test_performance_ensembling_images_and_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features and images ensembling and fit generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook are going see how to ensemble features with images in a classification problem.\n",
    "\n",
    "The main idea is to show how to :\n",
    "* ensemble CNN with other architectures to train a network using images and features together.\n",
    "* implement a fit generator with a network with multiple inputs.\n",
    "\n",
    "The dataset consists on features a and images of leaves from 100 different plants and the task consist of the Leaf Classification contest of [Kaggle](https://www.kaggle.com/c/leaf-classification):\n",
    "> The objective of this playground competition is to use binary leaf images and extracted features, including shape, margin & texture, to accurately identify 99 species of plants. Leaves, due to their volume, prevalence, and unique characteristics, are an effective means of differentiating plant species. They also provide a fun introduction to applying techniques that involve image-based features.\n",
    "\n",
    "The dataset loading code was taken from https://www.kaggle.com/abhmul/keras-convnet-lb-0-0052-w-visualization as well as some code.\n",
    "\n",
    "So, to brief the idea is to generate a leaf classifier as a excuse to make an hybrid DNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading the training data...\n",
      "Training data loaded!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras.preprocessing.image import img_to_array, load_img\n",
    "\n",
    "# A large amount of the data loading code is based on najeebkhan's kernel\n",
    "# Check it out at https://www.kaggle.com/najeebkhan/leaf-classification/neural-network-through-keras\n",
    "root = './all'\n",
    "np.random.seed(2016)\n",
    "split_random_state = 7\n",
    "split = .8\n",
    "image_max_size = 512\n",
    "\n",
    "\n",
    "def load_numeric_training(standardize=False):\n",
    "    \"\"\"\n",
    "    Loads the pre-extracted features for the training data\n",
    "    and returns a tuple of the image ids, the data, and the labels\n",
    "    \"\"\"\n",
    "    # Read data from the CSV file\n",
    "    data = pd.read_csv(os.path.join(root, 'train.csv'))\n",
    "    ID = data.pop('id')\n",
    "\n",
    "    # Since the labels are textual, so we encode them categorically\n",
    "    y = data.pop('species')\n",
    "    y = LabelEncoder().fit(y).transform(y)\n",
    "    # standardize the data by setting the mean to 0 and std to 1\n",
    "    X = StandardScaler().fit(data).transform(data) if standardize else data.values\n",
    "\n",
    "    return ID, X, y\n",
    "\n",
    "\n",
    "def load_numeric_test(scaler, standardize=False):\n",
    "    \"\"\"\n",
    "    Loads the pre-extracted features for the test data\n",
    "    and returns a tuple of the image ids, the data\n",
    "    \"\"\"\n",
    "    test = pd.read_csv(os.path.join(root, 'test.csv'))\n",
    "    ID = test.pop('id')\n",
    "    # standardize the data by setting the mean to 0 and std to 1\n",
    "    test = StandardScaler().fit(test).transform(test) if standardize else test.values\n",
    "    return ID, test\n",
    "\n",
    "\n",
    "def resize_img(img, max_dim=image_max_size):\n",
    "    \"\"\"\n",
    "    Resize the image to so the maximum side is of size max_dim\n",
    "    Returns a new image of the right size\n",
    "    \"\"\"\n",
    "    # Get the axis with the larger dimension\n",
    "    max_ax = max((0, 1), key=lambda i: img.size[i])\n",
    "    # Scale both axes so the image's largest dimension is max_dim\n",
    "    scale = max_dim / float(img.size[max_ax])\n",
    "    return img.resize((int(img.size[0] * scale), int(img.size[1] * scale)))\n",
    "\n",
    "\n",
    "def load_image_data(ids, max_dim=image_max_size, center=True):\n",
    "    \"\"\"\n",
    "    Takes as input an array of image ids and loads the images as numpy\n",
    "    arrays with the images resized so the longest side is max-dim length.\n",
    "    If center is True, then will place the image in the center of\n",
    "    the output array, otherwise it will be placed at the top-left corner.\n",
    "    \"\"\"\n",
    "    # Initialize the output array\n",
    "    # NOTE: Theano users comment line below and\n",
    "    X = np.empty((len(ids), max_dim, max_dim, 1))\n",
    "    # X = np.empty((len(ids), 1, max_dim, max_dim)) # uncomment this\n",
    "    for i, idee in enumerate(ids):\n",
    "        # Turn the image into an array\n",
    "        x = resize_img(load_img(os.path.join(root, 'images', str(idee) + '.jpg'), grayscale=True), max_dim=max_dim)\n",
    "        x = img_to_array(x)\n",
    "        # Get the corners of the bounding box for the image\n",
    "        # NOTE: Theano users comment the two lines below and\n",
    "        length = x.shape[0]\n",
    "        width = x.shape[1]\n",
    "        # length = x.shape[1] # uncomment this\n",
    "        # width = x.shape[2] # uncomment this\n",
    "        if center:\n",
    "            h1 = int((max_dim - length) / 2)\n",
    "            h2 = h1 + length\n",
    "            w1 = int((max_dim - width) / 2)\n",
    "            w2 = w1 + width\n",
    "        else:\n",
    "            h1, w1 = 0, 0\n",
    "            h2, w2 = (length, width)\n",
    "        # Insert into image matrix\n",
    "        # NOTE: Theano users comment line below and\n",
    "        X[i, h1:h2, w1:w2, 0:1] = x\n",
    "        # X[i, 0:1, h1:h2, w1:w2] = x  # uncomment this\n",
    "    # Scale the array values so they are between 0 and 1\n",
    "    return np.around(X / 255.0)\n",
    "\n",
    "\n",
    "def load_train_data(split=split, random_state=None):\n",
    "    \"\"\"\n",
    "    Loads the pre-extracted feature and image training data and\n",
    "    splits them into training and cross-validation.\n",
    "    Returns one tuple for the training data and one for the validation\n",
    "    data. Each tuple is in the order pre-extracted features, images,\n",
    "    and labels.\n",
    "    \"\"\"\n",
    "    # Load the pre-extracted features\n",
    "    ID, X_num_tr, y = load_numeric_training()\n",
    "    # Load the image data\n",
    "    X_img_tr = load_image_data(ID)\n",
    "    # Split them into validation and cross-validation\n",
    "    sss = StratifiedShuffleSplit(n_splits=1, train_size=split, random_state=random_state)\n",
    "    train_ind, test_ind = next(sss.split(X_num_tr, y))\n",
    "    X_num_val, X_img_val, y_val = X_num_tr[test_ind], X_img_tr[test_ind], y[test_ind]\n",
    "    X_num_tr, X_img_tr, y_tr = X_num_tr[train_ind], X_img_tr[train_ind], y[train_ind]\n",
    "    return (X_num_tr, X_img_tr, y_tr), (X_num_val, X_img_val, y_val)\n",
    "\n",
    "\n",
    "def load_test_data():\n",
    "    \"\"\"\n",
    "    Loads the pre-extracted feature and image test data.\n",
    "    Returns a tuple in the order ids, pre-extracted features,\n",
    "    and images.\n",
    "    \"\"\"\n",
    "    # Load the pre-extracted features\n",
    "    ID, X_num_te = load_numeric_test()\n",
    "    # Load the image data\n",
    "    X_img_te = load_image_data(ID)\n",
    "    return ID, X_num_te, X_img_te\n",
    "\n",
    "print('Loading the training data...')\n",
    "(X_num_tr, X_img_tr, y_tr), (X_num_val, X_img_val, y_val) = load_train_data(random_state=split_random_state)\n",
    "y_tr_cat = to_categorical(y_tr)\n",
    "y_val_cat = to_categorical(y_val)\n",
    "print('Training data loaded!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "some inspection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number of categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99\n"
     ]
    }
   ],
   "source": [
    "n_cats = len(np.unique(y_tr))\n",
    "print(n_cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(792, 192)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_num_tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(792, 512, 512, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_img_tr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the dataset consist of 792 samples of a vector of 192 features and a image.\n",
    "\n",
    "Here we resized the images from the original size, to 512x512 pixels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DNN using only features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we build a deep feed forward NN using only the features.\n",
    "\n",
    "We implement some callbacks such as Early Stopping and Reduce LR on plateau to avoid overfitting and to increase the accuracy of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import Sequential\n",
    "from keras import Model\n",
    "from keras.layers import ReLU, Dense, Conv2D, Softmax, Conv1D, Flatten, Dropout, Input,GlobalMaxPooling2D,MaxPooling2D, Convolution2D, Concatenate, merge\n",
    "from keras.callbacks import ReduceLROnPlateau, EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def features_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(64, input_shape = (192,), activation = 'relu', name='dense_first_layer'))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(128,activation = 'relu', name = 'dense_for_ens'))\n",
    "    model.add(Dense(n_cats,activation = 'softmax', name = 'out_layer'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_20 (Dense)             (None, 64)                12352     \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_21 (Dense)             (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "out_layer (Dense)            (None, 99)                12771     \n",
      "=================================================================\n",
      "Total params: 33,443\n",
      "Trainable params: 33,443\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# model \n",
    "# model = Sequential()\n",
    "# model.add(Dense(150, input_shape = (192,), activation = 'relu'))\n",
    "# model.add(Dropout(0.4))\n",
    "# model.add(Dense(120, activation = 'relu'))\n",
    "# model.add(Dropout(0.1))\n",
    "# model.add(Dense(110, activation = 'relu'))\n",
    "# model.add(Dense(105, activation = 'relu'))\n",
    "# model.add(Dense(100, activation = 'relu'))\n",
    "# model.add(Dense(n_cats,activation = 'softmax', name = 'out_layer'))\n",
    "\n",
    "model = features_model()\n",
    "\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "rlr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, \n",
    "                        patience=5, verbose=1, mode='auto', \n",
    "                        min_delta=0.01,  min_lr=0)\n",
    "erl = EarlyStopping(monitor='val_loss',\n",
    "                        patience=10, verbose=1, mode='auto', \n",
    "                        min_delta=0.01)\n",
    "\n",
    "callbacks = [rlr,erl]\n",
    "\n",
    "model.compile(optimizer='adam', loss= 'categorical_crossentropy', metrics = ['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 792 samples, validate on 99 samples\n",
      "Epoch 1/100\n",
      "792/792 [==============================] - 1s 836us/step - loss: 4.5903 - acc: 0.0114 - val_loss: 4.5628 - val_acc: 0.1111\n",
      "Epoch 2/100\n",
      "792/792 [==============================] - 0s 302us/step - loss: 4.5105 - acc: 0.0707 - val_loss: 4.3837 - val_acc: 0.1616\n",
      "Epoch 3/100\n",
      "792/792 [==============================] - 0s 303us/step - loss: 4.1789 - acc: 0.1086 - val_loss: 3.7947 - val_acc: 0.2727\n",
      "Epoch 4/100\n",
      "792/792 [==============================] - 0s 301us/step - loss: 3.5232 - acc: 0.1717 - val_loss: 3.0790 - val_acc: 0.3333\n",
      "Epoch 5/100\n",
      "792/792 [==============================] - 0s 303us/step - loss: 2.9595 - acc: 0.2462 - val_loss: 2.5956 - val_acc: 0.4343\n",
      "Epoch 6/100\n",
      "792/792 [==============================] - 0s 297us/step - loss: 2.5563 - acc: 0.3182 - val_loss: 2.2431 - val_acc: 0.5758\n",
      "Epoch 7/100\n",
      "792/792 [==============================] - 0s 296us/step - loss: 2.2460 - acc: 0.3965 - val_loss: 1.9904 - val_acc: 0.6061\n",
      "Epoch 8/100\n",
      "792/792 [==============================] - 0s 297us/step - loss: 2.0574 - acc: 0.4217 - val_loss: 1.8033 - val_acc: 0.6061\n",
      "Epoch 9/100\n",
      "792/792 [==============================] - 0s 300us/step - loss: 1.8654 - acc: 0.4785 - val_loss: 1.6455 - val_acc: 0.6465\n",
      "Epoch 10/100\n",
      "792/792 [==============================] - 0s 300us/step - loss: 1.7012 - acc: 0.5013 - val_loss: 1.5048 - val_acc: 0.6970\n",
      "Epoch 11/100\n",
      "792/792 [==============================] - 0s 302us/step - loss: 1.5712 - acc: 0.5581 - val_loss: 1.3987 - val_acc: 0.7273\n",
      "Epoch 12/100\n",
      "792/792 [==============================] - 0s 304us/step - loss: 1.4895 - acc: 0.5619 - val_loss: 1.2982 - val_acc: 0.7576\n",
      "Epoch 13/100\n",
      "792/792 [==============================] - 0s 299us/step - loss: 1.3686 - acc: 0.5997 - val_loss: 1.2068 - val_acc: 0.7879\n",
      "Epoch 14/100\n",
      "792/792 [==============================] - 0s 299us/step - loss: 1.3211 - acc: 0.6174 - val_loss: 1.1583 - val_acc: 0.7475\n",
      "Epoch 15/100\n",
      "792/792 [==============================] - 0s 301us/step - loss: 1.1973 - acc: 0.6730 - val_loss: 1.0879 - val_acc: 0.7576\n",
      "Epoch 16/100\n",
      "792/792 [==============================] - 0s 294us/step - loss: 1.1541 - acc: 0.6452 - val_loss: 1.0160 - val_acc: 0.8182\n",
      "Epoch 17/100\n",
      "792/792 [==============================] - 0s 301us/step - loss: 1.0976 - acc: 0.6831 - val_loss: 0.9783 - val_acc: 0.7475\n",
      "Epoch 18/100\n",
      "792/792 [==============================] - 0s 298us/step - loss: 1.0601 - acc: 0.6995 - val_loss: 0.8941 - val_acc: 0.8182\n",
      "Epoch 19/100\n",
      "792/792 [==============================] - 0s 299us/step - loss: 1.0037 - acc: 0.7083 - val_loss: 0.8937 - val_acc: 0.8081\n",
      "Epoch 20/100\n",
      "792/792 [==============================] - 0s 294us/step - loss: 0.9603 - acc: 0.7159 - val_loss: 0.8415 - val_acc: 0.8182\n",
      "Epoch 21/100\n",
      "792/792 [==============================] - 0s 298us/step - loss: 0.9358 - acc: 0.7247 - val_loss: 0.8356 - val_acc: 0.7879\n",
      "Epoch 22/100\n",
      "792/792 [==============================] - 0s 297us/step - loss: 0.8916 - acc: 0.7273 - val_loss: 0.7451 - val_acc: 0.8788\n",
      "Epoch 23/100\n",
      "792/792 [==============================] - 0s 300us/step - loss: 0.8229 - acc: 0.7639 - val_loss: 0.7420 - val_acc: 0.8182\n",
      "Epoch 24/100\n",
      "792/792 [==============================] - 0s 292us/step - loss: 0.7856 - acc: 0.7639 - val_loss: 0.7053 - val_acc: 0.8485\n",
      "Epoch 25/100\n",
      "792/792 [==============================] - 0s 293us/step - loss: 0.7910 - acc: 0.7601 - val_loss: 0.7228 - val_acc: 0.8384\n",
      "Epoch 26/100\n",
      "792/792 [==============================] - 0s 310us/step - loss: 0.7956 - acc: 0.7601 - val_loss: 0.6435 - val_acc: 0.8586\n",
      "Epoch 27/100\n",
      "792/792 [==============================] - 0s 302us/step - loss: 0.7138 - acc: 0.8043 - val_loss: 0.6676 - val_acc: 0.8586\n",
      "Epoch 28/100\n",
      "792/792 [==============================] - 0s 296us/step - loss: 0.6506 - acc: 0.8056 - val_loss: 0.6199 - val_acc: 0.8384\n",
      "Epoch 29/100\n",
      "792/792 [==============================] - 0s 299us/step - loss: 0.6674 - acc: 0.8043 - val_loss: 0.6269 - val_acc: 0.8182\n",
      "Epoch 30/100\n",
      "792/792 [==============================] - 0s 302us/step - loss: 0.6378 - acc: 0.8194 - val_loss: 0.5983 - val_acc: 0.8586\n",
      "Epoch 31/100\n",
      "792/792 [==============================] - 0s 298us/step - loss: 0.6169 - acc: 0.8182 - val_loss: 0.5765 - val_acc: 0.8586\n",
      "Epoch 32/100\n",
      "792/792 [==============================] - 0s 298us/step - loss: 0.6400 - acc: 0.7955 - val_loss: 0.6006 - val_acc: 0.8081\n",
      "Epoch 33/100\n",
      "792/792 [==============================] - 0s 297us/step - loss: 0.5895 - acc: 0.8220 - val_loss: 0.5584 - val_acc: 0.8384\n",
      "Epoch 34/100\n",
      "792/792 [==============================] - 0s 298us/step - loss: 0.5552 - acc: 0.8371 - val_loss: 0.5831 - val_acc: 0.8485\n",
      "Epoch 35/100\n",
      "792/792 [==============================] - 0s 298us/step - loss: 0.5540 - acc: 0.8434 - val_loss: 0.5424 - val_acc: 0.8687\n",
      "Epoch 36/100\n",
      "792/792 [==============================] - 0s 301us/step - loss: 0.5501 - acc: 0.8283 - val_loss: 0.5473 - val_acc: 0.8788\n",
      "Epoch 37/100\n",
      "792/792 [==============================] - 0s 298us/step - loss: 0.5532 - acc: 0.8422 - val_loss: 0.4987 - val_acc: 0.8990\n",
      "Epoch 38/100\n",
      "792/792 [==============================] - 0s 298us/step - loss: 0.5166 - acc: 0.8346 - val_loss: 0.5004 - val_acc: 0.8990\n",
      "Epoch 39/100\n",
      "792/792 [==============================] - 0s 298us/step - loss: 0.5202 - acc: 0.8384 - val_loss: 0.5376 - val_acc: 0.8586\n",
      "Epoch 40/100\n",
      "792/792 [==============================] - 0s 294us/step - loss: 0.5017 - acc: 0.8460 - val_loss: 0.4739 - val_acc: 0.9192\n",
      "Epoch 41/100\n",
      "792/792 [==============================] - 0s 293us/step - loss: 0.4859 - acc: 0.8396 - val_loss: 0.4487 - val_acc: 0.8990\n",
      "Epoch 42/100\n",
      "792/792 [==============================] - 0s 302us/step - loss: 0.4412 - acc: 0.8535 - val_loss: 0.4284 - val_acc: 0.8990\n",
      "Epoch 43/100\n",
      "792/792 [==============================] - 0s 303us/step - loss: 0.4096 - acc: 0.8775 - val_loss: 0.4708 - val_acc: 0.8687\n",
      "Epoch 44/100\n",
      "792/792 [==============================] - 0s 297us/step - loss: 0.4213 - acc: 0.8674 - val_loss: 0.4564 - val_acc: 0.8788\n",
      "Epoch 45/100\n",
      "792/792 [==============================] - 0s 302us/step - loss: 0.4403 - acc: 0.8510 - val_loss: 0.4161 - val_acc: 0.9192\n",
      "Epoch 46/100\n",
      "792/792 [==============================] - 0s 300us/step - loss: 0.4040 - acc: 0.8674 - val_loss: 0.4380 - val_acc: 0.8990\n",
      "Epoch 47/100\n",
      "792/792 [==============================] - 0s 307us/step - loss: 0.4040 - acc: 0.8763 - val_loss: 0.4426 - val_acc: 0.9394\n",
      "Epoch 48/100\n",
      "792/792 [==============================] - 0s 299us/step - loss: 0.4099 - acc: 0.8737 - val_loss: 0.4162 - val_acc: 0.9192\n",
      "Epoch 49/100\n",
      "792/792 [==============================] - 0s 298us/step - loss: 0.3635 - acc: 0.8939 - val_loss: 0.4067 - val_acc: 0.8889\n",
      "Epoch 50/100\n",
      "792/792 [==============================] - 0s 295us/step - loss: 0.3717 - acc: 0.8801 - val_loss: 0.3913 - val_acc: 0.8990\n",
      "Epoch 51/100\n",
      "792/792 [==============================] - 0s 297us/step - loss: 0.3576 - acc: 0.8914 - val_loss: 0.4358 - val_acc: 0.8788\n",
      "Epoch 52/100\n",
      "792/792 [==============================] - 0s 299us/step - loss: 0.3764 - acc: 0.8763 - val_loss: 0.4009 - val_acc: 0.8889\n",
      "Epoch 53/100\n",
      "792/792 [==============================] - 0s 296us/step - loss: 0.3573 - acc: 0.8902 - val_loss: 0.4013 - val_acc: 0.9091\n",
      "Epoch 54/100\n",
      "792/792 [==============================] - 0s 295us/step - loss: 0.3412 - acc: 0.8939 - val_loss: 0.3884 - val_acc: 0.9293\n",
      "Epoch 55/100\n",
      "792/792 [==============================] - 0s 297us/step - loss: 0.3144 - acc: 0.8977 - val_loss: 0.3889 - val_acc: 0.9091\n",
      "\n",
      "Epoch 00055: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 56/100\n",
      "792/792 [==============================] - 0s 302us/step - loss: 0.3199 - acc: 0.9091 - val_loss: 0.3721 - val_acc: 0.9091\n",
      "Epoch 57/100\n",
      "792/792 [==============================] - 0s 300us/step - loss: 0.2867 - acc: 0.9205 - val_loss: 0.3527 - val_acc: 0.9091\n",
      "Epoch 58/100\n",
      "792/792 [==============================] - 0s 303us/step - loss: 0.2916 - acc: 0.9192 - val_loss: 0.3362 - val_acc: 0.9192\n",
      "Epoch 59/100\n",
      "792/792 [==============================] - 0s 300us/step - loss: 0.2993 - acc: 0.9192 - val_loss: 0.3613 - val_acc: 0.9091\n",
      "Epoch 60/100\n",
      "792/792 [==============================] - 0s 296us/step - loss: 0.2687 - acc: 0.9205 - val_loss: 0.3693 - val_acc: 0.9192\n",
      "Epoch 61/100\n",
      "792/792 [==============================] - 0s 297us/step - loss: 0.3148 - acc: 0.9091 - val_loss: 0.3410 - val_acc: 0.9192\n",
      "Epoch 62/100\n",
      "792/792 [==============================] - 0s 300us/step - loss: 0.2589 - acc: 0.9280 - val_loss: 0.3432 - val_acc: 0.9394\n",
      "Epoch 63/100\n",
      "792/792 [==============================] - 0s 302us/step - loss: 0.2792 - acc: 0.9154 - val_loss: 0.3528 - val_acc: 0.9192\n",
      "\n",
      "Epoch 00063: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 64/100\n",
      "792/792 [==============================] - 0s 299us/step - loss: 0.2793 - acc: 0.9217 - val_loss: 0.3452 - val_acc: 0.9091\n",
      "Epoch 65/100\n",
      "792/792 [==============================] - 0s 298us/step - loss: 0.2616 - acc: 0.9331 - val_loss: 0.3430 - val_acc: 0.9091\n",
      "Epoch 66/100\n",
      "792/792 [==============================] - 0s 297us/step - loss: 0.2266 - acc: 0.9432 - val_loss: 0.3465 - val_acc: 0.9091\n",
      "Epoch 67/100\n",
      "792/792 [==============================] - 0s 298us/step - loss: 0.2632 - acc: 0.9205 - val_loss: 0.3500 - val_acc: 0.8990\n",
      "Epoch 68/100\n",
      "792/792 [==============================] - 0s 304us/step - loss: 0.2348 - acc: 0.9394 - val_loss: 0.3320 - val_acc: 0.9192\n",
      "\n",
      "Epoch 00068: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Epoch 00068: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f4e043e4cf8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_num_tr, y_tr_cat, batch_size = 10, epochs = 100, validation_data=(X_num_val, y_val_cat), callbacks=[rlr, erl])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit generator first implementation\n",
    "\n",
    "Here we implement a generator of keras following [a very *detailed example of how to use data generators with Keras\n",
    "*](https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly) and a nice discussion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "\n",
    "class DataGenerator(keras.utils.Sequence):\n",
    "    'Generates data for Keras'\n",
    "    def __init__(self, features, targets, \n",
    "                 n_classes =99, batch_size=32,  shuffle=True):\n",
    "        'Initialization'\n",
    "        self.n_classes = n_classes\n",
    "        self.n_vals = len(targets)\n",
    "        self.list_IDs = np.arange(self.n_vals)\n",
    "        self.batch_size = batch_size\n",
    "        self.features = features\n",
    "        self.shuffle = shuffle\n",
    "        self.targets = targets\n",
    "        self.targets_mc = keras.utils.to_categorical(targets, num_classes=self.n_classes)\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        \n",
    "    def __len__(self):\n",
    "        'Denotes the number of batches per epoch'\n",
    "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        'Generate one batch of data'\n",
    "        # Generate indexes of the batch\n",
    "        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
    "\n",
    "        # Find list of IDs\n",
    "        list_IDs_temp = [self.list_IDs[k] for k in indexes]\n",
    "\n",
    "        # Generate data\n",
    "        X, y = self.__data_generation(list_IDs_temp)\n",
    "        return X, y\n",
    "\n",
    "    def on_epoch_end(self):\n",
    "        'Updates indexes after each epoch'\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        if self.shuffle == True:\n",
    "            np.random.shuffle(self.indexes)\n",
    "\n",
    "    def __data_generation(self, list_IDs_temp):\n",
    "        'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)\n",
    "        # Initialization\n",
    "        X = self.features[list_IDs_temp]\n",
    "        y = self.targets_mc[list_IDs_temp]\n",
    "\n",
    "#        # Generate data\n",
    "#        for i, ID in enumerate(list_IDs_temp):\n",
    "#            # Store sample\n",
    "#            X[i,] = np.load('data/' + ID + '.npy')\n",
    "#\n",
    "#            # Store class\n",
    "#            y[i] = self.labels[ID]\n",
    "\n",
    "        return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we implement the generator for both, the training and the validation set. Note that at this point the generator only shuffles the data, but does not do nothing complicated.\n",
    "\n",
    "Later we are going to use the generator for more complex things."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'batch_size': 10,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True}\n",
    "\n",
    "training_generator = DataGenerator(X_num_tr,y_tr, **params)\n",
    "validation_generator = DataGenerator(X_num_val,y_val, **params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_24 (Dense)             (None, 64)                12352     \n",
      "_________________________________________________________________\n",
      "dropout_11 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "out_layer (Dense)            (None, 99)                12771     \n",
      "=================================================================\n",
      "Total params: 33,443\n",
      "Trainable params: 33,443\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "model = features_model()\n",
    "\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "rlr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, \n",
    "                        patience=5, verbose=1, mode='auto', \n",
    "                        min_delta=0.01,  min_lr=0)\n",
    "erl = EarlyStopping(monitor='val_loss',\n",
    "                        patience=10, verbose=1, mode='auto', \n",
    "                        min_delta=0.01)\n",
    "\n",
    "model.compile(optimizer='adam', loss= 'categorical_crossentropy', metrics = ['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "79/79 [==============================] - 1s 9ms/step - loss: 4.5869 - acc: 0.0266 - val_loss: 4.5557 - val_acc: 0.1667\n",
      "Epoch 2/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 4.4835 - acc: 0.0937 - val_loss: 4.3077 - val_acc: 0.1889\n",
      "Epoch 3/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 4.0572 - acc: 0.0949 - val_loss: 3.6819 - val_acc: 0.2222\n",
      "Epoch 4/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 3.4598 - acc: 0.1494 - val_loss: 3.0307 - val_acc: 0.3333\n",
      "Epoch 5/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 2.9366 - acc: 0.2443 - val_loss: 2.6364 - val_acc: 0.4333\n",
      "Epoch 6/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 2.6087 - acc: 0.3089 - val_loss: 2.3302 - val_acc: 0.5333\n",
      "Epoch 7/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 2.3386 - acc: 0.3494 - val_loss: 2.0883 - val_acc: 0.4889\n",
      "Epoch 8/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 2.1159 - acc: 0.3861 - val_loss: 1.9071 - val_acc: 0.5778\n",
      "Epoch 9/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 2.0018 - acc: 0.4532 - val_loss: 1.7731 - val_acc: 0.6222\n",
      "Epoch 10/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.8069 - acc: 0.4747 - val_loss: 1.5745 - val_acc: 0.6444\n",
      "Epoch 11/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 1.6964 - acc: 0.5101 - val_loss: 1.4896 - val_acc: 0.7000\n",
      "Epoch 12/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.5401 - acc: 0.5684 - val_loss: 1.2874 - val_acc: 0.7667\n",
      "Epoch 13/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 1.5106 - acc: 0.5582 - val_loss: 1.2935 - val_acc: 0.6889\n",
      "Epoch 14/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.4018 - acc: 0.5886 - val_loss: 1.1828 - val_acc: 0.7778\n",
      "Epoch 15/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 1.2713 - acc: 0.6152 - val_loss: 1.0922 - val_acc: 0.7556\n",
      "Epoch 16/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.2242 - acc: 0.6228 - val_loss: 1.0455 - val_acc: 0.7889\n",
      "Epoch 17/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.1787 - acc: 0.6620 - val_loss: 0.9206 - val_acc: 0.7556\n",
      "Epoch 18/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.1733 - acc: 0.6316 - val_loss: 0.9686 - val_acc: 0.7778\n",
      "Epoch 19/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.0488 - acc: 0.6873 - val_loss: 0.9413 - val_acc: 0.8000\n",
      "Epoch 20/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.0059 - acc: 0.7000 - val_loss: 0.8053 - val_acc: 0.8000\n",
      "Epoch 21/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.9484 - acc: 0.7152 - val_loss: 0.8842 - val_acc: 0.8000\n",
      "Epoch 22/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.9025 - acc: 0.7481 - val_loss: 0.7815 - val_acc: 0.8667\n",
      "Epoch 23/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.8827 - acc: 0.7342 - val_loss: 0.7814 - val_acc: 0.8444\n",
      "Epoch 24/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.8638 - acc: 0.7570 - val_loss: 0.7365 - val_acc: 0.8000\n",
      "Epoch 25/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.8569 - acc: 0.7203 - val_loss: 0.7544 - val_acc: 0.8222\n",
      "Epoch 26/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.7821 - acc: 0.7620 - val_loss: 0.6546 - val_acc: 0.8556\n",
      "Epoch 27/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.7855 - acc: 0.7544 - val_loss: 0.6938 - val_acc: 0.8667\n",
      "Epoch 28/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.7424 - acc: 0.7633 - val_loss: 0.6805 - val_acc: 0.8444\n",
      "Epoch 29/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.7156 - acc: 0.7937 - val_loss: 0.6421 - val_acc: 0.8889\n",
      "Epoch 30/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.7065 - acc: 0.7785 - val_loss: 0.6880 - val_acc: 0.8444\n",
      "Epoch 31/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.6690 - acc: 0.7987 - val_loss: 0.6108 - val_acc: 0.8556\n",
      "Epoch 32/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.6338 - acc: 0.7937 - val_loss: 0.5399 - val_acc: 0.8667\n",
      "Epoch 33/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.5963 - acc: 0.8165 - val_loss: 0.4597 - val_acc: 0.8778\n",
      "Epoch 34/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.6143 - acc: 0.8152 - val_loss: 0.5507 - val_acc: 0.8556\n",
      "Epoch 35/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.5695 - acc: 0.8127 - val_loss: 0.6099 - val_acc: 0.8444\n",
      "Epoch 36/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.5939 - acc: 0.8051 - val_loss: 0.5669 - val_acc: 0.8556\n",
      "Epoch 37/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.5569 - acc: 0.8266 - val_loss: 0.5878 - val_acc: 0.8556\n",
      "Epoch 38/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.5592 - acc: 0.8418 - val_loss: 0.4693 - val_acc: 0.8889\n",
      "\n",
      "Epoch 00038: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 39/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.4727 - acc: 0.8608 - val_loss: 0.5192 - val_acc: 0.8778\n",
      "Epoch 40/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.4791 - acc: 0.8532 - val_loss: 0.4174 - val_acc: 0.8889\n",
      "Epoch 41/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.4425 - acc: 0.8696 - val_loss: 0.4999 - val_acc: 0.8889\n",
      "Epoch 42/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.4808 - acc: 0.8367 - val_loss: 0.5048 - val_acc: 0.8889\n",
      "Epoch 43/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.4760 - acc: 0.8582 - val_loss: 0.5165 - val_acc: 0.9000\n",
      "Epoch 44/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.4395 - acc: 0.8646 - val_loss: 0.4579 - val_acc: 0.9222\n",
      "Epoch 45/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.4831 - acc: 0.8519 - val_loss: 0.4097 - val_acc: 0.9111\n",
      "\n",
      "Epoch 00045: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 46/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.4422 - acc: 0.8684 - val_loss: 0.4914 - val_acc: 0.8778\n",
      "Epoch 47/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.4134 - acc: 0.8937 - val_loss: 0.4870 - val_acc: 0.9000\n",
      "Epoch 48/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.4407 - acc: 0.8684 - val_loss: 0.4853 - val_acc: 0.8889\n",
      "Epoch 49/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.4121 - acc: 0.8722 - val_loss: 0.4352 - val_acc: 0.9111\n",
      "Epoch 50/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.4313 - acc: 0.8722 - val_loss: 0.4682 - val_acc: 0.9000\n",
      "\n",
      "Epoch 00050: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Epoch 00050: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f4dd064eda0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(training_generator, \n",
    "                   validation_data=validation_generator,\n",
    "                   epochs=100, callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train using images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we are going to use the images to train the classifier.\n",
    "\n",
    "First we use the same generator as before."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Using Custom Data generator for the images set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "params = {'batch_size': batch_size,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True}\n",
    "\n",
    "\n",
    "images_training_generator = DataGenerator(X_img_tr,y_tr, **params)\n",
    "images_validation_generator = DataGenerator(X_img_val,y_val, **params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 512, 512, 1)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im, y = images_training_generator.__getitem__(2)\n",
    "print(im.shape)\n",
    "plt.figure(figsize=(8,8))\n",
    "for i in range(9):\n",
    "    ax = plt.subplot(3,3,i+1)\n",
    "    ax.axis('off')\n",
    "    plt.imshow(im[i,:,:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we define the images model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "def images_model():\n",
    "    image = Input(shape=(image_max_size, image_max_size, 1), name='image')\n",
    "    # Pass it through the first convolutional layer\n",
    "    x = Conv2D(12, (4, 4), activation = 'relu')(image)\n",
    "    x = (MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))(x)\n",
    "\n",
    "    # Now through the second convolutional layer\n",
    "    x = (Conv2D(32, (4, 4),  activation = 'relu'))(x)\n",
    "    x = (MaxPooling2D(pool_size=(2, 2)))(x)\n",
    "\n",
    "    # Now through the second convolutional layer\n",
    "    x = (Conv2D(32, (4, 4),  activation = 'relu'))(x)\n",
    "    x = (MaxPooling2D(pool_size=(2, 2)))(x)\n",
    "\n",
    "    x = (Conv2D(16, (4, 4),  activation = 'relu'))(x)\n",
    "    x = (MaxPooling2D(pool_size=(2, 2)))(x)\n",
    "\n",
    "    #x = (Conv2D(16, (5, 5),  activation = 'relu'))(x)\n",
    "    #x = (MaxPooling2D(pool_size=(2, 2)))(x)\n",
    "\n",
    "    # Flatten our array\n",
    "    x = Flatten()(x)\n",
    "    # Define the pre-extracted feature input\n",
    "    x = Dropout(.5)(x)\n",
    "\n",
    "    # Add a fully connected layer just like in a normal MLP\n",
    "    x = Dense(100, activation='relu',name='dense_for_ens')(x)\n",
    "\n",
    "    # Get the final output\n",
    "    out = Dense(n_cats, activation='softmax')(x)\n",
    "    # How we create models with the Functional API\n",
    "    model = Model(inputs=image, outputs=out)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "image (InputLayer)           (None, 512, 512, 1)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_21 (Conv2D)           (None, 509, 509, 12)      204       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_21 (MaxPooling (None, 254, 254, 12)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_22 (Conv2D)           (None, 251, 251, 32)      6176      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_22 (MaxPooling (None, 125, 125, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_23 (Conv2D)           (None, 122, 122, 32)      16416     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_23 (MaxPooling (None, 61, 61, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_24 (Conv2D)           (None, 58, 58, 16)        8208      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_24 (MaxPooling (None, 29, 29, 16)        0         \n",
      "_________________________________________________________________\n",
      "flatten_6 (Flatten)          (None, 13456)             0         \n",
      "_________________________________________________________________\n",
      "dropout_17 (Dropout)         (None, 13456)             0         \n",
      "_________________________________________________________________\n",
      "dense_36 (Dense)             (None, 100)               1345700   \n",
      "_________________________________________________________________\n",
      "dense_37 (Dense)             (None, 99)                9999      \n",
      "=================================================================\n",
      "Total params: 1,386,703\n",
      "Trainable params: 1,386,703\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = images_model()\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "49/49 [==============================] - 3s 67ms/step - loss: 4.2916 - acc: 0.0689 - val_loss: 3.1654 - val_acc: 0.1979\n",
      "Epoch 2/100\n",
      "49/49 [==============================] - 2s 44ms/step - loss: 2.2260 - acc: 0.4247 - val_loss: 2.0634 - val_acc: 0.4375\n",
      "Epoch 3/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 1.1918 - acc: 0.6467 - val_loss: 1.9450 - val_acc: 0.4792\n",
      "Epoch 4/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.7051 - acc: 0.7615 - val_loss: 1.8631 - val_acc: 0.5417\n",
      "Epoch 5/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.4947 - acc: 0.8508 - val_loss: 2.1480 - val_acc: 0.5417\n",
      "Epoch 6/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.3235 - acc: 0.9107 - val_loss: 2.2132 - val_acc: 0.5729\n",
      "Epoch 7/100\n",
      "49/49 [==============================] - 2s 46ms/step - loss: 0.2062 - acc: 0.9362 - val_loss: 2.1845 - val_acc: 0.5000\n",
      "Epoch 8/100\n",
      "49/49 [==============================] - 2s 46ms/step - loss: 0.2029 - acc: 0.9324 - val_loss: 2.3952 - val_acc: 0.5208\n",
      "Epoch 9/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.1409 - acc: 0.9579 - val_loss: 2.3158 - val_acc: 0.5417\n",
      "\n",
      "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 10/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.0704 - acc: 0.9847 - val_loss: 2.5160 - val_acc: 0.6042\n",
      "Epoch 11/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.0453 - acc: 0.9911 - val_loss: 2.7751 - val_acc: 0.5625\n",
      "Epoch 12/100\n",
      "49/49 [==============================] - 2s 46ms/step - loss: 0.0400 - acc: 0.9847 - val_loss: 2.6472 - val_acc: 0.5833\n",
      "Epoch 13/100\n",
      "49/49 [==============================] - 2s 46ms/step - loss: 0.0383 - acc: 0.9923 - val_loss: 2.2637 - val_acc: 0.6146\n",
      "Epoch 14/100\n",
      "49/49 [==============================] - 2s 46ms/step - loss: 0.0154 - acc: 0.9974 - val_loss: 2.6097 - val_acc: 0.6146\n",
      "\n",
      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 00014: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f4cda4752e8>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(images_training_generator, \n",
    "                    validation_data=images_validation_generator,\n",
    "                    epochs=100, \n",
    "                    callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The intention of this notebook is not to generate the BEST model, however here worths to mention that this model overfits and is not generalizing well.\n",
    "Note the difference between the accuracy of the training and the validation set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['val_loss', 'val_acc', 'loss', 'acc', 'lr'])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = model.history.history\n",
    "history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4cf51041d0>]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "ax1 = plt.subplot(3,1,1)\n",
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.subplot(3,1,2, sharex=ax1)\n",
    "plt.plot(history['acc'])\n",
    "plt.plot(history['val_acc'])\n",
    "plt.subplot(3,1,3, sharex=ax1)\n",
    "plt.plot(history['lr'], color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing data augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we use the keras image data generator for the augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_augmentator= ImageDataGenerator(\n",
    "        #featurewise_center=True,\n",
    "        #featurewise_std_normalization=True,\n",
    "        rotation_range=30,\n",
    "        zoom_range=0.1,\n",
    "        width_shift_range=0.02,\n",
    "        height_shift_range=0.02,\n",
    "        horizontal_flip=True,\n",
    "        vertical_flip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "ims, ys = test_augmentator.flow(X_img_tr, y_tr, batch_size=9).next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "for i in range(9):\n",
    "    ax = plt.subplot(3,3,i+1)\n",
    "    ax.axis('off')\n",
    "    plt.imshow(ims[i,:,:,0])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data generator with augmentation\n",
    "\n",
    "we make a generator with augmentation.\n",
    "\n",
    "The key point here compared with the generator from above is that in the method \\_\\_data_generation we implement the ImageDataGenerator from keras.\n",
    "\n",
    "**Note**: the performance of this generator is very poor compared with the other. Try to find out why!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "class ImageGenerator(keras.utils.Sequence):\n",
    "    'Generates data for Keras'\n",
    "    def __init__(self, features, targets, \n",
    "                 n_classes =99, \n",
    "                 batch_size=32,  \n",
    "                 shuffle=True, \n",
    "                 repeats = 1):\n",
    "        \n",
    "        \n",
    "        self.n_classes = n_classes\n",
    "        self.n_vals = len(targets)\n",
    "        # since we are using data agumentation we repeat the number of times we show each image.\n",
    "        # we show the same original image but it can be rotated or flipper each time, so it is not the \"same\" image\n",
    "        self.list_IDs = np.repeat(np.arange(self.n_vals),repeats) #OJO con esto, deberian ser las imagenes validas si queremos hacer bien las cosas\n",
    "        self.batch_size = batch_size\n",
    "        self.features = features\n",
    "        self.shuffle = shuffle\n",
    "        self.targets = targets\n",
    "        self.targets_mc = keras.utils.to_categorical(targets, num_classes=self.n_classes)\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        \n",
    "        self.agumentator = ImageDataGenerator(\n",
    "        #featurewise_center=True,\n",
    "        #featurewise_std_normalization=True,\n",
    "        rotation_range=30,\n",
    "        zoom_range=0.1,\n",
    "        width_shift_range=0.01,\n",
    "        height_shift_range=0.01,\n",
    "        horizontal_flip=True,\n",
    "        vertical_flip=True)\n",
    "          \n",
    "    def __len__(self):\n",
    "        'Denotes the number of batches per epoch'\n",
    "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        'Generate one batch of data'\n",
    "        # Generate indexes of the batch\n",
    "        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
    "\n",
    "        # Find list of IDs\n",
    "        list_IDs_temp = [self.list_IDs[k] for k in indexes]\n",
    "\n",
    "        # Generate data\n",
    "        X, y = self.__data_generation(list_IDs_temp)\n",
    "        return X, y\n",
    "\n",
    "    def on_epoch_end(self):\n",
    "        'Updates indexes after each epoch'\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        if self.shuffle == True:\n",
    "            np.random.shuffle(self.indexes)\n",
    "\n",
    "    def __data_generation(self, list_IDs_temp):\n",
    "        'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)\n",
    "        # Initialization\n",
    "        X = self.features[list_IDs_temp]\n",
    "        y = self.targets_mc[list_IDs_temp]\n",
    "\n",
    "        X, y = self.agumentator.flow(X, y, batch_size=self.batch_size).next()\n",
    "        \n",
    "        # Generate data\n",
    "        for i, ID in enumerate(list_IDs_temp):\n",
    "            # Store sample\n",
    "            X[i,] = np.load('data/' + ID + '.npy')\n",
    "\n",
    "            # Store class\n",
    "            y[i] = self.labels[ID]\n",
    "\n",
    "        return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "from multiprocessing.pool import ThreadPool\n",
    "\n",
    "import keras\n",
    "import numpy as np\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.utils import OrderedEnqueuer\n",
    "\n",
    "\n",
    "class ImageGeneratorParallel(keras.utils.Sequence):\n",
    "    'Generates data for Keras'\n",
    "\n",
    "    def __init__(self, features, targets,\n",
    "                 n_classes=99,\n",
    "                 batch_size=32,\n",
    "                 shuffle=True,\n",
    "                 repeats=1,\n",
    "                 parallel = True):\n",
    "        self.n_classes = n_classes\n",
    "        self.n_vals = len(targets)\n",
    "        # since we are using data agumentation we repeat the number of times we show each image.\n",
    "        # we show the same original image but it can be rotated or flipper each time, so it is not the \"same\" image\n",
    "        self.list_IDs = np.repeat(np.arange(self.n_vals),\n",
    "                                  repeats)  # OJO con esto, deberian ser las imagenes validas si queremos hacer bien las cosas\n",
    "        self.batch_size = batch_size\n",
    "        self.features = features\n",
    "        self.shuffle = shuffle\n",
    "        self.targets = targets\n",
    "        self.targets_mc = keras.utils.to_categorical(targets, num_classes=self.n_classes)\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        self.pool = None\n",
    "        self.parallel = parallel\n",
    "\n",
    "        self.agumentator = ImageDataGenerator(\n",
    "            # featurewise_center=True,\n",
    "            # featurewise_std_normalization=True,\n",
    "            rotation_range=30,\n",
    "            zoom_range=0.1,\n",
    "            width_shift_range=0.01,\n",
    "            height_shift_range=0.01,\n",
    "            horizontal_flip=True,\n",
    "            vertical_flip=True)\n",
    "\n",
    "    def __len__(self):\n",
    "        'Denotes the number of batches per epoch'\n",
    "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        'Generate one batch of data'\n",
    "        # Generate indexes of the batch\n",
    "        if self.parallel:\n",
    "            if self.pool is None:\n",
    "                self.pool = ThreadPool(4)\n",
    "        indexes = self.indexes[index * self.batch_size:(index + 1) * self.batch_size]\n",
    "\n",
    "        # Find list of IDs\n",
    "        list_IDs_temp = [self.list_IDs[k] for k in indexes]\n",
    "\n",
    "        # Generate data\n",
    "        if self.parallel:\n",
    "            X, y = self.__data_generation_threads(list_IDs_temp)\n",
    "        else:\n",
    "            X, y = self.__data_generation(list_IDs_temp)        \n",
    "        return X, y\n",
    "\n",
    "    def on_epoch_end(self):\n",
    "        'Updates indexes after each epoch'\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        if self.shuffle == True:\n",
    "            np.random.shuffle(self.indexes)\n",
    "\n",
    "    def __data_generation(self, list_IDs_temp):\n",
    "        'Generates data containing batch_size samples'  # X : (n_samples, *dim, n_channels)\n",
    "        # Initialization\n",
    "        X = self.features[list_IDs_temp]\n",
    "        y = self.targets_mc[list_IDs_temp]\n",
    "        X = np.array(self.agumentator.flow(X, batch_size=self.batch_size, shuffle=self.shuffle).next())\n",
    "\n",
    "        return X, y\n",
    "\n",
    "    def __data_generation_threads(self, list_IDs_temp):\n",
    "        'Generates data containing batch_size samples'  # X : (n_samples, *dim, n_channels)\n",
    "        # Initialization\n",
    "        X = self.features[list_IDs_temp]\n",
    "        y = self.targets_mc[list_IDs_temp]\n",
    "        X = np.array(self.pool.map(lambda xi: self.agumentator.random_transform(self.agumentator.standardize(xi)), X))\n",
    "\n",
    "        return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = images_model()\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we are using different generators for the training and validation set.\n",
    "\n",
    "In the training set we use the same image `augmentation_image_repetition`\n",
    " times but the repetitions are different samples from the augmentator, so probably the differ each other and can be thought as different images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "#augmentation_image_repetition = 3\n",
    "#batch_size = 16\n",
    "#params = {'batch_size': batch_size,\n",
    "#          'n_classes': 99,\n",
    "#          'shuffle': True,}\n",
    "#images_validation_generator = DataGenerator(X_img_val,y_val, **params) # <-- no augmentation\n",
    "#\n",
    "#\n",
    "#\n",
    "#params['repeats'] = augmentation_image_repetition\n",
    "#images_training_generator = ImageGenerator(X_img_tr,y_tr, **params)\n",
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "using only augmentator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "flow() missing 1 required positional argument: 'x'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-180-e81c884c135f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0my_tr_cat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_tr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m model.fit_generator(ImageDataGenerator.flow(X_img_tr, y= y_tr_cat, batch_size=batch_size), \n\u001b[0m\u001b[1;32m     25\u001b[0m                     \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimages_validation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m                     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: flow() missing 1 required positional argument: 'x'"
     ]
    }
   ],
   "source": [
    "model = images_model()\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "augmentation_image_repetition = 1\n",
    "batch_size = 16\n",
    "params = {'batch_size': batch_size,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True,}\n",
    "images_validation_generator = DataGenerator(X_img_val,y_val, **params) # <-- no augmentation\n",
    "\n",
    "\n",
    "\n",
    "agumentator = ImageDataGenerator(\n",
    "            # featurewise_center=True,\n",
    "            # featurewise_std_normalization=True,\n",
    "            rotation_range=30,\n",
    "            zoom_range=0.1,\n",
    "            width_shift_range=0.01,\n",
    "            height_shift_range=0.01,\n",
    "            horizontal_flip=True,\n",
    "            vertical_flip=True)\n",
    "\n",
    "y_tr_cat = to_categorical(y_tr, 99)\n",
    "model.fit_generator(ImageDataGenerator.flow(X_img_tr, y= y_tr_cat, batch_size=batch_size), \n",
    "                    validation_data=images_validation_generator,\n",
    "                    epochs=100, \n",
    "                    callbacks = callbacks,\n",
    "                   )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "using augmentator in parallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "49/49 [==============================] - 19s 387ms/step - loss: 4.5787 - acc: 0.0191 - val_loss: 4.2784 - val_acc: 0.0208\n",
      "Epoch 2/100\n",
      "49/49 [==============================] - 15s 312ms/step - loss: 4.0272 - acc: 0.0702 - val_loss: 3.2079 - val_acc: 0.2083\n",
      "Epoch 3/100\n",
      "49/49 [==============================] - 17s 340ms/step - loss: 3.2917 - acc: 0.1416 - val_loss: 2.7439 - val_acc: 0.2812\n",
      "Epoch 4/100\n",
      "49/49 [==============================] - 15s 312ms/step - loss: 2.9124 - acc: 0.1722 - val_loss: 2.4835 - val_acc: 0.2917\n",
      "Epoch 5/100\n",
      "49/49 [==============================] - 17s 344ms/step - loss: 2.7143 - acc: 0.2270 - val_loss: 2.3889 - val_acc: 0.2812\n",
      "Epoch 6/100\n",
      "11/49 [=====>........................] - ETA: 12s - loss: 2.6702 - acc: 0.2045"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-169-4fc0cb091b91>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     18\u001b[0m                     \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimages_validation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m                     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m                     \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m                    )\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0mbatch_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m                 \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__len__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    593\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                 \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_done\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    662\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    663\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 664\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    665\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    659\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    660\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 661\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    662\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    663\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    293\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model = images_model()\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "augmentation_image_repetition = 1\n",
    "batch_size = 16\n",
    "params = {'batch_size': batch_size,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True,}\n",
    "images_validation_generator = DataGenerator(X_img_val,y_val, **params) # <-- no augmentation\n",
    "\n",
    "\n",
    "\n",
    "params['repeats'] = augmentation_image_repetition\n",
    "params['parallel'] = True\n",
    "images_training_generator = ImageGeneratorParallel(X_img_tr,y_tr, **params)\n",
    "\n",
    "model.fit_generator(images_training_generator, \n",
    "                    validation_data=images_validation_generator,\n",
    "                    epochs=100, \n",
    "                    callbacks = callbacks\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "49/49 [==============================] - 11s 232ms/step - loss: 4.6051 - acc: 0.0051 - val_loss: 4.5955 - val_acc: 0.0208\n",
      "Epoch 2/100\n",
      "49/49 [==============================] - 11s 215ms/step - loss: 4.5961 - acc: 0.0064 - val_loss: 4.5952 - val_acc: 0.0104\n",
      "Epoch 3/100\n",
      "49/49 [==============================] - 11s 220ms/step - loss: 4.5961 - acc: 0.0077 - val_loss: 4.5951 - val_acc: 0.0104\n",
      "Epoch 4/100\n",
      "41/49 [========================>.....] - ETA: 1s - loss: 4.5959 - acc: 0.0046"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-167-dbd007608916>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     18\u001b[0m                     \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimages_validation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m                     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m                     \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m                    )\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0mbatch_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m                 \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__len__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    593\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                 \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_done\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    662\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    663\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 664\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    665\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    659\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    660\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 661\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    662\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    663\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    293\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model = images_model()\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "augmentation_image_repetition = 1\n",
    "batch_size = 16\n",
    "params = {'batch_size': batch_size,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True,}\n",
    "images_validation_generator = DataGenerator(X_img_val,y_val, **params) # <-- no augmentation\n",
    "\n",
    "\n",
    "\n",
    "params['repeats'] = augmentation_image_repetition\n",
    "params['parallel'] = False\n",
    "images_training_generator = ImageGeneratorParallel(X_img_tr,y_tr, **params)\n",
    "\n",
    "model.fit_generator(images_training_generator, \n",
    "                    validation_data=images_validation_generator,\n",
    "                    epochs=100, \n",
    "                    callbacks = callbacks\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "augmentation_image_repetition = 3\n",
    "batch_size = 16\n",
    "params = {'batch_size': batch_size,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True,}\n",
    "images_validation_generator = DataGenerator(X_img_val,y_val, **params) # <-- no augmentation\n",
    "\n",
    "\n",
    "\n",
    "params['repeats'] = augmentation_image_repetition\n",
    "images_training_generator = ImageGeneratorParallel(X_img_tr,y_tr, **params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "147/148 [============================>.] - ETA: 0s - loss: 2.8056 - acc: 0.2032Epoch 1/100\n",
      "148/148 [==============================] - 54s 367ms/step - loss: 2.8008 - acc: 0.2035 - val_loss: 2.2919 - val_acc: 0.3333\n",
      "Epoch 2/100\n",
      "Epoch 1/100\n",
      "105/148 [====================>.........] - ETA: 15s - loss: 2.3886 - acc: 0.3048"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process ForkPoolWorker-2904:\n",
      "Process ForkPoolWorker-2900:\n",
      "Process ForkPoolWorker-2902:\n",
      "Process ForkPoolWorker-2903:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Process ForkPoolWorker-2905:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/pool.py\", line 125, in worker\n",
      "    put((job, i, result))\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/queues.py\", line 346, in put\n",
      "    with self._wlock:\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "Process ForkPoolWorker-2899:\n",
      "Process ForkPoolWorker-2901:\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/queues.py\", line 335, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "Process ForkPoolWorker-2898:\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "KeyboardInterrupt\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/ivan/coding_projects/testing3/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                     Traceback (most recent call last)",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m                 \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                 \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_done\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    663\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 664\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    665\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    660\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 661\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    662\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                     Traceback (most recent call last)",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    258\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0menqueuer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 259\u001b[0;31m                 \u001b[0menqueuer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    260\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mstop\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    497\u001b[0m         \u001b[0m_SHARED_SEQUENCES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muid\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1055\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1056\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_tstate_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1057\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36m_wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m   1071\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_stopped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1072\u001b[0;31m         \u001b[0;32melif\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1073\u001b[0m             \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-160-6467d43177f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m                     \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m                     \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m                     \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m                    )\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mval_enqueuer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m                 \u001b[0mval_enqueuer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mstop\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    494\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munfinished_tasks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    497\u001b[0m         \u001b[0m_SHARED_SEQUENCES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muid\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1056\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_tstate_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1057\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1058\u001b[0m             \u001b[0;31m# the behavior of a negative timeout isn't documented, but\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/coding_projects/testing3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36m_wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m   1070\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlock\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# already determined that the C code is done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1071\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_stopped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1072\u001b[0;31m         \u001b[0;32melif\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1073\u001b[0m             \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1074\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#model.fit_generator(images_training_generator, \n",
    "#                    validation_data=images_validation_generator,\n",
    "#                    epochs=100, \n",
    "#                    callbacks = callbacks\n",
    "#                   )\n",
    "\n",
    "model.fit_generator(images_training_generator, \n",
    "                    validation_data=images_validation_generator,\n",
    "                    epochs=100, \n",
    "                    callbacks = callbacks,\n",
    "                    use_multiprocessing=True,\n",
    "                    max_queue_size=12,\n",
    "                    workers=4\n",
    "                   )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we improved the performance in the validation set. Although the model it still overfitting, the performance is much better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4caa571940>]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = model.history.history\n",
    "plt.figure()\n",
    "ax1 = plt.subplot(3,1,1)\n",
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.subplot(3,1,2, sharex=ax1)\n",
    "plt.plot(history['acc'])\n",
    "plt.plot(history['val_acc'])\n",
    "plt.subplot(3,1,3, sharex=ax1)\n",
    "plt.plot(history['lr'], color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "one last try with more data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "image (InputLayer)           (None, 512, 512, 1)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 509, 509, 12)      204       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_17 (MaxPooling (None, 254, 254, 12)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 251, 251, 32)      6176      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_18 (MaxPooling (None, 125, 125, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 122, 122, 32)      16416     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_19 (MaxPooling (None, 61, 61, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 58, 58, 16)        8208      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_20 (MaxPooling (None, 29, 29, 16)        0         \n",
      "_________________________________________________________________\n",
      "flatten_5 (Flatten)          (None, 13456)             0         \n",
      "_________________________________________________________________\n",
      "dropout_16 (Dropout)         (None, 13456)             0         \n",
      "_________________________________________________________________\n",
      "dense_34 (Dense)             (None, 100)               1345700   \n",
      "_________________________________________________________________\n",
      "dense_35 (Dense)             (None, 99)                9999      \n",
      "=================================================================\n",
      "Total params: 1,386,703\n",
      "Trainable params: 1,386,703\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/100\n",
      "123/123 [==============================] - 30s 247ms/step - loss: 4.3976 - acc: 0.0307 - val_loss: 3.5240 - val_acc: 0.1146\n",
      "Epoch 2/100\n",
      "  2/123 [..............................] - ETA: 55s - loss: 3.7182 - acc: 0.0625 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ivan/coding_projects/testing3/lib/python3.6/site-packages/keras/callbacks.py:122: UserWarning: Method on_batch_end() is slow compared to the batch update (0.156538). Check your callbacks.\n",
      "  % delta_t_median)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123/123 [==============================] - 28s 228ms/step - loss: 2.7939 - acc: 0.2215 - val_loss: 2.2765 - val_acc: 0.3542\n",
      "Epoch 3/100\n",
      "123/123 [==============================] - 29s 238ms/step - loss: 2.1452 - acc: 0.3537 - val_loss: 1.8222 - val_acc: 0.4688\n",
      "Epoch 4/100\n",
      "123/123 [==============================] - 30s 241ms/step - loss: 1.7542 - acc: 0.4515 - val_loss: 1.6962 - val_acc: 0.4583\n",
      "Epoch 5/100\n",
      "123/123 [==============================] - 29s 237ms/step - loss: 1.4720 - acc: 0.5340 - val_loss: 1.4010 - val_acc: 0.5625\n",
      "Epoch 6/100\n",
      "123/123 [==============================] - 29s 236ms/step - loss: 1.2816 - acc: 0.5871 - val_loss: 1.2258 - val_acc: 0.6458\n",
      "Epoch 7/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 1.1728 - acc: 0.6032Epoch 7/100\n",
      "123/123 [==============================] - 29s 236ms/step - loss: 1.1728 - acc: 0.6026 - val_loss: 1.2132 - val_acc: 0.6458\n",
      "Epoch 8/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 1.0293 - acc: 0.6468Epoch 8/100\n",
      "123/123 [==============================] - 29s 239ms/step - loss: 1.0274 - acc: 0.6476 - val_loss: 1.0496 - val_acc: 0.6979\n",
      "Epoch 9/100\n",
      "123/123 [==============================] - 29s 232ms/step - loss: 0.9068 - acc: 0.6939 - val_loss: 1.0229 - val_acc: 0.6667\n",
      "Epoch 10/100\n",
      "123/123 [==============================] - 29s 239ms/step - loss: 0.8328 - acc: 0.7228 - val_loss: 1.1151 - val_acc: 0.6667\n",
      "Epoch 11/100\n",
      "123/123 [==============================] - 28s 231ms/step - loss: 0.7673 - acc: 0.7383 - val_loss: 0.9030 - val_acc: 0.7604\n",
      "Epoch 12/100\n",
      "\n",
      "123/123 [==============================] - 29s 239ms/step - loss: 0.7124 - acc: 0.7525 - val_loss: 1.1461 - val_acc: 0.6875\n",
      "Epoch 13/100\n",
      "123/123 [==============================] - 29s 237ms/step - loss: 0.6757 - acc: 0.7576 - val_loss: 0.8723 - val_acc: 0.7812\n",
      "Epoch 14/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.6408 - acc: 0.7743Epoch 14/100\n",
      "123/123 [==============================] - 29s 235ms/step - loss: 0.6392 - acc: 0.7746 - val_loss: 0.8392 - val_acc: 0.7708\n",
      "Epoch 15/100\n",
      "123/123 [==============================] - 29s 235ms/step - loss: 0.6333 - acc: 0.7818 - val_loss: 0.8746 - val_acc: 0.7292\n",
      "Epoch 16/100\n",
      "123/123 [==============================] - 30s 240ms/step - loss: 0.5857 - acc: 0.7917 - val_loss: 0.8709 - val_acc: 0.7500\n",
      "Epoch 17/100\n",
      "123/123 [==============================] - 28s 227ms/step - loss: 0.5207 - acc: 0.8204 - val_loss: 0.7457 - val_acc: 0.8333\n",
      "Epoch 18/100\n",
      "123/123 [==============================] - 28s 231ms/step - loss: 0.5286 - acc: 0.8204 - val_loss: 0.7046 - val_acc: 0.8125\n",
      "Epoch 19/100\n",
      "123/123 [==============================] - 29s 235ms/step - loss: 0.5204 - acc: 0.8158 - val_loss: 0.8120 - val_acc: 0.7917\n",
      "Epoch 20/100\n",
      "123/123 [==============================] - 29s 239ms/step - loss: 0.5088 - acc: 0.8120 - val_loss: 0.8379 - val_acc: 0.7604\n",
      "Epoch 21/100\n",
      "123/123 [==============================] - 28s 231ms/step - loss: 0.4693 - acc: 0.8316 - val_loss: 0.6556 - val_acc: 0.8125\n",
      "Epoch 22/100\n",
      "123/123 [==============================] - 29s 232ms/step - loss: 0.4403 - acc: 0.8460 - val_loss: 0.6564 - val_acc: 0.8229\n",
      "Epoch 23/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.4444 - acc: 0.8432Epoch 23/100\n",
      "123/123 [==============================] - 30s 242ms/step - loss: 0.4436 - acc: 0.8438 - val_loss: 0.6322 - val_acc: 0.8333\n",
      "Epoch 24/100\n",
      "  2/123 [..............................] - ETA: 1:15 - loss: 0.4493 - acc: 0.8281"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ivan/coding_projects/testing3/lib/python3.6/site-packages/keras/callbacks.py:122: UserWarning: Method on_batch_end() is slow compared to the batch update (0.150554). Check your callbacks.\n",
      "  % delta_t_median)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123/123 [==============================] - 28s 227ms/step - loss: 0.4225 - acc: 0.8516 - val_loss: 0.6081 - val_acc: 0.8438\n",
      "Epoch 25/100\n",
      "123/123 [==============================] - 30s 241ms/step - loss: 0.4066 - acc: 0.8534 - val_loss: 0.5879 - val_acc: 0.8646\n",
      "Epoch 26/100\n",
      "123/123 [==============================] - 29s 233ms/step - loss: 0.3997 - acc: 0.8567 - val_loss: 0.7076 - val_acc: 0.8229\n",
      "Epoch 27/100\n",
      "123/123 [==============================] - 29s 234ms/step - loss: 0.3944 - acc: 0.8656 - val_loss: 0.5359 - val_acc: 0.8646\n",
      "Epoch 28/100\n",
      "123/123 [==============================] - 29s 234ms/step - loss: 0.3707 - acc: 0.8643 - val_loss: 0.6669 - val_acc: 0.8229\n",
      "Epoch 29/100\n",
      "123/123 [==============================] - 29s 236ms/step - loss: 0.3627 - acc: 0.8692 - val_loss: 0.5994 - val_acc: 0.8750\n",
      "Epoch 30/100\n",
      "  1/123 [..............................] - ETA: 1:56 - loss: 0.3159 - acc: 0.906"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ivan/coding_projects/testing3/lib/python3.6/site-packages/keras/callbacks.py:122: UserWarning: Method on_batch_end() is slow compared to the batch update (0.162394). Check your callbacks.\n",
      "  % delta_t_median)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  4/123 [..............................] - ETA: 46s - loss: 0.3546 - acc: 0.8672"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ivan/coding_projects/testing3/lib/python3.6/site-packages/keras/callbacks.py:122: UserWarning: Method on_batch_end() is slow compared to the batch update (0.146257). Check your callbacks.\n",
      "  % delta_t_median)\n",
      "/home/ivan/coding_projects/testing3/lib/python3.6/site-packages/keras/callbacks.py:122: UserWarning: Method on_batch_end() is slow compared to the batch update (0.130120). Check your callbacks.\n",
      "  % delta_t_median)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123/123 [==============================] - 29s 235ms/step - loss: 0.3356 - acc: 0.8824 - val_loss: 0.4377 - val_acc: 0.8646\n",
      "Epoch 31/100\n",
      "123/123 [==============================] - 28s 231ms/step - loss: 0.3290 - acc: 0.8791 - val_loss: 0.7718 - val_acc: 0.7812\n",
      "Epoch 32/100\n",
      "123/123 [==============================] - 29s 232ms/step - loss: 0.3387 - acc: 0.8778 - val_loss: 0.5563 - val_acc: 0.8333\n",
      "Epoch 33/100\n",
      "Epoch 32/100\n",
      "123/123 [==============================] - 29s 237ms/step - loss: 0.3222 - acc: 0.8839 - val_loss: 0.7537 - val_acc: 0.7917\n",
      "Epoch 34/100\n",
      "123/123 [==============================] - 29s 235ms/step - loss: 0.3477 - acc: 0.8758 - val_loss: 0.6835 - val_acc: 0.8333\n",
      "Epoch 35/100\n",
      "123/123 [==============================] - 29s 232ms/step - loss: 0.3074 - acc: 0.8885 - val_loss: 0.7469 - val_acc: 0.7917\n",
      "\n",
      "Epoch 00035: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 36/100\n",
      "123/123 [==============================] - 27s 216ms/step - loss: 0.2634 - acc: 0.9017 - val_loss: 0.8431 - val_acc: 0.8229\n",
      "\bEpoch 37/100\n",
      "123/123 [==============================] - 30s 244ms/step - loss: 0.2514 - acc: 0.9078 - val_loss: 0.5876 - val_acc: 0.8333\n",
      "Epoch 38/100\n",
      "122/123 [============================>.] - ETA: 0s - loss: 0.2254 - acc: 0.9185Epoch 38/100\n",
      "123/123 [==============================] - 29s 232ms/step - loss: 0.2274 - acc: 0.9182 - val_loss: 0.7667 - val_acc: 0.8438\n",
      "Epoch 39/100\n",
      "123/123 [==============================] - 29s 235ms/step - loss: 0.2454 - acc: 0.9151 - val_loss: 0.5989 - val_acc: 0.8854\n",
      "Epoch 40/100\n",
      "123/123 [==============================] - 29s 238ms/step - loss: 0.2237 - acc: 0.9190 - val_loss: 0.6299 - val_acc: 0.8229\n",
      "\n",
      "Epoch 00040: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 00040: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f4cf581f208>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "augmentation_image_repetition = 5\n",
    "batch_size = 32\n",
    "\n",
    "model = images_model()\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()\n",
    "\n",
    "\n",
    "params = {'batch_size': batch_size,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True,}\n",
    "images_validation_generator = DataGenerator(X_img_val,y_val, **params) # <-- no augmentation\n",
    "params['repeats'] = augmentation_image_repetition\n",
    "images_training_generator = ImageGenerator(X_img_tr,y_tr, **params)\n",
    "\n",
    "\n",
    "model.fit_generator(images_training_generator, \n",
    "                    validation_data=images_validation_generator,\n",
    "                   epochs=100, callbacks = callbacks,\n",
    "                   use_multiprocessing=True,\n",
    "                    max_queue_size=10,\n",
    "                    workers=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that using data augmentation with 5 repetitions makes the network to generalize better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4cdaf43a20>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = model.history.history\n",
    "plt.figure()\n",
    "ax1 = plt.subplot(3,1,1)\n",
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.subplot(3,1,2, sharex=ax1)\n",
    "plt.plot(history['acc'])\n",
    "plt.plot(history['val_acc'])\n",
    "plt.subplot(3,1,3, sharex=ax1)\n",
    "plt.plot(history['lr'], color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.save('./model_image_5_repts.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features and Image generator\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we are going to build a generator that returns both, images and features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EnsembleGenerator(keras.utils.Sequence):\n",
    "\n",
    "    def __init__(self, x_tuple, targets, \n",
    "                 n_classes =99, \n",
    "                 batch_size=32,  \n",
    "                 shuffle=True,\n",
    "                 repeats = 1,\n",
    "                 validate = False):\n",
    "\n",
    "        # in validation mode we want the data as it comes, no agumentation or changes\n",
    "        self.validate = validate\n",
    "        if self.validate:\n",
    "            repeats = 1\n",
    "            \n",
    "        features, images = x_tuple[0], x_tuple[1]\n",
    "        self.n_classes = n_classes\n",
    "        self.n_vals = len(targets)\n",
    "        # since we are using data agumentation we repeat the number of times we show each image.\n",
    "        # we show the same original image but it can be rotated or flipper each time, so it is not the \"same\" image\n",
    "        self.list_IDs = np.repeat(np.arange(self.n_vals),repeats) #NOTE: this is not the best implementation for cases where you load the images from disk\n",
    "        self.batch_size = batch_size\n",
    "        self.features = features\n",
    "        self.images = images        \n",
    "        print('feature: {}'.format(self.features.shape))\n",
    "        print('images: {}'.format(self.images.shape))\n",
    "        self.shuffle = shuffle\n",
    "        self.targets = targets\n",
    "        self.targets_mc = keras.utils.to_categorical(targets, num_classes=self.n_classes)\n",
    "        self.indexes = np.arange(len(self.list_IDs)) #this is ok\n",
    "        \n",
    "        self.agumentator = ImageDataGenerator(\n",
    "                                    #featurewise_center=True,\n",
    "                                    #featurewise_std_normalization=True,\n",
    "                                    rotation_range=30,\n",
    "                                    zoom_range=0.1,\n",
    "                                    width_shift_range=0.01,\n",
    "                                    height_shift_range=0.01,\n",
    "                                    horizontal_flip=True,\n",
    "                                    vertical_flip=True)\n",
    "    def __len__(self):\n",
    "        'Denotes the number of batches per epoch'\n",
    "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        'Generate one batch of data'\n",
    "        # Generate indexes of the batch\n",
    "        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
    "\n",
    "        # Find list of IDs\n",
    "        list_IDs_temp = [self.list_IDs[k] for k in indexes]\n",
    "\n",
    "        # Generate data\n",
    "        Xi, yi = self.__image_generation(list_IDs_temp)        \n",
    "        Xf, yf = self.__feature_generation(list_IDs_temp)\n",
    "        if np.all(yf==yi):\n",
    "            y = yf\n",
    "        else:\n",
    "            print(np.argmax(self.targets_mc[list_IDs_temp],axis=1))\n",
    "            print(np.argmax(yf, axis=1))\n",
    "            print(np.argmax(yi, axis=1))\n",
    "            raise ValueError('not equal')\n",
    "                \n",
    "            \n",
    "        return ([Xf,Xi], y)\n",
    "\n",
    "    def on_epoch_end(self):\n",
    "        'Updates indexes after each epoch'\n",
    "        self.indexes = self.list_IDs\n",
    "        if self.shuffle == True:\n",
    "            np.random.shuffle(self.indexes)\n",
    "\n",
    "    def __feature_generation(self, list_IDs_temp):\n",
    "        y = self.targets_mc[list_IDs_temp]\n",
    "        X = self.features[list_IDs_temp]\n",
    "        return X, y\n",
    "        \n",
    "    def __image_generation(self, list_IDs_temp):\n",
    "        # Initialization\n",
    "        X = self.images[list_IDs_temp]\n",
    "        yr = self.targets_mc[list_IDs_temp]\n",
    "        if not self.validate:\n",
    "#            X, y = self.agumentator.flow(X, y, batch_size=self.batch_size).next()\n",
    "            X, y = self.agumentator.flow(X, yr, batch_size=len(list_IDs_temp), shuffle=False).next()            \n",
    "            if not np.all(y==yr):\n",
    "#                print('not equal after agumentator')\n",
    "                raise Exception('not equal after agumentator')\n",
    "        else:\n",
    "            y = yr\n",
    "        \n",
    "\n",
    "        return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature: (792, 192)\n",
      "images: (792, 512, 512, 1)\n",
      "feature: (99, 192)\n",
      "images: (99, 512, 512, 1)\n"
     ]
    }
   ],
   "source": [
    "batch_size = 16\n",
    "params = {'batch_size': batch_size,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True,\n",
    "         }\n",
    "params['repeats'] = 5\n",
    "ensemble_training_generator = EnsembleGenerator((X_num_tr,X_img_tr),y_tr, **params)\n",
    "params['validate']=True\n",
    "ensemble_validate_generator = EnsembleGenerator((X_num_val,X_img_val),y_val, **params)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(792, 192)\n",
      "(792, 512, 512, 1)\n"
     ]
    }
   ],
   "source": [
    "print(ensemble_training_generator.features.shape)\n",
    "print(ensemble_training_generator.images.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_model = features_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "lay = f_model.layers[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dense_first_layer\n",
      "dropout_30\n",
      "dense_for_ens\n",
      "out_layer\n"
     ]
    }
   ],
   "source": [
    "for lay in f_model.layers:\n",
    "    print(lay.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "feature_input (InputLayer)   (None, 192)               0         \n",
      "_________________________________________________________________\n",
      "dense_first_layer (Dense)    (None, 64)                12352     \n",
      "_________________________________________________________________\n",
      "dropout_30 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_for_ens (Dense)        (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "out_layer (Dense)            (None, 99)                12771     \n",
      "=================================================================\n",
      "Total params: 33,443\n",
      "Trainable params: 33,443\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_layers= []\n",
    "\n",
    "x = Input(shape=(192,), name = 'feature_input')\n",
    "model_layers.append(x)\n",
    "for lay in f_model.layers:\n",
    "    x = lay(x)\n",
    "    model_layers.append(x)\n",
    "    \n",
    "model = Model(inputs = model_layers[0], outputs = model_layers[-1])\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "image_input (InputLayer)        (None, 512, 512, 1)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 507, 507, 12) 444         image_input[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_49 (MaxPooling2D) (None, 253, 253, 12) 0           conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 250, 250, 32) 6176        max_pooling2d_49[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_50 (MaxPooling2D) (None, 125, 125, 32) 0           conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 122, 122, 32) 16416       max_pooling2d_50[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_51 (MaxPooling2D) (None, 61, 61, 32)   0           conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 58, 58, 16)   8208        max_pooling2d_51[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_52 (MaxPooling2D) (None, 29, 29, 16)   0           conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 26, 26, 16)   4112        max_pooling2d_52[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "feature_input (InputLayer)      (None, 192)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_53 (MaxPooling2D) (None, 13, 13, 16)   0           conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_50 (Dense)                (None, 100)          19300       feature_input[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_13 (Flatten)            (None, 2704)         0           max_pooling2d_53[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dropout_24 (Dropout)            (None, 100)          0           dense_50[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_25 (Dropout)            (None, 2704)         0           flatten_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_51 (Dense)                (None, 100)          10100       dropout_24[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_52 (Dense)                (None, 100)          270500      dropout_25[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 200)          0           dense_51[0][0]                   \n",
      "                                                                 dense_52[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_26 (Dropout)            (None, 200)          0           concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Dense_conc (Dense)              (None, 100)          20100       dropout_26[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_53 (Dense)                (None, 50)           5050        Dense_conc[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_54 (Dense)                (None, 99)           5049        dense_53[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 365,455\n",
      "Trainable params: 365,455\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#model = Sequential()\n",
    "#model.add(Dense(64, input_shape = (192,), activation = 'relu'))\n",
    "#model.add(Dropout(0.3))\n",
    "#model.add(Dense(128,activation = 'relu'))\n",
    "#model.add(Dense(n_cats,activation = 'softmax', name = 'out_layer'))\n",
    "\n",
    "feature = Input(shape=(192,), name = 'feature_input')\n",
    "xf = Dense(100, activation = 'relu')(feature)\n",
    "xf = Dropout(0.3)(xf)\n",
    "xf = Dense(100, activation='relu')(xf)\n",
    "\n",
    "image = Input(shape=(image_max_size, image_max_size, 1), name='image_input')\n",
    "# Pass it through the first convolutional layer\n",
    "xi = Conv2D(12, (6, 6), activation = 'relu')(image)\n",
    "xi = (MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))(xi)\n",
    "xi = (Conv2D(32, (4, 4),  activation = 'relu'))(xi)\n",
    "xi = (MaxPooling2D(pool_size=(2, 2)))(xi)\n",
    "xi = (Conv2D(32, (4, 4),  activation = 'relu'))(xi)\n",
    "xi = (MaxPooling2D(pool_size=(2, 2)))(xi)\n",
    "xi = (Conv2D(16, (4, 4),  activation = 'relu'))(xi)\n",
    "xi = (MaxPooling2D(pool_size=(2, 2)))(xi)\n",
    "if image_max_size>256:\n",
    "    xi = (Conv2D(16, (4, 4),  activation = 'relu'))(xi)\n",
    "    xi = (MaxPooling2D(pool_size=(2, 2)))(xi)\n",
    "xi = Flatten()(xi) # Flatten our array\n",
    "xi = Dropout(.5)(xi)\n",
    "xi = Dense(100, activation='relu')(xi)\n",
    "\n",
    "xe = Concatenate(axis = -1)([xf, xi])\n",
    "xe = Dropout(0.1)(xe)\n",
    "xe = Dense(100, activation='relu', name='Dense_conc')(xe)\n",
    "xe = Dense(50, activation='relu')(xe)\n",
    "\n",
    "# Get the final output\n",
    "out = Dense(n_cats, activation='softmax')(xe)\n",
    "# How we create models with the Functional API\n",
    "model = Model(inputs=[feature,image], outputs=out)\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "147/148 [============================>.] - ETA: 0s - loss: 1.9192 - acc: 0.4018Epoch 1/100\n",
      "148/148 [==============================] - 21s 144ms/step - loss: 1.9142 - acc: 0.4033 - val_loss: 1.6638 - val_acc: 0.5000\n",
      "Epoch 2/100\n",
      "Epoch 1/100\n",
      "148/148 [==============================] - 21s 143ms/step - loss: 1.3621 - acc: 0.5490 - val_loss: 1.2068 - val_acc: 0.6771\n",
      "Epoch 3/100\n",
      "148/148 [==============================] - 20s 136ms/step - loss: 1.0139 - acc: 0.6605 - val_loss: 1.0895 - val_acc: 0.7083\n",
      "Epoch 4/100\n",
      "148/148 [==============================] - 22s 145ms/step - loss: 0.8536 - acc: 0.7061 - val_loss: 0.8811 - val_acc: 0.7812\n",
      "Epoch 5/100\n",
      "148/148 [==============================] - 21s 144ms/step - loss: 0.7753 - acc: 0.7335 - val_loss: 0.5851 - val_acc: 0.7812\n",
      "Epoch 6/100\n",
      "148/148 [==============================] - 22s 148ms/step - loss: 0.6444 - acc: 0.7804 - val_loss: 0.7685 - val_acc: 0.7708\n",
      "Epoch 7/100\n",
      "148/148 [==============================] - 21s 141ms/step - loss: 0.5464 - acc: 0.8053 - val_loss: 0.6926 - val_acc: 0.8542\n",
      "Epoch 8/100\n",
      "148/148 [==============================] - 21s 139ms/step - loss: 0.4354 - acc: 0.8552 - val_loss: 0.6847 - val_acc: 0.8125\n",
      "Epoch 9/100\n",
      "148/148 [==============================] - 21s 139ms/step - loss: 0.4110 - acc: 0.8602 - val_loss: 0.6602 - val_acc: 0.8021\n",
      "Epoch 10/100\n",
      "\n",
      "148/148 [==============================] - 21s 143ms/step - loss: 0.3444 - acc: 0.8796 - val_loss: 0.6042 - val_acc: 0.8333\n",
      "\n",
      "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 11/100\n",
      "148/148 [==============================] - 19s 130ms/step - loss: 0.2785 - acc: 0.8982 - val_loss: 0.5505 - val_acc: 0.8438\n",
      "Epoch 12/100\n",
      "148/148 [==============================] - 20s 138ms/step - loss: 0.2492 - acc: 0.9139 - val_loss: 0.4949 - val_acc: 0.8542\n",
      "Epoch 13/100\n",
      "148/148 [==============================] - 22s 145ms/step - loss: 0.2120 - acc: 0.9341 - val_loss: 0.4424 - val_acc: 0.8854\n",
      "Epoch 14/100\n",
      "148/148 [==============================] - 21s 141ms/step - loss: 0.2079 - acc: 0.9244 - val_loss: 0.4291 - val_acc: 0.8438\n",
      "Epoch 15/100\n",
      "148/148 [==============================] - 21s 143ms/step - loss: 0.1870 - acc: 0.9345 - val_loss: 0.4846 - val_acc: 0.8854\n",
      "Epoch 16/100\n",
      "148/148 [==============================] - 22s 145ms/step - loss: 0.2008 - acc: 0.9345 - val_loss: 0.4786 - val_acc: 0.8854\n",
      "Epoch 17/100\n",
      "148/148 [==============================] - 22s 146ms/step - loss: 0.2124 - acc: 0.9299 - val_loss: 0.4234 - val_acc: 0.8750\n",
      "Epoch 18/100\n",
      "148/148 [==============================] - 21s 143ms/step - loss: 0.1676 - acc: 0.9434 - val_loss: 0.3657 - val_acc: 0.9167\n",
      "Epoch 19/100\n",
      "148/148 [==============================] - 21s 144ms/step - loss: 0.1770 - acc: 0.9400 - val_loss: 0.4758 - val_acc: 0.8854\n",
      "Epoch 20/100\n",
      "148/148 [==============================] - 20s 138ms/step - loss: 0.1466 - acc: 0.9519 - val_loss: 0.4738 - val_acc: 0.8646\n",
      "Epoch 21/100\n",
      "148/148 [==============================] - 21s 144ms/step - loss: 0.1646 - acc: 0.9430 - val_loss: 0.4169 - val_acc: 0.8854\n",
      "Epoch 22/100\n",
      "147/148 [============================>.] - ETA: 0s - loss: 0.1765 - acc: 0.9439Epoch 22/100\n",
      "148/148 [==============================] - 22s 146ms/step - loss: 0.1757 - acc: 0.9443 - val_loss: 0.3838 - val_acc: 0.9167\n",
      "Epoch 23/100\n",
      "148/148 [==============================] - 21s 140ms/step - loss: 0.1451 - acc: 0.9481 - val_loss: 0.4036 - val_acc: 0.9062\n",
      "\n",
      "Epoch 00023: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 24/100\n",
      "148/148 [==============================] - 22s 151ms/step - loss: 0.1214 - acc: 0.9599 - val_loss: 0.3596 - val_acc: 0.9167\n",
      "Epoch 25/100\n",
      "148/148 [==============================] - 20s 138ms/step - loss: 0.1228 - acc: 0.9599 - val_loss: 0.3685 - val_acc: 0.9167\n",
      "Epoch 26/100\n",
      "148/148 [==============================] - 20s 138ms/step - loss: 0.1023 - acc: 0.9683 - val_loss: 0.3711 - val_acc: 0.9062\n",
      "Epoch 27/100\n",
      "148/148 [==============================] - 21s 145ms/step - loss: 0.1105 - acc: 0.9603 - val_loss: 0.4139 - val_acc: 0.8958\n",
      "Epoch 28/100\n",
      "148/148 [==============================] - 21s 143ms/step - loss: 0.1057 - acc: 0.9628 - val_loss: 0.3636 - val_acc: 0.9271\n",
      "\n",
      "Epoch 00028: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Epoch 00028: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f250078abe0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(ensemble_training_generator, \n",
    "                    validation_data=ensemble_validate_generator,\n",
    "                    epochs=100, \n",
    "                    callbacks = callbacks,\n",
    "                    use_multiprocessing=True,\n",
    "                    max_queue_size=10,\n",
    "                    workers=4)\n",
    "            \n",
    "        # 'max_queue_size':5, \n",
    "         # 'workers':2, \n",
    "         # 'use_multiprocessing':True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.041666666666666664,\n",
       " 0.3333333333333333,\n",
       " 0.5520833333333334,\n",
       " 0.6875,\n",
       " 0.7291666666666666,\n",
       " 0.8020833333333334,\n",
       " 0.7916666666666666,\n",
       " 0.8020833333333334,\n",
       " 0.8229166666666666,\n",
       " 0.8645833333333334,\n",
       " 0.8541666666666666,\n",
       " 0.8854166666666666,\n",
       " 0.84375,\n",
       " 0.8854166666666666,\n",
       " 0.8645833333333334,\n",
       " 0.8854166666666666,\n",
       " 0.8958333333333334,\n",
       " 0.8645833333333334,\n",
       " 0.875,\n",
       " 0.8645833333333334,\n",
       " 0.8958333333333334,\n",
       " 0.90625,\n",
       " 0.90625,\n",
       " 0.8229166666666666,\n",
       " 0.9166666666666666,\n",
       " 0.9166666666666666,\n",
       " 0.9270833333333334,\n",
       " 0.9375,\n",
       " 0.8958333333333334,\n",
       " 0.9270833333333334,\n",
       " 0.8854166666666666,\n",
       " 0.8958333333333334,\n",
       " 0.90625,\n",
       " 0.9166666666666666,\n",
       " 0.9166666666666666,\n",
       " 0.9479166666666666,\n",
       " 0.9166666666666666,\n",
       " 0.9166666666666666]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.history.history['val_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "hasta aca warxhs",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-113-f4f80a988621>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hasta aca warxhs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mException\u001b[0m: hasta aca warxhs"
     ]
    }
   ],
   "source": [
    "raise Exception('hasta aca warxhs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator, NumpyArrayIterator, array_to_img\n",
    "\n",
    "# A little hacky piece of code to get access to the indices of the images\n",
    "# the data augmenter is working with.\n",
    "class ImageDataGenerator2(ImageDataGenerator):\n",
    "    def flow(self, X, y=None, batch_size=26, shuffle=True, seed=None,\n",
    "             save_to_dir=None, save_prefix='', save_format='jpeg'):\n",
    "        return NumpyArrayIterator2(\n",
    "            X, y, self,\n",
    "            batch_size=batch_size, shuffle=shuffle, seed=seed,\n",
    "            data_format=self.data_format,\n",
    "            save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format)\n",
    "\n",
    "\n",
    "class NumpyArrayIterator2(NumpyArrayIterator):\n",
    "    def next(self):\n",
    "        # Keeps under lock only the mechanism which advances\n",
    "        # the indexing of each batch\n",
    "        # see http://anandology.com/blog/using-iterators-and-generators/\n",
    "        with self.lock:\n",
    "            # We changed index_array to self.index_array\n",
    "            self.index_array, current_index, current_batch_size = next(self.index_generator)\n",
    "        # The transformation of images is not under thread lock so it can be done in parallel\n",
    "        batch_x = np.zeros(tuple([current_batch_size] + list(self.X.shape)[1:]))\n",
    "        for i, j in enumerate(self.index_array):\n",
    "            x = self.X[j]\n",
    "            x = self.image_data_generator.random_transform(x.astype('float32'))\n",
    "            x = self.image_data_generator.standardize(x)\n",
    "            batch_x[i] = x\n",
    "        if self.save_to_dir:\n",
    "            for i in range(current_batch_size):\n",
    "                img = array_to_img(batch_x[i], self.data_format, scale=True)\n",
    "                fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix,\n",
    "                                                                  index=current_index + i,\n",
    "                                                                  hash=np.random.randint(1e4),\n",
    "                                                                  format=self.save_format)\n",
    "                img.save(os.path.join(self.save_to_dir, fname))\n",
    "        if self.y is None:\n",
    "            return batch_x\n",
    "        batch_y = self.y[self.index_array]\n",
    "        return batch_x, batch_y\n",
    "\n",
    "print('Creating Data Augmenter...')\n",
    "imgen = ImageDataGenerator2(\n",
    "    rotation_range=20,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    vertical_flip=True,\n",
    "    fill_mode='nearest')\n",
    "imgen_train = imgen.flow(X_img_tr, y_tr_cat, seed=np.random.randint(1, 10000))\n",
    "print('Finished making data augmenter...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgen_train[-2][1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model\n",
    "from keras.layers import Dense, Dropout, Activation, Convolution2D, MaxPooling2D, Flatten, Input, Concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "next(imgen_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "def combined_model():\n",
    "\n",
    "    # Define the image input\n",
    "    image = Input(shape=(96, 96, 1), name='image')\n",
    "    # Pass it through the first convolutional layer\n",
    "    x = Convolution2D(8, 5, 5, input_shape=(96, 96, 1), border_mode='same')(image)\n",
    "    x = (Activation('relu'))(x)\n",
    "    x = (MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))(x)\n",
    "\n",
    "    # Now through the second convolutional layer\n",
    "    x = (Convolution2D(32, 5, 5, border_mode='same'))(x)\n",
    "    x = (Activation('relu'))(x)\n",
    "    x = (MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))(x)\n",
    "\n",
    "    # Flatten our array\n",
    "    x = Flatten()(x)\n",
    "    # Define the pre-extracted feature input\n",
    "    numerical = Input(shape=(192,), name='numerical')\n",
    "    # Concatenate the output of our convnet with our pre-extracted feature input\n",
    "    concatenated = Concatenate()([x, numerical])\n",
    "\n",
    "    # Add a fully connected layer just like in a normal MLP\n",
    "    x = Dense(100, activation='relu')(concatenated)\n",
    "    x = Dropout(.5)(x)\n",
    "\n",
    "    # Get the final output\n",
    "    out = Dense(99, activation='softmax')(x)\n",
    "    # How we create models with the Functional API\n",
    "    model = Model(input=[image, numerical], output=out)\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
    "\n",
    "    return model\n",
    "\n",
    "print('Creating the model...')\n",
    "model = combined_model()\n",
    "print('Model created!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.models import load_model\n",
    "\n",
    "\n",
    "def combined_generator(imgen, X):\n",
    "    \"\"\"\n",
    "    A generator to train our keras neural network. It\n",
    "    takes the image augmenter generator and the array\n",
    "    of the pre-extracted features.\n",
    "    It yields a minibatch and will run indefinitely\n",
    "    \"\"\"\n",
    "    while True:\n",
    "        for i in range(X.shape[0]):\n",
    "            # Get the image batch and labels\n",
    "            batch_img, batch_y = next(imgen)\n",
    "            # This is where that change to the source code we\n",
    "            # made will come in handy. We can now access the indicies\n",
    "            # of the images that imgen gave us.\n",
    "            x = X[imgen.index_array]\n",
    "            yield [batch_img, x], batch_y\n",
    "\n",
    "# autosave best Model\n",
    "best_model_file = \"leafnet.h5\"\n",
    "best_model = ModelCheckpoint(best_model_file, monitor='val_loss', verbose=1, save_best_only=True)\n",
    "\n",
    "print('Training model...')\n",
    "history = model.fit_generator(combined_generator(imgen_train, X_num_tr),\n",
    "                              samples_per_epoch=X_num_tr.shape[0],\n",
    "                              nb_epoch=89,\n",
    "                              validation_data=([X_img_val, X_num_val], y_val_cat),\n",
    "                              nb_val_samples=X_num_val.shape[0],\n",
    "                              verbose=0,\n",
    "                              callbacks=[best_model])\n",
    "\n",
    "print('Loading the best model...')\n",
    "model = load_model(best_model_file)\n",
    "print('Best Model loaded!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.models import load_model\n",
    "\n",
    "\n",
    "def combined_generator(imgen, X):\n",
    "    \"\"\"\n",
    "    A generator to train our keras neural network. It\n",
    "    takes the image augmenter generator and the array\n",
    "    of the pre-extracted features.\n",
    "    It yields a minibatch and will run indefinitely\n",
    "    \"\"\"\n",
    "    while True:\n",
    "        for i in range(X.shape[0]):\n",
    "            # Get the image batch and labels\n",
    "            batch_img, batch_y = next(imgen)\n",
    "            # This is where that change to the source code we\n",
    "            # made will come in handy. We can now access the indicies\n",
    "            # of the images that imgen gave us.\n",
    "            x = X[imgen.index_array]\n",
    "            yield [batch_img, x], batch_y\n",
    "\n",
    "# autosave best Model\n",
    "best_model_file = \"leafnet.h5\"\n",
    "best_model = ModelCheckpoint(best_model_file, monitor='val_loss', verbose=1, save_best_only=True)\n",
    "\n",
    "print('Training model...')\n",
    "history = model.fit_generator(combined_generator(imgen_train, X_num_tr),\n",
    "                              samples_per_epoch=X_num_tr.shape[0],\n",
    "                              nb_epoch=89,\n",
    "                              validation_data=([X_img_val, X_num_val], y_val_cat),\n",
    "                              nb_val_samples=X_num_val.shape[0],\n",
    "                              verbose=0,\n",
    "                              callbacks=[best_model])\n",
    "\n",
    "print('Loading the best model...')\n",
    "model = load_model(best_model_file)\n",
    "print('Best Model loaded!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_vec['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resize_img(img, max_dim=96):\n",
    "    \"\"\"\n",
    "    Resize the image to so the maximum side is of size max_dim\n",
    "    Returns a new image of the right size\n",
    "    \"\"\"\n",
    "    # Get the axis with the larger dimension\n",
    "    max_ax = max((0, 1), key=lambda i: img.size[i])\n",
    "    # Scale both axes so the image's largest dimension is max_dim\n",
    "    scale = max_dim / float(img.size[max_ax])\n",
    "    return img.resize((int(img.size[0] * scale), int(img.size[1] * scale)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "images = []\n",
    "for id_num in id_vec['id'].values:\n",
    "    im = load_img(images_folder + str(id_num) +'.jpg')\n",
    "    images.append(np.expand_dims(im,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_to_array(im).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "resize_img(images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_d0 = 0\n",
    "max_d1 = 0\n",
    "for im in images:\n",
    "    max_d0 = max(max_d0,im.shape[0])\n",
    "    max_d1 = max(max_d1,im.shape[1])   \n",
    "print(max_d0, max_d1)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "im.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "images_exp = []\n",
    "for im in images:\n",
    "#    print(im.shape)\n",
    "    s0,s1,s3 = im.shape\n",
    "    extra_0 =   max_d0 -s0\n",
    "    extra_1 =   max_d1 - s1\n",
    "    expanded = np.zeros((max_d0, max_d1, 1))\n",
    "    b0, b1 = int(np.floor(extra_0/2)), int(np.floor(extra_1/2))\n",
    " #   print(b0,b1)\n",
    "    expanded[b0:b0+s0,b1:b1+s1,:] = im\n",
    "    images_exp.append(expanded)\n",
    "\n",
    "images_exp = np.array(images_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "idd = 250\n",
    "print(images_exp[idd].shape)\n",
    "plt.figure()\n",
    "plt.imshow(images_exp[idd][:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(32, (4, 4), input_shape=(1089, 1633,1),\n",
    "               activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(units=128, activation='relu'))\n",
    "model.add(Dense(units=99, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(in_layer, output_layer)\n",
    "model.compile(optimizer = \"adam\", loss = \"categorical_crossentropy\",\n",
    "            metrics=[\"accuracy\"])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#in_layer = Input(shape=(1089, 1633,1))\n",
    "#x = Conv2D(16,(4,4), activation = 'elu')(in_layer)  # single stride 4x4 filter for 16 maps\n",
    "#x = Conv2D(32,(4,4), activation = 'elu')(x)         # single stride 4x4 filter for 32 maps\n",
    "#x = Dropout(0.5)(x)\n",
    "#x = Conv2D(64,(4,4), activation = 'elu')(x)         # single stride 4x4 filter for 64 maps\n",
    "#x = Dropout(0.5)(x)\n",
    "#x = Conv2D(128, (1,1))(x)                           # finally 128 maps for global average-pool\n",
    "#x = GlobalMaxPooling2D()(x)                     # pseudo-dense 128 layer\n",
    "#output_layer = Dense(99, activation = \"softmax\")(x) # softmax output\n",
    "#\n",
    "#model = Model(in_layer, output_layer)\n",
    "#model.compile(optimizer = \"adam\", loss = \"categorical_crossentropy\",\n",
    "#            metrics=[\"accuracy\"])\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(images_exp, target_mc, batch_size = 1, epochs = 50, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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