4sX4xsUxODPP0djI/+X/6vyKlpv\nb2/++c9/0r17dwIDA3FxcaFz584VjsioPlpnZ2fS0tLQ/HU3LyYmBhcXlwofzBJVqVKFtLS7/4ma\nl5f/1zaOpgzJpiz97iyfHollaPuGjAr0MyyvYl/cj5ZfqC+1T95fy+5sI0oLGD6cRk8+ybp//ANF\nX/oa/l34xx/zSMeOfNC8OUX5xT/TFyIjSb18mb4rV9L6uec4+dFHpgjbNCpx1EFGRgb79u1j//79\nuLi4MH78eHbs2EFYWFiF2jHqV9nrr7/OmDFjuHr1KsOGDeP1119nxowZ9xW4pfHy9CAtPZ2CgtLd\nA4k3k3B3q46dnUzbez+Wf3+O1VG/M6htfWb1b1VinddfXQaJf3Uh/N2dLoM7XQiiJJ29Pb2WLOHi\n7t1k37yJe6NGuDdqhFuDBgA4Vq+Oe6NGOLq64vrII7R8+ml+37nTkGTvOPvXfZb63bqZ+hTUVYld\nBwcPHqRu3bq4ubmh0+kICgri5MmTFQ7JqAzSokULNm3aRGxsLIqi0LBhQ+zt7/7ntrVp2bwZBw//\nwqkzZ2nbJsCwPD8/n/MXLtK+XRszRme9ln9/jhX7LxD+WD3mDix9Df1rueKg0xLzZ2qpdTFXU9EA\nLR65+wiGh52dkxNVPT3xCw3Fv2/fEusURaHViBEEPPMM302ezJ8HDwKgvUuxcGfZ3dZZt8qraGvX\nrs2vv/5KXl4eDg4OHD58mJYtW1a4HaOucFhYGKGhoYSEhFCvXr0KH8SShQQ/yeq1G/j4k89LJNrP\nt3xNbl4eYaG9zBiddfpg33lW7L/AgDb1eGfQY3fdxtnBjiea1uL7szf4PeGWYYhXdl4hW479QQOP\narSUoV13VZCdzReDB5da7uzpSd+VK7m4Zw8n164l8dQpbqekoC8qoumAAex74w3yMjIM27f+5z8B\niD961GSxm0QlDu8KCAggODiYAQMGYGdnx6OPPsqQIUMq3I5GudujJP8jPj6e3bt3s2fPHjQajWES\n8Nq1a5d/hNyUCgdlam+/+x6ffL6Vnk8EEti1E5evxLLpsy20fawVH39o+QO59bsWmjsEg08OXeGd\nnaeo7ebMuJ5N0f5Pf1nNao50blx81/ZqSjZDV0Zjp9XwbBdfqjna8+UvcVy6mcGq5zoZtrMEcwZb\nzjW+l+r16jExNrbUI7hBCxfSadIkbv3xB8c//JCc1FTqde1Ky6efJvXSJda0bWsxY2lnl5+OyqXE\nRxu9raaOabpNjEq0fxcXF8eKFSvYsWOH4YmxMllBov3fuQ7c3dwI6d2T8WNGWcVcB5aUaKdvOcHX\nJ6/ec/3jDT1YP6qr4XtsUiZL9p7lWGwyBUV6Hq3txlgLnOvAWhLthCtXOLp8Od9MnFhiXZuRI3ls\n1Ci8mjdH5+hIZnw8v+/cSdRbb5GTWrr7xlwqJdFe/9HobTW1Ax/4eEYdx9hE+/eqVqvVEhISwsiR\nI8vf0QoSrbWzpERrq6wh0dqCykm0Pxu9raZ21/I3qgRG9dFGRERQWFhI7969Wbp0KXXr1lU7LiGE\nuD/WOqnMggULaNSokdqxCCHEg7PARGvUOFpXV1ebnetACGFrtBX4mC6ictnyXAdCCBtjra8bt+W5\nDoQQNsYa3xkGtj3XgRDC1lheH61RiXbq1Kkl5jpIS0sr8bYFIYSwGNZ2M+zUqVMkJSXRvHlzNm3a\nxKRJk3BwcKBLly7UqlXLVDEKIUQFWNnNsFmzZhkmjzl58iQrV65k+PDhuLq6MnPmTJMEKIQQFWKB\nfbRlJtqioiLc3IpnUNq9ezdDhw4lODiYiRMn8scff5gkQCGEqJjKm/i7spSZaPV6veE1LocOHaJj\nx46GdUVFMvu9EMICWeDwrjJvhoWGhvLMM8/g7u5OlSpVaNeuHQB//PEH1apVM0mAQghRIRZ4M6zM\nRDtmzBg6depEUlISXbp0MQzv0uv1vPnmmyYJUAghKsbKEi1A69atSy1r2LChKsEIIcQDs7aKVggh\nrI/lPbUqiVYIYVukohVCCLVJRSuEEKrSSEUrhBBqk0QrhBDqkopWCCHUJolWCCHUZcJHa40liVYI\nYWOkohVCCHVJRSuEECqTm2FCCKE2y0u0lldjCyHEg6jkNyz8+OOP9O7dm+DgYNasWXNfIUmiFULY\nmMp7w4Jer2fu3LmsXbuWnTt3smvXLi5fvlzhiCTRCiFsSyW+YeHUqVPUr1+fOnXqYG9vT2hoKPv2\n7atwSJJohRA2pvIq2sTERHx8fAzfvb29uXnzZoUjkpthQgjb8lAO76pSU/VDPOy0gxaYOwSbN1uR\na2w1KjHneHt7c/36dcP3xMREvLy8KtyO5aV+IYSwEC1btuTq1avEx8eTn5/Prl27ePLJJyvcjnQd\nCCHEPeh0Ot58801GjhyJoigMHjwYX1/fCrejURRFUSE+IYQQf5GuAyGEUJkkWiGEUJkkWiGEUJnN\nJ9qmTZsyZcoUw/eioiI6duzIyy+/DMAPP/zAhx9+aK7wLFp5185YPXr0ID09vVJi2rZtG0lJSZXS\nljVYuXIlffv2pV+/foSHh3Pq1ClzhyTug82POnBycuLixYvk5+fj4ODAgQMHSjzp0aNHD3r06PHA\nxykqKkKn0z1wO5akvGtnrIq+lVSv16PV3r0G2Lp1K35+fnh6elY4DmsTExNDdHQ027dvx87OjvT0\ndAoKClQ5li3+/FoSm69oAQIDA4mKigJg165dhIaGGtZt27aNuXPnAjBt2jTefvtthg0bRlBQEN9+\n+61huwULFhAWFka/fv3YvXs3AEePHmX48OGMGTOmRJu2pKxrd+vWLV555RX69evHsGHDuHDhAgDp\n6em88MILhIWFMWPGDP4+sCUyMpKIiAjCw8OZNWuWYV2bNm1YsGABAwYMICYmhg8++IDBgwcTFhbG\nzJkzAdi7dy9nzpxh8uTJhIeHk5+fz2+//caIESMYNGgQo0aNIjk52URXRn1JSUm4u7tjZ1dcD7m5\nueHp6XnXc75y5QoRERGGfePj4wkLCwPgzJkzd71GI0aMYN68eQwaNIiNGzeSmprK+PHjiYiIICIi\nghMnTpj+pG2VYuPatGmjXLhwQRk3bpySl5en9O/fXzl69KgyevRoRVEUZevWrcrcuXMVRVGUqVOn\nKhMmTFAURVEuXbqkBAUFKYqiKN98840ycuRIRVEUJTk5WenevbuSlJSkHDlyRGndurUSHx9vhjNT\nX3nXbu7cucry5csVRVGUQ4cOKf379zcs/+CDDxRFUZSoqCiladOmSlpamnLp0iVl9OjRSmFhoaIo\nijJ79mxl+/btiqIoSpMmTZRvvvnGcOxbt24Z/j158mRl//79iqIoyjPPPKP89ttviqIoSkFBgTJ0\n6FAlNTVVURRF2bVrlzJt2jS1LofJZWdnK/3791eCg4OV2bNnK0ePHi3znAcMGKBcu3ZNURRFWbNm\njbJy5coyt3/mmWeUt956y3C8SZMmKcePH1cURVGuX7+u9OnTx2TnautsvusAwN/fn/j4eHbu3Em3\nbt1KVFj/q2fPngD4+vqSkpICwIkTJwyVXM2aNWnfvj2nT5+matWqBAQEULt2bfVPwkzKunbHjx9n\n2bJlAHTs2JFbt26RlZXFsWPHWL58OQDdunXD1dUVgMOHD3P27FkGDx6Moijk5eXh4eEBFA8M79Wr\nl6HtQ4cOsXbtWnJycsjIyMDPz4/u3bsDGGKIjY3l4sWLhsHker3+vh6PtFTOzs5s27aNY8eOcfjw\nYV599VXGjBlzz3Pu3bs3u3fv5sUXX2TPnj28//775V6jkJAQw78PHTrElStXDNf39u3b5OTk4OTk\nZNoTt0EPRaKF4r7YhQsXsnHjRtLS0u65nYODg+Hf90rIf1/+MPwQGnvtoOz+WEVRCA8P59VXXy21\nztHR0bBvfn4+c+bMYevWrXh7e7N8+XLy8vLu2p6fnx+bN2+u4BlZD41Gw+OPP87jjz+Ov78/n3zy\nyT3POSQkhAkTJhAUFIRGo6FevXr8/vvvZV6jv//8KorCF198gb29vWrn87Cy+T7aO0lx8ODBjB07\nFj8/vwrv265dO3bv3o1eryc1NZVjx44REBCgSryWpLxr17ZtWyIjIwE4cuQI7u7uVK1alXbt2rFj\nxw4AoqOjycjIAKBTp0588803pKamAsV9vDdu3ChxLIC8vDw0Gg3u7u5kZ2ezd+9ew7qqVauSlZUF\nQMOGDUlLSyMmJgaAwsJCLl26VOnXwVxiY2P5448/DN/PnTtH48aN73nOdevWRavVsmLFCkOlWpFr\n1KVLFzZs2GD4fv78eVXO62Fk8xXtnSrJ29ubZ5555r72DQoKIiYmhv79+6PRaJgyZQo1a9a8r5nW\nrUl5127cuHFMnz6dfv364ezszLvvvgvA2LFjee211wgLC6NNmzaGkQq+vr5MnDiRkSNHotfrsbe3\nZ9asWfj4+JSohF1cXIiIiCA0NBRPT09atmxpWDdw4EBmzZqFk5MTn3/+OUuXLuXtt98mMzMTvV7P\ns88+S+PGjdW8LCZz+/Zt5s6dS1ZWFjqdjvr16zNnzhyGDBlyz3MOCQlh0aJFTJw4EQB7e/t7XqP/\n/evjjTfeYM6cOfTr1w+9Xk+7du2YPXu2qU/bJslcB0IIoTKb7zoQQghzk0QrhBAqk0QrhBAqk0Qr\nhBAqk0QrhBAqk0QrhBAqk0QrhBAqk0QrhBAq+/9iisevSeJXswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f163c53dfd0>"
      ]
     },
     "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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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38HtGHnYc/A/OXryNlzu01E+xuqDFQdrMzExs3boVMTExiI2NRVVVFQ4ePKhx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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f163cedcad0>"
      ]
     },
     "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": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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foKAg7OzstMufeeYZ1QrTt+Tbt1m6eCEnTxxDURTate/I9BmzqPWXi4CiYpdP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\nXLlyhYSEBO1g8tLS0se6PdJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f163d10c090>"
      ]
     },
     "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": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/v8R2JGiFEDbFmEO0ixYteuTyhQsXlqodCVohhE2xs8BrwyRohRA2Re51IIQQ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\n0AohhMr+D7P7xfdT2J6UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f163d0fb150>"
      ]
     },
     "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": [
    "## Making Live Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def severity_estimator(image_path, model):\n",
    "    print \"Determining severity of damage...\"\n",
    "    urllib.urlretrieve(image_path, 'save.jpg') # or other way to upload image\n",
    "    img = load_img('save.jpg', 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",
    "    \n",
    "    d = {0: 'Minor', 1: 'Moderate', 2: 'Severe'}\n",
    "    for key in d.iterkeys():\n",
    "        if pred_label[0] == key:\n",
    "            print \"Assessment: {} damage to vehicle\".format(d[key])\n",
    "    print \"Severity assessment complete.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Determining severity of damage...\n",
      "Assessment: Minor damage to vehicle\n",
      "Severity assessment complete.\n"
     ]
    }
   ],
   "source": [
    "car_severity_estimator('https://www.nerdwallet.com/blog/wp-content/uploads/2015/12/exterior-car-damage-384x233.jpg', ft_d3_model3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Determining severity of damage...\n",
      "Assessment: Minor damage to vehicle\n",
      "Severity assessment complete.\n"
     ]
    }
   ],
   "source": [
    "car_severity_estimator('http://1.bp.blogspot.com/-ToQS-qIxYbo/UDNuV5OcVQI/AAAAAAAABdo/tjeQywWiOo0/s200/Key+scratch.jpg', ft_d3_model3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Determining severity of damage...\n",
      "Assessment: Severe damage to vehicle\n",
      "Severity assessment complete.\n"
     ]
    }
   ],
   "source": [
    "car_severity_estimator('http://1.bp.blogspot.com/-hzoFer11Gcw/Uw_b0Z8HGAI/AAAAAAAAA4M/XjV-NrRl0iM/s1600/20140227_140935.jpg', ft_d3_model3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Determining severity of damage...\n",
      "Assessment: Minor damage to vehicle\n",
      "Severity assessment complete.\n"
     ]
    }
   ],
   "source": [
    "severity_estimator('http://cdn.xl.thumbs.canstockphoto.com/canstock23892567.jpg', ft_d3_model3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Determining severity of damage...\n",
      "Assessment: Moderate damage to vehicle\n",
      "Severity assessment complete.\n"
     ]
    }
   ],
   "source": [
    "severity_estimator('https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcQsEqgcb4vdQlWgsE7EaxfMed5n-v3MzE5nzxdayWgreSTBt2z5', ft_d3_model3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking at Edge Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "scrolled": 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       0.79      0.71      0.75        48\n",
      "          1       0.53      0.65      0.59        55\n",
      "          2       0.80      0.71      0.75        68\n",
      "\n",
      "avg / total       0.71      0.69      0.70       171\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Cp4cPx57cXIQ8EKhZXc3J1h1tQkICfH19MW3aNACAn58fDh48WP/40KFDsWvXLtx3330N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\nqbNNf2l7pQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb33656a190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_categorical_model(ft_d3_model3, validation_data_dir, validation_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "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": 88,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "minor1 = get_edge_cases(ft_d3_model3, 'data3_1/validation/01-minor/', 0, 1) # true minor, predicted moderate - 12\n",
    "minor2 = get_edge_cases(ft_d3_model3, 'data3_1/validation/01-minor/', 0, 2) # true minor, predicted severe - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "moderate1 = get_edge_cases(ft_d3_model3, 'data3_1/validation/02-moderate/', 1, 0) # true moderate, predicted minor - 9\n",
    "moderate2 = get_edge_cases(ft_d3_model3, 'data3_1/validation/02-moderate/', 1, 2) # true moderate, predicted severe - 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "severe1 = get_edge_cases(ft_d3_model3, 'data3_1/validation/03-severe/', 2, 0) # true severe, predicted minor - 0\n",
    "severe2 = get_edge_cases(ft_d3_model3, 'data3_1/validation/03-severe/', 2, 1) # true severe, predicted moderate - 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True, True, True, True, True)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(minor1) == 12, len(minor2) == 2, len(moderate1) == 9, len(moderate2) == 10, len(severe1) == 0, len(severe2) == 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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ADIqFxEwWjd3ruuP3qr4WdoVandcwxl4c+MoWEYQABvPBOcQBDoBGs8ZH0Eo7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\nSSUqwGqbRuSedPH5FJJRapv1KSSS3GX/2Q==\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('data3_1/validation/01-minor/', minor1) # moderate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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h5kZiDBmbrXGB565elXk74MyVBLDW2QjKxW5ALg77mPPY+WlTbXGJBBIJH9c6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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('data3_1/validation/01-minor/', minor2) # severe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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J4ciBqq1PEX7IAMxMQItrNs2+5m+5KXiIqC8kaRMAC8WOp3QgaRANsct7kPzS\nATN3ZuQNuairRJglwE7xBI4Ra6f1sTMDncE6QNbixsFBWq23DURbhu3/AEUjiGs06xu7xryufFVS\n0ib/ANh9ac1Yc6Lc9/hx0F1WeT37h/RRVw2sYkQJMcSbePcqzueo+tymqmYgae+f7KN8DcRug8Of\nPuSUhdpx4d3zTR9X8E8g7jrr7PmkDb+GsSdNEjRpA7696cXQ7Xx+SWba6c9dZ+CRkA+vBNJTt3f/\nAHCXJ37vmgGCp5Wtz5JT705sd9vruTefyQDSCLd/1dITzmOSfYA2uY8oKCOA0iRPhqbwkCNbN7e2\nPYPqUhpAam0d0m6XJ3d3zlK8cTHh8kAwMA1M8vem9Xafhx9gUz6W6RyIm/hG9LSdH3iLQeB0Inim\nEPj9cEreEa7gpGsLg5wE5YLjG4mAe6THiE11Qkzvj+3uQDCN/Hy9miQj63pwHL+3yRCAcwQRAmJm\nwNtTY8Eh4JxdOg80rBx8voWKAaCfhzT4+vrekjdPhp70rJ+H9UyLCE9tUAQfcPmhAdlhjJE37I1/\nIU97AKbSAATMkaoQuhzIti0W9bTGUR2joNzHEe0DyVVzjmcP1WHxvdCFEUqtceqYd+VxnfM6zxUb\nd3cfaDKEJLNraeB95UQ9Y+PwQhTTRVmiW2+u0qNP1PH5IQko9p7Z/b9jXR7go2a+fvCEJGHb+8/B\nJhtR9bkISM6qL/u+5NJ7I8fehCASg48dded0rdT3n/uQhBGN39w9xRhRLwDftN96EIMh+J96Wr6r\nT3/BCEAjXECx3j3KSjqPzH4JEIIr3EiCSRa3mPcmnQd5+CRCKIHaeA96dSFikQgH0WiBbc7+CUxI\nhMEUg+XuCEICKpqlQhAf/9k=\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('data3_1/validation/02-moderate/', moderate1) # minor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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QTOrSBbrteFu4LsHWd3qpawa95wFt7Nm/mNF6nkB2Hoh67XC42vy/RDHnWE7K1HA+we0N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\ne5U8vqmxx/xKvifmf7JJK/s/ShJJJUf/2Q==\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('data3_1/validation/02-moderate/', moderate2) # severe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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IsOBYmd4mdhs6iQSFMnwYti5oTkzZNFBd3awm23cyB4bMlJGL3sE1Bq3WMqq+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\nGxuGtIeizkmFChskIRZg2cdUgbmFChUMWHifSFChRNjP/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c to continue, q to quitq\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Finished for now.'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view_images('data3_1/validation/03-severe/', severe2) # moderate"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}