02-second_gate-damaged_or_whole.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data1a - whole cars vs. all damaged cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "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",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "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": 3,
   "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",
    "\n",
    "    # loads the weights of the VGG16 networks\n",
    "    # note: when there is a complete match between model definition\n",
    "    # and your weights savefile, you can simply call model.load_weights(filename)\n",
    "        f = h5py.File(weights_path)\n",
    "        for k in range(f.attrs['nb_layers']):\n",
    "            if k >= len(model.layers): \n",
    "                # we don't look at the last (fully-connected) layers in the savefile\n",
    "                break\n",
    "            g = f['layer_{}'.format(k)]\n",
    "            weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]\n",
    "            model.layers[k].set_weights(weights)\n",
    "        f.close()\n",
    "        print('VGG16 Model with partial weights loaded.')\n",
    "    else:\n",
    "        print('VGG16 Model with no weights Loaded.')\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "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": 5,
   "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', 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",
    "    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": 6,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def train_binary_model():\n",
    "\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",
    "\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",
    "    \n",
    "    model = Sequential()\n",
    "    model.add(Flatten(input_shape=train_data.shape[1:])) # 512, 4, 4\n",
    "    model.add(Dense(256, activation = 'relu', W_regularizer=l2(0.01)))\n",
    "    model.add(Dropout(0.5)) \n",
    "    model.add(Dense(1, activation = 'sigmoid')) # should activation be sigmoid for binary problem?\n",
    "\n",
    "    model.compile(optimizers.SGD(lr=0.0001, momentum=0.9),\n",
    "              loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\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": 7,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def finetune_binary_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', W_regularizer=l2(0.01)))\n",
    "    top_model.add(Dropout(0.5))\n",
    "    top_model.add(Dense(1, activation='sigmoid'))\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",
    "    # compile the model with a SGD/momentum optimizer \n",
    "    # and a very slow learning rate\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                 optimizer = optimizers.SGD(lr=0.00001, momentum=0.9), # reduced learning rate by 1/10\n",
    "                  metrics=['accuracy'])\n",
    "    \n",
    "    # prepare data augmentation configuration\n",
    "    train_datagen = ImageDataGenerator(rescale=1./255,\n",
    "                                       rotation_range=40,\n",
    "                                       width_shift_range=0.2,\n",
    "                                       height_shift_range=0.2,\n",
    "                                       shear_range=0.2,\n",
    "                                       zoom_range=0.2,\n",
    "                                       horizontal_flip=True,\n",
    "                                       fill_mode='nearest')\n",
    "\n",
    "    test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "    train_generator= train_datagen.flow_from_directory(train_data_dir,\n",
    "                                                     target_size=(img_height, img_width),\n",
    "                                                     batch_size=8,\n",
    "                                                     class_mode='binary')\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='binary')\n",
    "    \n",
    "    \n",
    "    checkpoint = ModelCheckpoint(fine_tuned_model_path, monitor='val_acc', \n",
    "                                 verbose=1, save_best_only=True, \n",
    "                                 save_weights_only=False, mode='auto')\n",
    "    # fine-tune the model\n",
    "    fit = model.fit_generator(train_generator,\n",
    "                              samples_per_epoch=nb_train_samples,\n",
    "                              nb_epoch=nb_epoch,\n",
    "                              validation_data=validation_generator,\n",
    "                              nb_val_samples=nb_validation_samples,\n",
    "                              verbose=1,\n",
    "                              callbacks=[checkpoint])\n",
    "    \n",
    "    with open(location+'/ft_history.txt', 'wb') as f:\n",
    "        json.dump(fit.history, f)\n",
    "    \n",
    "    return model, fit.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def evaluate_binary_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='binary', # categorical for multiclass\n",
    "                                shuffle=False)\n",
    "    \n",
    "    predictions = model.predict_generator(generator, len(labels))\n",
    "    \n",
    "    # use for multiclass\n",
    "    # pred_labels = np.argmax(predictions, axis=1)\n",
    "    \n",
    "    pred_labels = [0 if i <0.5 else 1 for i in predictions]\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": 9,
   "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": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "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": "markdown",
   "metadata": {},
   "source": [
    "## Testing Image Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(rotation_range=40,\n",
    "                             width_shift_range=0.2,\n",
    "                             height_shift_range=0.2,\n",
    "                             shear_range=0.2,\n",
    "                             zoom_range=0.2,\n",
    "                             horizontal_flip=True,\n",
    "                             fill_mode='nearest') # omitted rescaling to keep the images displayable\n",
    "\n",
    "img = load_img('data1a/training/00-damage/car_damage39.JPEG') # this is a PIL image \n",
    "x = img_to_array(img) # this is a Numpy array with shape (3, 150, 150)\n",
    "x = x.reshape((1,) + x.shape) # this is a Numpy array with shape (1, 3, 150, 150)\n",
    "\n",
    "# the .flow() command below generates batches of randomly transformed images\n",
    "# and saves the results to the 'preview/' directory\n",
    "os.makedirs('data1a_preview')\n",
    "i = 0\n",
    "for batch in datagen.flow(x, batch_size=1,\n",
    "                         save_to_dir='data1a_preview', save_prefix='damage_car',\n",
    "                         save_format='jpeg'):\n",
    "    i +=1\n",
    "    if i > 30:\n",
    "        break # otherwise the generator would loop indefinitely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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T5vhi582QSKcFGAIOcjg4/KvOjB82Y2+b06H/AOvSAb5xQ/OrL+tFP+0TL8sq\nLJ/vcH86KAP/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 quitq\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Finished for now.'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view_images('data1a_preview/', os.listdir('data1a_preview/'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# path to the model weights file\n",
    "location = 'data1a'\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": 53,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG16 Model with partial weights loaded.\n",
      "Found 1840 images belonging to 2 classes.\n",
      "Found 460 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# do not rerun!!\n",
    "save_bottleneck_features(location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1840 samples, validate on 460 samples\n",
      "Epoch 1/50\n",
      "1808/1840 [============================>.] - ETA: 0s - loss: 2.0395 - acc: 0.7489Epoch 00000: val_acc improved from -inf to 0.83478, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 2.0218 - acc: 0.7489 - val_loss: 0.5771 - val_acc: 0.8348\n",
      "Epoch 2/50\n",
      "1808/1840 [============================>.] - ETA: 0s - loss: 0.6006 - acc: 0.8313Epoch 00001: val_acc improved from 0.83478 to 0.91522, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.6036 - acc: 0.8299 - val_loss: 0.2331 - val_acc: 0.9152\n",
      "Epoch 3/50\n",
      "1808/1840 [============================>.] - ETA: 0s - loss: 0.4741 - acc: 0.8446Epoch 00002: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.4686 - acc: 0.8467 - val_loss: 0.2335 - val_acc: 0.9087\n",
      "Epoch 4/50\n",
      "1808/1840 [============================>.] - ETA: 0s - loss: 0.3686 - acc: 0.8711Epoch 00003: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.3682 - acc: 0.8701 - val_loss: 0.3974 - val_acc: 0.8826\n",
      "Epoch 5/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3340 - acc: 0.8914Epoch 00004: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.3475 - acc: 0.8891 - val_loss: 0.3642 - val_acc: 0.8739\n",
      "Epoch 6/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2420 - acc: 0.9200Epoch 00005: val_acc improved from 0.91522 to 0.91739, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.2433 - acc: 0.9201 - val_loss: 0.3008 - val_acc: 0.9174\n",
      "Epoch 7/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2467 - acc: 0.9189Epoch 00006: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2465 - acc: 0.9185 - val_loss: 0.3784 - val_acc: 0.9000\n",
      "Epoch 8/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2292 - acc: 0.9265Epoch 00007: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2274 - acc: 0.9272 - val_loss: 0.2816 - val_acc: 0.9087\n",
      "Epoch 9/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1809 - acc: 0.9463Epoch 00008: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1802 - acc: 0.9462 - val_loss: 0.3720 - val_acc: 0.9065\n",
      "Epoch 10/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1458 - acc: 0.9518Epoch 00009: val_acc improved from 0.91739 to 0.92609, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.1446 - acc: 0.9522 - val_loss: 0.3840 - val_acc: 0.9261\n",
      "Epoch 11/50\n",
      "1808/1840 [============================>.] - ETA: 0s - loss: 0.1654 - acc: 0.9508Epoch 00010: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1683 - acc: 0.9505 - val_loss: 0.5276 - val_acc: 0.8870\n",
      "Epoch 12/50\n",
      "1808/1840 [============================>.] - ETA: 0s - loss: 0.1653 - acc: 0.9580Epoch 00011: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1629 - acc: 0.9587 - val_loss: 0.4011 - val_acc: 0.9130\n",
      "Epoch 13/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1109 - acc: 0.9693Epoch 00012: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1150 - acc: 0.9685 - val_loss: 1.0670 - val_acc: 0.8413\n",
      "Epoch 14/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0913 - acc: 0.9682Epoch 00013: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0906 - acc: 0.9685 - val_loss: 0.4366 - val_acc: 0.9217\n",
      "Epoch 15/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0843 - acc: 0.9764Epoch 00014: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0839 - acc: 0.9766 - val_loss: 0.8310 - val_acc: 0.8826\n",
      "Epoch 16/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0889 - acc: 0.9720Epoch 00015: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0881 - acc: 0.9723 - val_loss: 0.6803 - val_acc: 0.9130\n",
      "Epoch 17/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0989 - acc: 0.9775Epoch 00016: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0982 - acc: 0.9777 - val_loss: 0.5355 - val_acc: 0.9130\n",
      "Epoch 18/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0766 - acc: 0.9786Epoch 00017: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0760 - acc: 0.9788 - val_loss: 0.6416 - val_acc: 0.9087\n",
      "Epoch 19/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0615 - acc: 0.9857Epoch 00018: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0610 - acc: 0.9859 - val_loss: 0.5524 - val_acc: 0.9152\n",
      "Epoch 20/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0830 - acc: 0.9814Epoch 00019: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0856 - acc: 0.9810 - val_loss: 0.5695 - val_acc: 0.9239\n",
      "Epoch 21/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0320 - acc: 0.9918Epoch 00020: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0317 - acc: 0.9918 - val_loss: 0.6150 - val_acc: 0.9217\n",
      "Epoch 22/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0523 - acc: 0.9863Epoch 00021: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0519 - acc: 0.9864 - val_loss: 0.5873 - val_acc: 0.9152\n",
      "Epoch 23/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0294 - acc: 0.9923Epoch 00022: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0292 - acc: 0.9924 - val_loss: 0.6814 - val_acc: 0.9152\n",
      "Epoch 24/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1102 - acc: 0.9742Epoch 00023: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1092 - acc: 0.9745 - val_loss: 0.6623 - val_acc: 0.9065\n",
      "Epoch 25/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0396 - acc: 0.9912Epoch 00024: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0393 - acc: 0.9913 - val_loss: 0.5876 - val_acc: 0.9152\n",
      "Epoch 26/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0456 - acc: 0.9896Epoch 00025: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0457 - acc: 0.9891 - val_loss: 1.3881 - val_acc: 0.8609\n",
      "Epoch 27/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0681 - acc: 0.9885Epoch 00026: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0683 - acc: 0.9880 - val_loss: 0.7240 - val_acc: 0.9087\n",
      "Epoch 28/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0328 - acc: 0.9918Epoch 00027: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0326 - acc: 0.9918 - val_loss: 0.6951 - val_acc: 0.9174\n",
      "Epoch 29/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0458 - acc: 0.9868Epoch 00028: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0454 - acc: 0.9870 - val_loss: 0.6841 - val_acc: 0.9217\n",
      "Epoch 30/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0432 - acc: 0.9868Epoch 00029: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0428 - acc: 0.9870 - val_loss: 0.8315 - val_acc: 0.9043\n",
      "Epoch 31/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0746 - acc: 0.9885Epoch 00030: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0740 - acc: 0.9886 - val_loss: 0.6835 - val_acc: 0.9239\n",
      "Epoch 32/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0345 - acc: 0.9912Epoch 00031: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0342 - acc: 0.9913 - val_loss: 0.6900 - val_acc: 0.9239\n",
      "Epoch 33/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0660 - acc: 0.9890Epoch 00032: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0654 - acc: 0.9891 - val_loss: 0.7180 - val_acc: 0.9174\n",
      "Epoch 34/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0396 - acc: 0.9890Epoch 00033: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0392 - acc: 0.9891 - val_loss: 0.7252 - val_acc: 0.9239\n",
      "Epoch 35/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0443 - acc: 0.9890Epoch 00034: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0439 - acc: 0.9891 - val_loss: 0.8171 - val_acc: 0.9196\n",
      "Epoch 36/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0354 - acc: 0.9929Epoch 00035: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0381 - acc: 0.9918 - val_loss: 0.7282 - val_acc: 0.9261\n",
      "Epoch 37/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0280 - acc: 0.9934Epoch 00036: val_acc improved from 0.92609 to 0.92826, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.0278 - acc: 0.9935 - val_loss: 0.7321 - val_acc: 0.9283\n",
      "Epoch 38/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0247 - acc: 0.9934Epoch 00037: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0245 - acc: 0.9935 - val_loss: 0.7137 - val_acc: 0.9152\n",
      "Epoch 39/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0333 - acc: 0.9929Epoch 00038: val_acc improved from 0.92826 to 0.93043, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.0330 - acc: 0.9929 - val_loss: 0.7178 - val_acc: 0.9304\n",
      "Epoch 40/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0487 - acc: 0.9907Epoch 00039: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0570 - acc: 0.9902 - val_loss: 0.8290 - val_acc: 0.9130\n",
      "Epoch 41/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0347 - acc: 0.9934Epoch 00040: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0344 - acc: 0.9935 - val_loss: 0.7334 - val_acc: 0.9304\n",
      "Epoch 42/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0415 - acc: 0.9907Epoch 00041: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0411 - acc: 0.9908 - val_loss: 0.7444 - val_acc: 0.9196\n",
      "Epoch 43/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0205 - acc: 0.9973Epoch 00042: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0203 - acc: 0.9973 - val_loss: 0.8543 - val_acc: 0.9130\n",
      "Epoch 44/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0117 - acc: 0.9962Epoch 00043: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0116 - acc: 0.9962 - val_loss: 0.8684 - val_acc: 0.9196\n",
      "Epoch 45/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0824 - acc: 0.9879Epoch 00044: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0817 - acc: 0.9880 - val_loss: 0.8198 - val_acc: 0.9196\n",
      "Epoch 46/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0557 - acc: 0.9879Epoch 00045: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0552 - acc: 0.9880 - val_loss: 0.8696 - val_acc: 0.9109\n",
      "Epoch 47/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0205 - acc: 0.9951Epoch 00046: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0204 - acc: 0.9951 - val_loss: 0.8945 - val_acc: 0.9152\n",
      "Epoch 48/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0334 - acc: 0.9923Epoch 00047: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0331 - acc: 0.9924 - val_loss: 0.7544 - val_acc: 0.9261\n",
      "Epoch 49/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0292 - acc: 0.9912Epoch 00048: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0290 - acc: 0.9913 - val_loss: 0.8779 - val_acc: 0.9152\n",
      "Epoch 50/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0506 - acc: 0.9940Epoch 00049: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0546 - acc: 0.9935 - val_loss: 0.8414 - val_acc: 0.9152\n"
     ]
    }
   ],
   "source": [
    "d1a_model1, d1a_history1 = train_binary_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1840 samples, validate on 460 samples\n",
      "Epoch 1/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 6.2585 - acc: 0.5658Epoch 00000: val_acc improved from -inf to 0.86522, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 6.2277 - acc: 0.5652 - val_loss: 0.3505 - val_acc: 0.8652\n",
      "Epoch 2/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.8007 - acc: 0.7423Epoch 00001: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.8013 - acc: 0.7424 - val_loss: 0.9405 - val_acc: 0.7065\n",
      "Epoch 3/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.4736 - acc: 0.7571Epoch 00002: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.4696 - acc: 0.7582 - val_loss: 0.5963 - val_acc: 0.8152\n",
      "Epoch 4/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.5086 - acc: 0.7752Epoch 00003: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.5057 - acc: 0.7755 - val_loss: 1.2184 - val_acc: 0.7609\n",
      "Epoch 5/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.5419 - acc: 0.7725Epoch 00004: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.5411 - acc: 0.7728 - val_loss: 1.4495 - val_acc: 0.6326\n",
      "Epoch 6/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.2464 - acc: 0.7878Epoch 00005: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.2429 - acc: 0.7886 - val_loss: 0.4358 - val_acc: 0.8370\n",
      "Epoch 7/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.0519 - acc: 0.7752Epoch 00006: val_acc improved from 0.86522 to 0.90435, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 1.0464 - acc: 0.7766 - val_loss: 0.2698 - val_acc: 0.9043\n",
      "Epoch 8/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.1533 - acc: 0.7796Epoch 00007: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.1499 - acc: 0.7799 - val_loss: 0.2686 - val_acc: 0.8826\n",
      "Epoch 9/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.2429 - acc: 0.7730Epoch 00008: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.2438 - acc: 0.7739 - val_loss: 0.7058 - val_acc: 0.7913\n",
      "Epoch 10/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.1075 - acc: 0.7961Epoch 00009: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.1140 - acc: 0.7957 - val_loss: 1.5126 - val_acc: 0.6239\n",
      "Epoch 11/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.0500 - acc: 0.7884Epoch 00010: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.0477 - acc: 0.7880 - val_loss: 0.5701 - val_acc: 0.8261\n",
      "Epoch 12/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.9962 - acc: 0.7971Epoch 00011: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.9943 - acc: 0.7962 - val_loss: 0.4896 - val_acc: 0.8522\n",
      "Epoch 13/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.0980 - acc: 0.7823Epoch 00012: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.0966 - acc: 0.7826 - val_loss: 0.4380 - val_acc: 0.8304\n",
      "Epoch 14/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.9107 - acc: 0.7758Epoch 00013: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.9115 - acc: 0.7766 - val_loss: 1.0205 - val_acc: 0.7087\n",
      "Epoch 15/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.9746 - acc: 0.7939Epoch 00014: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.9742 - acc: 0.7940 - val_loss: 0.8522 - val_acc: 0.7696\n",
      "Epoch 16/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.9239 - acc: 0.7911Epoch 00015: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.9235 - acc: 0.7908 - val_loss: 0.2495 - val_acc: 0.8935\n",
      "Epoch 17/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.8490 - acc: 0.8136Epoch 00016: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.8449 - acc: 0.8152 - val_loss: 0.3170 - val_acc: 0.8739\n",
      "Epoch 18/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.8507 - acc: 0.8021Epoch 00017: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.8465 - acc: 0.8038 - val_loss: 0.2686 - val_acc: 0.8957\n",
      "Epoch 19/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.7706 - acc: 0.8103Epoch 00018: val_acc improved from 0.90435 to 0.91522, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 0.7683 - acc: 0.8109 - val_loss: 0.2381 - val_acc: 0.9152\n",
      "Epoch 20/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.7894 - acc: 0.8169Epoch 00019: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.7894 - acc: 0.8168 - val_loss: 0.8669 - val_acc: 0.6870\n",
      "Epoch 21/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.8075 - acc: 0.7961Epoch 00020: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.8052 - acc: 0.7967 - val_loss: 0.4581 - val_acc: 0.8500\n",
      "Epoch 22/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.9003 - acc: 0.7999Epoch 00021: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.8977 - acc: 0.8000 - val_loss: 0.2359 - val_acc: 0.9043\n",
      "Epoch 23/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.8024 - acc: 0.8048Epoch 00022: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.8074 - acc: 0.8027 - val_loss: 1.5645 - val_acc: 0.6370\n",
      "Epoch 24/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.7987 - acc: 0.8065Epoch 00023: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.7956 - acc: 0.8071 - val_loss: 0.2636 - val_acc: 0.8826\n",
      "Epoch 25/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.7039 - acc: 0.8169Epoch 00024: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.7025 - acc: 0.8174 - val_loss: 0.2951 - val_acc: 0.8717\n",
      "Epoch 26/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.7185 - acc: 0.8081Epoch 00025: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.7164 - acc: 0.8082 - val_loss: 0.2272 - val_acc: 0.9087\n",
      "Epoch 27/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6887 - acc: 0.8185Epoch 00026: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6887 - acc: 0.8174 - val_loss: 0.4386 - val_acc: 0.7717\n",
      "Epoch 28/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6994 - acc: 0.8306Epoch 00027: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6992 - acc: 0.8310 - val_loss: 0.3505 - val_acc: 0.8500\n",
      "Epoch 29/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6488 - acc: 0.8196Epoch 00028: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6487 - acc: 0.8196 - val_loss: 0.7872 - val_acc: 0.6978\n",
      "Epoch 30/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.7595 - acc: 0.7928Epoch 00029: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.7602 - acc: 0.7924 - val_loss: 0.7128 - val_acc: 0.7500\n",
      "Epoch 31/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6399 - acc: 0.8262Epoch 00030: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6383 - acc: 0.8272 - val_loss: 0.2425 - val_acc: 0.8957\n",
      "Epoch 32/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6535 - acc: 0.8141Epoch 00031: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6577 - acc: 0.8130 - val_loss: 0.2665 - val_acc: 0.8913\n",
      "Epoch 33/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6789 - acc: 0.8196Epoch 00032: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6768 - acc: 0.8201 - val_loss: 0.3022 - val_acc: 0.8587\n",
      "Epoch 34/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6789 - acc: 0.8032Epoch 00033: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6767 - acc: 0.8038 - val_loss: 0.3042 - val_acc: 0.8804\n",
      "Epoch 35/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6624 - acc: 0.8026Epoch 00034: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6602 - acc: 0.8033 - val_loss: 0.2668 - val_acc: 0.8913\n",
      "Epoch 36/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6905 - acc: 0.7993Epoch 00035: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6883 - acc: 0.8005 - val_loss: 0.2335 - val_acc: 0.9087\n",
      "Epoch 37/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6515 - acc: 0.8076Epoch 00036: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6517 - acc: 0.8076 - val_loss: 0.2781 - val_acc: 0.8870\n",
      "Epoch 38/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6726 - acc: 0.8004Epoch 00037: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6713 - acc: 0.8011 - val_loss: 0.3296 - val_acc: 0.8609\n",
      "Epoch 39/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6069 - acc: 0.8196Epoch 00038: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6040 - acc: 0.8212 - val_loss: 0.2971 - val_acc: 0.8739\n",
      "Epoch 40/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6552 - acc: 0.8125Epoch 00039: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6552 - acc: 0.8120 - val_loss: 0.2330 - val_acc: 0.9065\n",
      "Epoch 41/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6263 - acc: 0.8065Epoch 00040: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6271 - acc: 0.8076 - val_loss: 0.3064 - val_acc: 0.8630\n",
      "Epoch 42/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6524 - acc: 0.8120Epoch 00041: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6525 - acc: 0.8109 - val_loss: 0.2957 - val_acc: 0.8891\n",
      "Epoch 43/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6358 - acc: 0.8076Epoch 00042: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6347 - acc: 0.8076 - val_loss: 0.2831 - val_acc: 0.8891\n",
      "Epoch 44/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6358 - acc: 0.8152Epoch 00043: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6344 - acc: 0.8158 - val_loss: 0.2655 - val_acc: 0.8804\n",
      "Epoch 45/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6064 - acc: 0.8284Epoch 00044: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6100 - acc: 0.8261 - val_loss: 0.3150 - val_acc: 0.8652\n",
      "Epoch 46/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.5939 - acc: 0.8158Epoch 00045: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.5934 - acc: 0.8163 - val_loss: 0.4582 - val_acc: 0.8043\n",
      "Epoch 47/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6109 - acc: 0.8130Epoch 00046: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6097 - acc: 0.8136 - val_loss: 0.3644 - val_acc: 0.8304\n",
      "Epoch 48/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6218 - acc: 0.8169Epoch 00047: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6214 - acc: 0.8174 - val_loss: 0.6234 - val_acc: 0.7087\n",
      "Epoch 49/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6316 - acc: 0.8043Epoch 00048: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6310 - acc: 0.8038 - val_loss: 0.2586 - val_acc: 0.8913\n",
      "Epoch 50/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.5964 - acc: 0.8065Epoch 00049: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.5964 - acc: 0.8065 - val_loss: 0.3570 - val_acc: 0.8435\n"
     ]
    }
   ],
   "source": [
    "d1a_model2, d1a_history2 = train_binary_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1840 samples, validate on 460 samples\n",
      "Epoch 1/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.3105 - acc: 0.6672Epoch 00000: val_acc improved from -inf to 0.87391, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 2.2925 - acc: 0.6701 - val_loss: 0.3205 - val_acc: 0.8739\n",
      "Epoch 2/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.5933 - acc: 0.8026Epoch 00001: val_acc improved from 0.87391 to 0.91087, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.5903 - acc: 0.8033 - val_loss: 0.2904 - val_acc: 0.9109\n",
      "Epoch 3/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4857 - acc: 0.8339Epoch 00002: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.4836 - acc: 0.8348 - val_loss: 0.3724 - val_acc: 0.8435\n",
      "Epoch 4/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3741 - acc: 0.8547Epoch 00003: val_acc improved from 0.91087 to 0.91957, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.3723 - acc: 0.8543 - val_loss: 0.2301 - val_acc: 0.9196\n",
      "Epoch 5/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3110 - acc: 0.8788Epoch 00004: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.3165 - acc: 0.8783 - val_loss: 0.2448 - val_acc: 0.9130\n",
      "Epoch 6/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3369 - acc: 0.8947Epoch 00005: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.3351 - acc: 0.8951 - val_loss: 0.3967 - val_acc: 0.8978\n",
      "Epoch 7/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2634 - acc: 0.9052Epoch 00006: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2623 - acc: 0.9054 - val_loss: 0.4597 - val_acc: 0.8652\n",
      "Epoch 8/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2245 - acc: 0.9150Epoch 00007: val_acc improved from 0.91957 to 0.92174, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.2234 - acc: 0.9152 - val_loss: 0.2547 - val_acc: 0.9217\n",
      "Epoch 9/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2349 - acc: 0.9161Epoch 00008: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2408 - acc: 0.9152 - val_loss: 0.5481 - val_acc: 0.8543\n",
      "Epoch 10/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1948 - acc: 0.9211Epoch 00009: val_acc improved from 0.92174 to 0.93261, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.1952 - acc: 0.9207 - val_loss: 0.3178 - val_acc: 0.9326\n",
      "Epoch 11/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1858 - acc: 0.9364Epoch 00010: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1842 - acc: 0.9370 - val_loss: 0.2952 - val_acc: 0.9283\n",
      "Epoch 12/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1657 - acc: 0.9370Epoch 00011: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1649 - acc: 0.9370 - val_loss: 0.3708 - val_acc: 0.9043\n",
      "Epoch 13/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1471 - acc: 0.9468Epoch 00012: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1469 - acc: 0.9467 - val_loss: 0.3252 - val_acc: 0.9239\n",
      "Epoch 14/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1417 - acc: 0.9507Epoch 00013: val_acc improved from 0.93261 to 0.93478, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.1427 - acc: 0.9500 - val_loss: 0.3420 - val_acc: 0.9348\n",
      "Epoch 15/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1218 - acc: 0.9545Epoch 00014: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1210 - acc: 0.9549 - val_loss: 0.4210 - val_acc: 0.9239\n",
      "Epoch 16/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1079 - acc: 0.9583Epoch 00015: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1073 - acc: 0.9587 - val_loss: 0.4203 - val_acc: 0.9326\n",
      "Epoch 17/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1230 - acc: 0.9567Epoch 00016: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1228 - acc: 0.9565 - val_loss: 0.4516 - val_acc: 0.8978\n",
      "Epoch 18/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1174 - acc: 0.9594Epoch 00017: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1171 - acc: 0.9592 - val_loss: 0.3937 - val_acc: 0.9304\n",
      "Epoch 19/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0974 - acc: 0.9589Epoch 00018: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0979 - acc: 0.9587 - val_loss: 0.4033 - val_acc: 0.9283\n",
      "Epoch 20/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0898 - acc: 0.9682Epoch 00019: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0890 - acc: 0.9685 - val_loss: 0.6370 - val_acc: 0.9043\n",
      "Epoch 21/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0794 - acc: 0.9688Epoch 00020: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0799 - acc: 0.9685 - val_loss: 0.3834 - val_acc: 0.9239\n",
      "Epoch 22/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1203 - acc: 0.9622Epoch 00021: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1200 - acc: 0.9625 - val_loss: 0.6745 - val_acc: 0.8978\n",
      "Epoch 23/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1164 - acc: 0.9605Epoch 00022: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1155 - acc: 0.9609 - val_loss: 0.6159 - val_acc: 0.8978\n",
      "Epoch 24/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0792 - acc: 0.9698Epoch 00023: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0797 - acc: 0.9696 - val_loss: 0.5130 - val_acc: 0.9152\n",
      "Epoch 25/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0811 - acc: 0.9753Epoch 00024: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0804 - acc: 0.9755 - val_loss: 0.4694 - val_acc: 0.9261\n",
      "Epoch 26/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0799 - acc: 0.9709Epoch 00025: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0806 - acc: 0.9707 - val_loss: 0.4359 - val_acc: 0.9239\n",
      "Epoch 27/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0696 - acc: 0.9748Epoch 00026: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0710 - acc: 0.9739 - val_loss: 0.5154 - val_acc: 0.9261\n",
      "Epoch 28/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0563 - acc: 0.9759Epoch 00027: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0558 - acc: 0.9761 - val_loss: 1.0580 - val_acc: 0.8739\n",
      "Epoch 29/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0879 - acc: 0.9720Epoch 00028: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0879 - acc: 0.9717 - val_loss: 0.7458 - val_acc: 0.9022\n",
      "Epoch 30/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0788 - acc: 0.9775Epoch 00029: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0786 - acc: 0.9772 - val_loss: 0.6152 - val_acc: 0.9196\n",
      "Epoch 31/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0825 - acc: 0.9748Epoch 00030: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0842 - acc: 0.9745 - val_loss: 0.7022 - val_acc: 0.9022\n",
      "Epoch 32/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1039 - acc: 0.9726Epoch 00031: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1030 - acc: 0.9728 - val_loss: 0.5896 - val_acc: 0.9152\n",
      "Epoch 33/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0883 - acc: 0.9737Epoch 00032: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0879 - acc: 0.9739 - val_loss: 0.5587 - val_acc: 0.9152\n",
      "Epoch 34/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0733 - acc: 0.9797Epoch 00033: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0727 - acc: 0.9799 - val_loss: 0.6131 - val_acc: 0.9174\n",
      "Epoch 35/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0980 - acc: 0.9748Epoch 00034: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0971 - acc: 0.9750 - val_loss: 0.5912 - val_acc: 0.9087\n",
      "Epoch 36/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1004 - acc: 0.9726Epoch 00035: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0995 - acc: 0.9728 - val_loss: 0.5209 - val_acc: 0.9196\n",
      "Epoch 37/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0797 - acc: 0.9792Epoch 00036: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0791 - acc: 0.9793 - val_loss: 0.6981 - val_acc: 0.9087\n",
      "Epoch 38/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0511 - acc: 0.9792Epoch 00037: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0506 - acc: 0.9793 - val_loss: 0.6965 - val_acc: 0.9196\n",
      "Epoch 39/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0893 - acc: 0.9737Epoch 00038: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0939 - acc: 0.9723 - val_loss: 0.9763 - val_acc: 0.8978\n",
      "Epoch 40/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0811 - acc: 0.9775Epoch 00039: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0805 - acc: 0.9777 - val_loss: 0.9748 - val_acc: 0.8848\n",
      "Epoch 41/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0860 - acc: 0.9770Epoch 00040: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0853 - acc: 0.9772 - val_loss: 0.8273 - val_acc: 0.9065\n",
      "Epoch 42/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0674 - acc: 0.9792Epoch 00041: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0669 - acc: 0.9793 - val_loss: 0.8185 - val_acc: 0.9130\n",
      "Epoch 43/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0954 - acc: 0.9786Epoch 00042: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0947 - acc: 0.9788 - val_loss: 0.7035 - val_acc: 0.9196\n",
      "Epoch 44/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0727 - acc: 0.9841Epoch 00043: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0730 - acc: 0.9837 - val_loss: 0.7567 - val_acc: 0.9217\n",
      "Epoch 45/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0886 - acc: 0.9770Epoch 00044: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0878 - acc: 0.9772 - val_loss: 0.7136 - val_acc: 0.9217\n",
      "Epoch 46/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0830 - acc: 0.9825Epoch 00045: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0824 - acc: 0.9826 - val_loss: 0.7384 - val_acc: 0.9196\n",
      "Epoch 47/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0885 - acc: 0.9781Epoch 00046: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0881 - acc: 0.9783 - val_loss: 0.6690 - val_acc: 0.9217\n",
      "Epoch 48/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0659 - acc: 0.9808Epoch 00047: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0653 - acc: 0.9810 - val_loss: 0.7482 - val_acc: 0.9239\n",
      "Epoch 49/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0880 - acc: 0.9841Epoch 00048: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0873 - acc: 0.9842 - val_loss: 0.7051 - val_acc: 0.9130\n",
      "Epoch 50/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0658 - acc: 0.9819Epoch 00049: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0652 - acc: 0.9821 - val_loss: 0.8490 - val_acc: 0.9000\n"
     ]
    }
   ],
   "source": [
    "d1a_model3, d1a_history3 = train_binary_model() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1840 samples, validate on 460 samples\n",
      "Epoch 1/50\n",
      "1808/1840 [============================>.] - ETA: 0s - loss: 7.8198 - acc: 0.4994Epoch 00000: val_acc improved from -inf to 0.50000, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 7.7870 - acc: 0.4995 - val_loss: 5.9265 - val_acc: 0.5000\n",
      "Epoch 2/50\n",
      "1808/1840 [============================>.] - ETA: 0s - loss: 2.4645 - acc: 0.7102Epoch 00001: val_acc improved from 0.50000 to 0.87826, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 2.4314 - acc: 0.7114 - val_loss: 0.3232 - val_acc: 0.8783\n",
      "Epoch 3/50\n",
      "1808/1840 [============================>.] - ETA: 0s - loss: 0.6285 - acc: 0.7970Epoch 00002: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.6296 - acc: 0.7957 - val_loss: 0.3132 - val_acc: 0.8652\n",
      "Epoch 4/50\n",
      "1808/1840 [============================>.] - ETA: 0s - loss: 0.5124 - acc: 0.8059Epoch 00003: val_acc improved from 0.87826 to 0.90217, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.5182 - acc: 0.8049 - val_loss: 0.2548 - val_acc: 0.9022\n",
      "Epoch 5/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4857 - acc: 0.8196Epoch 00004: val_acc improved from 0.90217 to 0.91304, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.4837 - acc: 0.8196 - val_loss: 0.2401 - val_acc: 0.9130\n",
      "Epoch 6/50\n",
      "1808/1840 [============================>.] - ETA: 0s - loss: 0.4836 - acc: 0.8374Epoch 00005: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.4812 - acc: 0.8380 - val_loss: 0.2568 - val_acc: 0.9000\n",
      "Epoch 7/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4428 - acc: 0.8640Epoch 00006: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.4411 - acc: 0.8636 - val_loss: 0.2967 - val_acc: 0.8891\n",
      "Epoch 8/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4189 - acc: 0.8624Epoch 00007: val_acc improved from 0.91304 to 0.91739, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.4162 - acc: 0.8636 - val_loss: 0.2364 - val_acc: 0.9174\n",
      "Epoch 9/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3953 - acc: 0.8723Epoch 00008: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.3931 - acc: 0.8723 - val_loss: 0.3055 - val_acc: 0.9022\n",
      "Epoch 10/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3818 - acc: 0.8838Epoch 00009: val_acc improved from 0.91739 to 0.91957, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.3799 - acc: 0.8842 - val_loss: 0.2448 - val_acc: 0.9196\n",
      "Epoch 11/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3270 - acc: 0.8986Epoch 00010: val_acc improved from 0.91957 to 0.92391, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.3248 - acc: 0.8995 - val_loss: 0.2735 - val_acc: 0.9239\n",
      "Epoch 12/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3355 - acc: 0.8898Epoch 00011: val_acc improved from 0.92391 to 0.92609, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.3339 - acc: 0.8897 - val_loss: 0.2936 - val_acc: 0.9261\n",
      "Epoch 13/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3107 - acc: 0.8964Epoch 00012: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.3101 - acc: 0.8962 - val_loss: 0.2846 - val_acc: 0.9217\n",
      "Epoch 14/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2778 - acc: 0.9079Epoch 00013: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2756 - acc: 0.9087 - val_loss: 0.5155 - val_acc: 0.9043\n",
      "Epoch 15/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3105 - acc: 0.9084Epoch 00014: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.3128 - acc: 0.9082 - val_loss: 0.3046 - val_acc: 0.9109\n",
      "Epoch 16/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2617 - acc: 0.9178Epoch 00015: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2616 - acc: 0.9179 - val_loss: 0.3591 - val_acc: 0.9196\n",
      "Epoch 17/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2741 - acc: 0.9161Epoch 00016: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2762 - acc: 0.9163 - val_loss: 0.3367 - val_acc: 0.9217\n",
      "Epoch 18/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2708 - acc: 0.9167Epoch 00017: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2722 - acc: 0.9168 - val_loss: 0.4184 - val_acc: 0.9152\n",
      "Epoch 19/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2250 - acc: 0.9211Epoch 00018: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2272 - acc: 0.9207 - val_loss: 0.3571 - val_acc: 0.9000\n",
      "Epoch 20/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2702 - acc: 0.9271Epoch 00019: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2681 - acc: 0.9277 - val_loss: 0.4151 - val_acc: 0.9152\n",
      "Epoch 21/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2303 - acc: 0.9211Epoch 00020: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2288 - acc: 0.9212 - val_loss: 0.3610 - val_acc: 0.9261\n",
      "Epoch 22/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2603 - acc: 0.9276Epoch 00021: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2595 - acc: 0.9277 - val_loss: 0.3927 - val_acc: 0.9130\n",
      "Epoch 23/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2099 - acc: 0.9375Epoch 00022: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2081 - acc: 0.9380 - val_loss: 0.3816 - val_acc: 0.9217\n",
      "Epoch 24/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2082 - acc: 0.9364Epoch 00023: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2075 - acc: 0.9364 - val_loss: 0.4069 - val_acc: 0.9261\n",
      "Epoch 25/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1829 - acc: 0.9501Epoch 00024: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1820 - acc: 0.9500 - val_loss: 0.4131 - val_acc: 0.9239\n",
      "Epoch 26/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2113 - acc: 0.9391Epoch 00025: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2098 - acc: 0.9397 - val_loss: 0.3700 - val_acc: 0.9217\n",
      "Epoch 27/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1724 - acc: 0.9413Epoch 00026: val_acc improved from 0.92609 to 0.93261, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 1s - loss: 0.1719 - acc: 0.9413 - val_loss: 0.3987 - val_acc: 0.9326\n",
      "Epoch 28/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1750 - acc: 0.9534Epoch 00027: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1741 - acc: 0.9533 - val_loss: 0.4300 - val_acc: 0.9087\n",
      "Epoch 29/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.2065 - acc: 0.9479Epoch 00028: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.2049 - acc: 0.9484 - val_loss: 0.3828 - val_acc: 0.9326\n",
      "Epoch 30/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1650 - acc: 0.9518Epoch 00029: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1638 - acc: 0.9522 - val_loss: 0.4023 - val_acc: 0.9196\n",
      "Epoch 31/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1704 - acc: 0.9496Epoch 00030: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1697 - acc: 0.9495 - val_loss: 0.4250 - val_acc: 0.9239\n",
      "Epoch 32/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1668 - acc: 0.9496Epoch 00031: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1654 - acc: 0.9500 - val_loss: 0.4468 - val_acc: 0.9239\n",
      "Epoch 33/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1744 - acc: 0.9512Epoch 00032: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1744 - acc: 0.9505 - val_loss: 0.3958 - val_acc: 0.9304\n",
      "Epoch 34/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1486 - acc: 0.9600Epoch 00033: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1481 - acc: 0.9598 - val_loss: 0.4852 - val_acc: 0.9196\n",
      "Epoch 35/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1691 - acc: 0.9501Epoch 00034: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1677 - acc: 0.9505 - val_loss: 0.4998 - val_acc: 0.9217\n",
      "Epoch 36/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1463 - acc: 0.9633Epoch 00035: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1451 - acc: 0.9636 - val_loss: 0.4712 - val_acc: 0.9174\n",
      "Epoch 37/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1147 - acc: 0.9638Epoch 00036: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1142 - acc: 0.9636 - val_loss: 0.4785 - val_acc: 0.9152\n",
      "Epoch 38/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1567 - acc: 0.9600Epoch 00037: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1554 - acc: 0.9603 - val_loss: 0.5351 - val_acc: 0.9261\n",
      "Epoch 39/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1559 - acc: 0.9616Epoch 00038: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1550 - acc: 0.9620 - val_loss: 0.5171 - val_acc: 0.9239\n",
      "Epoch 40/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1181 - acc: 0.9616Epoch 00039: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1209 - acc: 0.9614 - val_loss: 0.5255 - val_acc: 0.9174\n",
      "Epoch 41/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1392 - acc: 0.9583Epoch 00040: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1433 - acc: 0.9582 - val_loss: 0.5459 - val_acc: 0.9196\n",
      "Epoch 42/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1210 - acc: 0.9605Epoch 00041: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1206 - acc: 0.9603 - val_loss: 0.5286 - val_acc: 0.9239\n",
      "Epoch 43/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1606 - acc: 0.9572Epoch 00042: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1593 - acc: 0.9576 - val_loss: 0.5299 - val_acc: 0.9239\n",
      "Epoch 44/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1397 - acc: 0.9655Epoch 00043: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1390 - acc: 0.9658 - val_loss: 0.6072 - val_acc: 0.9109\n",
      "Epoch 45/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0902 - acc: 0.9693Epoch 00044: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0901 - acc: 0.9690 - val_loss: 0.5534 - val_acc: 0.9217\n",
      "Epoch 46/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1975 - acc: 0.9529Epoch 00045: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1969 - acc: 0.9527 - val_loss: 0.5856 - val_acc: 0.9239\n",
      "Epoch 47/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1331 - acc: 0.9649Epoch 00046: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1319 - acc: 0.9652 - val_loss: 0.5738 - val_acc: 0.9130\n",
      "Epoch 48/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1622 - acc: 0.9567Epoch 00047: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1609 - acc: 0.9571 - val_loss: 0.5441 - val_acc: 0.9130\n",
      "Epoch 49/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.0893 - acc: 0.9693Epoch 00048: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.0886 - acc: 0.9696 - val_loss: 0.5837 - val_acc: 0.9217\n",
      "Epoch 50/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.1115 - acc: 0.9704Epoch 00049: val_acc did not improve\n",
      "1840/1840 [==============================] - 1s - loss: 0.1105 - acc: 0.9707 - val_loss: 0.5919 - val_acc: 0.9196\n"
     ]
    }
   ],
   "source": [
    "d1a_model4, d1a_history4 = train_binary_model() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1840 samples, validate on 460 samples\n",
      "Epoch 1/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 8.1049 - acc: 0.4984Epoch 00000: val_acc improved from -inf to 0.50000, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 8.1301 - acc: 0.4967 - val_loss: 1.4477 - val_acc: 0.5000\n",
      "Epoch 2/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.0480 - acc: 0.7264Epoch 00001: val_acc improved from 0.50000 to 0.89565, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 1.0447 - acc: 0.7272 - val_loss: 0.2837 - val_acc: 0.8957\n",
      "Epoch 3/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.8865 - acc: 0.7911Epoch 00002: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.8869 - acc: 0.7908 - val_loss: 0.9127 - val_acc: 0.7804\n",
      "Epoch 4/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.0019 - acc: 0.8130Epoch 00003: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.0081 - acc: 0.8120 - val_loss: 1.0178 - val_acc: 0.7891\n",
      "Epoch 5/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.8319 - acc: 0.8268Epoch 00004: val_acc improved from 0.89565 to 0.90435, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 0.8268 - acc: 0.8277 - val_loss: 0.2571 - val_acc: 0.9043\n",
      "Epoch 6/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.8213 - acc: 0.8273Epoch 00005: val_acc improved from 0.90435 to 0.90870, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 0.8192 - acc: 0.8283 - val_loss: 0.4097 - val_acc: 0.9087\n",
      "Epoch 7/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.7197 - acc: 0.8575Epoch 00006: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.7221 - acc: 0.8571 - val_loss: 0.2847 - val_acc: 0.8935\n",
      "Epoch 8/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6568 - acc: 0.8536Epoch 00007: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6661 - acc: 0.8527 - val_loss: 0.3284 - val_acc: 0.9022\n",
      "Epoch 9/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.7881 - acc: 0.8586Epoch 00008: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.7863 - acc: 0.8582 - val_loss: 0.3622 - val_acc: 0.8978\n",
      "Epoch 10/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6786 - acc: 0.8580Epoch 00009: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6740 - acc: 0.8592 - val_loss: 0.5047 - val_acc: 0.8630\n",
      "Epoch 11/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.5357 - acc: 0.8838Epoch 00010: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.5330 - acc: 0.8842 - val_loss: 0.6593 - val_acc: 0.8522\n",
      "Epoch 12/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6373 - acc: 0.8723Epoch 00011: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6446 - acc: 0.8717 - val_loss: 0.3457 - val_acc: 0.9065\n",
      "Epoch 13/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.5248 - acc: 0.8931Epoch 00012: val_acc improved from 0.90870 to 0.91304, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 0.5230 - acc: 0.8929 - val_loss: 0.2509 - val_acc: 0.9130\n",
      "Epoch 14/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6398 - acc: 0.8712Epoch 00013: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6545 - acc: 0.8690 - val_loss: 1.4439 - val_acc: 0.7870\n",
      "Epoch 15/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.6099 - acc: 0.8816Epoch 00014: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.6112 - acc: 0.8810 - val_loss: 0.8991 - val_acc: 0.8304\n",
      "Epoch 16/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.5595 - acc: 0.8986Epoch 00015: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.5581 - acc: 0.8984 - val_loss: 0.2168 - val_acc: 0.9109\n",
      "Epoch 17/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.5027 - acc: 0.9008Epoch 00016: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.5002 - acc: 0.9016 - val_loss: 0.4504 - val_acc: 0.8913\n",
      "Epoch 18/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4594 - acc: 0.8991Epoch 00017: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4594 - acc: 0.8995 - val_loss: 0.2799 - val_acc: 0.9109\n",
      "Epoch 19/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4531 - acc: 0.9030Epoch 00018: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4506 - acc: 0.9038 - val_loss: 0.3746 - val_acc: 0.8891\n",
      "Epoch 20/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.5338 - acc: 0.8832Epoch 00019: val_acc improved from 0.91304 to 0.91957, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 0.5325 - acc: 0.8837 - val_loss: 0.4264 - val_acc: 0.9196\n",
      "Epoch 21/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.5051 - acc: 0.8980Epoch 00020: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.5274 - acc: 0.8967 - val_loss: 0.4723 - val_acc: 0.9065\n",
      "Epoch 22/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4723 - acc: 0.9013Epoch 00021: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4707 - acc: 0.9016 - val_loss: 0.3880 - val_acc: 0.8913\n",
      "Epoch 23/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4435 - acc: 0.9041Epoch 00022: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4455 - acc: 0.9038 - val_loss: 0.3404 - val_acc: 0.9109\n",
      "Epoch 24/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4474 - acc: 0.9046Epoch 00023: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4472 - acc: 0.9049 - val_loss: 0.8022 - val_acc: 0.8261\n",
      "Epoch 25/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4542 - acc: 0.9062Epoch 00024: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4520 - acc: 0.9071 - val_loss: 0.2834 - val_acc: 0.9152\n",
      "Epoch 26/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.5664 - acc: 0.9013Epoch 00025: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.5631 - acc: 0.9022 - val_loss: 0.4133 - val_acc: 0.9130\n",
      "Epoch 27/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4748 - acc: 0.9112Epoch 00026: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4755 - acc: 0.9103 - val_loss: 0.4500 - val_acc: 0.8826\n",
      "Epoch 28/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3938 - acc: 0.9167Epoch 00027: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3929 - acc: 0.9168 - val_loss: 0.4786 - val_acc: 0.9022\n",
      "Epoch 29/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4502 - acc: 0.9052Epoch 00028: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4490 - acc: 0.9054 - val_loss: 0.4426 - val_acc: 0.9000\n",
      "Epoch 30/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4925 - acc: 0.9079Epoch 00029: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4976 - acc: 0.9082 - val_loss: 0.4358 - val_acc: 0.9087\n",
      "Epoch 31/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4673 - acc: 0.9145Epoch 00030: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4681 - acc: 0.9141 - val_loss: 0.3148 - val_acc: 0.9174\n",
      "Epoch 32/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4436 - acc: 0.9178Epoch 00031: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4433 - acc: 0.9174 - val_loss: 0.4760 - val_acc: 0.9109\n",
      "Epoch 33/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3849 - acc: 0.9205Epoch 00032: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3906 - acc: 0.9196 - val_loss: 0.9908 - val_acc: 0.8152\n",
      "Epoch 34/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3806 - acc: 0.9271Epoch 00033: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3798 - acc: 0.9272 - val_loss: 0.4932 - val_acc: 0.8783\n",
      "Epoch 35/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3996 - acc: 0.9183Epoch 00034: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3983 - acc: 0.9185 - val_loss: 0.3842 - val_acc: 0.9109\n",
      "Epoch 36/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4214 - acc: 0.9057Epoch 00035: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4196 - acc: 0.9060 - val_loss: 0.4483 - val_acc: 0.8913\n",
      "Epoch 37/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4041 - acc: 0.9189Epoch 00036: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4029 - acc: 0.9190 - val_loss: 0.4643 - val_acc: 0.9000\n",
      "Epoch 38/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3938 - acc: 0.9062Epoch 00037: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3961 - acc: 0.9054 - val_loss: 0.5113 - val_acc: 0.9109\n",
      "Epoch 39/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3988 - acc: 0.9249Epoch 00038: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3994 - acc: 0.9250 - val_loss: 0.4740 - val_acc: 0.8978\n",
      "Epoch 40/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3780 - acc: 0.9183Epoch 00039: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3798 - acc: 0.9185 - val_loss: 0.2994 - val_acc: 0.9065\n",
      "Epoch 41/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4019 - acc: 0.9139Epoch 00040: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4024 - acc: 0.9141 - val_loss: 0.3785 - val_acc: 0.9065\n",
      "Epoch 42/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4109 - acc: 0.9090Epoch 00041: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4096 - acc: 0.9092 - val_loss: 0.2955 - val_acc: 0.9152\n",
      "Epoch 43/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3851 - acc: 0.9167Epoch 00042: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3839 - acc: 0.9168 - val_loss: 0.4378 - val_acc: 0.8630\n",
      "Epoch 44/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3815 - acc: 0.9139Epoch 00043: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3798 - acc: 0.9141 - val_loss: 0.3371 - val_acc: 0.9087\n",
      "Epoch 45/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3907 - acc: 0.9205Epoch 00044: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3917 - acc: 0.9201 - val_loss: 0.5599 - val_acc: 0.8870\n",
      "Epoch 46/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3690 - acc: 0.9145Epoch 00045: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3693 - acc: 0.9147 - val_loss: 0.3480 - val_acc: 0.8935\n",
      "Epoch 47/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3545 - acc: 0.9145Epoch 00046: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3528 - acc: 0.9152 - val_loss: 0.3790 - val_acc: 0.9022\n",
      "Epoch 48/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.4100 - acc: 0.9128Epoch 00047: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.4076 - acc: 0.9136 - val_loss: 0.3991 - val_acc: 0.9130\n",
      "Epoch 49/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3496 - acc: 0.9106Epoch 00048: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3483 - acc: 0.9109 - val_loss: 0.3662 - val_acc: 0.9022\n",
      "Epoch 50/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.3211 - acc: 0.9287Epoch 00049: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.3195 - acc: 0.9293 - val_loss: 0.3511 - val_acc: 0.9043\n"
     ]
    }
   ],
   "source": [
    "d1a_model5, d1a_history5 = train_binary_model() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1840 samples, validate on 460 samples\n",
      "Epoch 1/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.6146 - acc: 0.7209Epoch 00000: val_acc improved from -inf to 0.86957, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 5.6131 - acc: 0.7212 - val_loss: 0.3661 - val_acc: 0.8696\n",
      "Epoch 2/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.4482 - acc: 0.8202Epoch 00001: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 5.4478 - acc: 0.8207 - val_loss: 0.3604 - val_acc: 0.8370\n",
      "Epoch 3/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.3899 - acc: 0.8487Epoch 00002: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 5.3895 - acc: 0.8489 - val_loss: 0.3313 - val_acc: 0.8543\n",
      "Epoch 4/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.3294 - acc: 0.8607Epoch 00003: val_acc improved from 0.86957 to 0.88696, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 5.3286 - acc: 0.8620 - val_loss: 0.2881 - val_acc: 0.8870\n",
      "Epoch 5/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.2851 - acc: 0.8679Epoch 00004: val_acc improved from 0.88696 to 0.90000, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 5.2860 - acc: 0.8668 - val_loss: 0.2657 - val_acc: 0.9000\n",
      "Epoch 6/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.2509 - acc: 0.8805Epoch 00005: val_acc improved from 0.90000 to 0.90435, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 5.2514 - acc: 0.8793 - val_loss: 0.2514 - val_acc: 0.9043\n",
      "Epoch 7/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.2176 - acc: 0.8914Epoch 00006: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 5.2183 - acc: 0.8913 - val_loss: 0.2502 - val_acc: 0.9000\n",
      "Epoch 8/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.1805 - acc: 0.8914Epoch 00007: val_acc improved from 0.90435 to 0.90652, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 5.1832 - acc: 0.8908 - val_loss: 0.2577 - val_acc: 0.9065\n",
      "Epoch 9/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.1556 - acc: 0.8947Epoch 00008: val_acc improved from 0.90652 to 0.90870, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 5.1550 - acc: 0.8951 - val_loss: 0.2353 - val_acc: 0.9087\n",
      "Epoch 10/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.1114 - acc: 0.8969Epoch 00009: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 5.1120 - acc: 0.8962 - val_loss: 0.2502 - val_acc: 0.9065\n",
      "Epoch 11/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.0858 - acc: 0.9008Epoch 00010: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 5.0842 - acc: 0.9016 - val_loss: 0.2307 - val_acc: 0.9065\n",
      "Epoch 12/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.0654 - acc: 0.9024Epoch 00011: val_acc improved from 0.90870 to 0.91087, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 5.0649 - acc: 0.9022 - val_loss: 0.2325 - val_acc: 0.9109\n",
      "Epoch 13/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.0142 - acc: 0.9156Epoch 00012: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 5.0144 - acc: 0.9152 - val_loss: 0.2244 - val_acc: 0.9087\n",
      "Epoch 14/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.9986 - acc: 0.9117Epoch 00013: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.9985 - acc: 0.9120 - val_loss: 0.2386 - val_acc: 0.9087\n",
      "Epoch 15/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.9654 - acc: 0.9260Epoch 00014: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.9654 - acc: 0.9261 - val_loss: 0.2176 - val_acc: 0.9087\n",
      "Epoch 16/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.9318 - acc: 0.9238Epoch 00015: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.9329 - acc: 0.9228 - val_loss: 0.2209 - val_acc: 0.9065\n",
      "Epoch 17/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.9179 - acc: 0.9205Epoch 00016: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.9171 - acc: 0.9212 - val_loss: 0.2512 - val_acc: 0.9000\n",
      "Epoch 18/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.8819 - acc: 0.9276Epoch 00017: val_acc improved from 0.91087 to 0.91304, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 4.8814 - acc: 0.9277 - val_loss: 0.2141 - val_acc: 0.9130\n",
      "Epoch 19/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.8515 - acc: 0.9364Epoch 00018: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.8517 - acc: 0.9364 - val_loss: 0.2149 - val_acc: 0.9109\n",
      "Epoch 20/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.8210 - acc: 0.9359Epoch 00019: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.8207 - acc: 0.9353 - val_loss: 0.2127 - val_acc: 0.9109\n",
      "Epoch 21/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.7969 - acc: 0.9370Epoch 00020: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.7984 - acc: 0.9364 - val_loss: 0.2207 - val_acc: 0.9130\n",
      "Epoch 22/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.7758 - acc: 0.9402Epoch 00021: val_acc improved from 0.91304 to 0.91522, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 4.7752 - acc: 0.9408 - val_loss: 0.2124 - val_acc: 0.9152\n",
      "Epoch 23/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.7462 - acc: 0.9391Epoch 00022: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.7462 - acc: 0.9391 - val_loss: 0.2108 - val_acc: 0.9130\n",
      "Epoch 24/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.7171 - acc: 0.9490Epoch 00023: val_acc improved from 0.91522 to 0.91739, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 4.7172 - acc: 0.9489 - val_loss: 0.2090 - val_acc: 0.9174\n",
      "Epoch 25/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.7048 - acc: 0.9419Epoch 00024: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.7041 - acc: 0.9424 - val_loss: 0.2123 - val_acc: 0.9130\n",
      "Epoch 26/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.6793 - acc: 0.9419Epoch 00025: val_acc improved from 0.91739 to 0.91957, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 4.6785 - acc: 0.9424 - val_loss: 0.2083 - val_acc: 0.9196\n",
      "Epoch 27/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.6457 - acc: 0.9485Epoch 00026: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.6456 - acc: 0.9484 - val_loss: 0.2239 - val_acc: 0.9152\n",
      "Epoch 28/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.6210 - acc: 0.9550Epoch 00027: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.6204 - acc: 0.9554 - val_loss: 0.2083 - val_acc: 0.9130\n",
      "Epoch 29/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.5971 - acc: 0.9507Epoch 00028: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.5964 - acc: 0.9511 - val_loss: 0.2152 - val_acc: 0.9152\n",
      "Epoch 30/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.5753 - acc: 0.9578Epoch 00029: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.5753 - acc: 0.9576 - val_loss: 0.2206 - val_acc: 0.9174\n",
      "Epoch 31/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.5550 - acc: 0.9512Epoch 00030: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.5550 - acc: 0.9511 - val_loss: 0.2054 - val_acc: 0.9174\n",
      "Epoch 32/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.5175 - acc: 0.9611Epoch 00031: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.5171 - acc: 0.9614 - val_loss: 0.2113 - val_acc: 0.9174\n",
      "Epoch 33/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.5022 - acc: 0.9594Epoch 00032: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.5016 - acc: 0.9598 - val_loss: 0.2062 - val_acc: 0.9130\n",
      "Epoch 34/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.4722 - acc: 0.9649Epoch 00033: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.4718 - acc: 0.9652 - val_loss: 0.2159 - val_acc: 0.9152\n",
      "Epoch 35/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.4541 - acc: 0.9633Epoch 00034: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.4543 - acc: 0.9630 - val_loss: 0.2056 - val_acc: 0.9130\n",
      "Epoch 36/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.4320 - acc: 0.9688Epoch 00035: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.4313 - acc: 0.9690 - val_loss: 0.2048 - val_acc: 0.9174\n",
      "Epoch 37/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.4148 - acc: 0.9600Epoch 00036: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.4141 - acc: 0.9603 - val_loss: 0.2097 - val_acc: 0.9130\n",
      "Epoch 38/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.3817 - acc: 0.9748Epoch 00037: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.3820 - acc: 0.9745 - val_loss: 0.2043 - val_acc: 0.9174\n",
      "Epoch 39/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.3684 - acc: 0.9688Epoch 00038: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.3681 - acc: 0.9690 - val_loss: 0.2034 - val_acc: 0.9152\n",
      "Epoch 40/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.3397 - acc: 0.9720Epoch 00039: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.3390 - acc: 0.9723 - val_loss: 0.2047 - val_acc: 0.9174\n",
      "Epoch 41/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.3293 - acc: 0.9677Epoch 00040: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.3290 - acc: 0.9679 - val_loss: 0.2106 - val_acc: 0.9152\n",
      "Epoch 42/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.2982 - acc: 0.9748Epoch 00041: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.2974 - acc: 0.9750 - val_loss: 0.2073 - val_acc: 0.9196\n",
      "Epoch 43/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.2747 - acc: 0.9786Epoch 00042: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.2749 - acc: 0.9788 - val_loss: 0.2117 - val_acc: 0.9152\n",
      "Epoch 44/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.2564 - acc: 0.9775Epoch 00043: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.2560 - acc: 0.9777 - val_loss: 0.2296 - val_acc: 0.9196\n",
      "Epoch 45/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.2377 - acc: 0.9742Epoch 00044: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.2374 - acc: 0.9745 - val_loss: 0.2058 - val_acc: 0.9152\n",
      "Epoch 46/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.2160 - acc: 0.9770Epoch 00045: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.2158 - acc: 0.9772 - val_loss: 0.2083 - val_acc: 0.9109\n",
      "Epoch 47/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.1945 - acc: 0.9819Epoch 00046: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.1955 - acc: 0.9804 - val_loss: 0.2062 - val_acc: 0.9196\n",
      "Epoch 48/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.1751 - acc: 0.9792Epoch 00047: val_acc improved from 0.91957 to 0.92174, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 4.1749 - acc: 0.9793 - val_loss: 0.2081 - val_acc: 0.9217\n",
      "Epoch 49/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.1514 - acc: 0.9825Epoch 00048: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.1533 - acc: 0.9815 - val_loss: 0.2047 - val_acc: 0.9152\n",
      "Epoch 50/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.1344 - acc: 0.9841Epoch 00049: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.1340 - acc: 0.9842 - val_loss: 0.2084 - val_acc: 0.9087\n"
     ]
    }
   ],
   "source": [
    "d1a_model6, d1a_history6 = train_binary_model() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1840 samples, validate on 460 samples\n",
      "Epoch 1/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.5955 - acc: 0.6957Epoch 00000: val_acc improved from -inf to 0.86087, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 5.5925 - acc: 0.6962 - val_loss: 0.3607 - val_acc: 0.8609\n",
      "Epoch 2/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 5.1507 - acc: 0.8306Epoch 00001: val_acc improved from 0.86087 to 0.88261, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 5.1508 - acc: 0.8293 - val_loss: 0.2857 - val_acc: 0.8826\n",
      "Epoch 3/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.9148 - acc: 0.8443Epoch 00002: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.9166 - acc: 0.8435 - val_loss: 0.3312 - val_acc: 0.8543\n",
      "Epoch 4/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.6986 - acc: 0.8602Epoch 00003: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.6991 - acc: 0.8598 - val_loss: 0.3208 - val_acc: 0.8652\n",
      "Epoch 5/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.4786 - acc: 0.8772Epoch 00004: val_acc improved from 0.88261 to 0.90435, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 4.4773 - acc: 0.8772 - val_loss: 0.2563 - val_acc: 0.9043\n",
      "Epoch 6/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.2703 - acc: 0.8788Epoch 00005: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 4.2693 - acc: 0.8788 - val_loss: 0.2845 - val_acc: 0.8783\n",
      "Epoch 7/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 4.0888 - acc: 0.8882Epoch 00006: val_acc improved from 0.90435 to 0.90652, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 4.0890 - acc: 0.8875 - val_loss: 0.2632 - val_acc: 0.9065\n",
      "Epoch 8/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 3.9334 - acc: 0.8739Epoch 00007: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 3.9314 - acc: 0.8750 - val_loss: 0.2245 - val_acc: 0.9065\n",
      "Epoch 9/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 3.7505 - acc: 0.8914Epoch 00008: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 3.7530 - acc: 0.8891 - val_loss: 0.2648 - val_acc: 0.9000\n",
      "Epoch 10/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 3.5932 - acc: 0.8882Epoch 00009: val_acc improved from 0.90652 to 0.91957, saving model to data1a/top_model_weights.h5\n",
      "1840/1840 [==============================] - 3s - loss: 3.5916 - acc: 0.8886 - val_loss: 0.2236 - val_acc: 0.9196\n",
      "Epoch 11/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 3.4468 - acc: 0.8849Epoch 00010: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 3.4467 - acc: 0.8837 - val_loss: 0.2224 - val_acc: 0.9130\n",
      "Epoch 12/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 3.3217 - acc: 0.8766Epoch 00011: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 3.3202 - acc: 0.8772 - val_loss: 0.2234 - val_acc: 0.9174\n",
      "Epoch 13/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 3.1890 - acc: 0.8816Epoch 00012: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 3.1890 - acc: 0.8810 - val_loss: 0.2961 - val_acc: 0.8717\n",
      "Epoch 14/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 3.0936 - acc: 0.8684Epoch 00013: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 3.0914 - acc: 0.8690 - val_loss: 0.2546 - val_acc: 0.9109\n",
      "Epoch 15/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.9633 - acc: 0.8772Epoch 00014: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.9623 - acc: 0.8777 - val_loss: 0.2364 - val_acc: 0.9022\n",
      "Epoch 16/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.8329 - acc: 0.8854Epoch 00015: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.8346 - acc: 0.8837 - val_loss: 0.3563 - val_acc: 0.8326\n",
      "Epoch 17/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.7567 - acc: 0.8657Epoch 00016: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.7567 - acc: 0.8658 - val_loss: 0.2495 - val_acc: 0.9109\n",
      "Epoch 18/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.6103 - acc: 0.8750Epoch 00017: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.6090 - acc: 0.8761 - val_loss: 0.2217 - val_acc: 0.9174\n",
      "Epoch 19/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.5175 - acc: 0.8827Epoch 00018: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.5163 - acc: 0.8832 - val_loss: 0.2290 - val_acc: 0.9109\n",
      "Epoch 20/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.4302 - acc: 0.8777Epoch 00019: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.4286 - acc: 0.8783 - val_loss: 0.2322 - val_acc: 0.9174\n",
      "Epoch 21/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.3312 - acc: 0.8679Epoch 00020: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.3305 - acc: 0.8685 - val_loss: 0.2548 - val_acc: 0.9022\n",
      "Epoch 22/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.2976 - acc: 0.8416Epoch 00021: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.2975 - acc: 0.8408 - val_loss: 0.2983 - val_acc: 0.9065\n",
      "Epoch 23/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.2398 - acc: 0.8191Epoch 00022: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.2395 - acc: 0.8185 - val_loss: 0.3207 - val_acc: 0.8717\n",
      "Epoch 24/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.1369 - acc: 0.8465Epoch 00023: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.1359 - acc: 0.8467 - val_loss: 0.2228 - val_acc: 0.9109\n",
      "Epoch 25/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.0828 - acc: 0.8251Epoch 00024: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.0810 - acc: 0.8266 - val_loss: 0.2565 - val_acc: 0.9065\n",
      "Epoch 26/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.0115 - acc: 0.8328Epoch 00025: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.0106 - acc: 0.8326 - val_loss: 0.2833 - val_acc: 0.8913\n",
      "Epoch 27/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 2.0201 - acc: 0.7566Epoch 00026: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 2.0194 - acc: 0.7565 - val_loss: 0.3520 - val_acc: 0.8804\n",
      "Epoch 28/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.9521 - acc: 0.6864Epoch 00027: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.9560 - acc: 0.6859 - val_loss: 0.3279 - val_acc: 0.8609\n",
      "Epoch 29/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.8652 - acc: 0.7993Epoch 00028: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.8644 - acc: 0.8000 - val_loss: 0.3796 - val_acc: 0.8587\n",
      "Epoch 30/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.8016 - acc: 0.7900Epoch 00029: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.8023 - acc: 0.7880 - val_loss: 0.2807 - val_acc: 0.9109\n",
      "Epoch 31/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.7753 - acc: 0.7270Epoch 00030: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.7751 - acc: 0.7266 - val_loss: 0.3355 - val_acc: 0.9022\n",
      "Epoch 32/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.6714 - acc: 0.7774Epoch 00031: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.6724 - acc: 0.7772 - val_loss: 0.2626 - val_acc: 0.9065\n",
      "Epoch 33/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.6440 - acc: 0.7834Epoch 00032: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.6440 - acc: 0.7826 - val_loss: 0.2530 - val_acc: 0.9174\n",
      "Epoch 34/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.5722 - acc: 0.7971Epoch 00033: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.5725 - acc: 0.7967 - val_loss: 0.3720 - val_acc: 0.8674\n",
      "Epoch 35/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.5297 - acc: 0.7686Epoch 00034: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.5286 - acc: 0.7696 - val_loss: 0.2985 - val_acc: 0.8978\n",
      "Epoch 36/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.4809 - acc: 0.7812Epoch 00035: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.4803 - acc: 0.7815 - val_loss: 0.2879 - val_acc: 0.8957\n",
      "Epoch 37/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.4104 - acc: 0.7769Epoch 00036: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.4107 - acc: 0.7755 - val_loss: 0.3162 - val_acc: 0.9196\n",
      "Epoch 38/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.4429 - acc: 0.6902Epoch 00037: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.4445 - acc: 0.6891 - val_loss: 0.3574 - val_acc: 0.8696\n",
      "Epoch 39/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.3849 - acc: 0.7056Epoch 00038: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.3823 - acc: 0.7065 - val_loss: 0.2898 - val_acc: 0.9065\n",
      "Epoch 40/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.3515 - acc: 0.6880Epoch 00039: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.3512 - acc: 0.6891 - val_loss: 0.3055 - val_acc: 0.8978\n",
      "Epoch 41/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.3198 - acc: 0.6902Epoch 00040: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.3190 - acc: 0.6913 - val_loss: 0.2561 - val_acc: 0.9087\n",
      "Epoch 42/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.2590 - acc: 0.7094Epoch 00041: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.2592 - acc: 0.7092 - val_loss: 0.3025 - val_acc: 0.8891\n",
      "Epoch 43/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.2328 - acc: 0.7133Epoch 00042: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.2330 - acc: 0.7136 - val_loss: 0.2462 - val_acc: 0.9152\n",
      "Epoch 44/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.1902 - acc: 0.6985Epoch 00043: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.1896 - acc: 0.6978 - val_loss: 0.2383 - val_acc: 0.9174\n",
      "Epoch 45/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.1759 - acc: 0.6897Epoch 00044: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.1797 - acc: 0.6875 - val_loss: 0.2416 - val_acc: 0.9152\n",
      "Epoch 46/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.1505 - acc: 0.6727Epoch 00045: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.1506 - acc: 0.6739 - val_loss: 0.3664 - val_acc: 0.8891\n",
      "Epoch 47/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.0775 - acc: 0.7094Epoch 00046: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.0790 - acc: 0.7087 - val_loss: 0.2493 - val_acc: 0.9043\n",
      "Epoch 48/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.0732 - acc: 0.6946Epoch 00047: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.0742 - acc: 0.6940 - val_loss: 0.4124 - val_acc: 0.9109\n",
      "Epoch 49/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 1.0350 - acc: 0.7127Epoch 00048: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 1.0343 - acc: 0.7136 - val_loss: 0.2289 - val_acc: 0.9130\n",
      "Epoch 50/50\n",
      "1824/1840 [============================>.] - ETA: 0s - loss: 0.9974 - acc: 0.7094Epoch 00049: val_acc did not improve\n",
      "1840/1840 [==============================] - 3s - loss: 0.9949 - acc: 0.7103 - val_loss: 0.2825 - val_acc: 0.8891\n"
     ]
    }
   ],
   "source": [
    "d1a_model7, d1a_history7 = train_binary_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 38 , val_acc: 0.930434782609 , val_loss: 0.717816673591\n"
     ]
    },
    {
     "data": {
      "image/png": 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dM3aGjYmJQUdHBzk5Oa5cuUJYWJisfXazvA0aNODYsWM0atSIhw8f8uDBAwBi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\nhRBCCCGEAOD/AeYbWHiVz+ZnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f017b2e66d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d1a_history1) # rmsprop, sigmoid, no regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 18 , val_acc: 0.915217391304 , val_loss: 0.238060471399\n"
     ]
    },
    {
     "data": {
      "image/png": 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R3ksxOlnZ2Qxc8h96fv4BWYXUmBQIBAKBoCJjdGEcGBhIz5496dGjB2vWrMm3PTExkYkT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\nJITZs2fXKFk0YsQI/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/On2KInw9TUVLmkdk5ODiYmJk+7SU3q7t27TJs2jYiICPr27Quo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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f017a60ed90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d1a_history2) # rmsprop, sigmoid, with l2 regularization (0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 13 , val_acc: 0.934782608696 , val_loss: 0.342017764477\n"
     ]
    },
    {
     "data": {
      "image/png": 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ApoZ6tjaxcQn0GTydGVNH8PXo/igo5HwPb3x8Imv++JOrN+6gqVEBTc0KGYGuZkaw+9Dv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\n+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f017a121390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d1a_history3) # rmsprop, sigmoid, with double dense and dropout layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 26 , val_acc: 0.932608695652 , val_loss: 0.398729373393\n"
     ]
    },
    {
     "data": {
      "image/png": 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+3r7Zkk+NVkNs0i2iE2MJPbKHWWEhHDh3glR1VsKlVCgp7u6JqTL76LMOHaev\nXaLtpEFM7jKAD+o2eWLM0Qk3GR48i/AzRx57zNLcAp1Wq3+uJncZ8NSpMTmJun6ZaSHBbD26DxOl\nksLObg++iShEcTdPShUqSpPKtd6IhDksLIzy5cvj7Jy3bxviExKJjIw0cFSvj4LU14cKWp/zu7+/\n/BvK3osnXuo5a3v70b1O8yc+HhkZia+vrz5BXb16NX369CEiIoJly5Zx9uxZFAoFsbGx7Ny5EwcH\nB7Rabda28AkJqFQqIiMj2bZtG++9957+OfTy8uLMmTOkpaWxfft2du7ciVar5c6dO2zbto3k5GTU\najVHjx7F1jarwpNarebYsWOEh4dTvnx5Tp48CUCFChUICQmhatWquLq6kpKSQmRkJI6Ojhw8eJDC\nhQu/1Ofsv17/V+43lEqdxp97t9Gsal3cHJxybT815BfmbvodABsLKwIq1qSub2Xq+lbFp0gJDh8+\nnOPmDx6OLtQvn3V/Uso99kYdY9muDfxzMoLP5k9ift/Rz/wGHXX9MhP+WMg/pyKy3W+mNMXd3pm3\nCpXgckIMy3ZtZNmujZRyLUqDMlWpUtiHm9cTOHT4JBFHTpGUdE9/rJurE0O6t+Ojji1xd3v0Jnzm\n+mWGLP6e41fP4+bgRPfarbkUcY0LFy9Tsaovq09uJyzxMOVPl8bHp5T+6/q9UceYsmYJhy+eQalQ\n0rrmO7xXpTa1fSrhYp99brGHo0uOo4yZGg2xSYn6hDk6IZZUdRrd321NCffH/9MVcnJh8sdf0Hf+\nRAYsnMrakTMxNTFBo9UwaPF33FermPXJcIq4ZN9yuUbZCoR+/SOf/jSe0MhwAGwtrWlc+W3qlKtC\n3XKVeespP+OnUSqVdA9oTUDFGgwLnsn2YwfYfuyA/nEbCyuKuXlS1NWdq/E32X0ygt0ns36uTrb2\n1PGpTKWSZfFyy0rqirl54mCd9YIVdiqS4cGzuH4rjrKFi/N998FU9c76MFPMzZO336qYLZa4O7f4\n89+trAz7m1X/bmXVv1t5q0hxqpUuz/Vb8UQn3OR6YhwZmsxsx5XxLEYd36znoZZPJf31/9/O4wfp\nv2AKg5d8z9HLUXz7Yd9sI+BarVb/ISFVnUbdclWo5VNRn7AWc/PE1d6Ra4mxDA+exfZjBzgw+hO+\n7tCHjvXey3X0+Gr8TaavX8ba/TvR6XRULF4GCzNzohNj+ffM0WxtZ/UcRrs6uS9my2+bNm2iRYsW\neW5vYmLd4ghKAAAgAElEQVRqsE1oXjeG3HDndVXQ+vw69Df0YiTm18691HN6FPJ4Yr8e9rlcuXI0\nbtwYKysrlEolQUFBrF27FnNzc7Zs2YJSqaRRo0b4+PhQuHBhlEol/v7+xMTEYGVlhb+/P46OjpQt\nW1Z/LWtra8qVK4eDgwM7duwgJCQEW1tbRo4cSZEiRfD398fc3JxKlSrh6Jj1Pm1hYUGlSpW4evUq\nd+/e1Z8rPDwcFxcX/Pz8sLOz099/7NgxUlNT8/xze54PPpIYG8jkNUtYsn0dP/+9mhVDJueYaD00\nd9NK5m76nRLuhZnRYyhVSvk812iTk609gdXqEVCpJl1njWbLkb0MXvI9sz8ZnqdpFVfjb/L92qWs\nO7ATHaC7q4NYLah0kAbpGZlc5wbXeTDS6ayAQgou6q5xKfE6SzTr4aYOLmspWsSDhnWrU82/AtWr\nVsCnbIls0x90Oh0Lt65h8uolZGgyaVf7Xb7p+ClOtvbwwaP/vF1jWvHJ3LH8tHkVJ66cp19gR37a\nvIrdp7J+2Zv512VYm66ULVL8mZ8vUxMTirp6UNTVgzrlKufpmJY1GrDl6F7W7d/Fj6G/M6BlZxZs\nWcOBcydp5l+XoFo5f+1UyMmFVcO/Y/uxA3g4OFOp5FsvdU5uMTdPfh86le3H9pOqTtPP/3W2c8iW\n8MUm3eLfqCP8e/oo4WeOsDEi7LFqGw7WthRycuFszFVMlEq+aPEhA1p2zvXbBw9HF/oFfshnzTqw\nN+oYv4WFsjnyX87GZE2dcbFzoELx0g/mJXtiqtbQqVmbPI/YNqpYg01fz6XX3HEs27WRU9GXmP/Z\naDydXPXTSg6cO4mDjR2zu3zxxKkSxdw8WTl0Cr+FbWb8HwsYFjyTDYd2M63rIIq6ejzWPu7OLeZs\n+I0VYaFkajT4epVixPvdaVSxhv78qnQ11xPjiE64ye2Uu7xXNW+7H+YnlUrF3r17GTduXJ6PkakU\nQrxcYz7ozZgPer/y61pbW1OjRg1GjRql/3CcnJyMs7MzSqWS/fv3c+PGo281dTrdY+eoXr06GzZs\noGbNmpw7d46zZ88CkJKSgrW1NTY2NiQmJhIWFkbNmjUBsLW1JSUlRZ8YPzxvtWrVGDlyJL1790aj\n0bB9+3a+++47gz4HTyKJ8X+oM9KZ9dcKNkaEMfqDXjStUvu5znM25gpLd/6FnZU1VxNu0nbSIFYM\nnoRvMe/H2i7Zvo4pa36hiIs7fwyb9tho4/OwNDNnSf+xdJo+krX7d2JjacXkj7944ojYwzf+X3eH\notFq0KXosI43Z1TPT2jRrAHxCUnEJ9wiPv4WsfG3iE+4RXp6BpYWFlhYmJNpouHsvascTzjP3aIp\nNGxSg0UDvn3iHGStVsuEVQtZsHUNHo4uTOs6kIBKNXNs+1aREmwaM5eBi6ax9eg+/dfj9XyrMCKo\nB5VLvvXCz9ezmtC5H/vPnmDmX79S2NmN79Yuxc3eialdBjx11NHc1Izm/nUNFpdCoaBx5VpPbVPI\nyYWgWu8SVOtddDodl+NvcOFGdPZ5uA+mYFQqUZYpXQc8NoUjN0qlkroP5ronpdwj7s4tvFwLYWOZ\nfcFjZGTkM09jKOFemPVfzWT40lms27+L5mM/p32dJizevhZ1RjrN/OvmaSGiQqGgc4PmvONXjRFL\nZ7PrxCHqjuyGTQ6LMu+rVWRqNJRwL8ywtl1pWb3BYx80rcwtKFO4GGUKF3um/uQnKysr9u/f/0zH\nSLk2IYxHYGAg/fv3Z+bMmUBWtYq+ffvSqlUrKlSogLf3o5wlp/e2Dz/8kJEjRxIYGIi3tzcVKlQA\nwMfHh3LlytGsWTM8PT2zje5+8MEHfPLJJ3h4eLB06VL9eX19fWnbti3t2rXTt/Px8SEmJsZg/X8S\nhS6njwEvUVhYGJMmTUKn0xEUFETv3tk/Gd27d49Ro0YRHR2NpaUlkyZNonTpx+es/pchvv44fPEM\nQ5ZM5/zNaP19A1p0YnCbj/XzTPNCp9Px4fdfEn7mCEsHjOdK/A2+WfkT9lY2BA8YT42yFfRt/9iz\nhSG/TMfN3ok1I2dkm7P6/56nz3dTU/hg2jBORV+kT9N2tPCpx42bCVhYmGNuYcq5hGh2nDnAvgvH\nydRq0Kl0KK7q+PCd9/hy8CfPXO4sWXWfnj98y96oY9T2qcTi/t9iZ5V9EVamRsOw4Bn8+e82yngW\nY8WQSRR2fvzDwP/3V6vVMm/zKvafPU7fZu2pU67KM8X2soWdiqTT9JH620sHjH9icp9Xr8PXeg/p\ndDqDl4B7kf7qdDoWb1/H+D9+RqPV4mLnwMSP+hNYrd4zx63T6Vi9dxvBOzeQnvH4ok0LMzM61nuP\nDnWbvvC84dfpZ/ysIiMj6dJnHKci1ud3KK/Em/yzel4Frc8Frb9Q8Pr8PP016IixVqtl/PjxBAcH\n4+7uTrt27QgICMj2KWT+/PmUK1eOuXPncunSJcaNG0dwcLAhw8pGla7m+7VLWbg1BK1OS7eA1rSu\n0ZABC6cye+NvHLtyjh96f5n1FX8e/H34X8LPHOEdv+r6RMnJ1p7BS77nw+lfsuCzMQRUqsnGQ2EM\nC56Jo40dK4dOeWpS/LwcrG35bfBkgqYM4ectq5m/4A+I1YGHAjyUKKyyEgjdfR3c0FLZvSwT5w+g\nckWf57qenZUNywZNpN/Pk/n78L90mDac5YMm6uf8qtLVfD5/EluP7qNyybdYPmhinp9XpVJJv8CO\n9Avs+FyxvWz1y/vTLaA1wTvW06l+sxdOil83r3tdZIVCwSeN21KpRFl2nThIz3fbPja3/FnO1b5O\nE9rXefKCPpFFyrUJIYydQRPj48ePU7x4cYoUyUr6AgMD2bFjR7bE+OLFi/pR5FKlShETE8Pt27fz\nvEr6RRw4d4Khv8zgclwMJdwL8333wfoFRZu+nkv/BVP452QEgeP6sajfNzlOhfgvVbqacX/8jJmJ\nKd92/FR///u1AnCwsaPPvPH0+OEbuge0JnjnX1hbWLJi8GR8ipY0WB9VyWmYnzdFZ6pDUdwERXEF\nOnSYKk0obe9FaRsvHLCh+ocVeL/Vuy9c4s3SzJz5fUfz5bLZ/L7nb9pOHszKoVOws7Kh+5yvOXDu\nBPXLV2Xh59889tX6m+abDn14t1LN56pPLF6O6mXKU71M+fwOo8BQp2eg0WjyvJOkEEK8aQyaGMfF\nxWXbTcnDw4MTJ7KXJfHx8WHbtm34+/tz/Phxbt68SWxsrMET4zX7tjNwUdbE7t5NghjWtitWFo9K\nZTnZ2rN04HhmrP+V2RtW0GrSQKZ1Hcj7T1hcBfDz339yLTGOT99rj7enV7bHAirWYOWQKXSbPYZF\n29ZiaW7B0oHjqVQy95qsz2vbzn0MHD6FO3eTadqiDucsrmFvZcOH9ZvRuuY7z1Rr9lmYmpjwXbdB\nONs6MG/zH7SeNBBnW3tOX7tEi2r1md1r+AuVkXtdmJma0rBCtfwOQ4hXSqVSY2trnd9hCCGEQeT7\n4rtevXoxceJE2rZtS9myZSlXznAbUzyk0WqYvnYZ5qZmrBo2Df/Svjm2M1GaMKxtVyqVKMuARVP5\nYuFU/jkZwdgP+z42BeDG7Xjmhv6Bm70TA1p2yvF81cuUZ/WX0/kuJJiejdtQs6xfrrEeOnySgcOn\n4l2yMNMnl8TNNfcPDBkZmUybuYR5C3/HwtyM7yYO4cP2zV/p1+MKhYJR7XvibGfPhFULiU1K5KOG\ngUz8qN8zzdkWQrxeUlVpkhgLIYyWQRNjDw+PbOU+4uLicHfPvtDK1taWyZMn6283atQIL6/so605\neZGi3PsuniQ6MZamvjXgrirXc7lgztT3P2XmtlWE7NvBzqMH+LRBa2p5P1pI992WlaSlq+lTryXn\nTkc99Xyf12kFqdpcrxtx5AxTZi4nPT2DK1djqN3oIzq1b0LzJrUxNX08uczIzORgxGnWbtzNuQvR\neBZyZcTAjyhVohCHDx9+6rUMpbpbKUY1+5i7qhQa+1bn6JGjuR/0QH4XXs8PBa3PBa2/xkDmGQsh\njJlBE2M/Pz+io6OJiYnBzc2NTZs2MWPGjGxtkpOTsbS0xMzMjFWrVlGjRg1sbHL/iv9FVlWO37Ic\ngJGde1PaM2/llfyB5g0bs2DrGqavXcqUv1fQolp9Jnz0ORduXiP8wnGqlPJhyIefvJQR77V/7WDS\n9KWYmpiw5KfxHDx0mN/XbGfRsr/Ys+8447/uT523syozXLx8jZWrQvlz7VYSbyUB0CrwHaaNH4yd\nnWGmSzyL5/lZFbSVs1Dw+lzQ+gvG8UFAahkLIYyZQRNjExMTxowZQ48ePdDpdLRr1w5vb29+//13\nFAoFHTp04OLFi4wYMQKlUkmZMmWYOHGiIUMi8uIZIi6cJqBizTwnxQ+ZmpjwWbMPaFq5FkN+mc7G\niDD+jTqK/YOSZOM6ffZSkuIly0IYM34u9nY2BP88kZrVK+LqZMnnfbowZcZiflu1iQ8+HkKzxnW5\nczeZfQePAeDkZE+v7u3o1L45ZcuUeOE4hBDi/0ktYyGEMTP4HOP69etTv379bPd17Pio5FblypXZ\nsmWLocPQW7hlDQC9mwY99zm8Pb1Y8+V0gnf8xZQ1S0hKuUeHuk2pUur5ypw9pNPpmD5nKTPnLsPN\n1YkVS6ZRvtyjShjOzg5MmzCYzh0C+WrsHDZvy9peuPbblfmoQwvea1wXC4s3f1GbEOL1lapS5XcI\nQghhMPm++O5VupYYS2hkOOWLeb9wiS0TpQk9G7fV1yT++J0WL3S+zEwN30ycS/Cv6ylW1JOVwdMo\nUTzn2saV/N7ir1U/cODQCTw8XChVougLXVsIIfJKRoyFEMasQCXGS7avQ6vT0qtJ0Eur0FDCvfAL\nbzoRcyOOfkMmcTDiBOXeKsWKJVPxcHd56jFKpZJaNaV+rhDi1ZI5xkIIY2bYumivUKZGQ/zd2098\n/F7qfVaG/Y2HowutajR4hZE93cbNu2ncshcHI04Q+F591vw2K9ekWAgh8kuaJMZCCCNmNInxlDVL\nqDa4Ez//vRqdTvfY4yvDNpOSlkr3gNaYm5rlQ4TZqVRpDB89nT5fjCU9I5PvJg7h5znf4GBvm9+h\nCSHEE8mIsRDCmBnNVIpribFodVrGr1rA0ctn+b77YP2WwxmZmSzevhYrcws6N2iez5HCqTMX+XzQ\nBM5fvEr5cqWZN3M0pb2frUKGEELkh9RUSYyFEMbLaBLjVHXWi3WVUj5sOLSbczeusLDft5TyKEJo\n5B5u3E6gW0Drx3ase5Vu3brD3AUrCV6+jvSMDD7pFsSoob2kkoQQ4o2hSpPFd0II42U0UykeJsar\nR3xP94DWnI25SuC4z9l2dB8LtqxBoVDQ8902+RJbcvJ9ps8JplZAZxYs+RM3NyeWLpjE2K8+l6RY\nCPFGkXJtQghjZjQjxip1GlbmFliYmTO+8+dUKvkWI5bOovucbwB4r2odSnrkXP7MYDGlqVm6Yj1z\nf/6NpKR7uLo48eXgnnTu0EISYiHEG0nKtQkhjJnRJMap6WlYW1jqb7er/S4+RUrQ68exXEuMo0/T\ndga57umoi3w1dg737qU89lhCYhK3bt/B3s6G4YN68EnXIGxsrAwShxBCvAqy+E4IYcyMJzFWp2Fj\nkT3prFC8NFvHzudaYhy+XqVe+jXT0zPoP2QSUecu4+j4+NxlczNTPuvVkc96d8Qph8eFECK/JCcn\n89VXX3H+/HmUSiWTJk2iUqXca6OrJDEWQhgxo0mM76vTKOT4eP1fOysbgyTFAD/+vJKoc5fp3KEF\n0yYMNsg1hBDCECZOnEiDBg2YM2cOmZmZpKXlLeGVxFgIYcyMZvGdSp19KoWhnTt/hdk//UohDxdG\nj+j9yq4rhBAvKiUlhYiICIKCggAwNTXF1jZvNdRljrEQwpgZRWKcqdGQnpnxyhJjjUbDkFHfk5GR\nyeSxA7G3k005hBBvjuvXr+Pk5MTIkSNp27YtY8aMydOIsaWlhcwxFkIYNaNIjB+WantViXHwr+s5\nfPQ0rQLfoUlAnVdyTSGEeFkyMzM5ffo0nTp1Yu3atVhaWrJgwYJcj7OyspCpFEIIo2YUc4wfJcaG\nr/hw7Xosk6cvwtHRnvFj+hn8ekII8bIVKlSIQoUK4efnB0DTpk1ZtGhRrseZmii5c+cekZGRhg7x\ntVBQ+vlfBa3PBa2/UDD7/CyMJDHOKjhv6BFjnU7HiDEzUKnSmDJuIK4uTga9nhBCGIKrqyuenp5c\nvnyZkiVLsn//fry9vXM9ztHBgdtJd/H3938FUeavyMjIAtHP/ypofS5o/YWC1+fn+RBgJInxgxFj\nc8Mmxn+u3cru8Aga1qtOUOvGBr2WEEIY0ujRoxk6dCiZmZl4eXkxefLkXI+xsrIg9YZMpRBCGC/j\nSowNOGIcn3CbsZPmYW1tydTxg1AoFAa7lhBCGJqPjw9r1qx5pmOsrSxRqdLQ6XTyGiiEMErGsfgu\n3bCJcWqqih59x3DnbjIjh3xC0SKFDHIdIYR4nVlZZb3GpqVJyTYhhHEyjsT4wYixlQES48xMDX0H\njufIsTMEtWlM94/bvvRrCCHEm+BhYiy1jIUQxsqoEmObl5wY63Q6vvx6Jtt37adB3WpMnzRMvj4U\nQhRYFpZmAKSqVPkciRBCGIaRJMaGqUoxfc5SVv4ZSsUKZVnww7eYmRnFlGwhhHguFpbmgIwYCyGM\nl5Ekxi9/jvGvv29k5txlFCvqybKFk7C1tX5p5xZCiDeRqUXW4IDsfieEMFaSGOdg645/GfnNLJyd\nHFjxy1TcXJ1fynmFEOJNZvYwMU6VxFgIYZyMKjG2egl1jPcdOEbfgROwsDBn2cJJlCpR9IXPKYQQ\nxsDELOstQ5UmibEQwjgZRWKsekkjxhs2/0On7sPJzMxk/uyvqVKp3MsITwghjIKpmQkgI8ZCCONl\nFInx/QeL72wsrZ77HAt/WU3fAeMxNzdj+cLJvPvO2y8rPCGEMAoK04cjxrL4TghhnAyeGIeFhfHe\ne+/RtGlTFixY8NjjKSkpfPrpp7Ru3ZqWLVsSEhLyzNd4kS2htVot46b8xLeT5uHm6sSa32ZRv261\nZz6PEEIYO6VpVrnK1FQp1yaEME4GrT+m1WoZP348wcHBuLu7065dOwICAvD29ta3WbFiBWXKlGH+\n/Pncvn2bZs2a0apVK0xN8x7a8y6+U6vTGTRiKus37aJ0qWL8ungKXkVlVzshhMhR1kwKGTEWQhgt\ng44YHz9+nOLFi1OkSBHMzMwIDAxkx44d2dooFAru378PwP3793F0dHympBhAlZ6GQqHA0twiz8ck\n3blH555fsn7TLqr7V2DdH3MkKRZCiKdRZo0Yq2SOsRDCSBk0MY6Li8PT01N/28PDg/j4+GxtOnfu\nzIULF6hbty6tW7dm1KhRz3ydVHUaVuYWedqVLj09g0XBa6jbuAv7DhyleZN6rAz+DidH+2e+rhBC\nFCQ6pQ6QqhRCCOOV71u5hYeH4+vry7Jly4iOjqZ79+789ddf2NjY5Pkcqeo0bCyevvBOp9OxZfu/\njJ/6M1euxmBvZ8OYEX3o1b0dJiYmL9oNIYQwelqyEmPZ4EMIYawMmhh7eHhw48YN/e24uDjc3d2z\ntQkJCaF3794AFCtWjKJFi3Lp0iX8/Pyeeu7IyEj9v+8k38PMxDTbff91/uI1lvy6kVNnLqFUKgls\nWocPgxpjb2/D0aNHn7d7r9yT+mesClp/oeD1uaD1902nQQPIltBCCONl0MTYz8+P6OhoYmJicHNz\nY9OmTcyYMSNbm8KFC7Nv3z78/f1JTEzkypUreHl55Xpuf39//b8zf9HiZu+Q7b6HQtZvZ8hXcwBo\n3KgWo4f3obR3sRfs2asXGRmZY/+MVUHrLxS8Phe0/sKb/0FAo9MCMmIshDBeBk2MTUxMGDNmDD16\n9ECn09GuXTu8vb35/fffUSgUdOjQgb59+zJy5EhatmwJwLBhw3B0dHym66Sq055YkWLthu0A/LZk\nKg3qVX+xDgkhRAGWqXs4YiyJsRDCOBl8jnH9+vWpX79+tvs6duyo/7e7uzuLFy9+7vNnZGaSocnM\nsYaxTqfj+MnzFCnsLkmxEEK8oExtJiAjxkII4/XG73yXmv7kGsZx8bdIvJWEX/kyrzosIYQwOmkZ\n6ZibmckcYyGE0cr3qhQv6n5a1g5M1paPJ8YnTp0HoIJv2VcakxBCvO4aNWqEra0tSqUSU1NTVq9e\nnesxqnQ1VlYWMpVCCGG03vjEWPWU7aBPnDoHQMUKMmIshBD/pVAoWL58OQ4ODnk+JisxtpTEWAhh\ntIx6KsXDEWO/8jJiLIQQ/6XT6dBqtc90TFp6GlZWljLHWAhhtN78xFj99MTYw90FdzfnVx2WEEK8\n1hQKBT169CAoKIhVq1bl6RiVWo21laXMMRZCGK03firFkxLjxFtJ3IxNIKDh2/kRlhBCvNZWrlyJ\nu7s7t2/fpnv37pQqVYpq1ao99Zjb9+5iozHjfqqKiIgIFArFK4o2f7zpdaefR0Hrc0HrLxTMPj8L\nI0qMs28JffL0BQCpSCGEEDl4uAups7MzjRs35sSJE7kmxigVuLu5EnXuKn5+FbGwMH8FkeaPgroB\nTUHqc0HrLxS8Pj/PhwAjmErxoCrF/40YHz/5cOGdzC8WQoj/UqlU3L9/H4DU1FTCw8MpUyb3QQTV\ngznGAKo0mU4hhDA+RjRinD0x1pdqkxFjIYTIJjExkX79+qFQKNBoNLRs2ZK6devmepwqPWuOMUBq\nahqODnaGDlUIIV6pXBPj2NhYChUq9CpieS6pTyjXdvLUeZydHChcyC0/whJCiNeWl5cX69evf+bj\nVOlqLJ2ypk+o0qQyhRDC+OQ6laJdu3b079+fffv2vYp4ntnDxNjqPyPGd+4mE339Jn7lyxj94hAh\nhHhVdDod5pYPEuNUSYyFEMYn18R4586dBAQEMGvWLJo3b86KFStISUl5FbHlSU51jE+efli/WKZR\nCCHEy2RmkfVFo8wxFkIYo1wTY3Nzc9q0acMff/zBhAkTWLhwIfXr12f8+PHcunXrVcT4VKkPvs6z\n+U9VipOysYcQQhiE6YPEODVVlc+RCCHEy5enqhQxMTFMnz6dIUOGUKtWLRYtWoSLiws9e/Y0dHy5\nymnx3cOKFDJiLIQQL5eJWdbbhowYCyGMUa6L7/r06cP58+fp2LEjISEhODk5AVC1alVCQ0MNHmBu\ncppKceL0eeztbCjm5ZlfYQkhhFEyMTMBsqpSCCGEsck1MW7bti2NGzfGxMTkscc2btxokKCexf8v\nvktOvs+ly9ep/XZlWXgnhBAvmdI063VVqlIIIYxRrlMp7O3tSU1N1d++d+/ea1WhQqVOQ6FQYGmW\ntVL6dNRFQKZRCCGEIShNs942ZMRYCGGMck2Mp02bhq2trf62ra0t06ZNM2hQzyJVnYa1uaV+dPjh\nxh4VZeGdEEK8fA/eNWSOsRDCGOWaGOt0umxTEpRKJRqNxqBBPYv7ahU2lo8qUhw/lbXwTna8E0II\nA3gwqy5VJSPGQgjjk2tibGNjw7Fjx/S3jx07hrW1tUGDehap6rRsm3ucPHUeGxsrSpUomo9RCSGE\ncdI9GCdRSWIshDBCuS6+GzZsGJ9//jmlS5cG4MKFC8ydO9fggeWVSp2Gs61D1r9VaZy/GE21quVR\nKvNUiU4IIcQz0Cq0gCTGQgjjlGtiXKVKFTZt2sTRo0cBqFy5Mg4ODgYPLK9S09P0pdpOR11Cq9Xi\n5yvTKIQQwhA06ABQqWSOsRDC+OSaGAM4ODjQoEEDQ8fyzNIzM8jUaLC2sACy6hcD+FWQhXdCCGEI\nGl3WGpNUlex8J4QwPrkmxlFRUXzzzTdERUWRnp6uv//MmTMGDSwvHu16l7X47oTseCeEEAaVqX2Q\nGEu5NiGEEcp1Iu63337LwIEDKV68OLt376Z3794MGjToVcSWq1R11ojFw6kUJ06dx9LCnNKliuVn\nWEII8cqEhoaSkpICwOzZs+nZsycnT5402PXSNRkolUop1yaEMEq5Jsbp6enUqlULnU6Hu7s7gwYN\nYsuWLa8itlw9GjG2RK1O5+z5y5Tz8cbU9PFd+oQQwhj99NNP2Nracvz4ccLDw2nTpg0TJkww2PXS\n0tVYW1nKiLEQwijlmhg/rO7g4OBAVFQUSUlJJCUlGTywvPhvYnz2/BUyMzUyjUIIUaCYmmbNiPv3\n339p3749LVu2RK023GhuqjoNKysL2RJaCGGUck2MAwMDSUpKonfv3nz44Yc0bNiQzp07v4rYcqVP\njM0tOfFgY4+KsvBOCFGAKBQKQkNDCQ0NpVatWgBkZGQY7HoqGTEWQhixpy6+02q11KpVCycnJ+rX\nr8/BgwdRq9XZtojOTVhYGJMmTUKn0xEUFETv3r2zPb548WI2bNiAQqEgMzOTixcvsn//fuzt7XM9\n98PE2MrCkpPHLgBQwbd0nmMTQog33ejRo1m0aBHt2rXDy8uLK1euULNmzTwdq9VqCQoKwsPDg/nz\n5+fpGFV6GlZWlty9l/IiYQshxGvpqYmxUqlk2LBhbNiwAQAzMzPMzMzyfHKtVsv48eMJDg7G3d2d\ndu3aERAQgLe3t75Nz5496dmzJwC7du1i6dKleUqK4VFibGNpRVz8LQC8inrmOT4hhHjTVa1alXnz\n5ulvlyhRgjFjxuTp2GXLluHt7a1fvJcbc1MzVOlqrGTEWAhhpHKdSlG8eHGuX7/+XCc/fvw4xYsX\np0iRIpiZmREYGMiOHTue2H7jxo0EBgbm+fz/nUpxO+kuJiZK7O1snitWIYR4E02ZMoXk5GQyMzPp\n1KkTlStXZv369bkeFxsby+7du2nfvn2er2VlbvEgMbYgPSODzEzNi4QuhBCvnVwT4/v379OqVSt6\n9erFgAED9H/yIi4uDk/PRyO4Hh4exMfH59g2LS2N8PBwmjZtmsfQsy++u510FydHB9kKWghRoOzd\nuyfzP9EAACAASURBVBc7OzvCw8Px8PBgy5YtLFmyJNfjJk2axPDhw1EoFHm+luWDxNjaKqtEpmwL\nLYQwNrlu8NGqVStatWpl8EB27txJ1apV8zyNArK2g4ZHibGrs5OhwhNCiNfaoUOHaNy4MR4eHrkm\nu//88w+urq6UK1eOAwcO5Pka1haWpKapsHqYGKepsZNv6YQQRiTXxLht27bPfXIPDw9u3Lihvx0X\nF4e7u3uObUNDQ2nRokWezx0ZGcnFy5cAuHr5CnfuJOPp4UJkZORzx/u6M+a+5aSg9RcKXp8LWn8N\nwcXFhW+++YY9e/bQu3dvMjMz0WiePsXh8OHD7Ny5k927d6NWq7l//z7Dhw9n2rRpT79YppYUVSqp\nmvsAHDx4CM9Cri+rK6+dgvj7WdD6XND6CwWzz88i18T4iy++yHH0Yfbs2bme3M/Pj+joaGJiYnBz\nc2PTpk3MmDHjsXbJyckcOnSI77//Po9hg7+/P6EXsn64pUuURqfTUdyrCP7+/nk+x5skMjLSaPuW\nk4LWXyh4fS5o/QXDvCFNnz6dv/76i7Zt2+Lg4MD169fp3r37U48ZPHgwgwcPBuDgwYMsWbIk96QY\ncHZ04vqdBLyKFgbAu3QZyr1V6sU78RoqqL+fBanPBa2/UPD6/Dyvubkmxu+8847+32q1mi1btmSr\nKvE0JiYmjBkzhh49eqDT6WjXrh3e3t78/vvv/2vvzsOjru7Fj79nn8meyTLZQ5gYkrDJrqKAoCKC\nRTaXori1tL1FrFuv0Mf7uy23XKt1aa1ea2srcr3Q68Yt0ltb8UK0yhbQsCSBQCBhQvY9mSWz/P6Y\nZCSSQMBMwsx8Xs+TJ5mZ7/d8z5nwHD458/l+DgqFgjvuuAOAjz76iGuvvRa9Xn9Rne/o3hK6y+6t\n2Wk0Rl/U+UIIEeiMRiN333035eXlHDt2jKysLBYvXuyXaxm0OrpcTnR6HYBUphBCBJ2LTqVYvHix\nr7zaQMyYMYMZM2b0eu7OO+885xqXkrLRc/OdrdMBQGysBMZCiNBy8OBBVq9ejVarxePx4HQ6eeml\nlxg9evSAzp86dSpTp04d0LEGnTcg1ui8/3XI7ndCiGBzwcD46xQKBTU1Nf7oy0XrCYytHd7vRgmM\nhRAh5uc//znr16/37Xr3+eefs27dOjZv3jzo1zJouwNjrQqQFWMhRPC5qBxjj8dDaWkp11xzjd87\nNhDW7sC4o82bUiGBsRAi1FitVl9QDHD11Vfz9NNP++VaBq033U3ZHRhbbXa/XEcIIYbLReUYq1Qq\nHnzwQcaPH+/XTg1Up92GUqGktcW7a5MxduCl3oQQIhgYDAZ2797t2wZ6z549GAwGv1xL371irFJ3\nB8ayYiyECDJ+Ldfmbx12K+F6PU3NrYCsGAshQs/atWt5+OGH0Wq1AHR1dfHrX//aL9fqSaVQaryf\nIkqOsRAi2Fxwm7i77rqLlpYW3+Pm5maWL1/u104NVKfd5t0OulECYyFEaBo3bhx/+9vfeOmll3jp\npZf461//yqpVq/xyrZ7AWOFdMJYcYyFE0LnginFnZyfR0V8FnDExMXR0dPi1UwNltdsw6PQ0NngD\ndwmMhRChSKPRkJOT43vs8Xj8ch2DrrukptK7ptIpW0ILIYLMBVeM3W43VqvV97ijowOn0+nXTg1U\np8Pm2w5ao1ETERE23F0SQohhd6EtoS9Vz4oxSm/gLakUQohgc8EV4wULFnD//fdz1113AbBp0ya+\n9a1v+b1jF+LxeLypFDo9dU31GGOj/fafgRBCXG7Kysr6fc1fixc9gbFH4Q2MJZVCCBFsLhgYf+97\n3yMxMZGPP/4Y8G7Ocdttt/m9YxficHbhcrt9K8apKabh7pIQQgyZlStX9vuarnsjjsHWExi7FT0r\nxlKuTQgRXAa0wcel7kznTz2bexg0elrbOhgjpdqEECGkZ7FiKPXUMXbhBmTFWAgRfC6YY/zQQw/R\n3Nzse9zU1MTDDz/s104NRE9grO6+CURuvBNCCP/qWTF2eVwA2CTHWAgRZC4YGFdWVhITE+N7HBsb\nS0VFhV87NRA9gbHCI4GxEEIMhbDuqhRdbm8Os6wYCyGCzQUDY5fLhcvl8j3u6urC4XD4tVMD0RMY\n0901CYyFEMK/elaMbV0O9Hqd5BgLIYLOBXOMr732Wh555BFWrFgBwIYNG5gxY4bfO3YhnQ5vYOxx\nenPdYmMkx1gIIfxJ371ibHXYCTPoZcVYCBF0LhgYP/roo/z2t7/l6aefRqFQMGvWLKZNmzYUfTuv\nTpu3trLL4Q2MZcVYCCH8q2fF2OqwYTDopI6xECLoXDCVQqPRsGrVKl5++WXmzJnDn//8Z9auXTsU\nfTuvnlSKLoc3181olMBYCCH8yRcY2+2EGQyyYiyECDrnXTF2Op1s376dd999ly+//BKn08nrr7/O\nlVdeOVT961dPYOywdgGyYiyEEP7myzF22LtXjCXHWAgRXPpdMV6/fj0zZ87kT3/6E7feeis7d+4k\nOjr6sgiK4ascY7vVOzEbpY6xEEL4lVatQalQ+nKMrVYbbrd7uLslhBCDpt8V4z/96U9ceeWVrFy5\nkquuugrgstpyuWfF2NreExjLirEQQviTQqHAoNVhddiIM3gXI+x2BwaDfph7JoQQg6PfwPiTTz5h\n69atPPPMM7S0tHDbbbf1Kts23HoC4442K3qdViZmIYQYIIfDwfLly+nq6sLlcjF37lxWrVo1oHMN\nOh2ddhtphkTAW8tY5l8hRLDoN5UiKiqK5cuX89577/Hyyy/T2tqK3W5n+fLlbN68eSj72KdOu7cq\nRXtrJ7Gx0ZfVarYQQlzOtFotb775Jlu2bGHLli0UFBRQVFQ0oHO9K8beHGNAKlMIIYLKBatSAOTm\n5vKTn/yEgoIC7r77brZv3+7vfl1Qz4pxa3O7pFEIIcRFMhgMgHf12Ol0Dvw8rd6XYwyy+50QIrhc\nsI7x2TQaDfPmzWPevHn+6s+AfZVjbMOYK4GxEEJcDLfbzeLFi6moqGD58uWMGzduQOf1rBiHhXUH\nxlYJjIUQweOiAuPLiW9LaLdUpBBCiIulVCrZsmUL7e3t/NM//RNlZWVkZ2ef95zCwkKcji5sDjtN\njY0AfPllEa6ujqHo8pArLCwc7i4MuVAbc6iNF0JzzBcjYANja3e5NlyyuYcQQlyqiIgIpk2bxief\nfHLBwHjSpEkk7IyDqnIyR2QAkJaewaRJk4aiq0OqsLAwKMd1PqE25lAbL4TemC/lj4AB5Rhfjjps\nNpQKJXikVJsQQlyMxsZG2traALDZbHz22WeMHDlyQOcadN4UCpXW+9+H1SqbfAghgkfArhh32m3o\n1Bo6cUhgLIQQF6Guro4nn3wSt9uN2+3mlltuYebMmQM6t2f3O7XW+9+H5BgLIYJJ4AbGDhtqhbf7\nsRIYCyHEgI0aNYr333//ks4N614xVmp6VowlMBZCBA+/p1IUFBRw8803M3fuXF577bU+j9m9eze3\n3XYbCxYs4J577hlQu1a7DbXC231ZMRZCiKHRs2KsVHtrx0tgLIQIJn5dMXa73axbt4433niDxMRE\nli5dypw5czCbzb5j2tra+NnPfsYf/vAHTCYTjd13Ol9Ip92G1uPtvgTGQggxNHoCY1SSYyyECD5+\nXTEuKioiMzOT1NRUNBoN8+fPP2dzkK1bt3LTTTdhMpkAMBqNA2q7026D7h2qpVybEEIMDYPWm0qh\nUHkfS46xECKY+DUwrqmpITk52ffYZDJRW1vb65iTJ0/S0tLCPffcw5IlS9iyZcuA2nZ73LidbkBy\njIUQYqj0rBh7lB5AtoQWQgSXYb/5zuVyceTIETZs2EBnZyd33nknEyZMIDMz88LnOtyEhenR67RD\n0FMhhBD6nsBY4Q2MZUtoIUQw8WtgbDKZqKqq8j2uqakhMTHxnGNiY2PR6XTodDomT55MSUnJgAJj\nu9VBdJghZHZxCZVx9gi18ULojTnUxhsMDDpvYOymZ8VYcoyFEMHDr4Hx2LFjqaiowGKxkJCQwLZt\n23j++ed7HTNnzhz+7d/+DZfLhcPhoKioiPvvv39A7TsdLpKSEkJiF5dQ3K0mlMYLoTfmUBsvBMcf\nAj05xi6P9yaPzk7rcHZHCCEGlV8DY5VKxVNPPcUDDzyAx+Nh6dKlmM1mNm/ejEKh4I477sBsNnPt\ntdfyrW99C6VSye23337BbUl7uBwujImSXyyEEEOlJ8fYifceD1kxFkIEE7/nGM+YMYMZM2b0eu7O\nO+/s9fjBBx/kwQcfvPjGXVKqTQghhlJPYOzocqDVaCTHWAgRVPy+wYdfuTzESqk2IYQYMj2pFFaH\nHYNBh01WjIUQQSSwA2O3rBgLIcRQ6lkx9gbGeskxFkIElcAOjCWVQgghhlRPVQqrw0ZsbBT1jc14\nPJ5h7pUQQgyOwA6MZcVYCCGG1NkrxmkpJjo6rDS3tA1zr4QQYnAEdmDsAqNRAmMhhBgqZ+cYp6Wa\nALBYaoazS0IIMWgCPDD2yIqxEEIMIb3Wu9OoNzBOAqDSUj2cXRJCiEET4IGxpFIIIcRQUilV6DRa\nrHYbaSneFePTsmIshAgSgR0YuyE2Rsq1CSHEUDJodb1SKU5XSWAshAgOAR0YRxjC0Gj8vkeJEEKI\nsxi0OmxnB8aSSiGECBIBHVXGRslqsRBCXKzq6mp+/OMf09DQgFKpZNmyZaxYsWLA5xu0etptncQZ\nY9DrdZJKIYQIGgEdGBujY4a7C0IIEXBUKhVr1qwhLy+Pjo4OFi9ezPTp0zGbzQM636DTUdfaiEKh\nIC3FxOmqWj/3WAghhkbAplJ43B7iYyUwFkKIi5WQkEBeXh4A4eHhmM1mamsHHtz25BgDpKWaaG5u\npb290y99FUKIoRSwgbFUpBBCiG/u9OnTlJSUMG7cuAGfY9DqcLpcOJxdvpJtcgOeECIYBG5g7Aaj\nUXKMhRDiUnV0dLB69WrWrl1LeHj4gM/ra5MPyTMWQgSDwM0xdkFsjKwYCyHEpXA6naxevZqFCxdy\nww03DOicwsJCAKwdHQDs3reXLrs3heLzXXuJjdL4p7PDpGe8oSTUxhxq44XQHPPFCNzA2A3GWFkx\nFkKIS7F27Vqys7O59957B3zOpEmTAEj5cgccK+KKUTnERiTw3G/+C4VS63s9GBQWFgbVeAYi1MYc\nauOF0BvzpfwRELipFJJjLIQQl6SwsJCtW7eya9cubrvtNhYtWkRBQcGAzzfozkqlSJFNPoQQwSNw\nV4xdHgmMhRDiEkyaNIni4uJLPt+g1QHewDg3dQQajVpyjIUQQSFwV4zdsmIshBDDoScwtjlsKJVK\nUpISZPc7IURQCNzAWFIphBBiWPhWjO3dtYzTkqirb8Jmdwxnt4QQ4hsL2MBY4Ybo6Mjh7oYQQoSc\ns8u1Ab48Y4vkGQshAlzABsZatRa1WjXc3RBCiJBzdo4x4KtlbJE8YyFEgAvYwDhMpxvuLgghREgK\n81WlsAH4dr+rlDxjIUSAC9jAOFwXNtxdEEKIkNTfirFUphBCBLqADYwjwyQwFkKI4WDQfS0wllrG\nQoggEbCBcVR4xHB3QQghQpK+5+a77qoUyUkJKJVKWTEWQgS8gA2MYyKkIoUQQgyHr1IpvDnGGo2a\nJFO8BMZCiIDn98C4oKCAm2++mblz5/Laa6+d8/qePXuYPHkyixYtYtGiRbzyyisDatcYFTXYXRVC\nCDEAPYFxp93mey4t1UR1TT1dXc7h6pYQQnxjft0S2u12s27dOt544w0SExNZunQpc+bMwWw29zpu\n8uTJvPrqqxfVdlyMbO4hhBDDoaeOsa07xxi8gfGefQc5U11HRnrycHVNCCG+Eb+uGBcVFZGZmUlq\naioajYb58+ezffv2QWk7PtY4KO0IIYS4OF+/+Q7kBjwhRHDwa2BcU1NDcvJXKwcmk4na2tpzjjtw\n4AALFy5k5cqVlJWVDajtxDgJjIUQYjh8PccYpGSbECI4+DWVYiBGjx7Njh07MBgM7Ny5kx/+8Id8\n+OGHFzwvKT5+CHonhBDi6zQqNSqlss8VY9n9TggRyPwaGJtMJqqqqnyPa2pqSExM7HVMeHi47+eZ\nM2fy05/+lObmZmJiYs7btrW5icLCwsHt8GVOxhv8Qm3MoTbeYKFQKDBo9b0D4zTZ/U4IEfj8GhiP\nHTuWiooKLBYLCQkJbNu2jeeff77XMfX19cR3r/4WFRUBXDAoBpg1Y8bgd/gyVlhYyKRJk4a7G0Mm\n1MYLoTfmUBsvBNcfAgatzlfHGCBVcoyFEEHAr4GxSqXiqaee4oEHHsDj8bB06VLMZjObN29GoVBw\nxx138OGHH7Jp0ybUajV6vZ4XXnjBn10SQggxCAxaXa8cY71OS0J8rKRSCCECmt9zjGfMmMGMr63u\n3nnnnb6fly9fzvLly/3dDSHEZai5uZn77rsPhUJBXV0dSqUSo9GIQqHg7bffRq2+8BS1du1aVq5c\nyYgRI/o95q233iI6OpoFCxYMYu9Dm0Gno7W5o9dzaalJHDpyDLfbjVIZsPtHCSFC2LDffCeECF0x\nMTFs2bIFgN/85jeEh4dz//33n3Ocx+NBoVD02cb69esveB354/tca9euZceOHcTFxbF169aLPl//\ntRxj8FamOPBlMTW1DSQnJQxWV4UQYsjIn/RCiMtORUUF8+fP5/HHH2fBggXU1dXxL//yLyxdupRb\nb7211w6Z3/72tykpKcHlcjFlyhSee+45Fi5cyJ133kljYyMAL774Im+++abv+Oeee45ly5Yxb948\nvvjiCwCsViurV69mwYIFrF69miVLllBSUjL0gx8iixcv5vXXX7/k8w1aHfYuB2632/ec1DIWQgQ6\nWTEWQgCw7ulX+eCvOwe1zQU3z+SpJ79/SeeWl5fz7LPPkp+fD8Djjz9OVFQULpeLFStWMHfu3HN2\n0Wxra2PatGk89thjPP3007z77rt897vf7bP9t99+m48//pjf/OY3/P73v2fjxo0kJCTw61//mpKS\nEpYsWXJJ/Q4UkydPxmKxXPL5Ybru3e+67ITpDMBXlSkslhqmTBzzzTsphBBDTFaMhRCXpfT0dF9Q\nDLB161YWL17MokWLOHHiBMePHz/nHIPBwLXXXgt4a6T3F/jdeOONvmN6Skru37+fW265BYDc3Fyy\ns7MHdTzBxrfJh/3cWsaVcgOeECJAyYqxEAKAp578/iWv7vpDWFiY7+dTp07x5ptv8u677xIREcET\nTzyB3W4/5xyNRuP7WaVS4XK5+mxbq9Ve8BiPx/NNuh/0vtr97qzAWHa/E0IEOAmMhRCXpbMD0/b2\ndiIiIggPD6e2tpZPP/30nGo3Xz/nYk2cOJH//d//ZdKkSZSWlnLixIlLbitYnV2Hub2lDYC9Bwqp\nMXoD4k6rt3zbkeKjQVGzORjGcLFCbcyhNl4IzTFfDAmMhRCXpbOrUIwePRqz2cy8efNITU3ttTHI\n2cf1V7miv3bPdvfdd/Pkk0+yYMECzGYzZrOZyMjIbzCCy9/F/iFx9vuecWwfHN7NyOxsxmfl+J6P\niYmitc0a8Ju3hOoGNKE05lAbL4TemC/ljwAJjIUQl4VVq1b5fs7IyOD999/v9fozzzzT53lvvfWW\n7+c9e/b4fr7lllt8OcM/+tGP+jw+Pj6eDz/8EACdTscvf/lLtFotp06d4sEHHyQ5OfkbjOjy9thj\nj7F7926am5uZNWsWDz300EXdcPhVKoWt1/NpKYmUnag8b4k9IYS4XElgLIQQQGdnJ/fee68v53jd\nunVBvUnFc889943ON+jOzTEGb57xoSNlNDa2EBcX842uIYQQQ00CYyGEACIjI3nvvfeGuxsBQ6/1\nlms7NzD2lmyrtFRLYCyECDjBuxwihBDCb/pPpZDKFEKIwCWBsRBCiIvWVx1jOKtkm+x+J4QIQBIY\nCyGEuGg9gbHta6kU6alf7X4nhBCBRgJjIYQQF82g6zvHODW1Z/e76iHvkxBCfFMSGAshhs2KFSv4\nxz/+0eu5DRs28NOf/rTfcyZMmABAbW0tDz/8cJ/H3HPPPRw+fPi8196wYUOv3fO+973v0d7ePtCu\nh7z+coxjoiMJDzdIjrEQIiBJVQohxLC59dZb+eCDD5g+fbrvub/85S888cQT/Z7TUxs3MTGRX/3q\nV5d87Q0bNrBw4UJ03WXHfvvb315yW6Gory2hwfv7yTFncqCohGX3PMrjq+9j2pRxw9FFIcQgcrld\n7Cot4s97dtLa2cGUK0Zz1ahx5KaOOG9py6b2Vpo72ojQhxFhCEOv0V5SjXOny0VtSyNWuw1blwOr\nw4bN4f0eaQjnqlGDM89IYCyEGDY33XQTL774Ik6nE7VajcVioa6ujvz8fO677z5aW1txOp08/PDD\nzJkzp9e5FouF73//+2zduhW73c6aNWsoLS0lKysLh8PhO+5f//VfOXToEHa7nblz57Jq1So2btxI\nbW0tK1asIDY2lg0bNjB79mzee+89YmJi+OMf/+gr3bZ06VLuvfdeLBYL3/3ud5k0aRIHDhzAZDLx\nH//xH2i12iF9zy4Xhn7KtQG8+MyT/PTfX+HjnXtYvOtHXHfNJB57+F6mTBwz1N0UQnSzOux0uZwX\ndY7H46Ho5DG27P6YP+/eQU1Lo++1rXt3AhAdHsnUK0ZzVc44TDFxnKy1cKLGQnmN93tLR1uvNtUq\nFRH6MCINYcSGR5GekERmQgoZCUmMSPR+D9PpKbWcpLiynOLT5RRXnqC06hT2Lgf9+fyZN0mPT7qo\n8fVFAmMhBADr/vs1tu39ZFDbnD/lOp66fWW/r0dHRzN27FgKCgqYPXs227Zt4+abb0av1/Pyyy8T\nHh5OU1MTd9xxxzmB8dk2bdqEwWBg27ZtlJaWsnjxYt9rjz76KFFRUbjdbu69915uuukm7rnnHt54\n4w02btxIdHQ08NVK9OHDh3n//fd55513cLlc3H777UybNo3IyEgqKip44YUXWLduHT/60Y/48MMP\nufXWWwfp3Qos/VWlAMg2Z7Dx90+z78Bhnv/1BnZ+uo9PPitk5rWT+c59S8jPNWNKjAuJnfEq6s6Q\nFmcK6s1ixOBzulx8efIonxV/we6jBwGIi4whLjIaY2QUxu6fFSiwOmxYHXbf6qnVYaepvZW6libq\nWhupa2mmrrWRNmsnAOF/NBAXGU1sZDTGiCiMEdHotVo8Hg9ujwdP95fb46bweDHlNRbAGwAvn3kL\nC6ddT3JsPHuOHmLX0SJ2lx7k71/s4u9f7Oo1BrVKRWZCMlOy84mLjKHDZqXN1km7tZN2WwftNiul\nVacoOnXsgu+HVq0hJyUTc1Ia4XoDBq0evVaLQavDoNWTYkwgLc40KO+9BMZCiGE1f/58tm3bxuzZ\ns/nLX/7C+vXrcbvdPPfcc+zbtw+lUkltbS0NDQ3ExcX12cbevXtZsWIFAKNGjWLUqFG+17Zt28bb\nb7+N0+mkvr6esrIycnJyfJP/1xUWFnLjjTf6UixuvPFG9u3bx/XXX09qaqqv7dGjR2OxWAb77QgY\nYb6b72z9HjN5wmj+64/PsLfwEL/89Rvs/HQfOz/dB0BEeBjZ5nTMIzPIHplBSnICapUKpUqFSqlE\npVKiVCqJjAgnd1QWsTFRQzKuwfTSB5v4xXt/ZO6Eq3nl+z9BpwnNTxeCWVN7K3//Yhe1LY20dLbT\n0tHW/b2ddlsnCdGxpMcnkRGfREZCMukJSaTHmVAqFdgcDmxd3i97l4MOWyf7jxfzj+Iv2X30IO22\nzm/cP4VCQVxkNGlxSSREx9DS0opT6aGhrYWSyhPYnV3nPV+v1bFw6ixuu+p6Zo6ZjFat8b2WZUrl\njuvmAlDVWMuu0oM0d7SRZUoly5RKWpwJtUp13vY9Hg81zY1U1J3hVF0Vp+qqOVVbRafdSk7KCPLS\ns8hNzWJkUtoF2xosEhgLIQB46vaV513d9Zc5c+bw9NNPc+TIEWw2G/n5+bz//vs0NzezZcsWlEol\ns2fP7nWj3ECdPn3alxYRERHBmjVreqVZXKyz0yZUKtUl9SlY9Jdj3Jcpk8bwpw2/ZPfeInZ8spey\nE5UcP1HB4eLjfFFUOqDrJSclkDdqJPl5ZvJHjSTnihGkpiQSFRnxjcbhL7//23v84r0/olQo+fDA\n5zzw6//H71f9P181D3H58Hg8tFk7qWluoLalgdbODvLTzWQkJPX5qYbH46Hw+BE2/t82Pti7s9/g\nUqNSX3TqQg9zUhqLcmczPe9Krh41DoNOR0Nbi++rsa2ZhrYWFCgw6HS+lVO9RoteqyM2IoqE6FiM\nEdG9AsrCwkImTZrkG0en3UZDWzMOZxdKhRIUCpQKBQoUKBSQEBU7oH+zKcZEFl/d/6d6/VEoFCTF\nxpEUG8fUnMsj1UoCYyHEsAoLC2Pq1KmsXbuWBQsWANDW1obRaESpVLJr1y6qqqp8x/e1yjtlyhS2\nbt3KtGnTOHr0KKWl3mCrvb2dsLAwwsPDqa+vp6CggGnTpgEQERFBe3s7MTExvdqdPHkya9asYeXK\nlbhcLj766COeffZZv74Hgahn9bPT3v+K8ddNmzKu1414TqeLSks1x8pOUVffiMvlxu1243K5cbld\nuF1uGptaKC49wZGS43y8czcf79zdq82I8DBSkhNISU70fk9KJCkpniRTPKbEOJJM8cTGRA1p2sZb\nO//Cv25+FVO0kf96/Gn+/Z3X+ejL3dz9wk/Y8PA6Igxhg3KdE9Wn2bbvE2LCI5k5ZhIZCcn9Huvx\neDhZW8XnpUUYtDqm5YwlxZgwKP3oab+5ow1LQy3NHW3YuxzYnV04urpwOLtwOB0YdHrGZGRjTkof\n0Epic0cb5TUWymstnKyp4mRtFeU1FtptnYzOyGbCyFwmjMxldIYZfR+r8R6Phw6blbrWJqqbG6hp\naqC6uZ6a5gaqmxo4fvoUHW87qGluOKceN0BitJHJ2flMvWIMk68YTWZCMn/es4ONO7ZRcrocW2Gq\nRwAAGV5JREFU8K6afnvGPHJSMokOjyA6LLL7ewRatYbmjjYq66upqK+moq6ayrpqTjfUoFAo0Gm0\n6DVa73etDr1GS156FtfkXklybPw5/QnTGQYlh7aHQqEgXG8gXG8YtDaDgQTGQohhN3/+fB566CFe\neOEFwFut4gc/+AHf+ta3GDNmDGaz2XdsXwHOXXfdxZo1a5g/fz5ms5kxY7wrD7m5ueTl5TFv3jyS\nk5N9KyUAt99+O9/5zncwmUxs2LDB125+fj6LFi1i6dKlvuNyc3NDOm2iL0qlEr1W12dAMVBqtYqs\nzFSyMlMHdHxjYwtHSo9TXHKCY8crOFNdx5nqOqrO1HK07FS/5+l1WhISjERHRRAZGUF0VDiRERFE\nRYVjjI1m0oTRTJ6Qj8HwzVdz39/1MU+++SuMEdFsevwX5KRm8toP/4XVr/2CD/YVcNdzT7LxkZ8T\nEx55Se03d7Sxdc9O3v7s7+w/XtzrtZGmNGaOmcSsMZO5Oncc7TYr/yj+gk+PHODT4gNYGmp7HZ+Z\nkMxVo8Zx1aixXDVqHC3WdvaVHfYGozUWymuqOFlrweqwE64PI0JvIEJv8P1s7+rC0lBLVWMdVU11\nA/63oNfqyE8fydiMbMZkZpMYbaSyvobK+mpO1Z2hst4bQLZaO845V6NSo9dqKTtTyf/s/j/fc/kZ\nZrKT0mnpbKO+tdn71dZ83j4pFQrio2K5IjmdxOg4EmOMmGLiCNPpKSo/yt5jh/hL4af8pfDTXuep\nVSoWTJ7BPdfP55rcK8/7R1dsRBSxEVGMG5EzoPdGDD+Fp6/ll8vc2R8FhIpQG3OojRdCb8yhNl4I\n7DH31fexq5eSEBXLx//2u2Hq1Vc6Oqy+IPlMTT3VX/uqrWukrb2Djg5rn+drNGomjM/j6qnjuXra\nlSg9diZMuJJOqw2r1U6n1Upnp42uLidxcTGkJCWcE0j/df8/+N4r6wjXGfjvHz/LmMxs32tOl4vH\n/vAc737+EaMzzPzXo/9OXFQMbrebEstJdpUWsau0iMLjxeg1Wkwx3o+XTTHer+iwCHYeKuTvX3yO\nvftj7+tGT2Tx1bNpt3ay81AhnxYf8K3gq1UqnC6X7/ox4ZFckzue6XlXYnXY2VVaxJ6jh/oMPs+m\n02iJ0Btot1n7rQhgjIgmxZhAalwCycZE4iKj0Wm0aNUa9BoNWrX356aOVg6dKuNQRRmllpO9+nc2\ng1ZHRkIS6fFJvnzVEYkpZJlSSTEmoFQoKa+xcKC8hAMnSjlwooQjFcd9aQtatYa4yGgSomKJi4oh\nISrW914mxcZ7v8fEU1F2nKlTpvY7do/HQ2V9NXvLDrP32GGOVVUwa8xk7rhuLonRxvO+b5erQJ6D\nLsWljFdWjIUQQlwSg1ZH2ZlKpjz2bcJ0esJ1BsL0BsJ0elKNiUzPv5LpuVcSGzF4N855PB6qmxto\nt3ZiTkrzVXsIDzeQbc4g25zR63iX28XeY4c5WnWKyeZ8rkjOpL2jk7a2DlrbOqg6U8vuvUV8vudL\n9u0/zJ59B/nVK//Z98VVeP/X9AAuiI6IINmUQHJSAsTCJ01foFapePrOh8lNzfpax+GHs2+nvraR\nncf3M/2x+0iKiKPa1kC7/aubrEzRRmwOO/vKjuD2uM/pwhXJGSy79iYWXTW718ft987+Fg5nF/vK\njrDz0D7+UfwF0WERTM+fwHX5Exidbu5VGeP7Ny/D5XZRcvokn5cWsefoQRoaG7lyVD5Ziam+gDQp\nJs53XpfTSYfd6q0uYO1Aq9aQHBt/SXnTti4HpadPcvDUMZraW0mLN5GRkExGfBLxUTEXTH0ZmZTG\nyKQ0llx9g6+96qZ6YiOiiDKEDyh1xqI8ed7XFQqFt08Jyb7riOAngbEQQohLcs+sBfzPnh1Y7TZa\nOtupaqzrdTPexh0foFAoGJuRzbX5E7kufwLjs0YRaQi7YODicrto6WjndEMNxZXlHKk8QfHpExSf\nLqepvRWA+KgYrsufyMzRk7hu9ERMMd6qJVaHnU8O7+fDA5/x9y920dje4ms3ISqWGaMnMXPMJGaM\nnsiY/GxunH01jW0tFFeUs3PvPgqLj1BmOYVSp8KpdOHAid3jwPW1QLUVG63uSkq6KqAe8IDzyy5+\n8H8/Q6vRkDUilfS0JKrO1HH8RAV2R/dNWllK2tM6KWvpxGPzoGiFtIhErho1jllTpqDTamhsaqWq\noZYzTfXUtTTR1NFKlDKcOHs0xwtO8eJnG1GrlajVajLSkhk/bhSj87K5Jnc81+SOP+f9tNkdHD12\nkuKS4+h0WvJyRzJyRDqjM8yMzjDznRsXnbO65nB0cbKiCoNeR3JSAhq1mhh15CWngZxNr9EyPiuH\n8VmDk2Kg12gZkZgyKG2J0CaBsRBCiEvy0IK7eGjBXb2ec7vddNptlFad4tMj+/nkyAEKy45QdOoY\nr/zvnwBQKZW9blKKDo9ErVTR1NFKU3srje2ttHa293mj5YjEFK4aNQ6DVsc/jhzg/V0f8/6ujwHI\nTcsiLS6RfxR/4QvQE6ON3DNrAaMzzOw9dpiCw4W8+/lHvPv5R4A3z7a+tZkO+9dSLLxFN9Ao1MRF\nxZAYFUt8dCyx4VE4XU46HTasdjudDhsdVisul4sFedehu1pL2YkKjh+voOxEJaXHTqLX6xiVk0XO\nFSPIvWIEV2Rn4tS7aaxt5kTJafbuP8SXRaW8Xfghb//Xh5f8+1CplIy6IovxY0cxdvQVdHZaOVJy\ngsPFZZSdqMDl6h3YazUarsjOJC93JHk5WViqLLzz5084ecrCyVNVnK6qwe32npOWamLqpLFMmTSG\nqZPHkpOdKbWZRVCSwFgIIcSgUSqVRBjCmGTOY5I5j4dvXU6n3cruo4f49MgBjp2poKWjnZbONlo6\n2rE01OLoLnelVqmIDY8iKcZIbuoIYiOiMEUbyUvPIi/dTG7qiF530Hs8HkotJyk4XMjOw/vZVVpE\nyelyskyp3DxxOjdPnM6ErFG+AO7uWfO9Ob2ny9lxuJCdh/ZRXFne/XG596P8npqzrbUNXH/NdcSE\nR15yRQuPx0NTcysx0ZH9B5ELvd9sdgdFB0s5UFSCUqEgOjqSmOgIoqMiiY6OJCoyAqVSgdPpxOl0\neb9cLhyOLo4dP8WXB0v58mAph46UcaTkOJve/uoS4eEGJozPY3ReNvm5Zmw2OyVHyykuPU7J0ZMc\nLi47p1uJCUYmTxxNZnoKLa1t7N1/iPf+/BHv/dn7B0V0VAQjMlNRKEBB9/ujUHgrHYQZSElOIDUl\nkZTkRFKTE0lJSUSr0VBpqaaysppKSzUVp89w2lKDw+HgCvMIcnNGMConi1FXZJGcFH/e972z08rJ\niipOnrJwqqKK8lNVtLS2kZOdyei8bEbnZZOWagqJTWQuZx6Ph892f8GpiiquvXoiGen9V065XEhg\nLIQQwq/CdAauHzuF68dOOec1j8eDzWHH6XYRob9wisXZFAoFuWlZ5KZlsXLuUmxdDhpam0kxJvTb\njlKpJD/DTH6GmX+ad3u/bRcWFn7j3GiFQoExNnpAx+p1WqZOHsvUyWMv+jrjxuSwZOGNgLcE3tGy\nkxw8fIzwMAOj87PJTE/uNzB3uVycrKiipLScE+UnmHP9dWSmpxAe3ruEl8fj4fiJSvYUHmTPvkPs\nLTxI6bGT0L2q790wx3uso+v8m0acTalUolapzqlnHRUZTnpasm8jHpfLhdvtweV209HRSW1dYz8t\n9m4jP9eMeWQ6Ho8Hu6MLx1lfjU1NxMbEoFar0KjVqDVqNGo1SpUSm82O1WrHarNh7bRhtdmxO7pI\njI8lNcVEaqqJtBQTqSmJmBLjaG/vpL6hibr6JuobmqhvaKapuZWoyAiSTHG+8oGmxHiSTHHEGS+c\nR+3xeDhtqaG49ARW21cpSmefZ0owkj0yA6MxekDtDaTegtVq42jZKUqOlmOz2UlMMBIfH0tivJGE\n+FjCwi5c3s3j8bB9x25efHkjB778qnpKTnYmN1x/NXNmTWPyxDGo1d6yfQ5HF6eraqisPMOpyjM4\nnU7mzLqKzIyhT4/xe2BcUFDA+vXr8Xg8LFmyhJUr+95AoKioiLvuuosXXniBm266yd/dEkKIkDbQ\nudnfFArFoG16oddoSY1LHJS2ApVarSI/10x+rvnCB+PdqMaclY45K53CwvB+z1MoFL6bG799+/zz\ntmm12TlTXYelqpaqqhqqztRhOVOL3eEgPTWJjLRk0tJMpKcmkZKciEKh4GSFhdKjJyk9Vk7J0XJK\nj57kZIUFpcK7C6JCqUClVKFUKtDrdFx3zSRGZKYworvcX2ZGCpGR4Rw95l0BP1x8nCPFx9m97yC7\n9hZd9Pt4Np1WgyHMgFql4lRFFbv3HfxG7QGEhekZkZHKiMxURmSkMCIzhfTUJM7U1HO4uIwjxcc5\nUnKcltb2AbUXEx2JeaT392gemQ5ATU0D1bUN1NTWU1PbQG1tIwolJCclktRd49vUHbS3tXVQUlpO\n8dFyTp6ynDeADg83kJGezMTx+Uya4P0yZ6WjUChwu938798+5Vev/Kfvk4ibb5zONdMmUPCPfXz6\n2X5e+d1mXvndZmKiI8m5YgSWqhrOVNf70nZ6PLXuN4zJz2b+zTOZf/MMzFnpffbHarPjdrnP+WPu\nUvk1MHa73axbt4433niDxMREli5dypw5c3rVJO057rnnnuPaa6/1Z3eEEEIw8LlZiEth0OsYOSKN\nkSPSBnxOdvfW4PNvnvGNrp2anMj1M74qwdbZaeW0pQa1Ro1Wo0ar1aLVatBpNRQVFTFu/HicXU66\nulNUupxO3C43er0Wg16PwaBDddZmJF1dTqpr6jltqeF0VQ2WqhpqahuIigwnPi6WhPhY4rq/x8ZE\n0dLS1h2cNlBdU09NbT1nquuoqKzmZIWFIyXH+xyHQqEga0QqM66dTH6umego7w6PZ4erHrfbe2Nn\neSVlJyr48mAphQeOnNOWWq0iMSGO0XlmWtvaaG3rZPe+qj6D3+ioCKZOGkPuqJHk5mQRGRFOXUMj\n9fVN1NZ5V8Nrahs4cfI0xSUneOtPHwAQExPFxPF5nLZUc7TsFEqlkoXzr+ehHywnb9RIAB68dzFW\nm53Pdh3go//bxfYdu9iz7yBJpnimTBxNRnoKmenJZGQk43B08ZcPC/jks/0cOlLGL55/nbxRI7l6\n6niaW9uorWukpqaB2roGWlrb0Wo0fPbxf3orxHxDfg2Mi4qKyMzMJDXVW7x9/vz5bN++/ZzJd+PG\njcydO5eDB7/5X2FCCCHOb6BzsxCBLizMQM4VI/p8Ta1WYdDrQK8bcHsajZr0tCTS0wa2A50pMa7f\n63s8Hurqmzh5ykL5KQuVp6tJTDAyOi+bvFFZA0pZOFtXl5NTlVWcKK9EpVL50jaMsdG+VJqeyiNd\nXU5q673BZXVNHXq9jtyckRfM7T77WsWlJyg8cJjCA0coPHCEj3fuRqVScvviuaz6/rf7XOE16HXM\nmXUVc2Zdhcfjwel0odH0HYretewWmlva+PvHn7PtrzvZ+ck+iktP+F6PiY4kyRTPuDGjyLkic8Bp\nSxfi18C4pqaG5OSvEq1NJtM5wW9NTQ0fffQRGzduZM2aNf7sjhBCCAY2Nwsh/EuhUJCYYCQxwXhJ\nueVfp9GofSvvAzk2tfvGSMi7pGuNG5PDuDE53H/PIgDq6htRKpTExcUMqA2FQtFvUNwjJjqSZYtu\nYtmim2hr66DsRAVxxhgSE+PQ687dBnwwDPvNd+vXr+eJJ57wPQ7AjfiEEEIIIUJaQrx/dwOMjAxn\nwviLD+Ivll8DY5PJRFVVle9xTU0NiYm9b4w4dOgQjzzyiLesTVMTBQUFqNVq5syZc962CwsL/dLn\ny1mojTnUxguhN+ZQG+/lYiBzc19C7fcVauOF0BtzqI0XQnPMF8OvgfHYsWOpqKjAYrGQkJDAtm3b\neP7553sds337dt/Pa9as4frrr79gUBxK+3wLIcRgG8jc/HUy7wohQoFfA2OVSsVTTz3FAw88gMfj\nYenSpZjNZjZv3oxCoeCOO+7w5+WFEEL0ob+5WQghQp3CI0m9QgghhBBCIBudCyGEEEIIgQTGQggh\nhBBCABIYCyGEEEIIAVwGdYwvVkFBAevXr8fj8bBkyRJWrlw53F0aVGvXrmXHjh3ExcWxdetWAFpa\nWnjkkUewWCykpaXx4osvEhkZOcw9HTzV1dX8+Mc/pqGhAaVSybJly1ixYkXQjtvhcLB8+XK6urpw\nuVzMnTuXVatWBe14e7jdbpYsWYLJZOLVV18N+vHOnj2biIgIlEolarWad955JyDHHOxzLoTevBtq\ncy7IvCvz7sDHHFArxm63m3Xr1vH666/zwQcfsG3bNo4f73uf8UC1ePFiXn/99V7Pvfbaa1x99dV8\n+OGHTJs2jd/+9rfD1Dv/UKlUrFmzhm3btrF582beeustjh8/HrTj1mq1vPnmm2zZsoUtW7ZQUFBA\nUVFR0I63x5tvvtmr8kGwj1ehULBx40a2bNnCO++8AwTemENhzoXQm3dDbc4FmXd7BPt4B2PeDajA\nuKioiMzMTFJTU9FoNMyfP79XHeRgMHnyZKKiono9t337dhYt8m65uGjRIj766KPh6JrfJCQkkJfn\n3c0mPDwcs9lMTU1NUI/bYDAA3lUMp9MJBPfvubq6mp07d7Js2TLfc8E8XvDu4ul2u3s9F2hjDoU5\nF0Jv3g3FORdk3oXgHi8MzrwbUIFxTU0NycnJvscmk4na2tph7NHQaGxsJD4+HvBOaI2NjcPcI/85\nffo0JSUljB8/noaGhqAdt9vt5rbbbmP69OlMnz6dcePGBfV4169fz49//GMUCoXvuWAeL3hXLh54\n4AGWLFnC22+/DQTemEN1zoXQmXdDZc4FmXch8OagizUY827A5RgLev0jDyYdHR2sXr2atWvXEh4e\nfs44g2ncSqWSLVu20N7ezg9/+EOOHTsWtOPdsWMH8fHx5OXlsXv37n6PC5bx9ti0aROJiYk0Njby\nwAMPkJWVFbS/41AQjL+rUJpzQebdvgTLeHsMxrwbUIGxyWSiqqrK97impobExMRh7NHQiIuLo76+\nnvj4eOrq6jAajcPdpUHndDpZvXo1Cxcu5IYbbgBCY9wRERFMnTqVTz75JGjHu3//fj7++GN27tyJ\n3W6no6ODJ554gvj4+KAcb4+eucloNHLDDTdQVFQUcL/jUJ1zIfjnn1Cdc0Hm3WAcb4/BmHcDKpVi\n7NixVFRUYLFYcDgcbNu2jTlz5gx3twbd1zcjnD17Nu+99x4A77//flCOee3atWRnZ3Pvvff6ngvW\ncTc2NtLW1gaAzWbjs88+w2w2B+14H330UXbs2MH27dt5/vnnmTZtGs8++yzXX399UI4XwGq10tHR\nAUBnZyeffvopOTk5Afc7DpU5F0Jv3g2lORdk3pV5d+BjDrgtoQsKCvj5z3+Ox+Nh6dKlQVc66LHH\nHmP37t00NzcTHx/PQw89xA033MDDDz/MmTNnSE1N5cUXXzznRpFAVlhYyN13301OTg4KhQKFQsEj\njzzCuHHj+NGPfhR04y4tLeXJJ5/E7Xbjdru55ZZb+MEPfkBzc3NQjvdse/bs4Q9/+AOvvvpqUI+3\nsrKSVatWoVAocLlc3HrrraxcuTIgxxzscy6E3rwbanMuyLwr8+7AxxxwgbEQQgghhBD+EFCpFEII\nIYQQQviLBMZCCCGEEEIggbEQQgghhBCABMZCCCGEEEIAEhgLIYQQQggBSGAshBBCCCEEEGA73wkx\nELNnz0av16PT6fB4PCgUCl5++WVSUlIG7RoWi4UlS5awa9euQWtTCCECkcy5IphIYCyC0ksvvYTZ\nbPbrNYJtj3khhLhUMueKYCGpFCIo9bVvTW5uLi+99BK33XYb8+bN429/+5vvtYKCAhYtWsTChQu5\n//77qaio8L32zjvvsHDhQhYuXMiyZctobGz0XeOFF15g0aJFzJs3j/379/t/YEIIcRmSOVcEC1kx\nFkFp9erVvo/11Go177zzDgBqtZotW7ZQXl7OnXfeyeTJkwH453/+Z9566y1GjhzJO++8w+OPP85/\n//d/s3v3bn73u9+xadMmjEYjVqsVlUqF1WqlubmZiRMn8sgjj7B161aeffZZNm3aNJzDFkKIYSFz\nrggWEhiLoNTfx3pLly4FICsrizFjxvDll18CkJeXx8iRIwFYsmQJP/vZz+js7GTnzp0sXLgQo9EI\ngMFg8LUVHh7OzJkzAbjyyiv5xS9+4dcxCSHE5UrmXBEsJJVCBKW+Ptb7+vP9/TxQWq3W97NSqcTl\ncl10G0IIEQxkzhXBQgJjEVLee+89AE6ePElxcTHjx49n/PjxlJaWUl5e7jsmPz+fsLAwZs2axf/8\nz//Q0NAAQGdnJw6HAzh3Yr+UiV4IIYKZzLki0EgqhQg6CoWiV76bQqFg3bp1ADidThYtWoTNZmPd\nunW+j+ueeeYZHnvsMVwuF0ajkWeffRaAqVOnsnLlSu677z6USiU6nY5XX33Vd52vX1cIIUKNzLki\nmCg88ieXCBG5ubl88cUX6PX64e6KEEIEPZlzRSCSVAoRMhQKhXz0JoQQQ0TmXBGIZMVYCCGEEEII\nZMVYCCGEEEIIQAJjIYQQQgghAAmMhRBCCCGEACQwFkIIIYQQApDAWAghhBBCCEACYyGEEEIIIQD4\n/1WFEa7xcr8CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f017a121510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d1a_history4) # rmsprop, sigmoid, with aggresive dropout (.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 19 , val_acc: 0.919565217391 , val_loss: 0.426407991819\n"
     ]
    },
    {
     "data": {
      "image/png": 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H+/Xe4quPsnOFyunqc+zGJeZt+Y15Qya81E6lUjH9zxWcu3uNzg1akCpP4+j1\ni6zav40JngO4H/CI02f9Ma1gQu8eHqzY8zdJaal8M2gcxga5Z6G7N25DNRt7Plo5l+1XjpP1hxZL\nR03NcwwJicms27+Tn/f/g45Um8wsBd+v3cDfmb4kJCaRkJBMQmIScfGJ6txUAPRB0lCbpMgU5i/5\njflLfsPR3gaPdk1p364ZI1p05/QdfyauWIzKX4F9JWsmT/iAvj07FLjAztTUhN9//poduw4xa+7P\nzPjmp2cHJOAgRWIsARVU0bXlI48+9O/WEX093QLfgwZOLlSqaMm9sOAC2xUkLjmRtYd2Ym5cAXtz\na3ZdOEZVazu8//mJBd//ztoNO9CV6bB04TQG9ffMda5KpcLnwjHm/P0r8drJVNQ2Ieb8U9CVYORm\nyOrj24nNTGTBsEnoy3RznXf0+kWWeK/n9uOH+fZNKpEyuLUnn/X4ABtTCyD7ff30jyUcvnEBIyvD\nAnN20+Tp7Ll0EoDpG3+mjkM1nCvlvVCwMG6FBPL5uh+4FRKIZfmKLB4+mQ71snOrfxk3g+6N2zBj\n489s9z9MXYdq/DB6KrJyMvwCbhMS84SQ6AhCop8Qm5zAV31HU8PW8bX78r4SqRSCIGi6Yg2MIyMj\nsbZ+/pWypaUlN27cyNXG2dmZQ4cO4ebmxvXr14mIiODJkyclFhjvPH+UmyEBNKpaC/e6jWlZsz51\nHKplL9bJzOCT1Qs54H+Wgd9/wcbPFmBq9PJsUXxKEqv2bcX73BGcrOxoWbMeLWs2oI5D1beWhnHw\n6jkAVoz5Eg/XJrmO2ZlbcSngFuGx0ThYvPyVfmFcD8rOAf1pzBd8v3MD6w77oEhSYKGowM1bD1Ao\nldjbWfNYFcWhoPNUs7JnydBPCY+K4mrAPRZv/4Nfl/5DfEii+poxyXF4HduNnZklw9rlnf/sXKky\nu2etoN+Cz/A+d4SElGSWfzgN/yt3OHzsPFeu3ibkcQQJJEMdLVBCxu10JK7a3A0L4u7tQLS0pBgb\nG2JibEQlWyvsbC2xt7fBwc6asMxolh/exGejhmEnseTI8fOcOHWZP7y8+cMru7A4NaRILKQMm9yD\n+eMm5luy7NiNS8z6ayW6OjLszLIX8dmbWzHnh/GcOHmJa/EPCE2KQiKR0LupO1N7Di/y++FcyZHD\n1y4Qm5SQ57+1V1m9fxsp8jSm9R5Br6bt6P7tZJb6/EkVS1vmzvyELh1bUaG8MdWrOeY670ncU6Zv\n/IlDV89gxv1hAAAgAElEQVSjL9Nl7uDxjHDvzs5/j6KjrUWj5nUY+8s8tp45yN3QIH7/ZA6VzCw5\nf+86S3as51LALSQSCX2btWdo26555qlHhoTSsY17rudMjA3p3dydwzcusP/KGfWHvbzs9z9LijyN\nxtVqc/HBTT7+dT6+s1cW+QNrVEIsK/b8w1/H96BQKhnUqjOzB459aSFm5wYtaFK9DvO2/Ma2M4fo\n+PW4PK8n09ZhlEdPzQyMi3k1uCAIQkkr8cV3Y8aMYcGCBfTu3Zvq1avj4uJSomW7giKzE73XTvya\nisblcx3T05Hx24TZfOm1nC2nD9B70Wf8PXUxthWzZ7tS5WmsO+zDqn3bSEhNxlDPgNN3/Dl9xx92\nrMfEwJBmzq60qeXGoFadX3s2F+DwteyApblLvZeO2ZtnB1+PY568dmB87dmM8V+/7ebpvVhUlVR4\nnd8NN7Mg/llauqkEakohA+7vekjdbc+KexsDdbVIsknDs04rmjdw5fvl65m/+XeyzOCLPqMKTEWp\nYGjMlNb9+fHodo5cv0Dd0f3IuqEAZfZX4VaO5qRZKFCgZJhrF9qMaMiX//4ERnDqr/UYGhrkO9P4\nnfd6AJq41KFVrQYM7NuZjIxMLly+weFj5wgIDKG1hxuLj2/gVKg/KkneKfh3QoMY9+u3ZCgyMdDV\nU6eR/Fen+s2Y1nvka89k1rDNDozvhj2iubNrkc59mhjP+iO7sDQx5YO2XdGX6bLh0/n0XDCFz//4\ngUpmVjRpVDfXOYmpKfy2fxtrDnmTKk+nmbMr34/8DEcLGwD6936errBj+jJmbvyZLacP4DnvE2rb\nO3Hqtv+zcTfniz4jCwwO/WKT8nzevW5jZNo67L9ypsDcau9zRwD4fuRn/HXClzUHvfnSazkrP55e\nqOoQ8SlJrN6/jT8O7SQtQ46DhQ0Lh06kTe2G+Z5TwdCYHz+cRrdGbVhzYAdGBuWyPxCZWWFvbo2D\nuTW2FS1KPNWquIhUCkEQNF2xBsaWlpa5yn1ERkZiYWGRq42hoSGLFi1SP3Z3d8fOzu6V1/7vNn+h\ncdHcfRJMe5f8/6gVxu3gQMrJ9Ai6H8CjfP64Dq7bhoyUNHb6n6TL3E+Y3XUEtyMesfXSUeLTkjHS\n1Wdkc0+61GlGeqac66EPuR4awLXQAPZfOcP+K2e4cMOfUS26FKlvOWMOi48mIOIxTSrX5PaNl/NP\nVSnZ+5Sfunwe/bSsor0AQJYqi4t3b6JKVXHM7yKmFYxxUdpxXxaGTj0ZszqNAAnM278BVCo62jVC\nYazgSeRTZDId6taqSox+IrtunybcKIraLnYMGt6BNdf3oJUmQT9Rle82jalp6fzy+3ZOnbsGEqCG\nFMylmLQ0ZkKz3tSq7sh0n9/JjFPwSbs+dKzZCIDKZtZcenSHC/6XqFAu/0VYZ29cASAzLilXHwx0\noUfnpkBTADpHN2b39bMs/HMV3V1z5y3Hpybxv22/kiJP44tOQ2hRtQ7J6WlEJsXyJCGWyMRYEtJS\naO5UmxpW9qRExuIXmbv+cH7++7rIMrID88PnTqKbosjrlHxtOLuPVHk6HzTukOvfydT2A5jv68Xw\nZTP5od8ELIwrIM/MwPfGOXZcOUGyPI3y+oaMbNubDjUb8vRxBE8f552zPrhuG0y19FlzajenbvtT\n19aJoU07UsPKnuQnT/F78rRI481Rx6YyfiH32XvsEJbGL397FJ+axImbflSzqER8eBSdqzTgpNVl\ndl08jrW+CZ61m+Z7z/TMDPZcP4u3/wlS5OlUMDBiRDNPOrg0RFsuKdQWouXRYpr7f3aKy4C4sEji\nwiJfeX5pJVIpBEHQdMUaGNepU4eQkBDCwsIwNzfH19eXZcuW5WqTlJSEnp4eOjo6bN26lcaNG1Ou\n3Ktr2f53leHf65ay5fQB+rfvShWrSq/VX2WWkqjf4nC2daRhw4ID7IYNG+KybysLt61lypYVABjo\n6vFp9w/4uFO/XPmz7rRR/xwcFUHfJVM5eOcS80d/WmD1hhe9uLLy8v7tAAxo6/nS65CVlcWe8xez\nHxjIXmv16dI1XmSqFOgrdNl/4FeqVskun+Z97giT1yxhzcU9ZCoVKLKUrPlkzksL3iA7zzT15685\ndPU8J8PucFeRPROvCFDw82/b2b5p+Us5tgEPQ5g682seBAZTtUolhg/pRdvWjVh32gevY7v55/5R\nrGPMCY2L5uNO/Zg+8HmFk5aPb3Hp0R2k5Q1wq5v/mMP+Xopl+Yq4t2yTbxuAytWrcvyrq3hfPcXU\nwR+qq3KkZcgZ+P0XRCfH80XvkUzuPqRwL2oh5LV6Vt+8PD8e3kqqVlaR3svohDj2rbmAVQUzvhz6\nMXovzGC6ubmhbVKO2Zt+YemxbQxt05Wf9/xNZEIsJgaGfNV3FKPb98JAV79Q92rYsCF9PTxJSkul\nUbXCb4Vc0GrhAcmR+HndJzQjgS5uHV46vvbQTrJUWQxr30N9jY1VHen0zQTWndlLjzYdqOuYe5Fd\nRFwMW07t589je4hKiKV8OSNm9hjKSPce6OvqFbrfb6IwQff7TATGgiBoumLNWdDS0mL27NmMHj2a\nbt26qWvdbd68mS1btgDZi++6deuGp6cnp0+fZubMma91r4TU5OzrPXn82v2NiI0hQ5FJZUvbQrWf\n4DmA70d+hrlxBT5s35uzS7yY1nvES4vKXuRgYc24Tv1Ilaez7rDPa/Xz4NVzSCQSPFwbv3Ts59V/\n8/uv2wAICCv6a7Hd5yDL1v8JwEf9+qiDYoA+zTyY0mMoITFPiIiLYXq/D/MMiiG75uGPH07DzsyS\n5f/+xanb/rSu5Ub/dh25ev0eX8xcmqtg+P5Dp+naZwIPArN3Uftu3kQ+HNEHp8p2fDt0IlN6DCU4\nOoLz967ToV5TZvT/MNf96jhUA7IXT+YnLjmRiLiYAvNWc5gamTDBcyCxyQms2rcVeLahw7qlXAm8\nQ59mHkzqNviV13lTTtZ2aEml3At9VKTzVu3bSnqGnMndBucKinOM8ujJCPce3A0NYtamlSSmpTCx\n6yDOLPFiYtfBhQ6KczhXqlykoPhVciqF7L9yNs/j3mcPoyWV0qPx8w84NqYWrBjzJZlKBeN+/Zb4\nlCQUSiUH/c8x8qfZNPnfUH7w+ZOU9DQ+7TaEs0v+ZLzngHcWFGuCdLlIpRAEQbMVe45x69atad26\nda7nBg0apP65Xr16HDhw4I3vkybPTh/IyRF+HTnnOhYyMAYY3NqTwa09X93wBUPaePLTnr9Zd2QX\nH3fqV6TFQnHJiVwOuEWDKi6YGeeu2Xvi1CW+X74emY42GVkqzl+/hkqlKvRuXHsPnOSzL79Dt7qM\nDJR4uDV5qc3UnsNQKpXo6ugwvnP/Aq9XvpwRq8fPpveiz8hUKpjZ/yOcLO0IDHqM97+HcalRhY8/\n7M/3P23g51Wb0NPTZeXSmfTu4ZFrZk0ikfC/XsOxNTXn4oObfPvBxJcWMdZ2yK59fSMk/8D49uPs\njTlq2ee/W9yLPurQmw1H/+X3g94Mb9eDv0/uZdfF4zSqWovvR76bXc50dWRUsazEvbBHhX4vI+Of\n4nVsNzam5gxs2SnfdnMHjwdAR0uLCV0GYmFSMgte82JuUoFGVWtxKeAW0QlxuepTPwgP4XrwAzzq\nNnnp/4F2dRoxuetgftrzN4N/+JKohDgi47PTOVwdqzOkTRd6Nm5bohVlSjMxYywIgqYruVVub1la\nRvYv7KDI8Fe0zN+jqOxzCztj/LoMdPX5sH0vElKS2HRib4FtVSoVp89dISExe0b86I2LKLOy6FAv\ndw5laNgTPpm6AB1tbbb/tRx9dInPSGa7z6FC9enYyYtM+Oxb9PV1caprj5ZUSu08AkiJRMKXfUcx\npcfQQgVprpWr89fnC1k9fha17J3Q05Wx9pe5WFmasfCHNfQcOJmfV23Cwc6a3dtW0ruHR77XGtza\nkx8/nEY5vZdnM60rmFHRyISbwQ/yPT+ndFhhZowB9HX1mNprOOkZckatmM2yXRuxN7Ni7cSv3+ni\nqhq2jiSmpRARF12o9r/s3YI8M4PJ3YYU2E9tLS0WDJ3IN4PHv1dBcY7ODZqjUqnUFVhy5Cy669PM\nPa/T+LzXMJo7u3IjOIA0eToj2nXnwDer8J2zkg/adBFB8RsQgbEgCJpOYwLj1Gczxo+i3nzGuPKz\nFfjFaaRHT8rp6vP7ge2kZ+b/9eRW7wMMHP4/Jk1bypnz/hzyPw9kf9WcI12ewdhJc4mLS2Te7Im4\n1a+Ja/UaSHQkzPp2BWHhBS8GOnfhGh9NmIOWVMq6VfN59DSc6jaOb+0r5ubOrnRt2Er92NKiIutW\nzUdXpoP/tTu4t2nM3p2rqelcuJncvEgkEmo7VONxTCRxyYl5trkV8mzG2K7w9xnQoiPVrO25ERyA\nkb4B6z+d91K1kuJWo5IjAHcLkU4RERfDpuO+2JlZMqDlqze8eJ/lpOns8zutfi4rKwvv80cw1DOg\nY/3meZ6nJdVi3aS5eH06H79l/7Bg2KRCf0sgFEyUaxMEQdNpXGD8JjPGQc+C6uKeMYbsNINh7boR\nmRDL9jMH82zzJDKGuQt/RU9Pl6TkVAaOnMbBK+dwMLemms3z3N8581dy7cY9BvTpxNBB2fWBa9g5\nAJCsSGXKl0vIynq5OoVKpWLjP7sZ+tFXKLOyWPPLXCraVSAtQ05dx2rFMOrnXOvUYNO6JSyZ/zle\nvy8s1Ha+r1LnWTpFTgD8X7cfP0RPpoujZeE/+GhrafHt0IlUt3Fg9fhZJVKbNuee98IevbLtSt/N\nyBWZTO42JM/awaWJvbk1teydOHPnKompKQBcfHCTsKdRdG3YKtemIv9lqG+Ah2sTkT/8lolybYIg\naDqNCYzTMrID47DYqAJnYAvyKDIcEwPDQleKeFNjOvZBV1uHVfu2oVAqcx1TqVR8NedHEhKT+Xr6\neBZ9PZ4KDsZkZGWiiFYQG5sAwJYd+9m0ZQ+1XKqycO4UdXqDnZkVAPWauHD2/NXnm1c8ExefyNiJ\n3/DVnB/R05Xxx6/zcG/ThOtB97PPq1yjuIdP08auDB3U7a3Vra5t/yzPOI90igxFJg/CQ3CpVLnI\nm6y0cKnH0W/XFFjftjg5FzIwjk9J4p+T+3Awt6Zf85crOZRGnRu0IFOp4Oj17EorO16RRiEUTlBQ\nEL169aJ379706tULNzc3/vzzz1eeJ1IpBEHQdBoTGKc+yzFWqVSEROVdc7UgyiwlwdEROFrYvJNF\nVQCW5SsyoGUngqMj2H3pRK5jO/89wqGj52jetB5DB3XDubojnQe0BCD0+hM69hzLpi17mPH1ckyM\nDVmz8ptc5c9yNvlo37kpFU3Ls+iHNdx/8AiA8xev0aH7GPYePEXTRnU5uHsNHm2zc5ZzNvb4b6mr\n0iBnAd7NPGaMA8JDyFQqCp1f/D5xsLBGV0f2ylSKc3evkaHIpE8zjzfaPOZ9kpNOsf/KGdIzM/C9\ndBLrCmY0q1G0zU6E3CpXroyPjw87d+7E29sbfX19OnR49YcpkUohCIKm05jAOKcqBTxfRFcU4bHR\nZCgyi1SR4m0Y17k/WlIpK303q9MdoqJjmf3tSvT19fhhwf+QSqWoVCpO3r2CsYEh00aPIio6li9m\nLSNdnsGKH2bgYJ87PcDePHvG+GlaIt99+znyjEwmT1vE98vX03/YVKKinzJtyii2blyKrfXzTVeu\nBd1Hpq2D87O81tLEwdwaY/1yeS7Au1XEihTvEy2pFtVt7HkQHowyS5lvu9N3rgLQsmb9d9W1Yuds\n64iDhQ3HblzC9/JJEtNS6NXUvUR3x9Q0Z8+exd7eHmvrV++SKVIpBEHQdBrx10WhVJKhyFQ/fp2S\nbeqFd+84MHawsKZH47bcC3vEkesXUKlUzPh6OfHxicycNkYd8D56+oSwp1G4123ElAlD2bZxKS41\nqjD7y49p3+7lXb7U20JHP6Fzh5YM6NOJG7cesPyXjdhYm7Pj7+VM+WQYWlrP0wrkmRncfvwQl0qV\nS+WWthKJhFoOVXkYGUZyWmquY0WtSPG+qWHriFyRSXAB34acueOPga4e9as4v8OeFS+JRELnBs1J\nkacxf8vvAPRtln/lEqHo9u7dS9euXQvVVqRSCIKg6TQiMM7JL65U0RJ4voiuKJ6Xaiv+ihT/9UmX\ngQD87LuZXb7H2HfoNE0b1WXEBz3VbS4G3QaeV6No2tiVw3vWMu6jgXle08TAEBMDQ0KiswOpebMm\n0qRhHfr26sDBf9fQqEHtl865G/qITKUC18qlL40iRx37qqhUKnUgnCPnsbNt5ZLo1hvLWYB3N588\n44i4mOxtwqvXKfWL7v7L81k6RUxiPLXsnXCuVDrfw/dRZmYmR48exdOzcLXYRSqFIAiaTiMSEXMq\nUrjYVSb0aSSPXqMyhXpzD4t3O2MM2buGdarfjAP+57h39CF6err8sGharq+LLz66g7aWFm1rNyr0\nde3NrbgfHoJKpcLIqBze//xUYPvrj7IX3rk6Fv/Cu+Ki3ugj+AGNq2cH/yqVilshgTha2JTaGrbq\nwDg0iC5uLV86fiYnjcJFc9IocjSo4oKFiSlRCbH0aSpmi9+mkydPUqtWLUxNC1fHOi4uodRva10U\nZWmsOcramMvaeKFsjrkoNCowNjU0waqC2WvNGOcExlXecSpFujyDhIQkerm246D/OVIc5DRqWhML\ny+d/qJ7EPSUgKoyWLvUL3G76v+zNrbkRHEBUQiyW5Su+sv3VoGcL70rzjHHO1tAv7IAXERdDfEoS\nzZ1L74KtnFnS/CpTnLnjD2hWfnEOqVRK/xYd+Ou4L72ativp7mgUX19funXrVuj2EqkWbm5uxdij\n94efn1+ZGWuOsjbmsjZeKHtjfp0PARoRGOfsemegq4ejhQ0X7t8gPTMDvSLkyT6KCseknFGRS7Wl\nyzPYtecoNao5UqdWtVw5uy9SqVTcuPUA3/0nOHbyEjFP40hISCJd/sJiFiPQq2vApYjbtJ8zlu9G\nfEarWg04fC17U4//7nb3Kjkl20KinxQqML726D76Ml2qWdu/su37qoqVLfoyXW4EPw+Mb4eU3oV3\nOawrmGGsXy7PwFilUnH6tj+mhia4aGiawZd9RvFZz2FF+n9aKFhaWhpnz55l3rx5hT5H5BgLgqDp\nNCMwfjZjrC/To7KlLefvXSc4KrzQmzEos5SERD/B5TUWZi1eupY167cDYGJsSPMm9WjZwo2Wzerj\nVNmOq9fv4rv/JL77TxISmp3vq6crw8rKHCtLM0yMDTExMcLE2AhzswoMGtCZv87uZdW+rQxe+hVD\nWnuq85+LGhg7PFuAFxITQaNqtQpsmyZP535YMG5OLmjnE9yXBlpSLWraOXE16C5pGXL0ZbqlfuEd\nZC9Cq2HryJWHd1760BcUGUZEXAzdGrbW2GoNUqkUPakIit8mfX19zp8/X6Rzcn2QFwRB0EAaERjn\npFIY6OpS3jA7FeJREQLjsKdRZCgyi7zwLuBhCOs37sSukhWtmjfg1Nkr7Dt0mn2HsrewNTDQIzU1\nu2/lyunTu7sHXTu3pm2rRujr578j11d9R9PFrSVT1y3j75P7AHCoaKWuNFFYdubPZ4xf5WZIIFmq\nrFK98C5HbYeq+AXe5m5oEPWrOJfqUm0vqlHJkUsBt3gY8ZiaL4zltDqNol5JdU0oI8SMsSAImk4j\nAuMXUyly0geKUrJNXZGiiAvv5i1ajUKh5Ovp4/Hs2AqA4JBwTp+7wqmzV7h5O4AG9Vzo1rk1rVs2\nQk+38DNedR2r4zvnZ1b6bmal72Y6uBR917UXS7a9yrWchXfvYMe74vZ8a+gA6ldx5vbjh5iUM8K6\ngnkJ9+zN5FTUuBMWlDswvp0dGLfQwIV3wvslPV2OSqV6Z5sgCYIgvGsaERinvpBKkbNBR1EC49ep\nYXz81CWOHD9P86b16NzheZUAB3sbHOxt+GBg4Re05EemrcPnPYcxudsQrvr7F/l8W1NzJBIJwdGv\n3gnw2rOFd66lcMe7/8pZgHcjOICU9DQeRYXTrEbdUv/HvMazTVfuhQarn1NmKTlz9xq2FS1wtHj3\npQaFskeekVmkD/mCIAiliUYkJKY+q2Osr6uH47NZ0qLsfve8VFvhAovMTAXfLPgVqVTKNzM+KfaA\nS1tL67Xuoasjw7qCGY9jXj1jfP3RfYz1y2lEcFXNxh6Ztg43gwO4GxqESqUq1fnFOWrYOAC5K1Pc\nCgkkISWJli71S33gL5QOIp1CEARNphGBcZo6x1gPfV09rCuYFXHGOGdzj8LNGP+1eTcPAoMZ3L8L\ntVze77xVOzMrIuJikGfmv2gmMTWFwCeh1HGsphGLt2TaOjjbOnInNEidIlLa84sBTI1MsDAxzRUY\na+I20ML7TQTGgiBostIfBfF85zt9mS4Ajpa2hMdGq3OPX+VRVBjlC1mqLS4+kR9WeGFkWI4vPhv1\n+p1+RxzMrVGpVITFRufb5kZw6d/Y479qO1QlQ5GJz4VjANS0K/2BMUANWwdCn0aSlJYCwBl1frFY\neCe8GyIwFgRBk2lEYJz6wowxQOVn6QDBhUinUCizS7UVNoVg2Qov4uMTmTJxGGYVK7xmj9+d55Up\n8s8zvvboAYBGVKTIkZNnfCUwe8fAqtZ2JdyjtyNno4/7YcHIMzO48OAmNWwdsDAp3M5lgvCmRMk2\nQRA0mUYsvnuxKgU8T4l4FBWuDiTyE/Y0ikylolBpFPcfPMLr7104OtgyeljvN+z1u2H/LDAuqDKF\nJi28y/Fi6kQ1awd0NWRjCPXW0GGPyFAqSM+Qi2oUwjslZowFQdBkGhEYP69K8SyV4tnsb2HyjB9F\nFa4ihUqlYu6iVSiVWXw9fRwymc6bdPmdsTfLXoxYUGWKa0H3qWhkgm1Fi3fVrWLnYlcFLakUZVaW\nRiy8y5ETGN8Le0T4s/SYliIwFt4hERgLgqDJNCow/u+McWEC4/8uvPO/dodf12whKSmFdLkcuTyD\n9HQ5aelyHoc+oVVzNzq4Ny+OYRQL+1ekUtwJDSL0aSSd6jfXqKoG+jJdqlrbcy/sEbXsNScwrv5C\nZQp5ZgZSiZSmNeqWcK+EskSkUgiCoMk0KjDWl2UHxg7PZowLU7LtxVJtp89dYdS4Werd6iQSCXp6\nuujpydCVyXCpUYX5syeWqgDSwsQUXR1ZviXbtp4+AEDf5u3fZbfeiToOVbkX9khjFt4BlNPTx97M\nipvBAaTI03CtXB1jg3Il3S2hDBEzxoIgaDKNCIzTn+UY6z+bMdaX6WJjal7IVIrs4DnkfgSfTV2M\nSgVrVn5DB/fmaGu/Xv3g94lEIsHezCrPbaEzFJl4nzuCqaEJ7V2blEDvitcnXQZhW9GSZs6aNaPq\nXKkyB6+eA0QahfDuicBYEARNpjFVKaQSKbraz/N+HS1siIiLUdc4zk9QZBjlZPpM/mwhEqmUDb99\nS5dOrdHR0S71QXEOe3NrElKTiU9JyvX8kWsXeJqUQJ9m7si0S0fOdFFUs7FnWu8RaEm1Srorb1VO\nnjFAy5qiTJvwbqWni1QKQRA0l2YExhnpGOjq5Qpk1ZUpClh0plAqeRQVTkpMKnq6uvy9bgltWjUq\n9v6+azkl2/6bTrH1zEEABrTs9M77JLy+nMBYV0eGW9VaJdsZocxJl4sZY0EQNJdGBMZpcrl64V0O\ndWBcQDrFinWbyFJloaPUZrPX9zRppFlfuedwUC/Aex4YRyXEcvT6Reo4VNWoqg1lgXMlRwAaVa2F\nnoaUoRNKD5FKIQiCJiv2wPjkyZN07tyZTp068fvvv790PDk5mXHjxtGzZ0+6d++Ot7d3ke+RmpGu\nLtWWQ12yLZ8FeH9t3sPS370AGNzNk/quLkW+b2lhZ/ZyZQrvc0dQZmWJ2eJSqIatIzP7f8SM/h+W\ndFeEUiwpKYnJkyfj6elJ165duXbtWqHOSxOpFIIgaLBiXXyXlZXF/Pnz2bBhAxYWFvTr1w8PDw+c\nnJ5XCdi0aRPVqlVj9erVxMbG4unpSY8ePdDWLnzX0uTplC9nlOu5gkq2+fnfZta8FRjY65NGBo1r\n1XnNEZYO9ubZtYxzUilUKhVbTh1Apq1DrybtSrJrwmuQSCSM9xxQ0t0QSrkFCxbQpk0bVqxYgUKh\nID294PUYOUQqhSAImqxYZ4yvX7+Og4MDtra26Ojo0LVrV44cOZKrjUQiISUlBYCUlBTKly9fpKAY\nsne++28qhYOFDRKJ5KWSbTFP4/h48lyUyizadGgIgKNl4baDLq3sn80YBz9LpfB/eJcHESF0qt+c\nCobGJdk1QRBKQHJyMpcvX6Zv374AaGtrY2hoWKhzRSqFIAiarFgD48jISKytrdWPLS0tiYqKytXm\ngw8+ICAggJYtW9KzZ09mzJhRpHsolEoyFJkvpVLo6ciwrmCWa8ZYoVAy4bNviXgSzZefjyZDSwG8\nete70s5Q3wBTQxP1ttDPF911LMluCYJQQkJDQ6lQoQLTp0+nd+/ezJ49u/AzxiKVQhAEDVbii+9O\nnz5NzZo1OX36ND4+PsybN089g1wY/9317kWVLW158kLJtu+Xr+PMOX86tW/BJ2MHExQZhqmhCSYG\nhZspKc3szK0IfRpJqjyNXReOYVXBjNa1GpR0twRBKAEKhYLbt28zZMgQdu7ciZ6eXp5rQPIiFzvf\nCYKgwYo1x9jS0pLw8OepDJGRkVhYWORq4+3tzdixYwGwt7enUqVKPHz4kDp1Cs779fPzAyA2JRGA\n9JRU9XM5DKXZK/b3Hj9MxMMYVv72D9ZWZowc0olLly8REh1BVYtKL533vnqTfhpp6ZKhyOTbP1eR\nlJZKp5qNuep/9S327u0rLe/L21TWxlzWxvu+sLKywsrKSv17tlOnTqxdu7ZQ50ZEPCkz71tZGeeL\nytqYy9p4oWyOuSiKNTCuU6cOISEhhIWFYW5ujq+vL8uWLcvVxsbGhnPnzuHm5kZMTAyPHj3Czs7u\nlRdAcMEAACAASURBVNd2c3MDni+us7G0Vj+Xo3F0EAduXSRRqeDn37ajp6fLxrWLcalRhUdR4Siz\nsqhdufpL572P/Pz83qifrkFXOR1wnX+vnwFgcr8RVHmPU0jedLylUVkbc1kbL7w/f5DMzMywtrYm\nKCiIypUrc/78+VyLoguib2BQJt63svrvsyyNuayNF8remF/nd26xBsZaWlrMnj2b0aNHo1Kp6Nev\nH05OTmzevBmJRMLAgQMZP34806dPp3v37gBMmzaN8uXLF/oeaQWkUuQsqvtp/V8kp6Ty8w8zcKmR\nXbM3J6CurOEL73LkVKaIT0micbXa73VQLAhC8Zs1axb/+9//UCgU2NnZsWjRokKdJ3KMBUHQZMUa\nGAO0bt2a1q1b53pu0KBB6p8tLCz4448/Xvv6aRnZK6TzzDG2yA7+olPiGTWsF316tlcfu/HoQXab\nMhIg5lSmALHoThAEcHZ2ZseOHUU6RyqVinJtgiBotGIPjItbzuK7/1alALC3sAYVSA2kzPziYwBu\nhQSyeMc6jt24hI6WNnUdq7/T/paUnBljfZku3Rq2fkVrQRCEl+npyUS5NkEQNFrpD4wz8k+lSElM\nRSVXITPSISIhhh82bODfiycAaObsyld9R5eZGWPbihY0rFqTFi71MdQ3KOnuCIJQCunp6opUCkEQ\nNFrpD4wLyDE+e/H/7N15WJTl+sDx78wAM8MOKoioqLjvirvmhkuKG4L7rqWnk1rayZP281THMrNs\n08o6aamZWrmUYmlqirhLKu65K6igssg6wyy/PxCURFlkQGbuz3V5xTDvct8MvdzzzP0+z1FIN6PX\nGOjy+gSMJhON/Wrx75BxdGwQgEKhKOlwS42dSsWGWR+XdhhCiDJMo1FLK4UQwqqV+cI4u8dY4/Bw\nYRyx9wikAh5QzasSrw4cS1DAMzZVEAshRHHRaBxITi74PPNCCFHWlPnC+HEjxnv2H8HpjpqvP5xD\nu3pNsFOpSjo8IYSwGhq1mlu3Eko7DCGEsJhSX/nuST1quraYG3FcuhxN2xZN6NiwuRTFQgjxhKSV\nQghh7cp+YZx9893fWin27DsCQIe2suyxEEIUB43GgcxMA0ajsbRDEUIIiyjzhXHOdG3q3NO1Rez7\nE4D2bZuVeExCCGGNNPeuszIzhRDCWllNYfzgiLHZbGbPviOU83Snbu3qpRWaEEJYFY0muzCWdgoh\nhHXKtzC+efNmScRRZNmzUmgf6DG+cOkaN2Nv065NU5TKMl/7CyHEU0GjcQCQPmMhhNXKt2oMDQ1l\nypQp7Nu3ryTiKbS8Vr6T/mIhhCh+2nsjxukyYiyEsFL5FsY7duwgMDCQjz/+mN69e7Ny5UpSUlJK\nIrYCSc9j5buIe4Wx9BcLIUTxkR5jIYS1y7cwdnBwYMCAAaxZs4a3336b//3vf3Ts2JE5c+Zw586d\nkojxsdJ1OlRKJQ529gCYTCb2HjiKbyUvqlWtVMrRCSGE9ZBWCiGEtStQA25MTAwLFizglVdeoW3b\ntnz99deUK1eOCRMmWDq+fKXpM9A6aHJWszt1+gKJiXdp36aZrHAnhBDFSK2+VxhLK4UQwkrlu/Ld\npEmTOHfuHEOHDmXdunV4eHgA0Lx5czZv3mzxAPOTpsvI3Uax/15/cTvpLxZCiOIkrRRCCGuXb2Ec\nHBxM9+7dUeWxctymTZssElRhpOkycs1IsSd7/uI20l8shBDFSaZrE0JYu3wLY1dXV9LS0nBxcQHg\n7t27nDx5krZt21o8uILI0OvwcHYFIDPTwP5DUdSsUZWK3uVLOTIhhHh6de3aFWdnZ5RKJXZ2dvz0\n00/57iM9xkIIa5dvj/H8+fNxdnbOeezs7Mz8+fMtGlRhPNhKcTTqDGlpGTIbhRBC5EOhULBixQo2\nbNhQoKIYHhwxllYKIYR1yrcwNpvNuW5iUyqVGI1GiwZVUJkGA5lGQ86qdxE58xdLYSyEEI9jNpsx\nmUyF2ie7x1ink8JYCGGd8i2MnZycOHbsWM7jY8eO4ejoaNGgCur+qndZF+s9+/5EoVDQtnXT0gxL\nCCGeegqFgvHjxxMSEsIPP/xQoH1yWimkx1gIYaXy7TF+9dVXefHFF6lZsyYA58+fZ9GiRRYPrCCy\nV71zdNCQnp5B5JFTNKxfEw9311KOTAghnm6rVq3Cy8uL+Ph4xo0bR40aNWjRosVj98lppZAeYyGE\nlcq3MG7WrBlhYWEcPXoUgKZNm+Lm5mbxwAoi7d6qd1q1hkORJ9BnZspsFEIIUQBeXl4AeHp60r17\nd44fP55vYXz58qV7/71KZGSkxWMsbbaQ49/ZWs62li/YZs6FkW9hDODm5kanTp0sHUuhZejuLwe9\nR+YvFkKIAklPT8dkMuHk5ERaWhoRERFMnjw53/2aNmkCgLu7BwEBAZYOs1RFRkZafY5/Z2s521q+\nYHs5F+VNQL6F8ZkzZ3jjjTc4c+YMev39Gy5Onz5d6JMVt5wRYwc1l65cA6BBvZqlGZIQQjz1bt++\nzeTJk1EoFBiNRvr27UuHDh3y3U+maxNCWLt8C+M333yTl19+mXfffZevv/6alStX4uTkVBKx5Svt\ngRHjxKRkANzdXEozJCGEKFGbN2+mY8eOODs788knnxAVFcW0adNo2LDhI/epUqUKP//8c6HPJdO1\nCSGsXb6zUuj1etq2bYvZbMbLy4tp06axZcuWkogtX9mzUjiqNSQk3MXZyREHB/tSjkoIIUrOF198\ngbOzM1FRUURERDBgwADefvtti5xLq5aV74QQ1i3fwlipzNrEzc2NM2fOkJCQQEJCgsUDK4jsEWOt\ng5rEpGTc3WW0WAhhW+zssj7427NnD4MGDaJv377oLNTqIK0UQghrl29hHBQUREJCAhMnTmTYsGF0\n7tyZESNGlERs+copjNUaEpPuShuFEMLmKBQKNm/ezObNm2nbti0AmZmZFjlXditFurRSCCGs1GN7\njE0mE23btsXDw4OOHTty8OBBdDpdriWi8xMeHs7cuXMxm82EhIQwceLEXM8vWbKEjRs3olAoMBgM\nXLhwgf379+Pqmv9cxNmtFPZKO9LSMvBwfzqmkRNCiJLyf//3f3z99deEhoZSpUoVLl++TOvWrS1y\nLqVSiYO9vbRSCCGs1mMLY6VSyauvvsrGjRsBsLe3x96+4D28JpOJOXPm8O233+Ll5UVoaCiBgYH4\n+/vnbDNhwgQmTJgAwB9//MGyZcsKVBQDpN8bMTZmZi1rKq0UQghb07x5cz7//POcx9WqVWP27NkW\nO59G4yCtFEIIq5VvK4Wfnx/R0dFFOnhUVBR+fn74+vpib29PUFAQ27dvf+T2mzZtIigoqMDHz26l\nMOqNgMxIIYSwPfPmzSM5ORmDwcDw4cNp2rRpkWacKCiNRi2zUgghrFa+hXFqair9+vXj+eef56WX\nXsr5VxCxsbH4+PjkPPb29iYuLi7PbTMyMoiIiKBnz54FDP1+YazPyOqnk6WghRC2Zu/evbi4uBAR\nEYG3tzdbtmxh6dKlFjufRq2WVgohhNXKdx7jfv360a9fP4sHsmPHDpo3b17gNgqA9HsLfOjTs0Yv\n3KUwFkLYqEOHDtG9e3e8vb1RKBQWO49G40DynVSLHV8IIUpTvoVxcHBwkQ/u7e3N9evXcx7Hxsbi\n5eWV57abN2+mT58+BT52ZGQk0Teyjn3+r4sAJCXcseo1wK05t7zYWr5geznbWr6WUK5cOd544w12\n797NxIkTMRgMGI1Gi51PWimEENYs38J46tSpeY4+fPLJJ/kevFGjRly9epWYmBgqVKhAWFgYH374\n4UPbJScnc+jQIT744IMChg0BAQFo9/4CgKd7OQCaNm1stWuA2+L65raUL9hezraWL1jmjcCCBQv4\n5ZdfCA4Oxs3NjejoaMaNG1fs58mmVjuQkaHDbDZbdGRaCCFKQ76FcZcuXXK+1ul0bNmyJdesEo+j\nUqmYPXs248ePx2w2Exoair+/P6tXr0ahUDBkyBAAtm3bRocOHdBoNIUKPnu6trTkdEB6jIUQtsfT\n05ORI0dy6dIlzp07R/Xq1Rk4cKDFzqdRqzGbzej1majVDhY7jxBClIZCt1IMHDgwZ3q1gujYsSMd\nO3bM9b2hQ4c+dI6itGyk6TJQKZUkJaUAMiuFEML2HD9+nKlTp+Lg4IDZbMZgMLBw4UIaNGhgkfPd\nX/1OL4WxEMLq5FsY/51CoSA2NtYSsRRami4DR7WGxHuFsYeHjBgLIWzLO++8w9y5c3NWvdu3bx9z\n5sxh9erVFjlf9up3GRk63FwLvtiTEEKUBYXqMTabzZw9e5Z27dpZPLCCSNfr0DpoSLx9FwA3Vxkx\nFkLYlvT09JyiGKBt27bMmzfPYufTqO8XxkIIYW0K1WOsUqmYMGECTZo0sWhQBZWmy0Cr1pCQeBcX\nZyfs7Qs9AC6EEGWaVqvlwIEDOctAHzx4EK1Wa7Hz3W+lkMJYCGF9LDpdm6Wl6zLwdHYjITFBloMW\nQtikWbNm8dJLL+HgkFWwZmZm8umnnxZoX5PJREhICN7e3ixevLhA+9xvpZAp24QQ1iffle+GDRtG\nUlJSzuPExERGjBhh0aAKKl2vu9djnCw33gkhbFLjxo3ZunUrCxcuZOHChfz2229Mnjy5QPsuX768\nwLMMZcsujHU6KYyFENYn38I4LS0NNze3nMfu7u6kppb+qkeZBgOZRgNqO3vS0zNkqjYhhM2yt7en\ndu3a1K5dO2d2ivzcvHmTXbt2MWjQoEKdS3NvJgrpMRZCWKN8C2OTyUR6enrO49TUVAwGg0WDKoi0\ne8tB2ymzukFkOWghhMhSkIU35s6dy4wZMwq9SEdOK4X0GAshrFC+PcZ9+vRh3LhxDBs2DIBVq1bR\nr18/iweWnzRdVmGsUmTV9tJKIYSwJefPn3/kc/kNXuzcuZPy5ctTr149Dhw4UKjzZhfG6dJjLISw\nQvkWxpMmTcLLy4sdO3YAWYtzDBgwwOKB5Sd71TuFKWu0Q1ophBC2ZOLEiY98Tn1vSrVH+fPPP9mx\nYwe7du1Cp9ORmprKjBkzmD9//mP3i4yMJPbmDQDOnjlLZEXrvu5aYgnvp52t5Wxr+YJt5lwYBZrf\nrKgr01lS+r0RY4Up67HMSiGEsCXZgxVFMX36dKZPnw5kTe+2dOnSfItigICAAKJvZN2M7V3Rh4CA\ngCLH8LSLjIy06vzyYms521q+YHs5F+VNQL49xlOmTCExMTHncUJCAi+99FKhT1TcslspzIasm0zc\n3ax75EIIIZ4G93uMpZVCCGF98h0xvnbtGu7u7jmPPTw8uHr1qkWDKoj0ezffGfRGQFophBCiKFq1\nakWrVq0KvL3MSiGEsGb5jhgbjUaMRmPO48zMTPT60h8pyB4xNuqzbjKRVgohhLC8+wt8SGEshLA+\n+Y4Yd+jQgWnTpjF69GgAli1bRseOHS0eWH6yC+NMXVZh7CGtFEIIYXHSSiGEsGb5FsbTp0/nyy+/\nZN68eSgUCjp37kzr1q1LIrbHyp6VIiMt6+IsrRRCCGF5Go20UgghrFe+rRT29vZMnjyZzz77jMDA\nQH755RdmzZpVErE9VvasFNmFsZvMYyyEEBanUUsrhRDCej12xNhgMLB9+3bWrl3LsWPHMBgMLFmy\nhKZNm5ZUfI+U3UqRnpqOi7MTdnaqUo5ICCGsn7RSCCGs2SNHjOfOnUunTp1Ys2YNffv2ZdeuXbi5\nuT0VRTHcb6VIuZsubRRCCFFC5OY7IYQ1e+SI8Zo1a2jatCkTJ06kTZs2ACgUihILLD/ZI8YpyWlU\nruRVytEIIYRtUMt0bUIIK/bIwnj37t1s3LiR+fPnk5SUxIABA3JN21ba0u7NY6xP18uIsRBClBC1\ngz0KhUJaKYQQVumRrRSurq6MGDGCdevW8dlnn3H37l10Oh0jRoxg9erVJRljnrIX+MAI7nLjnRBC\nlAiFQoFa7SAjxkIIq5TvrBQAdevW5fXXXyc8PJyRI0eyfft2S8eVr+xZKTCBu4wYCyFEidFo1FIY\nCyGsUr7zGD/I3t6eXr160atXL0vFU2DZPcYYZQ5jIYQoSRq1AxkZ0kohhLA+BRoxfhql6XQoFUow\nSyuFEEKUJI1GTYZORoyFENanzBbG6foMHFRZA97SSiGEECVHK60UQggrVWYL4zRdBnaKrEU9pJVC\nCCFKjrRSCCGsVZktjNN1GajIKozd3aWVQgghSopGo0afmflUTeEphBDFoewWxnodCnPW19JjLIQQ\nJSd79Tu9PrOUIxFCiOJVqFkpiiI8PJy5c+diNpsJCQlh4sSJD21z4MAB3n33XQwGAx4eHqxYsSLf\n46bpM3A2aQHwdHcr9riFEMJa6fV6RowYQea9Ud+ePXsyefLkAu+vubf6XXqGDq1WY6kwhRCixFm0\nMDaZTMyZM4dvv/0WLy8vQkNDCQwMxN/fP2eb5ORk/vvf/7J06VK8vb2Jj48v0LENRiNmU9aQsaur\ns0XiF0IIa+Tg4MDy5cvRarUYjUaGDRtGx44dady4cYH2zx4xlj5jIYS1sWgrRVRUFH5+fvj6+mJv\nb09QUNBDi4Ns3LiRHj164O3tDYCnp2eBj2/MNOHq4oSdnapY4xZCCGun1WZ94qbX6zEYDIXaN6cw\nlinbhBBWxqKFcWxsLD4+PjmPvb29iYuLy7XN5cuXSUpKYtSoUYSEhLBhw4YCH9+gM8iMFEIIUQQm\nk4kBAwbQvn172rdvX+DRYgCNJquVQqZsE0JYG4v3GOfHaDRy6tQpli1bRlpaGkOHDqVZs2b4+fnl\nu68+I1PmMBZCiCJQKpVs2LCBlJQU/vnPf3L+/Hlq1qxZoH01ammlEEJYJ4sWxt7e3ly/fj3ncWxs\nLF5eXg9t4+HhgVqtRq1W06JFC86cOVOgwtiYaUKpNhMZGVnssT+NbCXPbLaWL9hezraW79PI2dmZ\n1q1bs3v37nwL4+zXKyHhDgBRUccxG9MsHmNpscXfT1vL2dbyBdvMuTAsWhg3atSIq1evEhMTQ4UK\nFQgLC+PDDz/MtU1gYCBvv/02RqMRvV5PVFQU48aNK9gJjOBXtTIBAQEWiP7pEhkZaRN5ZrO1fMH2\ncra1fOHp+YMUHx+Pvb09Li4uZGRksHfv3jxnDPq77Ndr3+GzAFT187Pa19BWfz9tKWdbyxdsL+ei\nXHMtWhirVCpmz57N+PHjMZvNhIaG4u/vz+rVq1EoFAwZMgR/f386dOhAv379UCqVDB48uMAf52Ey\ny+IeQghRSLdu3eK1117DZDJhMpno3bs3nTp1KvD+91sppMdYCGFdLN5j3LFjRzp27Jjre0OHDs31\neMKECUyYMKHwBzeCu5v0GAshRGHUqVOH9evXF3l/ma5NCGGtyuzKdwCYkFkphBCihOXMSiHTtQkh\nrEzZLoyNSCuFEEKUMGmlEEJYq7JdGMuIsRBClDhppRBCWKuyXRhLj7EQQpQ4tVpaKYQQ1qlsF8Yy\nK4UQQpQ4WflOCGGtynZhbJRWCiGEKGlaWflOCGGlynZhbAI3V+fSjkIIIWxKTo+xtFIIIaxMmS6M\nXbROqFSq0g5DCCFsSnYrRbq0UgghrEyZLow9XKS/WAghSprMSiGEsFZlujCWGSmEEKLkyTzGQghr\nVWYLY7PJjKebW2mHIYQQNkd6jIUQ1qrMFsayuIcQQpQOOzsVdnYqaaUQQlidslsYy3LQQghRajRq\ntbRSCCGsTtktjE3SYyyEEKVFo1Gj08uIsRDCupTdwlgW9xBCiFKj0TjIiLEQwurYlXYARWaSVgoh\nhCiKmzdvMmPGDO7cuYNSqWTQoEGMHj26UMfQqNUkJN61UIRCCFE6ym5hbDTj7iaFsRBCFJZKpWLm\nzJnUq1eP1NRUBg4cSPv27fH39y/wMTQaB5mVQghhdcp4K4VM1yaEEIVVoUIF6tWrB4CTkxP+/v7E\nxcUV6hgajdx8J4SwPmW3MJZWCiGEeGLR0dGcOXOGxo0bF2o/jUaN0WgiM9NgociEEKLkld3C2Ii0\nUgghxBNITU1l6tSpzJo1Cycnp0Ltq1E7ALL6nRDCupTZHmOFCdxcnUs7DCGEKJMMBgNTp06lf//+\ndOvWrUD7REZG5nydnp4GwIGDh/Cw0k/vHszXVthazraWL9hmzoVRZgtjtZ0DKpWqtMMQQogyadas\nWdSsWZMxY8YUeJ+AgICcr318fgeiqFOnLlUqV7RAhKUrMjIyV762wNZytrV8wfZyLsqbgDLbSqFV\nq0s7BCGEKJMiIyPZuHEj+/fvZ8CAAQQHBxMeHl6oY2juXYOllUIIYU3K7Iixo1pb2iEIIUSZFBAQ\nwOnTp5/oGBrNvR5jmbJNCGFFyuyIsbPWsbRDEEIIm3X/5jtZFloIYT3KbGHs6li4O6iFEEIUn+xW\ninRppRBCWJGyWxgXcmohIYQQxSenlUIKYyGEFbF4YRweHs6zzz5Lz549+eqrrx56/uDBg7Ro0YLg\n4GCCg4P5/PPPC3Rcd1frnB5ICCHKAo3m3s13OmmlEEJYD4vefGcymZgzZw7ffvstXl5ehIaGEhgY\niL+/f67tWrRoweLFiwt1bA9X1+IMVQghRCHkFMYyYiyEsCIWHTGOiorCz88PX19f7O3tCQoKYvv2\n7cVy7HJu7sVyHCGEEIUn07UJIayRRQvj2NhYfHx8ch57e3sTFxf30HZHjhyhf//+TJw4kfPnzxfo\n2OU9PIotTiGEEIUj07UJIaxRqc9j3KBBA3bu3IlWq2XXrl28+OKLbNmyJd/9vDylMBZCiNJyv5VC\neoyFENbDooWxt7c3169fz3kcGxuLl5dXrm2cHphdolOnTrz11lskJibi7v74Vonk+ASbW+9b8rV+\ntpazreVrTaSVomAiL5ymSbXa2KlUpR2KEKIALFoYN2rUiKtXrxITE0OFChUICwvjww8/zLXN7du3\nKV++PJDVkwzkWxQD9OjaBScn21n9zhbXN7elfMH2cra1fMG63ghIK0X+9pw+ypD3ZzA5aCivhYwv\n7XCEEAVg0cJYpVIxe/Zsxo8fj9lsJjQ0FH9/f1avXo1CoWDIkCFs2bKFVatWYWdnh0aj4aOPPirQ\nsW2pKBZCiKdBfHISni5ugLRSFMT+s1mDPct2/MKLvYfgopX594V42lm8x7hjx4507Ngx1/eGDh2a\n8/WIESMYMWKEpcMQQjyFEhMTGTt2LAqFglu3bqFUKvH09EShUPDjjz9iZ5f/JWrWrFlMnDiRatWq\nPXKblStX4ubmRp8+fYoxettz5NJZAhu3AmS6toI4dvkvAJLT01i9+zee7xFSyhEJIfJT6jffCSFs\nl7u7Oxs2bABg0aJFODk5MW7cuIe2M5vNKBSKPI8xd+7cfM8jb76Lx9GLZ+4XxuqsVgqdLPCRJ7PZ\nzLFLZ6ng6kFKRhr/27qOsV37Y1+AN3tCiNJTZpeEFkJYr6tXrxIUFMS//vUv+vTpw61bt/jPf/5D\naGgoffv2zbVC5vDhwzlz5gxGo5GWLVuyYMEC+vfvz9ChQ4mPjwfg448/Zvny5TnbL1iwgEGDBtGr\nVy+OHj0KQHp6OlOnTqVPnz5MnTqVkJAQzpw5U/LJP8WOXLz/87i/8p2MGOcl5k4cd5KTaFmrAUOf\neZbr8bfYdDi8tMMSQuRD3roKIQCYM28xm37bVazH7PNsJ2a/9o8i7Xvp0iXef/996tevD8C//vUv\nXF1dMRqNjB49mp49ez60imZycjKtW7fmlVdeYd68eaxdu5bnn38+z+P/+OOP7Nixg0WLFvH111+z\nYsUKKlSowKeffsqZM2cICZGPvf/u6KWzOaP30mP8eEcvnwWgSfU69G3ZkWU7NrL4tx8Z0LrLIz/9\nEEKUPhkxFkI8lapUqZJTFANs3LiRgQMHEhwczMWLF7lw4cJD+2i1Wjp06ABkzZEeExOT57G7d++e\ns032lJJ//vknvXv3BqBu3brUrFmzWPN52syaNYt27drRt2/fAu+TmJrM5bisn1d2K4X0GOft6MWs\nwrhp9TpUreBDUItnOHn1AntOHy3lyIQQjyMjxkIIAGa/9o8ij+5agqOjY87XV65cYfny5axduxZn\nZ2deffVVdHl8hG9vb5/ztUqlwmg05nlsBweHfLcxm81PEv5Tb+DAgYwaNYoZM2YUar+jl85S3ds3\na9RY7SCtFI9w7PJfKBQKGlerBcCkZ0PZeGgXX/z2Ix3qNyvl6IQQjyIjxkKIp9KDhWlKSgrOzs44\nOTkRFxdHREREvvsUVvPmzfn1118BOHv2LBcvXizyscqCFi1a4OrqWuj9/t5nLK0UDzOZTBy/fA7/\nipVzpmhrWr0Obeo0ZteJw5y6Zt2/W0KUZVIYCyGeSg/2YTZo0AB/f3969erFzJkzcy0M8uB2Bend\nfNQ2I0eOJC4ujj59+vD555/j7++Pi4vLE2RgfexUKo5eOpvzWKNxIDkl9ZGj7rbqws1oUjLSaFKt\nTq7v/6NnKAD/27q2NMISQhSAtFIIIZ4KkydPzvm6atWqrF+/Ptfz8+fPz3O/lStX5nx98ODBnK97\n9+6d0zP88ssv57l9+fLl2bJlCwBqtZoPPvgABwcHrly5woQJE/Dx8XmCjKxP/co1OHnlPHpDJg52\n9vjXqMqefUfoP2Qq77/zCvXq1MhzP4PRyM/7/uDDn1agNCn47d3PcXJyzHNba3DsUvaNd7Vzfb9r\n41bU9KnChv1/MGPgOHw8ypdGeKIIzGYzZ2MuU8e3mtw8aeWkMBZCCCAtLY0xY8bkjH7OmTMHpVI+\nVHuQr4snUYZzrPt9M7W8KjNpbD/slLBrzxF69p/IwL6dGTKwGw4OWb3ed9NT2XhkD5uO7SXNdL8X\nuUPwaF7/xxiq+1UqrVQKpKhLeP9+MKvVR6MzP3SMZ+u2ZNGNdbz73WLGtOv1xDEWN2tatrwgCprv\nttOHWbhjLc916EPfJu0tHJVl2dprXFhSGAshBODi4sK6detKO4wSVdie7B6tn+HXEwfIcFDk406Z\nWAAAIABJREFUtLMEdu3Ejl0HeO0/H/Hjhh0cPvoX018dzbbzBwiL3I3RbMJsNKNJsqd906bsuHqY\nOHMiM2Yv4j+z/smY4f2eyhG4yMjIXC07hRGz+VvsVCoG9ghCY++Q67kGjRuxJvIPfj9zmHeem/ZU\nLRP9JDmXRQXN12A0MvWHTwH4OWoP/x4xEa1aY+nwLMIWX+PCkuEQIYSwQa+88gpDhw7l0qVLdO7c\nmbVr8+97bVq9LgBHL+Ve+KRrp9b8sfkbnhsbwuWr15nyzXv8cngXhjQjzrEa/t1hLMe/X8uXs97A\nUa3Bu145tE4aXn/zE55/8Q0SEu9aJMfSoDdkcuraRepVrv5QUQygsXdgfLcBJKensWb3llKIUBTW\nzwf/4MqtG7g7uXDrbgIrd20u7ZCEBUlhLIQQNmjBggVERERw4sQJdu7cWaAFTbJmWXDMNTNFNicn\nLW+9/iJzPpiMQqtAm+zAnN7/5Oi6n5j6/AgcHbVoHdQENm5NXEoCn385m7atmvDr7xH06DeRsN/C\niTxyihOnznP+wlWuRd8k7lY8GWVsyekz0ZfQGzJpUr3OI7cZ0ak3dioVP+zZWoKRiaIwmUx8FrYG\nlVLJ96+8i5Nay+e//kC6XqYptFbSSiGEEKJAlEolTarVIeL0ERJTk3F3enjWjgPXTgCw+t35BNSs\n/9DzvQM6sPHQLg5ePsma5R/w6ecr+XDRciZOeTPPc2rUDgT16sTwQb1p3bJxkdou0tMzuHLtBrVr\n+lm8b/zYpb8AaFKt9iO38XRxo2ujVmw9uo/T0ZeoV7m6RWMqSZsOhRObeIcJ3YNLO5Ri8duRvfx1\n/QqD2/egcbXajOvWn0Vhq1m5azPPWUmOIjcpjIUQQhRYsxp1iTh9hKjLf9GxQe5exTt3E9lyZC91\nfKvR3L9envt3bdwKtb0Dmw/v5tXgMUybMprOHVuyY9cBdDo9GTo9Ol0mOp0evV7PsRN/sXbD76zd\n8Ds1qldm+KDehAb3oEJ5z3xjTU5OZdn3P/PV0p+4E59I7Zp+TBo/mOB+gajVD7c5FIfs6eya1qj7\n2O0Gtgtk69F9rN27jf8bnPey5WXNjYTbvLzkfTL0OlrXbkRDv7K9eqTZbObTjd+jUCh4sfcQACb2\nCOGbbT/z+eY1jOjUG62DupSjFMVNWimEEKVm9OjR7NmzJ9f3li1bxltvvfXIfZo1y1o1LC4ujpde\neinPbUaNGsXJkycfe+5ly5blWj1v0qRJpKSkFDR0m9W0RlaLQF7tFD/u/Z1Mo4HhHXs9cmTXSaOl\nc8MWnLtxlXPXrwLQrEk9Xpk6llmvTuS//zeZ9+ZM4+P5/+bzj2cT8ftyfvzuQwb260ZMTCxvz/+K\nFs8MYeyk1/ly6Y9EHjmF7m/tFgmJd1nw6be07jyMdz/4mszMTLp1acPFy9G8Mut92nQZzqdfrLRI\nb/Oxy3+hdVBTy6fqY7fr1qQNrlon1u/fgdFkHfNAf/TzCjLutRgsDFtVytE8uT+OH+LE1fP0adER\nf58qQNZo/9jAfsQlxfO99BpbJSmMhRClpm/fvmzatCnX9zZv3kxQUNAj98kuuLy8vPjkk0+KfO5l\ny5aRnp6e8/jLL7/E2dm5yMezFU3v9c4+uNAHZI2urQr/FbWdPQPbBj72GL1bdABgc+TufM+nUCho\n17opCxfM4s+9PzFn9mRq+fvx+459/PfdL+g3eDL1mvdlwNCpvP3el8yZt5g2nYfz4cLlKFVK/j19\nAgd2rWLZV3PZu2Ml/5gwmLS0DN77cAktOw5l9n8Xcut2/EO5JKUX/k1Smi6dv2Ku0MivFnYqFcAj\nFz/R2DvQp2VHYhPvsPf0sUKf62lz4cY11kRsoaZPFRr51WRzZATnb1wt7bCKzGw2s3BTVnE/JWho\nrucm9QzFUa3h881ryMgsWz3wIn9SGAshSk2PHj0IDw/HYDAAEBMTw61bt6hfvz5jx45l4MCB9OvX\nj+3btz+0b0xMDH379gVAp9Mxffp0goKCmDx5Mnr9/T9Wb775JqGhofTt25dFixYBsGLFCuLi4hg9\nejRjxowBoGvXriQmJgLwzTff0LdvX/r27cuyZctyzte7d29mz55Nnz59mDBhQq7z2Apv93JU8qzA\nkYtnck33dvDcCS7cjKZXiw54OD9+qenuTdpir7Ij7HD+hfGD3N1cGD96IL9v/B/7dqxk0YLXGTuy\nP7Vq+hF55BRffL2GxUt+wNFRw39mvsDBnauY+sIIXF2y3vD4+ngx+7V/cHj3Gv4z8wU83F1ZumI9\nHbqNZtGX35Oh03MxNoZhH7zG6KXv8N3OsJxz301OYfGSHxg+bgbfrFif502Bx6+cx2Q20aR6bW7G\n3ua1/3yEf6NeNGkTwpAx/+KNdz5j1Y+bOXLsNGlp6YS06wbA2n3bCvVzeBq9t+4bjCYT/x44jil9\nhmM2m/l88w+lHVaR7f/rOIfOn6R70zbUr+qf6zlPFzfGdu1PrIwaWyXpMRZCADDnh68IO1S4QiU/\nQS2fYfbgiY983s3NjUaNGhEeHk7Xrl0JCwvj2WefRaPR8Nlnn+Hk5ERCQgJDhgwhMPDRo5CrVq1C\nq9USFhbG2bNnGThwYM5z06dPx9XVFZPJxJgxY+jRowejRo3i22+/ZcWKFbi5uQH3R6JPnjzJ+vXr\n+emnnzAajQwePJjWrVvj4uLC1atX+eijj5gzZw4vv/wyW7ZsySnObUmzGnUJO7ybmDtxVC7vDcD3\nu34FYHjH3vnu7+roxDMNmrMj6iCXYmOo7u1bqPMrFAqqVvGhahUfgvtl/V6kpqZz9PgZ4hOS6Nal\nLVrNo3s/XVycmDR+EONHBbPyhzA++OQb3l3wNZ//9gNp5XQYTEZUSiWzV35Gea07B3ZE8f2aMFJS\n0wDYFXGYhYu/5x/PDWbU0L5otVlz2maveHflZAzt3xlFRoaOyr7eKBQKIvb+ScTeP3PlUNO/Ck6+\nGn45sIuRLXrTtGE97OxUhfpZPA3+vHCazZERNPevx7PN22M2m6nlU5V1+7czvf+onN+RsmThpu8B\nmBI0LM/nJ/UM4dsdP/PZ5jUM79Q7z6n5RNkkI8ZCiFIVFBREWFjWyNzmzZvp06cPJpOJBQsW0K9f\nP8aNG0dcXBx37tx55DEOHTpEv379AKhTpw516tyfKissLIyBAwcyYMAALly4wPnz54Gsj0rzWuAi\nMjKS7t27o1arcXR0pHv37hw+fBgAX1/fnGM3aNCAmJiY4vkhlDHZ7RTZfcaJqclsOhxOdW9f2tZp\nXKBj9A7IbqeIKJaYnJy0tG/TjL69Oj+2KH6Qvb0dY0f055MvZ+LRxZ27HmlkphuoqfMltGZnMo0G\nnvv0Tb5a8SOOjhpee+U5wrcuY/KkYaSkpvHW3C9o02UEX/xvNbfvJPDjtt8B2LJhD+5uzsx/ezoR\nv69g/x/fc/bIJn75YRHz357OhNEDadOyMddv3CLlchp6Yyb9//kSdZr1IXjYS7wz/yu2bt9LfEJS\nsfxsHqTT6UlMSiYtLR29PrPQi7z8ndlsZu5PSwCYFToBhUKBUqnkxaChGIxGvvjtx0fue+78Ff49\n+yNGjP83y1b+THx88edbFEcuniH85J90qNfskTeRlnN1Z0zXfsQm3mFV+K8lHOHTx2w289WWn5i9\n8jPSdRmlHc4TkRFjIQQAswdPfOzorqUEBgYyb948Tp06RUZGBvXr12f9+vUkJiayYcMGlEolXbt2\nzXWjXEFFR0fzzTffsG7dOpydnZk5c+YTtT84ONwfFVKpVEWKyRo0q5G90MdZ+rbqxPr9O9Bl6hn2\nmJvu/q5ns3b8e9nHbI6MyLnjv6TFJyfxzo9fsyZiCwqFguCWXUk8lsSO7Qc4d/AKVFagqK7Cv7cf\nm//7Gc6OjgDM/NfzTJowmK+/XcvS5et5e/5XvD3/K2ihQmmv4PWpExk7KjhXge7s7EhAs/oENLs/\nhZ3RaOSPQ4cY+9V/qNTEC7doJw7/eZKDh4/D/7K2qeXvR6sWDWnRvCGNGtSiRrXKhZ5RIy0tnW07\nD7Dp153s2HWQ9PTchYtKpcTe3p5ynq50aBtAy4CGtGjWgJr+VfN9Pf84foj9Z6Po2rgVbR54U9S/\nVWcWbFjG6vBfeanvcLzcsmYRMZvN7Nl/hC+X/MiOXQdytt+5+xD/eXsRnTq0JLhvID27tcPRUVuo\nPPMSn5BEdEwsXhU88argWaDp+nJ6i/vkPVqc7R89Q/l2+88sClvNsI69inXU2GQycfnWDU5dvcDp\n6Es4a7TUqFgZ/4qVqVrBBwc7+2I715MyGI3MWvEp3997g/DnhdMsnfoW3u7lSjmyopHCWAhRqhwd\nHWnVqhWzZs2iT58+ACQnJ+PpmfVHbP/+/Vy/fj1n+7xGuFq2bMnGjRtp3bo1f/31F2fPZn2knZKS\ngqOjI05OTty+fZvw8HBat24NgLOzMykpKbi7u+c6bosWLZg5cyYTJ07EaDSybds23n//fYv+DMqa\nRn61UCqUOX3G3+/6FTuVikHtuhf4GB7OrrSr24Tdp44QfTu2RD9uzzQYWLbjFz765TuS0lKoX6UG\n7415Oafgj9j3J999v4Ghg/qw6vgWwiJ38/6GZbw1/IWcY3h6uDFj2ngmTRjM0mXr2LR9F2e112hb\npykvPD/0UafORaVS0a1NG5r+Xoeoy+fYtGIRzvaO/HnsNIcij3Pw8Akij55k5ZorrFwTdm8fJX5V\nK1G7ph+1alajtr8f5cu5o9Go0WjUaO/9185OxcHDx9n0Wzg7dh0gIyPrTVz1apWp5V8Vg8GALtNA\nijGVu6ZUUkgn7lYiazb9xpq1vwHg7u5KQNP61K/nT0Wvcnh7lcPbqzze3uXwKu+JSqVk3tqlKBQK\nXgsZn5OX2WzGbDIzITCYN9csZuEvq3ixx5Ccgvjk6axPbVoGNGTS+EE0blibjb/uYsPG7WzfuZ/t\nO/ej0arp3KUFAW0a4FPNi1t344m5c4vrCbdIzUijagUfqnv5Ut0761/VChVJvptK1Im/OH7yHCdO\nniPq5F9Ex8TmxOVgb08lnwpUquRF5UreKBVGTv11HW+vclT0Lo9XBU9Ox15i69F9tKhZn3Z1mzz2\n9Svn6s7YwP588esPDJw7jed7htCnRUfs7R5fWhmMRpLTU0lMTeZuWipJaVn/TUi5y9nrVzh55Tyn\nrl0kVZee5/4qpZIq5StSo2JlQtoG0q9V51JbVj1dr+PFxXPZenQfDavWpLavH+v2bafv21NZ9vLb\nZXKObimMhRClLigoiClTpvDRRx8BWbNVvPDCC/Tr14+GDRvi73//5pe8/gAMGzaMmTNnEhQUhL+/\nPw0bNgSgbt261KtXj169euHj40NAwP15dwcPHsxzzz2Ht7c3y5Ytyzlu/fr1CQ4OJjQ0NGe7unXr\n2mzbRF6cNFrq+FYj6so5Ii+c4nT0RXoHdKCCm0ehjtM74Bl2nzrCr39G8HyPh1feM5vNZBoNxTo6\ntiPqIG+tXsyFm9G4OTrz5rAXGNu1X84sEgAd2jZH62AmICCAFi0bcu7GVZZsW0+T6rUfmnHDzdWZ\naVNG06xLfUZ+OIsWtfL+6P1xQtp14+ils/x84A8m9gzlmXbNeaZdcwAMBiPfb93Mf9d/ibudC16J\n7lz+K4bfLkXz2+978jlyFv8aVXi2RwfqNKtGkimVk1cvcOraBc7GXEFvyLy/YRVQVLGjsqsX7pku\n3DmbkFOo/p1CocC+kj2ZNUwob0Hvnv/AZDJhMj3QoqQAWqlY+vsGls5dC4asRWL69u7MxHGDaN60\nHrpMPSeuXqB8XQ+6erSh3HlPTl6+wJ20RLYkH2DL7wceOneezGDWm8GY9TUmsK+owqOGO26OLlQ0\neZIRm8H1G7fYu/9ozm6r19678VEFVFOiqKQEMxgvGvjk8+9o1rguTRrXxd3t4cVsIKsH+XLcdX77\ncw9TvprHOz9+zdiu/RjRqXfOTaj6zEz2n45ix9GD7DsXxdkblzE8Zoo+lVJJTZ8qNKhakwZV/KlX\npTppugwu3IzmYva/2Gh2RB1kR9RBthzZy9xRU/NccOdxTCYTOn0men0marUDmgc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8AAAG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\nWBhLkiRJgIWxJEmSBFgYS5IkSQD8AAUa15j8gQWbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f017a09c9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d1a_history5) # rmsprop, sigmoid, with l2 = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 18 , val_acc: 0.923913043478 , val_loss: 0.227474954919\n"
     ]
    },
    {
     "data": {
      "image/png": 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sX758OXp6egwbNuyejxkUFPSYvRbi7pRKJYnJ6Vy4GElaehZ6ejro6Wqjr6eL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\niAigxFhEREREBID/D90a+JzFmXs/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f016521b9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d1a_history6) # sgd with lr = 0.0001, sigmoid, with l2 = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 47 , val_acc: 0.921739130435 , val_loss: 0.208093595116\n"
     ]
    },
    {
     "data": {
      "image/png": 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NDRYWFkBZt/2JEyfKBcZRUVHyXmR7e3uSkpLIyspCX18fmUyGVCqtzioKwis7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\nFSg6bioIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0160d96910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WINNER\n",
    "plot_metrics(d1a_history6) # sgd with lr = 0.0001, sigmoid, with l2 = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 9 , val_acc: 0.919565217391 , val_loss: 0.223592086201\n"
     ]
    },
    {
     "data": {
      "image/png": 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SnoogCILSiMD4I7fi0HYmb/2ZXou+43loYGlPR/gPSk1LY/jqOXz16/yPosak8HHo1f0z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dZ0jtWNdKU8C5JrJde2LV6zGDHW6/UsWbKENWvWsG3bNpKTkzl//nxnd6tdTZkyhTVr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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f017a362f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(d1a_history7) # sgd with lr = 0.001 and decay 1e-6, sigmoid, with l2 = 0.001"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG16 Model with partial weights loaded.\n",
      "Found 1840 images belonging to 2 classes.\n",
      "Found 460 images belonging to 2 classes.\n",
      "Epoch 1/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.5562 - acc: 0.7773Epoch 00000: val_acc improved from -inf to 0.86304, saving model to data1a/ft_model.h5\n",
      "1840/1840 [==============================] - 180s - loss: 4.5560 - acc: 0.7766 - val_loss: 0.3395 - val_acc: 0.8630\n",
      "Epoch 2/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.4936 - acc: 0.8095Epoch 00001: val_acc improved from 0.86304 to 0.88261, saving model to data1a/ft_model.h5\n",
      "1840/1840 [==============================] - 181s - loss: 4.4931 - acc: 0.8098 - val_loss: 0.2819 - val_acc: 0.8826\n",
      "Epoch 3/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.4764 - acc: 0.8193Epoch 00002: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.4754 - acc: 0.8201 - val_loss: 0.3318 - val_acc: 0.8674\n",
      "Epoch 4/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.4490 - acc: 0.8313Epoch 00003: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.4485 - acc: 0.8315 - val_loss: 0.3328 - val_acc: 0.8674\n",
      "Epoch 5/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.4440 - acc: 0.8362Epoch 00004: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.4433 - acc: 0.8364 - val_loss: 0.2939 - val_acc: 0.8783\n",
      "Epoch 6/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.4324 - acc: 0.8422Epoch 00005: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.4319 - acc: 0.8429 - val_loss: 0.3385 - val_acc: 0.8522\n",
      "Epoch 7/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.4221 - acc: 0.8433Epoch 00006: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.4213 - acc: 0.8440 - val_loss: 0.2767 - val_acc: 0.8783\n",
      "Epoch 8/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3999 - acc: 0.8499Epoch 00007: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3992 - acc: 0.8505 - val_loss: 0.3544 - val_acc: 0.8435\n",
      "Epoch 9/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3918 - acc: 0.8597Epoch 00008: val_acc improved from 0.88261 to 0.88913, saving model to data1a/ft_model.h5\n",
      "1840/1840 [==============================] - 181s - loss: 4.3910 - acc: 0.8603 - val_loss: 0.2656 - val_acc: 0.8891\n",
      "Epoch 10/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3890 - acc: 0.8646Epoch 00009: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3882 - acc: 0.8652 - val_loss: 0.3126 - val_acc: 0.8587\n",
      "Epoch 11/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3973 - acc: 0.8433Epoch 00010: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3978 - acc: 0.8429 - val_loss: 0.3094 - val_acc: 0.8783\n",
      "Epoch 12/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3729 - acc: 0.8537Epoch 00011: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3723 - acc: 0.8543 - val_loss: 0.2948 - val_acc: 0.8696\n",
      "Epoch 13/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3754 - acc: 0.8472Epoch 00012: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3753 - acc: 0.8478 - val_loss: 0.2852 - val_acc: 0.8848\n",
      "Epoch 14/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3698 - acc: 0.8504Epoch 00013: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3692 - acc: 0.8511 - val_loss: 0.2815 - val_acc: 0.8804\n",
      "Epoch 15/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3396 - acc: 0.8690Epoch 00014: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3391 - acc: 0.8696 - val_loss: 0.3033 - val_acc: 0.8761\n",
      "Epoch 16/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3423 - acc: 0.8608Epoch 00015: val_acc improved from 0.88913 to 0.90870, saving model to data1a/ft_model.h5\n",
      "1840/1840 [==============================] - 181s - loss: 4.3411 - acc: 0.8614 - val_loss: 0.2635 - val_acc: 0.9087\n",
      "Epoch 17/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3366 - acc: 0.8766Epoch 00016: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3357 - acc: 0.8772 - val_loss: 0.2555 - val_acc: 0.8804\n",
      "Epoch 18/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3213 - acc: 0.8750Epoch 00017: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3205 - acc: 0.8755 - val_loss: 0.2883 - val_acc: 0.8739\n",
      "Epoch 19/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3396 - acc: 0.8630Epoch 00018: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3390 - acc: 0.8636 - val_loss: 0.2848 - val_acc: 0.8848\n",
      "Epoch 20/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3309 - acc: 0.8630Epoch 00019: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3316 - acc: 0.8625 - val_loss: 0.2586 - val_acc: 0.8913\n",
      "Epoch 21/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3077 - acc: 0.8679Epoch 00020: val_acc improved from 0.90870 to 0.91304, saving model to data1a/ft_model.h5\n",
      "1840/1840 [==============================] - 181s - loss: 4.3072 - acc: 0.8685 - val_loss: 0.2141 - val_acc: 0.9130\n",
      "Epoch 22/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3062 - acc: 0.8695Epoch 00021: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3059 - acc: 0.8696 - val_loss: 0.2792 - val_acc: 0.8783\n",
      "Epoch 23/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3028 - acc: 0.8695Epoch 00022: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3026 - acc: 0.8696 - val_loss: 0.2632 - val_acc: 0.8913\n",
      "Epoch 24/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2880 - acc: 0.8783Epoch 00023: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2872 - acc: 0.8788 - val_loss: 0.2775 - val_acc: 0.8826\n",
      "Epoch 25/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2839 - acc: 0.8783Epoch 00024: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2835 - acc: 0.8783 - val_loss: 0.2438 - val_acc: 0.9000\n",
      "Epoch 26/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.3013 - acc: 0.8690Epoch 00025: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.3005 - acc: 0.8696 - val_loss: 0.2715 - val_acc: 0.8848\n",
      "Epoch 27/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2759 - acc: 0.8854Epoch 00026: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2750 - acc: 0.8859 - val_loss: 0.2674 - val_acc: 0.8848\n",
      "Epoch 28/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2698 - acc: 0.8816Epoch 00027: val_acc improved from 0.91304 to 0.91522, saving model to data1a/ft_model.h5\n",
      "1840/1840 [==============================] - 181s - loss: 4.2688 - acc: 0.8821 - val_loss: 0.2231 - val_acc: 0.9152\n",
      "Epoch 29/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2617 - acc: 0.8783Epoch 00028: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2614 - acc: 0.8788 - val_loss: 0.2319 - val_acc: 0.9043\n",
      "Epoch 30/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2449 - acc: 0.8870Epoch 00029: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2443 - acc: 0.8875 - val_loss: 0.2453 - val_acc: 0.8978\n",
      "Epoch 31/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2612 - acc: 0.8684Epoch 00030: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2602 - acc: 0.8690 - val_loss: 0.2744 - val_acc: 0.8870\n",
      "Epoch 32/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2621 - acc: 0.8734Epoch 00031: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2619 - acc: 0.8734 - val_loss: 0.2041 - val_acc: 0.9130\n",
      "Epoch 33/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2414 - acc: 0.8837Epoch 00032: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2406 - acc: 0.8837 - val_loss: 0.3041 - val_acc: 0.8761\n",
      "Epoch 34/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2415 - acc: 0.8892Epoch 00033: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2410 - acc: 0.8891 - val_loss: 0.2281 - val_acc: 0.9065\n",
      "Epoch 35/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2293 - acc: 0.8854Epoch 00034: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2287 - acc: 0.8859 - val_loss: 0.2332 - val_acc: 0.9043\n",
      "Epoch 36/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2244 - acc: 0.8892Epoch 00035: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2236 - acc: 0.8897 - val_loss: 0.2641 - val_acc: 0.8870\n",
      "Epoch 37/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2149 - acc: 0.9023Epoch 00036: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2146 - acc: 0.9022 - val_loss: 0.2369 - val_acc: 0.9022\n",
      "Epoch 38/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2102 - acc: 0.8881Epoch 00037: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2098 - acc: 0.8886 - val_loss: 0.2300 - val_acc: 0.9087\n",
      "Epoch 39/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2121 - acc: 0.8881Epoch 00038: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2113 - acc: 0.8886 - val_loss: 0.2687 - val_acc: 0.8891\n",
      "Epoch 40/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.1856 - acc: 0.8990Epoch 00039: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.1847 - acc: 0.8995 - val_loss: 0.2432 - val_acc: 0.9000\n",
      "Epoch 41/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2067 - acc: 0.8832Epoch 00040: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2068 - acc: 0.8832 - val_loss: 0.2204 - val_acc: 0.9043\n",
      "Epoch 42/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.1993 - acc: 0.8985Epoch 00041: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.1985 - acc: 0.8989 - val_loss: 0.2349 - val_acc: 0.9000\n",
      "Epoch 43/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.2011 - acc: 0.8876Epoch 00042: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.2005 - acc: 0.8880 - val_loss: 0.2628 - val_acc: 0.8891\n",
      "Epoch 44/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.1889 - acc: 0.8985Epoch 00043: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.1883 - acc: 0.8989 - val_loss: 0.2275 - val_acc: 0.9065\n",
      "Epoch 45/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.1755 - acc: 0.8941Epoch 00044: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.1753 - acc: 0.8946 - val_loss: 0.2181 - val_acc: 0.9065\n",
      "Epoch 46/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.1508 - acc: 0.9023Epoch 00045: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.1504 - acc: 0.9027 - val_loss: 0.2400 - val_acc: 0.8913\n",
      "Epoch 47/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.1762 - acc: 0.8832Epoch 00046: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.1753 - acc: 0.8837 - val_loss: 0.2513 - val_acc: 0.8935\n",
      "Epoch 48/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.1647 - acc: 0.8925Epoch 00047: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.1640 - acc: 0.8929 - val_loss: 0.2330 - val_acc: 0.9000\n",
      "Epoch 49/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.1686 - acc: 0.8914Epoch 00048: val_acc did not improve\n",
      "1840/1840 [==============================] - 180s - loss: 4.1679 - acc: 0.8918 - val_loss: 0.2787 - val_acc: 0.8848\n",
      "Epoch 50/50\n",
      "1832/1840 [============================>.] - ETA: 0s - loss: 4.1478 - acc: 0.8963Epoch 00049: val_acc improved from 0.91522 to 0.91739, saving model to data1a/ft_model.h5\n",
      "1840/1840 [==============================] - 181s - loss: 4.1469 - acc: 0.8967 - val_loss: 0.2078 - val_acc: 0.9174\n"
     ]
    }
   ],
   "source": [
    "ft_model, ft_history = finetune_binary_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 50 , val_acc: 0.917391304348 , val_loss: 0.207779218418\n"
     ]
    },
    {
     "data": {
      "image/png": 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zrN6xmbsGDMXXo0eL7eMyUzGQyRqkVnOzd+JSajxF5aXYW9k06lNQVkJlbXWL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\nAUhjLIQQQgghBAD/B79z26igKGRFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0165829cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WINNER\n",
    "plot_metrics(ft_history) # sgd with lr = 0.0001, sigmoid, with l2 = 0.001"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Model Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ft_model = load_model(location+'/ft_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "with open('data1a/top_history.txt') as f:    \n",
    "    top_history = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "with open('data1a/ft_history.txt') as f:    \n",
    "    ft_history = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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', 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",
    "    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": 15,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def plot_acc_metrics(hist1, hist2, stop=50):\n",
    "    fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(4.25,6))\n",
    "                            \n",
    "    axes = axes.flatten()\n",
    "    \n",
    "    axes[0].plot(range(stop), hist1['acc'], label='Training', color='#FF533D')\n",
    "    axes[0].plot(range(stop), hist1['val_acc'], label='Validation', color='#03507E')\n",
    "    axes[0].set_title('Training')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "    axes[0].set_xlabel('Epoch')\n",
    "    axes[0].legend(loc='lower right')\n",
    "                             \n",
    "    axes[1].plot(range(stop), hist2['acc'], label='Training', color='#FF533D')\n",
    "    axes[1].plot(range(stop), hist2['val_acc'], label='Validation', color='#03507E')\n",
    "    axes[1].set_title('Fine-tuning')\n",
    "    axes[1].set_ylabel('Accuracy')\n",
    "    axes[1].set_xlabel('Epoch')\n",
    "    axes[1].legend(loc='lower right')\n",
    "                             \n",
    "    plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def evaluate_binary_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='binary', # categorical for multiclass\n",
    "                                shuffle=False)\n",
    "    \n",
    "    predictions = model.predict_generator(generator, len(labels))\n",
    "    \n",
    "    # use for multiclass\n",
    "    # pred_labels = np.argmax(predictions, axis=1)\n",
    "    \n",
    "    pred_labels = [0 if i <0.5 else 1 for i in predictions]\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": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pmZwIicDdqSYOhawGiSTBT0vkfqcho6FZ61cbpCD8wxXbST5jxgzef/99zSqX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\nkQ5KIpHUW/4fsHJ6tRCSFEMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86897a1bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_metrics(top_history, ft_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "epoch: 50 , val_acc: 0.917391304348 , val_loss: 0.207779218418\n"
     ]
    },
    {
     "data": {
      "image/png": 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AB8fXue75uDQGLPgfw0K82fnukzUuY7Cv3PNRbS2FQgHWNuLmVsvJkkYDmWmQ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dFeVsR9e5PC7Oxszp8/D8CFCxeMy1xSUsKoUaNYsWJF+JiJmQOBAB8/fgTg8+fP3Lp1i/T0dCOz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\nIiIiIgJoMBYRERERATQYi4iIiIgA8D/HQxKBy5UQTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe98e3eca50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WINNER\n",
    "plot_metrics(ft_history) # sgd with lr = 0.0001, sigmoid, with l2 = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "validation_labels = np.array([0] * validation_samples[0] + \n",
    "                             [1] * validation_samples[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 460 images belonging to 2 classes.\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.96      0.84      0.90       230\n",
      "          1       0.86      0.96      0.91       230\n",
      "\n",
      "avg / total       0.91      0.90      0.90       460\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm = evaluate_binary_model(ft_model, validation_data_dir, validation_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "heatmap_labels = ['Damaged', 'Whole']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cIDDw/AJUQUFB5ObmVilaJWER8SzVNG15w4YNBAYG0qFDB8pbbPKyHvv2Gxod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\n1EMYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f608be466d0>"
      ]
     },
     "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": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CImIqHhbnX2XJyMjgq6++ol+/fo5l69evJzY2FoDY2FjWrVtXsXor9G0Rkeqm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f608bbc0f50>"
      ]
     },
     "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": "markdown",
   "metadata": {},
   "source": [
    "## Making Live Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def car_categories_gate(image_path, model):\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",
    "    print \"Validating that damage exists...\"\n",
    "    print pred\n",
    "    if pred[0][0] <=.5:\n",
    "\n",
    "        print \"Validation complete - proceed to location and severity determination\"\n",
    "    else:\n",
    "        print \"Are you sure that your car is damaged? Please submit another picture of the damage.\"\n",
    "        print \"Hint: Try zooming in/out, using a different angle or different lighting\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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eGU+8Gh5UhbD5m+juNj5Ukwkd1fUq+jTRR/2Raf8AlilD0daOv2dHs/8AyVo8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\nugPTO9eT95T793nXR2Mkf//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('https://www.nerdwallet.com/blog/wp-content/uploads/2015/12/exterior-car-damage-384x233.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validating that damage exists...\n",
      "[[ 0.22355862]]\n",
      "Validation complete - proceed to location and severity determination\n"
     ]
    }
   ],
   "source": [
    "car_categories_gate('https://www.nerdwallet.com/blog/wp-content/uploads/2015/12/exterior-car-damage-384x233.jpg', ft_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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LSPEJ6J7BgQdPlMxguljajLG45oGp2bSfe4ttv+1ZZn2Y5rhlbx6YNZFa5A6t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\nWA9B6nALzN+K9KwGFbhKDWN1Nz5pjXiGEdNlxXwgfFkgK//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('http://1.bp.blogspot.com/-ToQS-qIxYbo/UDNuV5OcVQI/AAAAAAAABdo/tjeQywWiOo0/s200/Key+scratch.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validating that damage exists...\n",
      "[[ 0.45193103]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Validation complete - proceed to location and severity determination'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_categories_gate('http://1.bp.blogspot.com/-ToQS-qIxYbo/UDNuV5OcVQI/AAAAAAAABdo/tjeQywWiOo0/s200/Key+scratch.jpg', ft_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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734gKmC0yp6Fmx/OqUTSOEb6FF/Gl7cKZLeO4kTruSJiuP97p+tDjbcf1ZR3djG1Dx7DA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\nta7IpSyxqxUGNenzYKgH8+eKHorB5kv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lSV+fDLjIzkdDWVSny6disPJQrW6nk2iXACyL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RN0xuV1IwPmyeBx6DFGr1BpLqPaWXccWgYZ4YHr707jsfSYBztB3bsDp/nHrWKDQmdFON2QoGDn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\nDa6FqyxKiTKzJmIs/fd3HX0A/I+taeZFtCVbi2wNwmU918w8e3SpuVdH/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('https://www.carfax.com/media/zoo/images/rsz_frame-damage_85730e0a843d155e25e4b0f0e100bf65.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validating that damage exists...\n",
      "[[ 0.97538584]]\n",
      "Are you sure that your car is damaged? Please submit another picture of the damage.\n",
      "Hint: Try zooming in/out, using a different angle or different lighting\n"
     ]
    }
   ],
   "source": [
    "car_categories_gate('https://www.carfax.com/media/zoo/images/rsz_frame-damage_85730e0a843d155e25e4b0f0e100bf65.jpg', ft_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking at edge cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def get_edge_cases(model, directory, exp_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_prob = model.predict(x)\n",
    "        if pred_prob <=0.5:\n",
    "            pred = 0\n",
    "        else:\n",
    "            pred = 1\n",
    "        if pred != exp_result:\n",
    "            edge_list.append(name)\n",
    "    return edge_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 460 images belonging to 2 classes.\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.96      0.84      0.90       230\n",
      "          1       0.86      0.96      0.91       230\n",
      "\n",
      "avg / total       0.91      0.90      0.90       460\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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XKy0trd31MhMGYJWOvFgjMTFR6enpGjdunLxerwYNGqQJEyaorq5Os2bNUlFRkfr3769l\ny5a1v95AIBDosIo/R8Op4525e7hU0uDxpktAF1T18baQ9/H3370c9Ge/MenOkI8XKsYRAGAQ4wgA\nVomIcFdvSQgDsIu7MpgQBmAXt91FzWXfGQBgFzphAFZxWydMCAOwi7symBAGYBeeMQcAJjGOAABz\nXJbBhHC4/H7NWhW9UiJJumPc7br7rgmGK0I4+C7rq8W/zFN0zKUKNAe0bk2J1qws1ux592vEqBQ1\nnG3QgepDmv/wz1T36T8U9bXeeub5n+raxG/qTy9v1NNP/Mr0X8F13HZijiVqYbB33wcqfnWD1q76\njdat/p227XhD+w8cNF0WwqCxqUlLFj2n8bdM1j3ZD+j792brqgFX6s3tf1b2qEmaMOYH+vjDA5r6\nwN2SpIazDXr2Fy/oF//xa8OVu1iEE/yrCyCEw+CDjz5S4rWD1K1bN3k8Ht1w3VBVbAn9RiXo+o4f\nrdXf/7pXklT/j3p9sPdj+S6LUeUbb+uf986qevev8sX2kySdOXNWu9/+i841NBir2e0cxwn61RUQ\nwmGQMOBqvb1rt06eOqX6M2f0+hv/oxq/33RZCLPL4y7TNwYlqOrdv12wPXvCGL2xtdJQVTCt3TPh\noqIi5eTkdGQt1rr6qqs05d57lPvgLPXs0UPf/MY1ivDw/fdV0qNnDy1dvlBPL3xW9f+ob9k+7cF7\n1NjYqLJX2v+gSFzIbUvU2p0Ezz77bEfWYb3s22/T2lW/0W/zn1NU74t11ZVXmi4JYeLxePTM8oXa\nUPyatv73Gy3bb7/jVg2/+UY9OnORwers09FPW+5srXbCWVlZX/jeP5+vhODUfvKJoi+9VIdralSx\ndbtW/2aF6ZIQJj9d8qj27f1Yq39b1LLtphHf0eTpd+m+O2fqXMO5z/29rjKzdB2X/bu1GsLHjx9X\nYWGhoqKiLtgeCAR01113dWphtpn9aJ5OnTwlr9erxx99WBdf3Mt0SQiDoUnf0phxo/T+ng+0tuwF\nKRDQs0te0KNPzFRkpFcFq5dKOn9ybvHjv5Qkbdzxknr26qHIyEjdfMtNmj7xYX20r9rkX8NV3Pbl\n1WoIjxw5UnV1dRo4cOBn3ktOTu60omz0u4LlpkuAAbveek/XXZ36me07Rt79hb+TMZwG56uEZ8zB\nCJ4xh8/TEc+YO7Dxv4L+bFzGrSEfL1RcMQfAKl3lhFuwCGEAVnFc9ow5d1ULAJahEwZgF8YRAGCO\nVUvUAMB13JXBhDAAu7itE+bEHAAYRCcMwCqOy+5QSAgDsIvLxhGEMACrMBMGAASNThiAXVx2sQad\nMACrdPSDPpubm5Wdna37779fknTy5ElNmTJF6enpmjp1qk6fPh1SvYQwALs4TvCvIKxatUoDBgxo\n+bmgoEDDhg1TeXm5kpOTlZ+fH1K5hDAAq3TkM+Zqamq0bds23XnnnS3bKioqlJ2dLUnKzs7Wpk2h\nPaSVEAZglw7shJ988kk98sgjF4wujh8/rpiYGElS3759VVtbG1K5hDAAq3TUTHjr1q2KiYnRwIED\n1doDiEJdEsfqCAB26aB1wu+88442b96sbdu26ezZs6qrq9OPf/xjxcTE6NixY4qJidHRo0cVHR0d\n0nHohAFYpaNmwnPmzNHWrVtVUVGhZ555RsnJyVqyZIluvvlmFRcXS5LWr1+vtLS0kOolhAHgS8jN\nzdWbb76p9PR07dy5U7m5uSHtj6ctwwietozP0xFPW67d/eegPxs95NshHy9UzIQBWMVtD/okhAHY\nhcuWAQDBohMGYBXHcVdvSQgDsIvL7idMCAOwittu6k4IA7CLy07MEcIArEInDAAmEcIAYBCrIwDA\nnGBu1t6VuOsrAwAsQycMwC7MhAHAHCfCY7qEL4UQBmAVZsIAgKDRCQOwCzNhADCHK+YAwCQu1gAA\ng1x2Yo4QBmAVxhEAYBLjCAAwh04YAExyWSfsrmoBwDJ0wgCs4rbLlglhAHZhJgwA5rjtLmpOIBAI\nmC4CAL6qODEHAAYRwgBgECEMAAYRwgBgECEMAAYRwgBgECEcJtu3b9ett96q9PR0FRQUmC4HXUBe\nXp5SUlKUlZVluhQYRAiHQXNzsxYtWqTCwkJt2LBBpaWl2rdvn+myYNj48eNVWFhougwYRgiHQVVV\nleLj49W/f39FRkYqMzNTFRUVpsuCYUlJSYqKijJdBgwjhMPA7/crNja25Wefz6cjR44YrAhAV0EI\nA4BBhHAY+Hw+HTp0qOVnv9+vfv36GawIQFdBCIfB4MGDVV1drYMHD6qhoUGlpaVKS0szXRa6AO6f\nBe6iFibbt2/X4sWLFQgEdMcddyg3N9d0STBs7ty5qqys1IkTJxQTE6MZM2YoJyfHdFkIM0IYAAxi\nHAEABhHCAGAQIQwABhHCAGAQIQwABhHCAGAQIQwABhHCAGDQ/wEaZy7emWSboAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff520cedf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_binary_model(ft_model, validation_data_dir, validation_labels)\n",
    "# TP = 221, TN = 194, FP = 36, FN = 9\n",
    "# 9 were predicted to be damaged but were actually whole\n",
    "# 36 were predicted to be whole but were actually damaged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fp = get_edge_cases(ft_model, 'data1a/validation/00-damage/', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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T4mnC7nGh8xCrrReBp030o8eE08U60thHISPNZaxW59J+NhjiNiOBC9Emxztyum2yzMrC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\naCCEWGFOsQQQoW1OgIIIpKIQbU4GEaCC/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view_images('data1a/validation/00-damage/', fp)  # damaged, identifed as whole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fn = get_edge_cases(ft_model, 'data1a/validation/01-whole/', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c to continue, q to quitq\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Finished for now.'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view_images('data1a/validation/01-whole/', fn) #whole, identified as damaged"
   ]
  }
 ],
 "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
}