CNN_tutorial_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN tutorial\n",
    "\n",
    "This part of the tutorial will introduce a simple Convolutional Neural Network (CNN) architecture to classify images.\n",
    "You will extend the given architecture with your own ideas to improve the performance of the classifier. Finally, you can train a state-of-the-art CNN (ResNet50) by fine-tuning a model that was trained on ImageNet.\n",
    "As a toy example we use the cifar10 dataset. (source: https://www.cs.toronto.edu/~kriz/cifar.html)\n",
    "\n",
    "Run the code cell by cell individually and complete the missing parts. Carry out the experiments and answer the questions. Have fun!\n",
    "\n",
    "If you are waiting while a model is training, have a break on the playground and explore the effects of hyperparameters: https://playground.tensorflow.org\n",
    "\n",
    "### Tasks:\n",
    "1. Complement the model architecture given in the Figure (AlexNet like architecture).\n",
    "2. Train and evaluate the network on the original input images (RGB)\n",
    "3. Train and evaluate on the preprocessed input data\n",
    "    * Why is preprocessing the input data important?\n",
    "4. Modify and extend the CNN model architecture to improve performance and beat our baseline. For example:    \n",
    "    * higher model capacity\n",
    "    * stronger non-linearity (increase model depth/ # layers)\n",
    "    * stronger pooling (shrinkage -> global information)\n",
    "    * stronger regularization (dropout)\n",
    "5. Train and evaluate the modified model on the preprocessed input data\n",
    "\n",
    "#### Bonus / Homework ;-)\n",
    "\n",
    "6. Train and evaluate the modified model using data augmentation at train time \n",
    "    * We use the data augmentation generator from keras (ImageDataGenerator)\n",
    "    * Use the template below and set meaningful data augmentation parameters\n",
    "    * Did the data augmentation improve performance? Can you explain why?\n",
    "    * In what scenario might data augmentation be very useful?\n",
    "7. Run the ResNet50 model and compare the performance to our simple and shallow CNN model\n",
    "    * Compare the performance and the training curves to the previous models (AlexNet)?\n",
    "    * Can you observe overfitting?\n",
    "    * What could be tried to improve the performance of the ResNet50 model on the cifar10 dataset?\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\n",
    "from keras.optimizers import Adam\n",
    "from keras.datasets import mnist\n",
    "from keras.utils import np_utils\n",
    "from keras import initializers\n",
    "from keras import backend as K\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.utils.vis_utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SET PARAMETERS\n",
    "data_dir = 'data/'\n",
    "nb_classes = 10\n",
    "img_rows, img_cols, channels = 32, 32, 3  # input image dimensions for the cifar10 dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load image data (cifar10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((50000, 32, 32, 3), 'train samples')\n",
      "((10000, 32, 32, 3), 'test samples')\n"
     ]
    }
   ],
   "source": [
    "# Load cifar10 training and test sets from keras\n",
    "# from keras.datasets import cifar10\n",
    "# (X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
    "\n",
    "# Load cifar10 from local file\n",
    "cifar10_data = np.load(data_dir + '/cifar10.npz')\n",
    "X_train = cifar10_data['X_train']\n",
    "y_train = cifar10_data['y_train']\n",
    "X_test = cifar10_data['X_test']\n",
    "y_test = cifar10_data['y_test']\n",
    "\n",
    "\n",
    "# if grey scale images: data dimensions are not [images, height, width, channels] then add 1 dimension for channels\n",
    "if len(X_train.shape) == 3:\n",
    "    print('X_train original shape:', X_train.shape)\n",
    "    X_train = np.expand_dims(X_train, axis=3)\n",
    "    X_test = np.expand_dims(X_test, axis=3)\n",
    "    print('X_train expanded shape:', X_train.shape)\n",
    "\n",
    "if K.image_dim_ordering() == 'th':\n",
    "    # For Theano backend\n",
    "    X_train = X_train.reshape(X_train.shape[0], channels, img_rows, img_cols)\n",
    "    X_test = X_test.reshape(X_test.shape[0], channels, img_rows, img_cols)\n",
    "    input_shape = (channels, img_rows, img_cols)\n",
    "else:\n",
    "    # For TensorFlow backend\n",
    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, channels)\n",
    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, channels)\n",
    "    input_shape = (img_rows, img_cols, channels)\n",
    "    \n",
    "print(X_train.shape, 'train samples')\n",
    "print(X_test.shape, 'test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((40000, 32, 32, 3), 'train samples')\n",
      "((10000, 32, 32, 3), 'val samples')\n",
      "((10000, 32, 32, 3), 'test samples')\n"
     ]
    }
   ],
   "source": [
    "# Split training data into validation and train \n",
    "# --> split (X_train, y_train) into (X_train, y_train) and (X_val, y_val)\n",
    "nb_train_images = X_train.shape[0]\n",
    "shuffled_indices = np.arange(nb_train_images)\n",
    "np.random.shuffle(shuffled_indices)\n",
    "train_indices = shuffled_indices[:int(0.8*nb_train_images)]      # 80% for training\n",
    "val_indices = shuffled_indices[int(0.8*nb_train_images):]        # 20% for validation\n",
    "\n",
    "X_val = X_train[val_indices, :, :, :]\n",
    "y_val = y_train[val_indices]\n",
    "\n",
    "X_train = X_train[train_indices, :, :, :]\n",
    "y_train = y_train[train_indices]\n",
    "\n",
    "print(X_train.shape, 'train samples')\n",
    "print(X_val.shape, 'val samples')\n",
    "print(X_test.shape, 'test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# VISUALIZE IMAGES\n",
    "def plotImages( images, n_images=8):\n",
    "    fig, axes = plt.subplots(n_images, n_images, figsize=(10, 10))\n",
    "    axes = axes.flatten()\n",
    "    for img, ax in zip(images, axes):\n",
    "        ax.imshow(img)\n",
    "        ax.set_xticks(())\n",
    "        ax.set_yticks(())\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "plotImages(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('label integer encoded: y_train[0]: ', array([5], dtype=uint8))\n",
      "('label one-hot encoded: Y_train[0]: ', array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], dtype=float32))\n"
     ]
    }
   ],
   "source": [
    "# convert labels to categorical\n",
    "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
    "Y_test = np_utils.to_categorical(y_test, nb_classes)\n",
    "Y_val = np_utils.to_categorical(y_val, nb_classes)\n",
    "\n",
    "print('label integer encoded: y_train[0]: ', y_train[0])\n",
    "print('label one-hot encoded: Y_train[0]: ', Y_train[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN Model - AlexNet \n",
    "\n",
    "**Task**: Implement an AlexNet like CNN as visualized in the Figure below. \n",
    "\n",
    "* Use strides=(1,1) for all convolutional layers. \n",
    "* Use 'relu' as activation for both, conv and dense (fully connected) layers. \n",
    "* Spatial extent is reduced by using maxpooling. Set the pool_size0(3,3) and set the parameter strides, such that the spatial extent is reduced by factor 2 in both spatial dimensions.\n",
    "* For both dense layers use 128 neurons instead of 4096 to reduce the number of parameters for this tutorial.\n",
    "* Add dropout layers after the first and second convolutional layer as well as after both dense layers. Use the given dropout probabilities `prob_drop_conv = 0.2` and `prob_drop_hidden = 0.5`.\n",
    "\n",
    "**Questions to answer**:\n",
    "\n",
    "1. How does the Total number of trainable parameters change, when we change the number of neurons in the first dense layer from 128 to the original proposed 4096?\n",
    "2. AlexNet was designed for ImageNet input images with a size of 224x224 pixels. Concerning the first convolutional layer, what could we change to adapt better to our dataset?\n",
    "\n",
    "![title](figures/alexnet.png)\n",
    "*Figure 1: Visualization of the AlexNet(-like) architecture for the ImageNet challenge (1000 classes)*\n",
    "\n",
    "\n",
    "img source: http://cv-tricks.com/wp-content/uploads/2017/03/xalexnet_small-1.png.pagespeed.ic.q5Lnn1-u6h.png\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 96)        34944     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 96)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 16, 16, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 256)       614656    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 8, 8, 384)         885120    \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 8, 8, 384)         1327488   \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 8, 8, 256)         884992    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               524416    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 4,289,418\n",
      "Trainable params: 4,289,418\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# parameters:\n",
    "base_learning_rate = 0.0001\n",
    "batch_size = 128\n",
    "\n",
    "pool_size = (3, 3)                  # size of pooling area for max pooling\n",
    "prob_drop_conv = 0.2                # drop probability for dropout @ conv layer\n",
    "prob_drop_hidden = 0.5              # drop probability for dropout @ fc layer\n",
    "\n",
    "def AlexNet():\n",
    "\n",
    "    model = Sequential()\n",
    "\n",
    "    # conv1 layer\n",
    "    conv1 = Conv2D(filters=96, kernel_size=(11, 11), strides=(1,1), padding='same', activation='relu', \n",
    "                   input_shape=input_shape)\n",
    "    model.add(conv1)\n",
    "    model.add(MaxPooling2D(pool_size=(pool_size), strides=(2, 2), padding='same'))\n",
    "    model.add(Dropout(prob_drop_conv))\n",
    "\n",
    "    # conv2 layer\n",
    "    model.add(Conv2D(filters=256, kernel_size=(5, 5), strides=(1,1), padding='same', activation='relu' ))\n",
    "    model.add(MaxPooling2D(pool_size=pool_size, strides=(2,2), padding='same'))\n",
    "    model.add(Dropout(prob_drop_conv))\n",
    "\n",
    "    # conv3 layer\n",
    "    model.add(Conv2D(filters=384, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "    \n",
    "    # conv4 layer\n",
    "    model.add(Conv2D(filters=384, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "\n",
    "    # conv5 layer\n",
    "    model.add(Conv2D(filters=256, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=pool_size, strides=(2,2), padding='same'))\n",
    "\n",
    "    \n",
    "    model.add(Flatten())\n",
    "\n",
    "    # fc1 layer\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dropout(prob_drop_hidden))\n",
    "\n",
    "    # fc2 layer\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dropout(prob_drop_hidden))\n",
    "\n",
    "    # fc3 layer\n",
    "    model.add(Dense(nb_classes, activation='softmax'))\n",
    "\n",
    "    return model\n",
    "\n",
    "\n",
    "model_alexNet = AlexNet()\n",
    "model_alexNet.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of images per batch: 128\n",
      "batches per epoch: 312\n",
      "validation steps: 78\n"
     ]
    }
   ],
   "source": [
    "# Calculate the number of batches per epoch. \n",
    "# One epoch is defined as an update pass through the full training data. \n",
    "# One iteration is defined as an update through one mini-batch.\n",
    "batches_per_epoch = X_train.shape[0]//batch_size\n",
    "# the number of batches to see the full validation data:\n",
    "validation_steps = X_val.shape[0]//batch_size\n",
    "print('number of images per batch: {}'.format(batch_size))\n",
    "print('batches per epoch: {}'.format(batches_per_epoch))\n",
    "print('validation steps: {}'.format(validation_steps))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data generator to load the training data as batches\n",
    "\n",
    "def generator(features, labels, batch_size, patch_size, channels, nb_classes):\n",
    " # Create empty arrays to contain batch of features and labels#\n",
    " batch_features = np.zeros((batch_size, patch_size, patch_size, channels))\n",
    " batch_labels = np.zeros((batch_size, nb_classes))\n",
    "\n",
    " while True:\n",
    "    for i in range(batch_size):\n",
    "         # choose random index in features\n",
    "         index = np.random.choice(a=features.shape[0], size=1)\n",
    "\n",
    "         #Optional: Add some data augmentation here\n",
    "\n",
    "         batch_features[i] = features[index, :, :, :]\n",
    "         batch_labels[i] = labels[index]\n",
    "    yield batch_features, batch_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to re-initialize the weights before starting a new experiment/training\n",
    "\n",
    "def initialize_weights(model, layer_name=None):\n",
    "    session = K.get_session()\n",
    "    if layer_name is None:\n",
    "        for layer in model.layers: \n",
    "            if hasattr(layer, 'kernel_initializer'):\n",
    "                print('initialize weights of layer: {}'.format(layer.name))\n",
    "                layer.kernel.initializer.run(session=session)\n",
    "    else:\n",
    "        layer = model.get_layer(name=layer_name)\n",
    "        if hasattr(layer, 'kernel_initializer'):\n",
    "            print('initialize weights of layer: {}'.format(layer.name))\n",
    "            layer.kernel.initializer.run(session=session)\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to plot training curves\n",
    "\n",
    "def plot_train_val_accuracy_and_loss(history, legend_suffix=\"\"):\n",
    "    # list all data in history\n",
    "    print(history.history.keys())\n",
    "\n",
    "    # summarize history for accuracy\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "    plt.title('Accuracy')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train_'+legend_suffix, 'val_'+legend_suffix], loc='lower right')\n",
    "    plt.show()\n",
    "\n",
    "    # summarize history for loss\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title('Loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train_'+legend_suffix, 'val_'+legend_suffix], loc='upper right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model on original data\n",
    "\n",
    "The model was trained on a GPU (Titan Xp 12GB) for 11 epochs within 99 seconds (9 seconds per epoch). This code loads the weights after the 11th epoch. You will train one epoch more and then the training/validation curves are plotted for the entire 12 epochs. \n",
    "\n",
    "If you have not implemented the model correctly the weights of the pre-trained model will not match the model architecture. \n",
    "\n",
    "* Did the model learn anything yet? Is it better than a random classifier?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "312/312 [==============================] - 10s 32ms/step - loss: 1.2500 - acc: 0.5759 - val_loss: 1.1401 - val_acc: 0.6190\n",
      "['acc', 'loss', 'val_acc', 'val_loss']\n"
     ]
    },
    {
     "data": {
      "image/png": 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bT0TDajx7dQeGd7HZxqXNa6EgIoHAG8AlQAIQJSJzVXXjCad+oqrjvVWHMWXO0YPOlcGqDyC0Hlw7GTpe73dNRUey85gbs5fpK/cQm3iYkOBAhnZuyKheTelsaxC5xptXCj2B7aq6E0BEZgLDgRNDwRgDzrLWMdPg+6fhWBr0/iMM+jNU8q/O1NhEZ7+COWuc/QraNqjKM8Pbc3U5XpnUl3gzFBoD8UVuJwC9TnLedSJyIbAVeFBV4088QUTGAeMAwsPDvVCqMS7bt85pKkpYCU16w5CXoEFHt6sqMUdznF3Mpq/43y5mV3VqxKhe5W+/Al/ndkfzV8AMVc0WkbuBqcBFJ56kqpOASQCRkZFauiUa40VHDsCSl5x5ByG1YPib0HkkBJT9DeABNu8/zPQVcXyxOpGM7Dxa1Qvl6aERXFtOdjEri7wZColA0WUZw/hfhzIAqppa5OZk4EUv1mOMb8jLhq3fQsx02LYQtAB63AEXPenahjMlSVVZui2FSUt2smx7ChU8exuP8vO9jf2FN0MhCmglIs1xwuAmYFTRE0Skoaru89wcBmzyYj3GuEfVGUUUMx1iZ8GxQxDaAPreB11uhrqt3a7wvOXkFfD1ur1MWrKTzfszqFe1Io9d3oaRPcKpWeUs93AwrvFaKKhqnoiMBxbgDEl9X1U3iMgzQLSqzgXuF5FhQB5wEBjjrXqMcUXGflj3CcTMcLaiDKwI7a6CzqOgxSBnr4IyLiMrlxkr43h/2W72H86iVb1QXry+E8O7NKJikC09UdaIatlqoo+MjNTo6Gi3yzDm1HKzYOt856pg+/dO81BYT+gyEtpfCyE13K6wROxLP8aUn3czY0UcGdl59GlRm3EXtmBQm7rWROSDRGSVqkae6byy/2uKMb5AFRJXO0NKY2c7ex1XbQT9HoAuo6BOK7crLDGb9h3m3SU7mbt2LwWqDOnUiLsGNKdTmH+EXXlnoWDM+Ti8z9M8NB1StkBQJWg31AmC5gMhwD+aT1SVn7enMmnpTpZsTaZyhUBu6d2UO/o3p0kt/16dtbyxUDDmbOVmwZZ5ThDs+NFpHmrSG4a+5mxQXwb3Mj6V3PwC5q3bx6QlO9m47zB1Qivy6GVtuLlXODUqW+exP7JQMKY4VCEh2mke2vA5ZKVDtTAY8LAzr8DPlq8+kp3HzJVxTPl5N4lpx2hZtwovXNeRq7s2ts5jP2ehYMzpHN4La2c6VwWp2yAoBCKGO53GzS70m0lmxyUdzuL9n3cxfUUcGVl59Gpei2eGt2dwm3oEBFjncXlgoWDMiXKPweZ5zlXBjkWAQtN+zrLVEcP9bi0igC37M3h36U7mxCSSX6Bc0aEhd13Ygi5NrPO4vLFQMOa4tHhY+Q6s/tBpHqoeDgMfg843Qa0WbldX4lSVX3amMmnJThZvSSYkOJBRPcO5o38Lwmtb53F5ZaFgTPxK+OUN2PSVcztiOESOda4O/Kx5CCAvv4BvYvczackOYhMPUye0Ag9f0ppbeje1mcfGQsGUU/m5sGku/PImJEY7I4b6joee46B6mNvVeUVOXgEzo+J456edJKYdo0XdKjx3bUeu6dqYSsHWeWwcFgqmfDl2CFZNhZWT4HAi1Grp7GjWeaTfbnFZUKB8vX4fLy3YQtzBo0Q2rcnEYe35Q1vrPDa/Z6FgyoeU7bDiLWcUUe5RaH4hDHkFWl3ql01Exy3blsLz324iNvEwbRtU5YPbezCwtS1DYU7NQsH4L1XYtQR+fRO2LoDAYOh4g7OjmR9tYHMysYnpvPDtZpZuS6FxjRD+PaIzwzs3tisDc0YWCsb/5GY5y1P/+hYkxULlOjDwcWfPgtB6blfnVXtSM3npu618tXYvNSsH89RVEdzSO9wmnJlis1Aw/uPIAYh+H6ImQ2Yy1GsPw153rg6CK7ldnVelHMnmvz9sY9qKOIIChfGDL2DcwBa257E5axYKpuxL2uCMIlr/KeTnQKvLoM+fnAXp/Lzt/Eh2HpOX7uTdJTvJyivgph5NmPCHVtSr5t8haLzHQsGUTQUFsH2hM79g108QXBm6jnb6C/xomepTOT689LUftpFyJIcrOzbg4Uvb0LKuf46gMqXHQsGULTmZzgiiFW9D6nZnz4KLJ0K326ByLber87qCAmXe+n289N0W9qQepVfzWrx7a1u6hpf9vZ2Nb7BQMGVDeoIzt2DVB84SFI26wXXvObOPA8tHu/mJw0un3N6DQTa81JQwCwXj25K3wtKXYP0sQJ0NbHr/CZr08vv+guNOHF76yo2dGd6lMYE2vNR4gYWC8U3JW2HJi04YBIdAr3ug191Qs6nblZWauNSjvPTdFuau3UuNysE8OaQdt/RuaktSGK+yUDC+JXkL/PSis89xcAj0ux/63g9V6rhdWalJOZLN6z9uZ9qKPQQGCPcObsndA1va8FJTKiwUjG84sNm5Moj93BlJ1G8C9L2vXIXBicNLR3iGl9a34aWmFFkoGHedGAb9H4A+90GV2m5XVmpOHF56RYcGPHKZDS817rBQMO44sMlpJtrwBVSoUi7DQFVZsCGJ5+ZvYk/qUXo2r8WkW9vSzYaXGhdZKJjSdWAT/PQCbPjSEwYPQp/x5SoMALYlZfD3rzaybHsKreuHMmVMDwa1seGlxn0WCqZ0JG10momOh8GAh5wwKAcTzopKP5bLq99vY+ovu6lSIZC/D2vPzb3CCQr03+W7TdlSrFAQkc+B94D5qlrg3ZKMX0na6FwZbPwSKoTCgIehz73lLgzyC5TPouP514ItHDyaw8ie4TxyaRtq2faXxscU90rhTeB24DUR+QyYoqpbvFeWKfOSNnjCYA5UqAoDHimXYQCwas8hJs7dwPrEdCKb1mTqsJ50aFzd7bKMOalihYKqfg98LyLVgZGe7+OBd4GPVTXXizWasuTEMLjwUWcGcjkMgwOHs3h+/mY+X5NI/WoVefWmLgzr3Mj6DYxPK3afgojUBm4BRgNrgGlAf+A2YJA3ijNlyP5YJww2zYWK1eDCx5wVS8thGOTkFTDl51289sM2cvOVPw1qyb2DL6BKRevCM76vuH0KXwBtgI+Aoaq6z3PXJyISfZrHXQ68CgQCk1X1+VOcdx0wC+ihqqd8PuOD9q/3hMFXThgMfNwJg5DyOaxy0ZYD/OOrjexMyeTidvV4ckgEzepUcbssY4qtuL+6vKaqi052h6pGnuy4iAQCbwCXAAlAlIjMVdWNJ5xXFZgArCh21cZ9Fga/sTslk398vZEfNh+gRZ0qTLm9B4Pb+PfWn8Y/FTcUIkRkjaqmAYhITWCkqr55msf0BLar6k7PY2YCw4GNJ5z3D+AF4NGzqty4IzcLvvurs+Vlxeow8AnofU+5DYPM7DxeX7Sd95buIjhQ+MuVbRnTtzkVgmyIqSmbihsKd6nqG8dvqOohEbkLZ1TSqTQG4ovcTgB6FT1BRLoBTVR1noicMhREZBwwDiA8PLyYJZsSl7wVZt0OSbFO5/HAxyGkhttVuUJVmROzl+fmbyLpcDbXdQvj8cvb2DaYpswrbigEioioqkJh09B5DbAWkQDgFWDMmc5V1UnAJIDIyEg9n9c15yhmOsx72Fm5dNRn0PpStytyTWxiOhPnbiB6zyE6hVXnrVu629IUxm8UNxS+xelUfsdz+27PsdNJBJoUuR3mOXZcVaADsNgzRK8BMFdEhllnsw/JzoB5j8C6mdBsAFw7Cao1crsqV6Qeyeal77YyMyqOWpUr8OJ1nbi+exgBttmN8SPFDYXHcYLgj57bC4HJZ3hMFNBKRJrjhMFNwKjjd6pqOlC4LrKILAYesUDwIfvWwWdj4NAuGPRnZ85BQPnb4CUvv4CPf93DKwu3kpmTz9h+zbn/D62oHmL7Gxj/U9zJawXAW56vYlHVPBEZDyzAGZL6vqpuEJFngGhVnXsuBZtSoAor33U6lCvXhtu+gmb93a7KFct3pPD3uRvZkpRB/wvq8PTQCFrVr+p2WcZ4TXHnKbQCngMigMKeNFVtcbrHqeo3wDcnHPvbKc4dVJxajJcdOwRzxsPmr6HVpXD1W+Vqo5vjEg4d5blvNjNv/T7CaobwzujuXBpR32YjG79X3OajKcDTwL+BwTjrINmYO38TvxJmjYWMfXDps9D7XggoX3/NufkFvLloB2/9tB2Ahy9pzV0XtrB9kU25UdxQCFHVHzwjkPYAE0VkFXDS3/pNGVNQAD//B358FqqHwdjvIKy721WVuviDR7lvxhpi4tMY0qkhf7myHY1rhLhdljGlqrihkO0ZQrrN00+QCNhegf7gyAH44m7Y8SNEXA3DXoNK5W8Fz29j9/HYrHWowuujunJVp/I5wsqY4obCBKAycD/ODOTBOAvhmbJs52L4fBxkpcNV/4HuY6CctZln5ebz3DebmPrLHjo2rs7ro7rStLatVWTKrzOGgmei2ghVfQQ4gtOfYMqy/DxY/BwsfRnqtIbRX0D99m5XVep2pWQyfvpqNuw9zNh+zXn8ijZUDLK+A1O+nTEUVDVfRMrneER/lJ4As+6A+F+h6y1wxYvO9pjlzJyYRP7y+XqCAgN499ZILomo73ZJxviE4jYfrRGRucBnQObxg6r6uVeqMt6x+Rv48o9QkAfXToZON7hdUak7lpPP37/awMyoeLo3rclrI7taZ7IxRRQ3FCoBqcBFRY4pYKFQFuRlw8K/wYq3oWFnuH4K1G7pdlWlbltSBuOnr2FLUgZ/HNSShy5pTXBg+Rpya8yZFHdGs/UjlFWpO5yVTfethV73wCXPQFBFt6sqVarKrFUJ/G3OBipXCGTq2J4MbF3X7bKM8UnFndE8BefK4DdUdWyJV2RKzrrP4OsHICAIbpoObYe4XVGpy8zO46kvY/l8TSJ9WtTmPzd1ob4tb23MKRW3+ejrIt9XAq4B9pZ8OaZE5GTC/MdgzcfQpDdcNxlqNDnz4/zMxr2HGT99NbtTM3nw4taMv+gCAm1FU2NOq7jNR7OL3haRGcAyr1Rkzk/SRmdl05StMOBhGPQXCCxfG8arKtNWxPHM1xupERLMtDt706dlbbfLMqZMONdPi1aAbUDrS1Rh1Qfw7RPOnsmjv4CWg92uqtQdzsrlz7PXM2/9Pga2rsvLN3amTmj56kMx5nwUt08hg9/2KezH2WPB+IL8XGepitjZ0GKwsxFOaPnL7LXxaYyfsZq9aVk8cUVbxg1oYRvgGHOWitt8ZAvI+7Jf33QCYfBfYcAj5W5lU1XlvWW7eOHbzdSrWolP7+5N96a13C7LmDKpuFcK1wA/enZLQ0RqAINU9UtvFmeK4dAeWPQctBkCAx9zu5pSdygzh0dnreX7TQe4uF19XrqhEzUqn9f24caUa8XtU3haVb84fkNV00TkacBCwU2qMO9hkAC48kW3qyl10bsPct+MNaQcyeZvV0Vwe79mtgmOMeepuKFwsvaI8jWkxRdt+AK2L4TLnnP2QSgnCgqUt37awSsLt9K4Rgiz/9iXTmE13C7LGL9Q3A/2aBF5BXjDc/teYJV3SjLFcizNGWnUsAv0utvtakpNckY2D30aw9JtKQzp1JDnru1ItUrBbpdljN8obijcBzwFfIIzCmkhTjAYt/zwd8hMhlGfQkD5WO55+fYUJnwSQ/qxXP7vmg6M6hluzUXGlLDijj7KBJ7wci2muOJXQvT7zh7Kjbq4XY3X5Rcor/2wjdd+3EbzOlX4cGxP2jWs5nZZxvilYo1dFJGFnhFHx2/XFJEF3ivLnFJ+Lnw1AaqFweC/uF2N12Vk5TJmykpe/WEb13RtzFfj+1sgGONFxW0+qqOqacdvqOohESl/s6N8wfL/woGNcNMMqOjf22QnZ2Rz+wcr2bQvg+ev7chNPcPdLskYv1fcUCgQkXBVjQMQkWacZNVU42UHd8FPL0C7odD2Srer8ao9qZnc+v5KDhzOZvKtkQxua7+DGFMaihsKfwWWichPgAADgHFeq8r8nirMewgCgp0tNP1YbGI6Y6ZEkVdQwLS7etEtvKbbJRlTbhS3o/lbEYnECYI1OJPWjnmzMHOC2Nmw40cnEKo1crsar1m+I4VxH66iWqUgZo7rwwX1bIUVY0pTcZe5uBOYAIQBMUBv4Bd+uz2n8ZZjh5w5CY3LlHDaAAAbr0lEQVS6QY873a7Ga+at28eDn8TQrE5lpo7tScPqtneyMaWtuCunTQB6AHtUdTDQFUg7/UNMiVn4NBw9CENf9ds5CR/9spvxM1bTKaw6n93d1wLBGJcUt08hS1WzRAQRqaiqm0WkjVcrM449v8DqqdD3PmjYye1qSpyq8u+FW3ntx+1c3K4e/x3ZjZAK/hl8xpQFxQ2FBM88hS+BhSJyCNjjvbIMAHk5zh7L1ZvAoD+7XU2Jy8sv4Kk5scxYGc+NkWH885qOBAWWr2W/jfE1xe1ovsbz7UQRWQRUB7490+NE5HLgVSAQmKyqz59w/z04y2XkA0eAcaq6sfjl+7nlr0LyZmcpiwpV3K6mRGXl5nP/jDV8tzGJewe35JFL29iSFcb4gLNe6VRVfyrOeSISiLOA3iVAAhAlInNP+NCfrqpve84fBrwCXH62Nfml1B3w078g4mpofZnb1ZSo9GO53DU1mqg9B3l6aAS392vudknGGA9vLn/dE9iuqjsBRGQmMBwoDAVVPVzk/CrYhDjH8TkJQRXh8ufPfH4ZknQ4i9veX8mO5CO8elNXhnX23+G1xpRF3gyFxkB8kdsJQK8TTxKRe4GHgAqcYoiriIzDM1kuPLwcLHWw7lPYuRiufAmqNXS7mhKzI/kIt763krSjOUwZ05P+req4XZIx5gSu9+qp6huq2hJ4HHjyFOdMUtVIVY2sW7du6RZY2o4ehAV/hsaREHmH29WUmJj4NK5/azlZufnMHNfHAsEYH+XNUEgEmhS5HeY5diozgau9WE/ZsPApyEr3zElwPbNLxE9bkxn17q+EVgpi1h/70jGsutslGWNOwZufOlFAKxFpLiIVgJuAuUVPEJFWRW4OAbZ5sR7ft3sZrPkY+oyHBh3crqZEfLkmkTs+iKJp7SrM/mNfmtfxr1FUxvgbr/UpqGqeiIwHFuAMSX1fVTeIyDNAtKrOBcaLyMVALnAIuM1b9fi8vGz4+kGoEQ4DH3e7mhIxeelOnp23id4tajHp1kjbNtOYMsCbHc2o6jfANycc+1uR7yd48/XLlGX/gZStcPNsqFDZ7WrOi6ry/LebeeennVzZsQGv3NiFSsE2S9mYssCroWCKKWU7LH0JOlwHrS52u5rzkptfwBOz1zN7dQK39A7n78M6EBhgk9KMKSssFNym6ixlERQClz3ndjXn5WhOHvdOW82iLck8dElr7rvoApulbEwZY6HgtrUzYPdSuOrfULW+29Wcs0OZOYydGsXa+DT+eU1HRvUqB/NJjPFDFgpuykyFBX+FJr2g2xi3qzlniWnHuPW9FcQfOsabN3fn8g4N3C7JGHOOLBTctPApyD4MV/2nzM5J2JqUwa3vrSQzJ4+PxvakV4vabpdkjDkPFgpu2bUEYqbBgIehfoTb1ZyT6N0HGftBFBWDA/n07j60a1jN7ZKMMefJQsENuVnOnISazeDCR92u5px8vzGJe6evplGNED4c25Mmtcr2MFpjjMNCwQ3L/g2p22H0FxBc9radnLUqgcdnr6N9o2pMGdOD2qEV3S7JGFNCLBRKW/JWWPYKdLwRWp50UVif9tEvu3lqzgb6X1CHd0Z3p0pF+ydkjD+x/9Gl6fichODKcNk/3a7mrL390w6en7+Zi9vV5/VRXW2WsjF+yEKhNK35GPb8DENfg9CyswS4qvLvhVt57cftDO3ciFdu7Eyw7aVsjF+yUCgtR5LhuychvC90He12NcWmqjw7bxPvLdvFjZFhPHdtJ1u2whg/ZqFQWr57EnIyYWjZmZOQX6A8+WUsM1bGMaZvM/52VQQBFgjG+DULhdKwYxGsmwkXPgZ127hdTbHk5RfwyGdr+TJmL38a1JJHL2tj6xgZUw5YKHhb7jGY9xDUauFMVCsDsvPyuX/GGhZsSOLRy9pw7+AL3C7JGFNKLBS8benLcHAn3DoHgiu5Xc0ZHcvJ5+6PV7FkazJPD43g9n7N3S7JGFOKLBS86cBmZ/OcziOhxSC3qzmjI9l5jP0giqjdB3nhuo6M6GErnRpT3lgoeEtBgTMnoWIoXPqs29WcUdrRHG6bEkVsYjr/GdGF4V0au12SMcYFFgresnoqxP0Cw9+AKnXcrua0kjOyGf3eCnYmZ/LWzd24tL0tfW1MeWWh4A2xs+GbR6HZAOhys9vVnNa+9GPcPHkFe9OO8d6YSAa0KjuT6owxJc9CoaStfNcJhPA+MOJj8OFhnHGpRxk1+VfSjuby4dhe9Gxey+2SjDEus1AoKaqw+Hn46XlocyVc/75Pr4C6/cARbp78K9l5BUy/qxedwmq4XZIxxgdYKJSEgnyY/xhETXaai4a+BoG++6PdsDedW99biYgwc1xv2jawzXGMMQ7f/eQqK/Jy4Iu7YcPn0Pd+uOQZn24yWh13iDHvryS0YhAf39mLFnVD3S7JGONDLBTOR/YR+OQW2LnICYN+E9yu6LR+2ZHKnVOjqFO1ItPu7EVYTdstzRjzWxYK5yozFaZdD/vWwvA3oatvjzJatOUA93y0ivBalfn4zl7Ur+b7s6uNMaXPQuFcpMXDx9dCWpwzwqjtlW5XdFrz1+/j/plraF2/Kh/d0YtaVSq4XZIxxkdZKJyt5C3w0TVO09Etn0Ozfm5XdFqfr07gkc/W0qVJDabc3pPqIcFul2SM8WEWCmcjIdppMgqsALfPgwYd3a7otKat2MOTX8bSp0Vt3r010vZTNsackX1KFNf2H+CT0c42mqO/hFq+vXro5KU7eXbeJi5qW483b+5m+ykbY4rFq1uAicjlIrJFRLaLyBMnuf8hEdkoIutE5AcRaerNes7Z+lkwfYSzJ8LY73w6EFSVV7/fxrPzNjGkY0PevqW7BYIxpti8FgoiEgi8AVwBRAAjRSTihNPWAJGq2gmYBbzorXrO2cp3Yfad0KSn02RUtb7bFZ2SqvLc/M38+/utXNctjFdv6kKFoLKx9acxxjd48xOjJ7BdVXeqag4wExhe9ARVXaSqRz03fwXCvFjP2VGFRc/BN49AmyvgltlQqbrbVZ1SQYHy1JxYJi3ZyejeTfnX9Z0ICrRAMMacHW/2KTQG4ovcTgB6neb8O4D5J7tDRMYB4wDCw0th45ffLFtxCwx91aeXrcjNL+Dx2ev4fHUidw9swROXt7X9lI0x58QnPulE5BYgEhh4svtVdRIwCSAyMlK9WkxetmfZii+cGcoX/92nl63IyMrlT9NWs3RbCg9d0pr7LrrAAsEYc868GQqJQJMit8M8x35DRC4G/goMVNVsL9ZzZtlH4JObYediuOQf0O9+V8s5k71pxxj7QRTbDhyx7TONMSXCm6EQBbQSkeY4YXATMKroCSLSFXgHuFxVD3ixljMrY8tWxCamc8fUKDKz85kypgcXtrbNcYwx589roaCqeSIyHlgABALvq+oGEXkGiFbVucC/gFDgM0+TR5yqDvNWTaeUFu/MUk6Ph5umOR3LPmzR5gPcO301NUKCmfXHPrb0tXFNbm4uCQkJZGVluV2K8ahUqRJhYWEEB5/b6gVe7VNQ1W+Ab0449rci31/szdcvlqLLVoz+Apr2dbui0/ro1z08PSeWdg2r8f6YHrawnXFVQkICVatWpVmzZtaX5QNUldTUVBISEmje/NzmU/lER7NrytCyFQUFyvPfbmbSkp1c1LYe/x3Z1ZatMK7LysqyQPAhIkLt2rVJTk4+5+cov58q27/3LFtR37lC8OFZylm5+Tz4SQzzY/czundTnh4aYXMQjM+wQPAt5/v3UT5DYf0s+OIeqNcWbp7t07OUU49kc+eH0cTEp/HkkHbc0b+5/Sc0xnhN+QuFFZOciWlN+8HI6T49S3lH8hFunxJF0uEs3rq5G5d3aOh2ScYYP1d+2iBUYdE/Yf6j0OZKn1+2YsXOVK59czmZ2XnMHNfbAsGYk0hLS+PNN98868ddeeWVpKWleaEix9tvv82HH3542nOio6O5//5znwvVrFkzUlJSzvnxp1J+rhSWvgw/vQBdb4GrfHvZijkxiTz62TrCaoXwwZiehNe2vZSN7/v7VxvYuPdwiT5nRKNqPD20/SnvPx4Kf/rTn35zPC8vj6CgU/8f/+abb0553/nKy8vjnnvuOeN5kZGRREZGeq2Oc+W7n4wlrdMIZ7mK/g/57LIVqsrrP27n5YVb6dW8Fu+M7k6NyrZ1pjGn8sQTT7Bjxw66dOlCcHAwlSpVombNmmzevJmtW7dy9dVXEx8fT1ZWFhMmTGDcuHGA81t2dHQ0R44c4YorrqB///4sX76cxo0bM2fOHEJCQk76ejExMdxzzz0cPXqUli1b8v7771OzZk0GDRpEly5dWLZsGSNHjiQjI4PQ0FAeeeQRoqKiuOOOOwgICOCSSy5h/vz5xMbGsnjxYl566SW+/vprJk6cSFxcHDt37iQuLo4HHnig8CriVO/Ba1S1TH11795d/VFOXr4++lmMNn38a31g5hrNys1zuyRjzmjjxo2uvv6uXbu0ffv2qqq6aNEirVy5su7cubPw/tTUVFVVPXr0qLZv315TUlJUVbVp06aanJysu3bt0sDAQF2zZo2qqt5www360UcfnfL1OnbsqIsXL1ZV1aeeekonTJigqqoDBw7UP/7xj4XnPf300/qvf/1LVVXbt2+vy5cvV1XVxx9//Df1DhkypPD8Pn36aFZWliYnJ2utWrU0JyenWO/hZE7294IzafiMn7Hlp0/Bhx3OyuX2KVF8Gp3A/X9oxSs3dqZikG2MY8zZ6tmz528mbb322mt07tyZ3r17Ex8fz7Zt2373mObNm9OlSxcAunfvzu7du0/63Onp6aSlpTFwoLNu52233caSJUsK7x8xYsTvHpOWlkZGRgZ9+vQBYNSoUb8757ghQ4ZQsWJF6tSpQ7169UhKSir2eyhJ5af5yEclHDrK2A+i2Jmcyb+u78QNkU3O/CBjzElVqVKl8PvFixfz/fff88svv1C5cmUGDRp00uU4KlasWPh9YGAgx44dO+/XPhcn1pGXl1fs91CS7ErBResT0rnmzeXsS89i6tieFgjGnKWqVauSkZFx0vvS09OpWbMmlStXZvPmzfz666/n9VrVq1enZs2aLF26FICPPvqo8KrhVGrUqEHVqlVZsWIFADNnzjyr1yzp91AcdqXgku83JnHfjDXUqlKB6Xf2olX9qm6XZEyZU7t2bfr160eHDh0ICQmhfv3/TUS9/PLLefvtt2nXrh1t2rShd+/e5/16U6dOLexobtGiBVOmTDnjY9577z3uuusuAgICGDhwINWrF38ovDfew5mI0/9QdkRGRmp0dLTbZZyXqct38/evNtChcXUm3xZJvaq2qJ0pmzZt2kS7du3cLsOnHTlyhNDQUACef/559u3bx6uvvurV1zzZ34uIrFLVM46BtSuFUpRfoPzzm028t2wXF7erz2sju1C5gv0VGOPP5s2bx3PPPUdeXh5Nmzblgw8+cLuk07JPpFJyLCefBz5Zw4INSdzerxlPDokgMMA350sYU97de++9/Pzzz785NmHCBG6//fazfq4RI0acdGSSr7JQKAXJGc6idusS0nh6aAS39/PdFVmNMfDGG2+4XYJrLBS8bPuBDMZMiSLlSDbv3NKdS9s3cLskY4w5JQsFL/plRyp3fxRNhaBAPhnXh85NarhdkjHGnJaFghckHc7iyzWJvPTdFprVrsL7Y3rQpJYtameM8X0WCiVkR/IRFmzYz3cbkoiJd5bkHdCqDq+P6kb1kHPbQNsYY0qbhcI5KihQ1iWme4JgPzuSMwHoHFadRy9rw6UR9bmgXqjtkmaMDwkNDeXIkSPn/Tx9+/Zl+fLlpz3nzjvv5KGHHiIiIuKsn/+DDz4gOjqa119//VxLPGcWCmchJ6+AFbtSWbBhPws3JpF0OJugAKF3i9rc1rcZF7erT6MaJ19y1xi/N/8J2L++ZJ+zQUe44vmSfc7zcHyfhjMFAsDkyZNLoaKSZ6FwBpnZefy0NZkFG/bz4+YDZGTlERIcyKA2dbm0fX0ualOf6pWtecgYNzzxxBM0adKEe++9F4CJEycSFBTEokWLOHToELm5uTz77LMMHz78jM+lqjz22GPMnz8fEeHJJ59kxIgRLF68mKeeeuo3+zQcv+IoKChg/Pjx/PjjjzRp0oTg4GDGjh3L9ddfz6BBg3jppZeIjIwkNDSUCRMm8PXXXxMSEsKcOXOoX78+X331Fc8++yw5OTnUrl2badOm/WapDjdYKJxEypFsftiUxIINSSzbnkJOXgG1qlTgig4NuDSiAf1b1aFSsC1tbcxvuPAb/YgRI3jggQcKQ+HTTz9lwYIF3H///VSrVo2UlBR69+7NsGHDztiU+/nnnxMTE8PatWtJSUmhR48eXHjhhQCsXr2a2NjY3yzLffwxu3fvZuPGjRw4cIB27doxduzY3z13ZmYmvXv35v/+7/947LHHePfdd3nyySfp378/v/76KyLC5MmTefHFF3n55ZdL6KdzbiwUPOJSjzr9Axv3E73nEKoQVjOE0b2bcmlEfSKb1bIZyMb4mK5du3LgwAH27t1LcnIyNWvWpEGDBjz44IMsWbKEgIAAEhMTSUpKokGD088ROr5rWmBgIPXr12fgwIFERUVRrVq13+3TUPQxN9xwAwEBATRo0IDBgwef9LkrVKjAVVddBTh7NixcuBCAhIQERowYwb59+8jJyTnpa5S2chsKqsqGvYf5bsN+vtuYxOb9zvK7EQ2rMeEPrbg0ogHtGla1jmJjfNwNN9zArFmz2L9/PyNGjGDatGkkJyezatUqgoODadas2XnvQXC+eyUEBwcXfpYc3ysB4L777uOhhx5i2LBhLF68mIkTJ57X65SEcrWfQl5+Ab/sSGXi3A30f2ERV/13Ga8v2k71kGCeuiqCpY8N5psJA3jg4tZENKpmgWBMGTBixAhmzpzJrFmzuOGGG0hPT6devXoEBwezaNEi9uzZU6znGTBgAJ988gn5+fkkJyezZMkSevbsedrH9OvXj9mzZ1NQUEBSUhKLFy8+q9rT09Np3Lgx4CzL7QvKzZXCzJVxPP/tZtKO5lIxKIABreoy4eJW/KFtPWqHVjzzExhjfFL79u3JyMigcePGNGzYkJtvvpmhQ4fSsWNHIiMjadu2bbGe55prruGXX36hc+fOiAgvvvgiDRo0YPPmzad8zHXXXccPP/xAREQETZo0oVu3bme1X8LEiRO54YYbqFmzJhdddBG7du0q9mO9pdzsp7B4ywHmxuzl0vb1GdCqLlUqlps8NMZrbD+F/+2XkJqaSs+ePfn555/P2H/hbbafQjEMalOPQW3quV2GMcbPXHXVVaSlpZGTk8NTTz3leiCcL6+GgohcDrwKBAKTVfX5E+6/EPgP0Am4SVVnebMeY4xZv349o0eP/s2xihUrFu6jfLbOth/B13ktFEQkEHgDuARIAKJEZK6qbixyWhwwBnjEW3UYY7xLVcvUoIyOHTsSExPjdhlec75dAt4cfdQT2K6qO1U1B5gJ/GZaoaruVtV1QIEX6zDGeEmlSpVITU097w8iUzJUldTUVCpVOvd9373ZfNQYiC9yOwHodS5PJCLjgHEA4eHh51+ZMaZEhIWFkZCQQHJystulGI9KlSoRFhZ2zo8vEx3NqjoJmATO6COXyzHGeAQHB/vELFxTcrzZfJQINClyO8xzzBhjjI/yZihEAa1EpLmIVABuAuZ68fWMMcacJ6+FgqrmAeOBBcAm4FNV3SAiz4jIMAAR6SEiCcANwDsissFb9RhjjDmzMjejWUSSgeItZvJ7dYCUEizH1/jz+7P3Vnb58/srS++tqarWPdNJZS4UzoeIRBdnmndZ5c/vz95b2eXP788f31u5WiXVGGPM6VkoGGOMKVTeQmGS2wV4mT+/P3tvZZc/vz+/e2/lqk/BGGPM6ZW3KwVjjDGnYaFgjDGmULkJBRG5XES2iMh2EXnC7XpKiog0EZFFIrJRRDaIyAS3ayppIhIoImtE5Gu3aylpIlJDRGaJyGYR2SQifdyuqaSIyIOef5OxIjJDRM596U4fICLvi8gBEYktcqyWiCwUkW2eP2u6WWNJKBehUGRvhyuACGCkiES4W1WJyQMeVtUIoDdwrx+9t+Mm4MyK90evAt+qalugM37yPkWkMXA/EKmqHXA22rrJ3arO2wfA5SccewL4QVVbAT94bpdp5SIUKMbeDmWVqu5T1dWe7zNwPlQau1tVyRGRMGAIMNntWkqaiFQHLgTeA1DVHFVNc7eqEhUEhIhIEFAZ2OtyPedFVZcAB084PByY6vl+KnB1qRblBeUlFE62t4PffHAeJyLNgK7Aue0r6Jv+AzyGf27E1BxIBqZ4mscmi0gVt4sqCaqaCLyEs7viPiBdVb9ztyqvqK+q+zzf7wfqu1lMSSgvoeD3RCQUmA08oKqH3a6nJIjIVcABVV3ldi1eEgR0A95S1a5AJn7Q/ADgaVsfjhN8jYAqInKLu1V5lzrj+8v8GP/yEgp+vbeDiATjBMI0Vf3c7XpKUD9gmIjsxmnyu0hEPna3pBKVACSo6vEru1k4IeEPLgZ2qWqyquYCnwN9Xa7JG5JEpCGA588DLtdz3spLKPjt3g7i7Jj+HrBJVV9xu56SpKp/VtUwVW2G83f2o6r6zW+bqrofiBeRNp5DfwA2ulhSSYoDeotIZc+/0T/gJ53oJ5gL3Ob5/jZgjou1lIgysR3n+VLVPBE5vrdDIPC+qvrL3g39gNHAehGJ8Rz7i6p+42JNpvjuA6Z5flnZCdzucj0lQlVXiMgsYDXOCLk1lPElIURkBjAIqOPZB+Zp4HngUxG5A2dJ/xvdq7Bk2DIXxhhjCpWX5iNjjDHFYKFgjDGmkIWCMcaYQhYKxhhjClkoGGOMKWShYEwpEpFB/rjaq/EfFgrGGGMKWSgYcxIicouIrBSRGBF5x7OnwxER+bdnj4AfRKSu59wuIvKriKwTkS+Or6kvIheIyPcislZEVotIS8/ThxbZQ2GaZ8avMT7BQsGYE4hIO2AE0E9VuwD5wM1AFSBaVdsDP+HMaAX4EHhcVTsB64scnwa8oaqdcdb9Ob6aZlfgAZy9PVrgzEo3xieUi2UujDlLfwC6A1GeX+JDcBY6KwA+8ZzzMfC5Z0+EGqr6k+f4VOAzEakKNFbVLwBUNQvA83wrVTXBczsGaAYs8/7bMubMLBSM+T0Bpqrqn39zUOSpE8471zVisot8n4/9PzQ+xJqPjPm9H4DrRaQeFO7D2xTn/8v1nnNGActUNR04JCIDPMdHAz95dsFLEJGrPc9RUUQql+q7MOYc2G8oxpxAVTeKyJPAdyISAOQC9+JsgtPTc98BnH4HcJZMftvzoV90pdPRwDsi8oznOW4oxbdhzDmxVVKNKSYROaKqoW7XYYw3WfORMcaYQnalYIwxppBdKRhjjClkoWCMMaaQhYIxxphCFgrGGGMKWSgYY4wp9P8iUFeU0l/IkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_epoch = 1 # 4\n",
    "model=AlexNet()\n",
    "opt = Adam(lr=base_learning_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
    "\n",
    "# load the model weights from epoch 11\n",
    "weights_path = 'model_weights/alexnet_original_epoch11.h5'\n",
    "model.load_weights(filepath=weights_path, by_name=False, skip_mismatch=False)\n",
    "\n",
    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# re-initialize weights of the model to train from scratch\n",
    "#initialize_weights(model)\n",
    "\n",
    "history_original = model.fit_generator(\n",
    "        generator(features=X_train, labels=Y_train, batch_size=batch_size, patch_size=img_cols, channels=channels, \n",
    "                  nb_classes=nb_classes),\n",
    "        steps_per_epoch=batches_per_epoch, epochs=nb_epoch, \n",
    "        validation_data=generator(features=X_val, labels=Y_val, batch_size=batch_size, patch_size=img_cols, \n",
    "                                  channels=channels, nb_classes=nb_classes),\n",
    "        validation_steps=validation_steps)\n",
    "\n",
    "# merge history until epoch_11 with additional epoch\n",
    "history_epoch11 = {'acc': [0.11380709134615384, 0.13599258814102563, 0.19721554487179488, 0.2645983573717949, 0.328700921474359, 0.3811848958333333, 0.4226262019230769, 0.4607121394230769, 0.49291366185897434, 0.5213842147435898, 0.5494791666666666], 'loss': [2.864764799674352, 2.2487578705335274, 2.1015444237452288, 1.962251861890157, 1.823481919291692, 1.7024982212445674, 1.604735214358721, 1.525293638690924, 1.4359341340187268, 1.3694841491106229, 1.3113467792669933], 'val_acc': [0.17057291666666666, 0.23537660256410256, 0.34825721153846156, 0.4004407051282051, 0.43569711538461536, 0.4668469551282051, 0.49328926282051283, 0.5360576923076923, 0.5328525641025641, 0.5571915064102564, 0.5865384615384616], 'val_loss': [2.271790779553927, 2.125739351297036, 1.9180474495276427, 1.7726084635807917, 1.6616307405325084, 1.5195633050722954, 1.476634464202783, 1.3749462304971156, 1.3630957527038379, 1.3458387286235125, 1.2366121120941944]}\n",
    "for key in history_original.history.keys():\n",
    "    history_original.history[key] = history_epoch11[key] + history_original.history[key]\n",
    "    \n",
    "plot_train_val_accuracy_and_loss(history_original, legend_suffix='original')\n",
    "    \n",
    "# model.save_weights('model_weights/alexnet_original_epoch11.h5')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 88us/step\n",
      "Summary: Loss over the test dataset: 1.14, Accuracy: 0.60\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model trained on the original data\n",
    "evaluation = model.evaluate(X_test, Y_test, batch_size=256, verbose=1)\n",
    "print('Summary: Loss over the test dataset: %.2f, Accuracy: %.2f' % (evaluation[0], evaluation[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the input images\n",
    "\n",
    "We subtract the training mean and divide by the training standard deviation for each RGB channel separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PREPROCESSING: NORMALIZE THE INPUT DATA\n",
    "# as calculating the Mean and STD over the entire training data takes some time, the values are provide.\n",
    "\n",
    "def get_mean_and_std_per_channel(data_train, patch_size, channels=3):\n",
    "    N = data_train.shape[0]\n",
    "    img_concat = None\n",
    "    for i in range(data_train.shape[0]):\n",
    "        img = data_train[i, :, :, :]\n",
    "        print(i)\n",
    "        assert len(img.shape) == 3\n",
    "\n",
    "        if img_concat is None:\n",
    "            img_concat = img\n",
    "        else:\n",
    "            img_concat = np.concatenate((img_concat, img), axis=0)\n",
    "\n",
    "    assert img_concat.shape == (N * patch_size, patch_size, channels)\n",
    "    print('img_concat.shape', img_concat.shape)\n",
    "\n",
    "    # calculate mean\n",
    "    mean = np.mean(img_concat, axis=(0, 1))\n",
    "    mean = mean.reshape(1, 1, channels)\n",
    "    print('mean: ', mean)\n",
    "    print('mean.shape', mean.shape)\n",
    "    assert mean.shape == (1, 1, channels)\n",
    "\n",
    "    # control mean\n",
    "    img_concat_zero_mean = img_concat - np.tile(mean, [N * patch_size, patch_size, 1])\n",
    "    mean_control = np.mean(img_concat_zero_mean, axis=(0, 1))\n",
    "    print('mean_control', mean_control)\n",
    "\n",
    "    # calculate std\n",
    "    std = np.std(img_concat_zero_mean, axis=(0, 1))\n",
    "    std = std.reshape(1, 1, channels)\n",
    "    print('std: ', std)\n",
    "    print('std.shape', std.shape)\n",
    "    assert std.shape == (1, 1, channels)\n",
    "\n",
    "    # control std\n",
    "    img_concat_zero_mean_std_1 = np.divide(img_concat_zero_mean, np.tile(std, [N * patch_size, patch_size, 1]))\n",
    "    std_control = np.std(img_concat_zero_mean_std_1, axis=(0, 1))\n",
    "    print('std_control: ', std_control)\n",
    "    return mean, std\n",
    "\n",
    "def normalize_img_per_channel(image, mean_train, std_train, patch_size):\n",
    "    img_zero_mean = image - np.tile(mean_train, [patch_size, patch_size, 1])\n",
    "    img_norm = np.divide(img_zero_mean, np.tile(std_train, [patch_size, patch_size, 1]))\n",
    "    return img_norm\n",
    "\n",
    "def normalize_images_per_channel(images, mean_train, std_train, out_dtype='float32'):\n",
    "    images_norm = []\n",
    "    for i in range(images.shape[0]):\n",
    "        img = images[i, :, :, :]\n",
    "        assert len(img.shape) == 3\n",
    "        img_norm = normalize_img_per_channel(img, mean_train, std_train, patch_size=img.shape[0])\n",
    "        images_norm.append(img_norm)\n",
    "    return np.asarray(images_norm, out_dtype)\n",
    "\n",
    "# print('calculating mean and std per channel from the training data...')\n",
    "# mean_train_file = data_dir + '/cifar10_mean_train.npy'\n",
    "# std_train_file = data_dir + '/cifar10_std_train.npy'\n",
    "# MEAN_train, STD_train = get_mean_and_std_per_channel(data_train=X_train, patch_size=img_cols, channels=channels)\n",
    "# np.save(mean_train_file, MEAN_train)\n",
    "# np.save(std_train_file, STD_train)\n",
    "# #load mean, std from file\n",
    "# MEAN_train = np.load(mean_train_file)\n",
    "# STD_train = np.load(std_train_file)\n",
    "\n",
    "# Cifar10 mean and std from training data\n",
    "MEAN_train = [[[125.30691805, 122.95039414, 113.86538318]]]\n",
    "STD_train = [[[62.99321928, 62.08870764, 66.70489964]]]\n",
    "\n",
    "X_train_prepro = normalize_images_per_channel(images=X_train, mean_train=MEAN_train, std_train=STD_train, \n",
    "                                              out_dtype='float32')\n",
    "X_test_prepro = normalize_images_per_channel(images=X_test, mean_train=MEAN_train, std_train=STD_train, \n",
    "                                             out_dtype='float32')\n",
    "X_val_prepro = normalize_images_per_channel(images=X_val, mean_train=MEAN_train, std_train=STD_train, \n",
    "                                            out_dtype='float32')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model on preprocessed images\n",
    "\n",
    "The model was trained on a GPU (Titan Xp 12GB) for 11 epochs within 99 seconds (9 seconds per epoch). This code loads the weights after the 11th epoch. You will train one epoch more and then the training/validation curves are plotted for the entire 12 epochs. \n",
    "\n",
    "If you have not implemented the model correctly the weights of the pre-trained model will not match the model architecture. \n",
    "\n",
    "* What is the purpose of preprocessing the input data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "312/312 [==============================] - 8s 27ms/step - loss: 0.8637 - acc: 0.7174 - val_loss: 0.7946 - val_acc: 0.7487\n",
      "['acc', 'loss', 'val_acc', 'val_loss']\n"
     ]
    },
    {
     "data": {
      "image/png": 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9ozGlj6jqubcqRiIjIzU6OtrtMExplbofVn/q1BU+efE/lvT7bfwr5rvQN/79hb9qCPid4y/085WVwYmkbUSvXMq2mGhqpsfRptxeGrEXH83+bbuqDX9/V1GrFdRs8dtFf+dSmD4cjidD/xchYpg1F5UBIrJKVSPPtZ3dKRgDkJIIP78Bqz9x+gGqNnQu8i3757vge74HVS/yi2hqRhb/W76bD3/ez/6jDWhTrzWjBzQlpG1dfMiBQ3FOIkva8tv32EWQk/nbTio3cPoodv7i3LWM+AHqtS/S8zDFnyUFU7YdioOoCbDmf4BC+yHQ8xGo0dTtyAA4kJrBRz/H89mynaRmZHNR0xqMu7E9vZrXzDe2wAdqtXC+8svNce528ieKg1ug/S3Q7wVrLjKnZUnBlE1JWyHqVVg3FXx8IeJO6PGQc4dQDMQfPMbExbHMWJ1IVk4u/dvW5Z7eTWkfUrXgO/HxdZJbjabQ6irvBWtKFUsKpmzZHwOLx0PMV1AuELqOhosegMr13I4MgHWJR3h30Q6+27APPx8fbogIZlTvJoTW/GO9Y2O8wZKCKRv2/Ookg83fOh3FPf8K3e6DirXcjgxVZcm2g7y7aAe/7EimUkA5Rl/clGE9GlO7UqDb4ZkyxpKCKd12LYfF42D7DxBYBS4eA13vcTqLXXZygrr3FscSs+cotSsF8ET/VtzatSGVAq1spXGHJQVT+qhC/BKnqHz8EgiqAX2fgs4ji0XnasrxLP63Yhef/BLPvqMZNKlVgZduCGdQxwYElPN1OzxTxnk1KYhIP+B1wBd4X1VfPM02NwFjcYZ9rlXVW70ZkynFVJ3BWIvHQcJyZ6DYlf/nTNvg736bfNzBY3z0cxzTohNJz8qhR7Ma/N/1benT4gwT1BnjAq8lBRHxBd4CLgcSgZUiMlNVN+bbpjnwBNBDVQ+LSG1vxWNKsdxc2DLHSQZ710DlYLhqPHQcCn7utsmrKstiD/FBVBw/bt6Pn48PAzvUZ3iPUNrUd/+uxZhTefNOoQuwXVVjAURkMnAtsDHfNiOBt1T1MICqHvBiPKa0yc2BjV/D4lfgQIwzsGzgm9BuSIEnkfOWE9m5fLtuDx9ExRGz5yjVgvx44JJm3N69kXUem2LNm0mhAZCQbzkR6HrKNi0ARORnnCamsar6/ak7EpFRwCiAhg2Lx3PkxkU52bB+Gix5BZK3OVM4XPcetL2h0KafPl+Hj53I6y84kJpJs9oVeeH6cK7r2IBAP+svMMWf2x3N5YDmQB8gGFgsIuGqeiT/Rqr6HvAeOHMfFXWQppjIzoS1X8CSV505ieq0hcEfQ+uBzkAtF+1ISuPDqDhmrE4kIyuXXs1r8vKN7ejdvJb1F5gSxZtJYTcQkm852LMuv0RguapmAXEishUnSaz0YlympMlKh9WT4OfX4OhuqN8J+r8ELfq5OpGbqrJ0RzLvR8Xx0+YD+JfzYVCH+gzvGUqrutZfYEombyaFlUBzEQnFSQZDgFOfLPoauAX4SERq4jQnxXoxJlOSZKZC9Ifwy3/g2AFo2N3pM2h6qavJIDM7h1lr9/JBVByb9h6lRgV//npZc27v1oiaFQNci8uYwuC1pKCq2SJyPzAXp7/gQ1WNEZFngGhVnel57woR2QjkAH9T1WRvxWRKiOOHYMV7sOwdp3h8kz7Q60No3NPVZHDo2Ak+X7aTT5ftJCk1kxZ1KvLyDe0Y2KG+9ReYUsPqKZjiI+0ALP0PrPwATqRBy6ug12MQHOFqWNsPpPJBVDxfrk4kMzuXi1vUYkSvUHo2yz9TqTHFm9VTMCXHkQT45Q2nuE3OCQi7Dno9CnXCXAtJVYnafpAPouJYuCWJgHI+XN+pAcN7hNL8XDWPjSnBLCkY9yTvcKavXjvZWS4GtQyyc3L56tfdfBAVx+Z9qdSsGMAjl7fgtq4NqWH9BaYMsKRgit7+GGeMQcxX4OsPkcPhogedUpYuWrjlAM/P3sS2A2m0qluJcTc6/QU2H5EpSywpmKKTuAqWjHempPCv6NQx6H4/VHR3dpPN+47y/OxNLNl2kMY1gnj39giuDKtj/QWmTLKkYLxLFeKjnGQQuxACq0KfJ6DLKNenrz6QmsGEH7YyZWUClQL9+NfVbRjarRH+5XxcjcsYN1lSMN6hCtt+cJJBwnKoUBsuf8ZpKgpwt6M2IyuH95fE8s7CHZzIyeWui0J5sG8zqga5O1+SMcWBJQVTuHJzYdNMp89g3zqoEuKZsfR28CvvcmjKN2t38/L3W9ibksGVYXUY07+1lbo0Jh9LCqZw5GTB+unO00QHt0L1pnDtWxB+k+szlgIsj03m+TmbWJeYQniDKrx2cwe6NqnhdljGFDuWFMyFycqANZ878xId2eVMUnfjh9BmkOuT1AHEHzzGC99tYm7MfupVCWTCze25tn0Dm6TOmDOwpGDOz4ljEP0R/PImpO2DBpHQ/2XXJ6k76cjxE7zx43YmLYvH39eHx65owd09m1De3/1EZUxxZknB/HnrpsL3Y+B4MjTuBddPhNCLi0UyOJGdy6RlO3njx22kZmRxc+cQHr68hRW2MaaALCmYgss+AXP/ASv/CyHd4JbJENLF7agAZ1qKuTH7efG7TcQnH6dX85r8c0Brm8LamD/JkoIpmKN7YOqdkLjCGXB22b9dr3J20vrEFJ6dvZEVcYdoXrsiHw3rTJ8WtWzwmTHnoXj8VpviLT4Kpt0FJ447lc7CrnM7IgD2HEln/NwtfPnrbmpU8Of569pyc2QI5Xxt8Jkx58uSgjkzVVj6FvzwFFRvAnd+C7VbuR0VaZnZTFy0g/cWx6LAvX2a8pc+TakU6Od2aMaUeJYUzOllpsHM+51J61oPdMYcBLrbPp+Tq0yNTuCVeVs5mJbJwPb1+Xu/lgRXC3I1LmNKE0sK5o8OboPJt0HyNmdqiosedP3JoiXbknh+9iY270slslE1/ntHBB0bVnM1JmNKI0sK5vc2zoSv74VyATD0a2hysavh7D+awT+/2sD8TftpWD2It2/rRP+2da0T2RgvKVBSEJEvgQ+A71Q117shGVfkZMNPz8DPr0ODCLjpU6gS7Fo4qso3a/bw9MwYMrNzeKJ/K+7q0dhqGxjjZQW9U3gbGAa8ISLTgI9UdYv3wjJFKi0JZgyHuMXOLKb9XnTuFFySlJrJP79az7yN+4loVI1xN7ajSa2KrsVjTFlSoKSgqvOB+SJSBbjF8zoB+C/wmapmeTFG402J0TD1Dmd08rVvQ8fbXA1n1to9PPXNBo6dyOGfV7VmeM9QfG2eImOKTIH7FESkBnA7MBT4Ffgc6AncCfTxRnDGi1Rh1Ufw3eNQqS7cPQ/qtXctnOS0TJ76JobZ6/fSPqQqrwxuT7PadndgTFEraJ/CV0BLYBJwjaru9bw1RUSivRWc8ZKsdJj9qDO7abPL4Pr/uloF7bv1e3ny6w2kZmTz934tGdWriQ1AM8YlBb1TeENVF5zuDVWNLMR4jLcdjocpQ50COBc/7ny5NMX14WMneHpmDDPX7iG8QRXGD25Py7ruVmUzpqwraFJoIyK/quoRABGpBtyiqm97LzRT6LbNhxl3Awq3TIGW/VwL5YeN+3niy/WkpJ/g0ctbMLpPU/zs7sAY1xU0KYxU1bdOLqjqYREZifNUkinucnOdWskL/s8pgnPzp860FS5IOZ7Fv2fF8OWvu2ldrzKfDu9Cm/o2k6kxxUVBk4KviIiqKoCI+ALu11g055Z+GL68B7bNhXZD4OoJ4O/OtBA/bXbuDg6mneDBvs25/5Jm+JezuwNjipOCJoXvcTqVJ3qW7/GsM8XZvvUw5XZI2Q1XjYfOI1yZruJoRhbPztrItFWJtKxTiffv6Ex4cJUij8MYc24FTQqP4ySCv3iWfwDeP9eHRKQf8DrgC7yvqi+e8v5dwDhgt2fVf1T1nPs1BbB2Csx6CMpXhWFzXCuGs3hrEo/PWMf+oxncd0lTHuzb3EYlG1OMFXTwWi7wjuerQDxNTG8BlwOJwEoRmamqG0/ZdIqq3l/Q/ZpzyF8drVFPGPwRVKxd5GGkZWbz/OxNfLFiF01rVeDLe3vQIaRqkcdhjPlzCjpOoTnwAtAGyCt2q6pn663sAmxX1VjPPiYD1wKnJgVTWIpJdbSftx/k79PXsSclnXt6N+Hhy1sQ6Gd3B8aUBAW9YnwEPA1MAC7BmQfpXD2EDYCEfMuJQNfTbHeDiPQGtgIPq2rCqRuIyChgFEDDhg0LGHIZUwyqox3LzObF7zYzadlOQmtWYPro7kQ0cm9QnDHmzyvoox/lVfVHQFR1p6qOBQYUwvFnAY1VtR1OP8Unp9tIVd9T1UhVjaxVq1YhHLYUST8Msx+DT66BwKow8idXEsLy2GT6v76Ez5bvZHiPUOY82MsSgjElUEHvFDJFxAfYJiL343QMn2timt1ASL7lYH7rUAZAVZPzLb4PvFzAeExuLqz5DOaPdRJD5xFw6b+KvDpa+okcXp67mY9+jqdh9SAmj+xG1yY1ijQGY0zhKWhSeAgIAh4EnsVpQrrzHJ9ZCTQXkVCcZDAEuDX/BiJSL988SgOBTQWMp2zbvRrmPAa7V0FIN7hqHNRrV+RhRMcf4rFpa4lPPs6d3RvxeP9WBPlb3SZjSrJz/gZ7niK6WVUfA9Jw+hPOSVWzPXcVc3EeSf1QVWNE5BkgWlVnAg+KyEAgGzgE3HV+p1FGHEt2CuGs+gQq1ILrJkK7m4t87EFGVg6vzNvC+1FxNKhanv+N7MpFTWsWaQzGGO8QzyDls28kskxVuxVBPOcUGRmp0dFlbGLW3BxY9TH89CxkHIWuo6HP4xBY9APA9hxJZ/Rnq1iXmMJtXRvyxFWtqRhgdwfGFHcisqogE5gW9Lf5VxGZCUwDjp1cqapfnmd8pqASVjhNRXvXQuNeTlNR7dauhLIi7hD3fr6KjKxc3hsawRVhdV2JwxjjPQVNCoFAMnBpvnUKWFLwlrQkpxN5zWdQqR7c8AG0vcGVaSpUlc+X72LszBhCqgcxeVQEzWrbFNfGlEYFHdFcoH4EUwhysiH6A/jpecg6Dj0egt5/hwB3qpBlZucwdmYMX6xIoE/LWrw+pCNVyvu5EosxxvsKOqL5I5w7g99R1eGFHlFZtvMXmPM32L8BmlwC/V+GWi1cC+fA0Qz+8vlqVu08zL19mvLoFS2tXrIxpVxBm4++zfc6ELgO2FP44ZRRqfvgh6dg3RSoEgI3TYLW17jSVHTSr7sOM/qzVRxNz+Y/t3bk6nb1XYvFGFN0Ctp8NCP/soh8AUR5JaKyJCcLlk+EhS9CTib0egx6PepavYOTpkYn8ORXG6hdOYAZf7nIiuAYU4ac77OEzYGin3qzNIlb7DQVJW2G5ldAvxehRlNXQ8rKyeX52Zv4+Jd4ejSrwX9u6US1ClZLyZiypKB9Cqn8vk9hH06NBfNnpeyGef+EmK+gaiO4ZTK06OdqUxFAclom936+muVxhxjRM5Qx/VtRzmomG1PmFLT5yJ4/vFDZJ2DZW7BoHGgO9PkH9HgQ/Mq7HRkbdqdwz6RVJKVl8upN7bm+U7DbIRljXFLQO4XrgJ9UNcWzXBXoo6pfezO4UmP7j/Dd3yF5O7S6Gq58Hqo1djsqAL5Zs5vHZ6yjWpA/00d3p12wFcIxpiwraJ/C06r61ckFVT0iIk8DlhTO5sgupwrapllQvQncNgOaX+Z2VADk5Covf7+ZiYtj6dy4Gm/fFkGtSgFuh2WMcVlBk8LpGpdtwpuzWfk+zH3S6Svo+5RTCa1c8bjoHjl+gge++JUl2w4ytFsj/nV1G/zLWf+BMabgF/ZoEXkVp+YywH3AKu+EVArs+MkpfNOsL1z9GlQNOfdnisiWfamMmhTNniPpvHh9OEO6WCU7Y8xvCpoUHgD+BUzBeQrpB5zEYE6Vug9mjHQmrbtpkutjDvL7fsNeHpm6lgoB5Zg8qptVRjPG/EFBnz46BozxciwlX24OzBjhzFk0+ONikxByc5XX5m/ljZ+20z6kKhNvj6BulUC3wzLGFEMFakgWkR88TxydXK4mInO9F1YJtehliF8CA16BWi3djgaA1IwsRk2K5o2ftjM4Ipgpo7pZQjDGnFFBm49qquqRkwuqelh8KhfMAAAcd0lEQVREbERzfrGLYNFL0P5W6HDrubcvAjuS0hj1aTTxycf598Aw7ujeCHF5kJwxpngraFLIFZGGqroLQEQac5pZU8us1P1Os1HNFjBgvNvRAPDT5v089MUa/Mr58NndXenetIbbIRljSoCCJoV/AlEisggQoBcwymtRlSS5OfDlSMhMhTu+Af8Kroajqry9cAfj522hTb3KTBwaQXC14tG3YYwp/gra0fy9iETiJIJfcQatpXszsBJjySsQtwgGvgl12rgayrHMbP42fS1z1u9jYPv6vHRDO8r7+7oakzGmZCnoNBcjgIeAYGAN0A1Yyu/Lc5Y98VGw8AVodzN0HOpqKLuSjzNqUjRb96fyz6taM6JXqPUfGGP+tIIOY30I6AzsVNVLgI7AkbN/pJRLS4Lpd0P1pjDgVVdnOd2yL5WBb0WxNyWDj4d1YWTvJpYQjDHnpaB9ChmqmiEiiEiAqm4WkeLxzKUbcnPhq1GQcQRun+Fa/WSAvSnp3PnhCvx9fZh6T3ca13S3T8MYU7IVNCkkesYpfA38ICKHgZ3eC6uY+3mCM5XF1a9B3bauhZGSnsVdH64kLTPbEoIxplAUtKP5Os/LsSKyAKgCfO+1qIqznUvhp+eh7Q0QcZdrYWRm5zDq02hiD6bx8bAuVjLTGFMo/vRMp6q6yBuBlAjHkmH6cKjWyLlLcKndPjdXeXTqWpbHHeK1mzvQo1lNV+IwxpQ+Nv11QeXmwtej4fhBGDEfAt37y/z/5mzi23V7GdO/FYM6NnAtDmNM6WNJoaCWvgnb5sFV46Fee9fCeH9JLO9HxXFn90bc07uJa3EYY0onr1ZWEZF+IrJFRLaLyBlnWRWRG0REPQPkip9dy2H+v6HNtdB5hGthfLtuD8/N3kS/sLo8dU2YPXZqjCl0XksKIuKLU5SnP9AGuEVE/jDkV0Qq4YyDWO6tWC7I8UNOP0LVEGfUsksX4mWxyTwyZS2dG1fjtSEd8PWxhGCMKXzevFPoAmxX1VhVPQFMBq49zXbPAi8BGV6M5fyowtf3Qtp+uPEjCKziShhb9qUy8tNoGtYI4r93RBLoZ1NXGGO8w5tJoQGQkG850bMuj4h0AkJUdbYX4zh/S9+Crd/BFc9Bg06uhLA3JZ27PlpBeT9fPh7WmapB/q7EYYwpG1zraBYRH+BV4K4CbDsKz6ysDRsWUU3hxGiY/zS0uhq63lM0xzzFycFpqRnO4DSb7dQY423evFPYDeSvWB/sWXdSJaAtsFBE4nEm2Zt5us5mVX1PVSNVNbJWrVpeDNkj/TBMGwaV68O1/3GlHyEzO4d7JjmD0yYOjbDBacaYIuHNO4WVQHMRCcVJBkOAvJJkqpoC5I26EpGFwGOqGu3FmM5NFb65H1L3wvC5UL5akYeQm6s8Nm0dy2JtcJoxpmh57U5BVbOB+4G5wCZgqqrGiMgzIjLQW8e9YMsnwuZv4fJ/Q3CEKyG88N0mZq3dY4PTjDFFzqt9Cqo6B5hzyrqnzrBtH2/GUiC7V8O8J6HlVdDtXldC+CAqjv8uscFpxhh3eHXwWomSkQLTh0GlunDtW670I8xet5fnZm+0wWnGGNfYNBfg9CPMfABSEmHYdxBUvchDWBabzMNT1hDR0AanGWPcY3cKACvfh43fQN+nIKRLkR9+6/5URn0aTUj18rx/pw1OM8a4x5LCnjUw9x/Q/Aro/kCRH/5k5bRAP18+Gd7FBqcZY1xVtpuPMo7CtLsgqCYMehd8ijZHHs3IYthHzuC0Kfd0s8FpxhjXld2koAqzHoIju+Cu2VChRpEePjM7h3s+XcX2A07ltLD67syrZIwx+ZXd5qNVH0HMl3DpP6FR9yI99MnBaUtjkxk3uB09m9vgNGNM8VA2k8K+9fDdGGjaF3o8XOSHf/H7zcxau4fH+7Xiuo7BRX58Y4w5k7KXFDJTnX6E8tXguolF3o/wYVQc7y2O5c7ujRh9sQ1OM8YUL2WrT0EVvn0EDsXCnbOgYhFMrpfP7HV7eXb2Rq4Mq2OD04wxxVLZulP4dRKsnwp9/gGNexbpoZfnG5z2+pCONjjNGFMslZ2ksD8G5vwNmvSBXo8U6aG37ncqp9ngNGNMcVd2kkLcEgisCtf/F3yK7qK8LyXDBqcZY0qMstOn0G00dLilSOssH83I4q6PVtjgNGNMiVF27hSgSBNC/sFp794eYYPTjDElQtm5Uyhi//p6A0tjk5lwc3sbnGaMKTHK1p1CEVkWm8zU6ETu7dPUBqcZY0oUSwqFLDsnl7EzY2hQtTwP9m3udjjGGPOnWFIoZP9bsYvN+1J5ckBre/TUGFPiWFIoRIeOneCVeVvp0awG/drWdTscY4z50ywpFKJxc7dwLDObsTaFhTGmhLKkUEjWJ6YweeUu7ryoMc3rVHI7HGOMOS+WFApBbq7y9MwN1Kjgz0OXWeeyMabksqRQCL5es5vVu47w936tqBzo53Y4xhhz3iwpXKDUjCxe+G4zHUKqcmMnG5NgjCnZbETzBXrzp+0cTMvk/Tsi8bHpsI0xJZzdKVyA7QfS+DAqjpsiQmgfUtXtcIwx5oJZUjhPqsq/Z8VQ3t+Xv/Vr6XY4xhhTKLyaFESkn4hsEZHtIjLmNO+PFpH1IrJGRKJEpI034ylMP2zcz5JtB3n4shbUrBjgdjjGGFMovJYURMQXeAvoD7QBbjnNRf9/qhquqh2Al4FXvRVPYcrIyuHZ2RtpUaciQ7s3cjscY4wpNN68U+gCbFfVWFU9AUwGrs2/gaoezbdYAVAvxlNo3lscS8KhdMYODMPP11rgjDGlhzefPmoAJORbTgS6nrqRiNwHPAL4A5d6MZ5CkXj4OG8v3M6A8Hpc1NTqJBhjShfX/8xV1bdUtSnwOPDk6bYRkVEiEi0i0UlJSUUb4ClemLMZgH8MaO1qHMYY4w3evFPYDYTkWw72rDuTycA7p3tDVd8D3gOIjIx0rYnpl+0Hmb1+L49c3oIGVcu7FYYxf5CVlUViYiIZGRluh2JcFhgYSHBwMH5+5ze7gjeTwkqguYiE4iSDIcCt+TcQkeaqus2zOADYRjGVlZPL2FkxhFQvz6jeTdwOx5jfSUxMpFKlSjRu3Nhm6C3DVJXk5GQSExMJDQ09r314LSmoaraI3A/MBXyBD1U1RkSeAaJVdSZwv4hcBmQBh4E7vRXPhZq0dCdb96fx3tAIK55jip2MjAxLCAYRoUaNGlxIM7tXp7lQ1TnAnFPWPZXv9UPePH5hOZiWyYT5W+nVvCaXt6njdjjGnJYlBAMX/v/A9Y7mkmDc91tIP5HD01Y8xxhTyllSOIe1CUeYuiqB4T1DaVa7otvhGGOMV1lSOIvcXOWpmTHUrBjAA5c2czscY4qtI0eO8Pbbb//pz1111VUcOXLECxGVfhUreuePVJs6+yymr05kbcIRXr2pPZWseI4pIf49K4aNe46ee8M/oU39yjx9TdgZ3z+ZFO69997frc/OzqZcuTNfZubMmXPG9y7UuY5dEDk5Ofj6lq0HS+xO4QyOZmTx8veb6dSwKoM6NHA7HGOKtTFjxrBjxw46dOhA586d6dWrFwMHDqRNG2e6s0GDBhEREUFYWBjvvfde3ucaN27MwYMHiY+Pp3Xr1owcOZKwsDCuuOIK0tPTz3i8Pn368NBDD9GhQwfatm3LihUrABg7dixDhw6lR48eDB06lJycHP72t7/RuXNn2rVrx8SJEwFYuHAhvXv3ZsCAAbRs2ZLRo0eTm5sLOH+BP/roo7Rv356lS5fy448/0rFjR8LDwxk+fDiZmZkArFy5kosuuoj27dvTpUsXUlNTz3i8vXv30rt377x4lyxZQk5ODnfddRdt27YlPDycCRMmALBjxw769etHREQEvXr1YvNmZ8BsXFwc3bt3Jzw8nCefPO0438KhqiXqKyIiQovCM7NitPGYb3V94pEiOZ4xF2Ljxo2uHj8uLk7DwsJUVXXBggUaFBSksbGxee8nJyerqurx48c1LCxMDx48qKqqjRo10qSkJI2Li1NfX1/99ddfVVV18ODBOmnSpDMe7+KLL9YRI0aoquqiRYvyjv30009rp06d9Pjx46qqOnHiRH322WdVVTUjI0MjIiI0NjZWFyxYoAEBAbpjxw7Nzs7Wyy67TKdNm6aqqoBOmTJFVVXT09M1ODhYt2zZoqqqQ4cO1QkTJmhmZqaGhobqihUrVFU1JSVFs7Kyzni88ePH63PPPaeqqtnZ2Xr06FGNjo7Wyy67LO+cDh8+rKqql156qW7dulVVVZctW6aXXHKJqqpec801+sknn6iq6n/+8x+tUKHCGX8+p/v/gDMU4JzXWGs+Oo1t+1P55Jd4hnRuSNsGVdwOx5gSp0uXLr8bPPXGG2/w1VdfAZCQkMC2bduoUaPG7z4TGhpKhw4dAIiIiCA+Pv6sx7jlllsA6N27N0ePHs3rmxg4cCDlyzszDsybN49169Yxffp0AFJSUti2bRv+/v506dKFJk2a5O0rKiqKG2+8EV9fX2644QYAtmzZQmhoKC1atADgzjvv5K233qJv377Uq1ePzp07A1C5cuWzHq9z584MHz6crKwsBg0aRIcOHWjSpAmxsbE88MADDBgwgCuuuIK0tDR++eUXBg8enHeeJ+9Mfv75Z2bMmAHA0KFDefzxxwvwL/HnWVI4haoydlYMQf6+/O1KK55jzPmoUKFC3uuFCxcyf/58li5dSlBQEH369DntdBwBAb/VJfH19T1r8xH88Xn8k8v5j62qvPnmm1x55ZW/23bhwoVn/HxgYOB59yOc6XgAixcvZvbs2dx111088sgj3HHHHaxdu5a5c+fy7rvvMnXqVF577TWqVq3KmjVrTrv/ongk3voUTvH9hn38vD2ZR69oSfUK/m6HY0yJUKlSJVJTU0/7XkpKCtWqVSMoKIjNmzezbNmyQjnmlClTAIiKiqJKlSpUqfLHu/orr7ySd955h6ysLAC2bt3KsWPHAFixYgVxcXHk5uYyZcoUevbs+YfPt2zZkvj4eLZv3w7ApEmTuPjii2nZsiV79+5l5cqVAKSmppKdnX3G4+3cuZM6deowcuRIRowYwerVqzl48CC5ubnccMMNPPfcc6xevZrKlSsTGhrKtGnTACfJrF27FoAePXowefJkAD7//PNC+Rmejt0p5JN+IofnZm+iVd1K3Na1odvhGFNi1KhRgx49etC2bVvKly9PnTq/jfzv168f7777Lq1bt6Zly5Z069atUI4ZGBhIx44dycrK4sMPPzztNiNGjCA+Pp5OnTqhqtSqVYuvv/4agM6dO3P//fezfft2LrnkEq677rrTHuOjjz5i8ODBZGdn07lzZ0aPHo2/vz9TpkzhgQceID09nfLlyzN//vwzHm/hwoWMGzcOPz8/KlasyKeffsru3bsZNmxYXgf3Cy+8ADgX/L/85S8899xzZGVlMWTIENq3b8/rr7/OrbfeyksvvcS11177h1gLizj9DyVHZGSkRkdHe2XfE37Yyus/bmPyqG50a1Lj3B8wppjYtGkTrVuXnenc+/Tpw/jx44mMjDyvzy9cuJDx48fz7bffFnJkxcPp/j+IyCpVPecPzJqPPBIOHefdRTu4pn19SwjGmDLLmo88npu9ER8R/nFVK7dDMcZ43Hffffz888+/W/fQQw+xcOHCC9pvnz596NOnzwXto7SypAAs2ZbE3Jj9/O3KltSrYsVzjCku3nrrLbdDKHPKfPNRVk4u/561kUY1ghjR6/yKUhhjTGlR5pPCJ7/Es/1AGk9d3YaAcmVrjhNjjDlVmU4KB1IzeG3+Ni5pWYu+ra14jjHGlOmk8NJ3W8jMzuGps8z+aIwxZUmZTQqrdx1mxupE7u7ZhNCaFc79AWNMofFWLYDSYuzYsYwfP96VY5fJp49yc5WxM2OoU9mK55hS6LsxsG994e6zbjj0f7Fw93mBLrTWQWHUWyiNyuSdwtToBNYlpvCPq1pTIcD+UxhzocaMGfO7x0fHjh3Lc889R9++fenUqRPh4eF88803BdrXn6l1sGrVKi6++GIiIiK48sor2bt3L1DwegsZGRkMGzaM8PBwOnbsyIIFCwAn4Tz22GO0bduWdu3a8eabbwKc8XhvvPEGbdq0oV27dgwZMgSARYsW0aFDBzp06EDHjh3z5oYaN25cXr2Fp59+Ou+8n3/+eVq0aEHPnj3ZsmXLef9bXLCCzK9dnL4utJ7CkWMntOMz8/TGd37W3NzcC9qXMcWF2/UUVq9erb17985bbt26te7atUtTUlJUVTUpKUmbNm2a9zt3tloABa11cOLECe3evbseOHBAVVUnT56sw4YNU9WC11sYP3583mc2bdqkISEhmp6erm+//bbecMMNmpWVpapOPYizHa9evXqakZGhqr/VRbj66qs1KipKVVVTU1M1KytL586dqyNHjtTc3FzNycnRAQMG6KJFizQ6Olrbtm2rx44d05SUFG3atKmOGzfufP4pVNXqKfwpE+Zv5cjxE4wd2KVIpqE1pizo2LEjBw4cYM+ePSQlJVGtWjXq1q3Lww8/zOLFi/Hx8WH37t3s37+funXrnnN/Ba11sGHDBi6//HLA+eu+Xr16efsoSL2FqKgoHnjgAQBatWpFo0aN2Lp1K/Pnz2f06NF5zUvVq1dnw4YNZzxeu3btuO222xg0aBCDBg0CnFlNH3nkEW677Tauv/56goODmTdvHvPmzaNjx44ApKWlsW3bNlJTU7nuuusICgrKi9EtZSopbNmXyqRlO7m1a0PC6lvxHGMK0+DBg5k+fTr79u3j5ptv5vPPPycpKYlVq1bh5+dH48aNT1tH4XQKUutAVQkLC2Pp0qV/ah/56y38GWc73uzZs1m8eDGzZs3i+eefZ/369YwZM4YBAwYwZ84cevTowdy5c1FVnnjiCe65557fff611147r5i8ocz0KagqT8/cQKXAcjx6uRXPMaaw3XzzzUyePJnp06czePBgUlJSqF27Nn5+fixYsICdO3cWeF8FrXWQlJSUd5HOysoiJiYm7/2C1Fvo1atXXm2CrVu3smvXLlq2bMnll1/OxIkTyc7OBuDQoUNnPF5ubi4JCQlccsklvPTSS6SkpJCWlsaOHTsIDw/n8ccfp3PnzmzevJkrr7ySDz/8kLS0NAB2797NgQMH6N27N19//TXp6emkpqYya9asAv+sCluZuVOYvX4vy2IP8dygtlSz4jnGFLqwsDBSU1Np0KAB9erV47bbbuOaa64hPDycyMhIWrUq+GSTBal14O/vz/Tp03nwwQdJSUkhOzubv/71r4SFOeOOClJv4d577+Uvf/kL4eHhlCtXjo8//piAgABGjBjB1q1badeuHX5+fowcOZL777//tMdr0aIFt99+OykpKagqDz74IFWrVuVf//oXCxYswMfHh7CwMPr3709AQACbNm2ie/fugNNx/tlnn9GpUyduvvlm2rdvT+3atfPKfLqhzNRTWLDlAF8s38U7t0fg62N9CaZ0KU31FAqj1sGF1lso6S6knkKZuVO4pGVtLmlZ2+0wjDGmWCszScEYU7ysX7+eoUOH/m5dQEAAy5cvv+BaBxdab6Es82pSEJF+wOuAL/C+qr54yvuPACOAbCAJGK6qBe+NMsbkUdUS9Zh1eHg4a9ascTuMUudCuwS89vSRiPgCbwH9gTbALSLS5pTNfgUiVbUdMB142VvxGFOaBQYGkpycfMEXBFOyqSrJyckEBgae9z68eafQBdiuqrEAIjIZuBbYeHIDVV2Qb/tlwO1ejMeYUis4OJjExESSkpLcDsW4LDAwkODg4PP+vDeTQgMgId9yItD1LNvfDXx3ujdEZBQwCqBhw4aFFZ8xpYafnx+hoVY50Fy4YjF4TURuByKBcad7X1XfU9VIVY2sVatW0QZnjDFliDfvFHYDIfmWgz3rfkdELgP+CVysqplejMcYY8w5ePNOYSXQXERCRcQfGALMzL+BiHQEJgIDVfWAF2MxxhhTAF4d0SwiVwGv4TyS+qGqPi8iz+BM4TpTROYD4cBez0d2qepZpwcUkSTgfB9brQkcPM/PlgSl+fzs3Equ0nx+JencGqnqOdvfS9w0FxdCRKILMsy7pCrN52fnVnKV5vMrjedWLDqajTHGFA+WFIwxxuQpa0nhPbcD8LLSfH52biVXaT6/UnduZapPwRhjzNmVtTsFY4wxZ2FJwRhjTJ4ykxREpJ+IbBGR7SIyxu14CouIhIjIAhHZKCIxIvKQ2zEVNhHxFZFfReT8S3EVUyJSVUSmi8hmEdkkIt3djqmwiMjDnv+TG0TkCxE5/6k7iwER+VBEDojIhnzrqovIDyKyzfO9mpsxFoYykRQKOI13SZUNPKqqbYBuwH2l6NxOegjY5HYQXvI68L2qtgLaU0rOU0QaAA/iTI3fFmcA6xB3o7pgHwP9Tlk3BvhRVZsDP3qWS7QykRTIN423qp4ATk7jXeKp6l5VXe15nYpzUWngblSFR0SCgQHA+27HUthEpArQG/gAQFVPqOoRd6MqVOWA8iJSDggC9rgczwVR1cXAoVNWXwt84nn9CTCoSIPygrKSFE43jXepuXCeJCKNgY7AcncjKVSvAX8Hct0OxAtCcSoOfuRpHntfRCq4HVRhUNXdwHhgF840NimqOs/dqLyijqqenKZnH1DHzWAKQ1lJCqWeiFQEZgB/VdWjbsdTGETkauCAqq5yOxYvKQd0At5R1Y7AMUpB8wOAp239WpzEVx+o4Jkiv9RS5/n+Ev+Mf1lJCgWaxrukEhE/nITwuap+6XY8hagHMFBE4nGa/C4Vkc/cDalQJQKJqnryzm46TpIoDS4D4lQ1SVWzgC+Bi1yOyRv2i0g9AM/3Ej/bc1lJCuecxrukEqdS+wfAJlV91e14CpOqPqGqwaraGOff7CdVLTV/barqPiBBRFp6VvUlX7naEm4X0E1Egjz/R/tSSjrRTzETuNPz+k7gGxdjKRTeLLJTbKhqtojcD8zlt2m8Y1wOq7D0AIYC60VkjWfdP1R1josxmYJ7APjc88dKLDDM5XgKhaouF5HpwGqcJ+R+pYRPCSEiXwB9gJoikgg8DbwITBWRu3Gm9L/JvQgLh01zYYwxJk9ZaT4yxhhTAJYUjDHG5LGkYIwxJo8lBWOMMXksKRhjjMljScGYIiQifUrjbK+m9LCkYIwxJo8lBWNOQ0RuF5EVIrJGRCZ6ajqkicgET42AH0WklmfbDiKyTETWichXJ+fUF5FmIjJfRNaKyGoRaerZfcV8NRQ+94z4NaZYsKRgzClEpDVwM9BDVTsAOcBtQAUgWlXDgEU4I1oBPgUeV9V2wPp86z8H3lLV9jjz/pycTbMj8Fec2h5NcEalG1MslIlpLoz5k/oCEcBKzx/x5XEmOssFpni2+Qz40lMToaqqLvKs/wSYJiKVgAaq+hWAqmYAePa3QlUTPctrgMZAlPdPy5hzs6RgzB8J8ImqPvG7lSL/OmW7850jJjPf6xzs99AUI9Z8ZMwf/QjcKCK1Ia8ObyOc35cbPdvcCkSpagpwWER6edYPBRZ5quAlisggzz4CRCSoSM/CmPNgf6EYcwpV3SgiTwLzRMQHyALuwymC08Xz3gGcfgdwpkx+13PRzz/T6VBgoog849nH4CI8DWPOi82SakwBiUiaqlZ0Ow5jvMmaj4wxxuSxOwVjjDF57E7BGGNMHksKxhhj8lhSMMYYk8eSgjHGmDyWFIwxxuT5f8ydX5hvLw16AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train the model with preprocessed images\n",
    "import time\n",
    "nb_epoch = 1 \n",
    "\n",
    "model=AlexNet()\n",
    "opt = Adam(lr=base_learning_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
    "\n",
    "# load the model weights from epoch 11\n",
    "weights_path = 'model_weights/alexnet_prepro_epoch11.h5'\n",
    "model.load_weights(filepath=weights_path, by_name=False, skip_mismatch=False)\n",
    "\n",
    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# re-initialize the weights of the model to train again from scratch\n",
    "# initialize_weights(model)\n",
    "\n",
    "history_preprocessed = model.fit_generator(\n",
    "        generator=generator(features=X_train_prepro, labels=Y_train, batch_size=batch_size, patch_size=img_cols, \n",
    "                            channels=channels, nb_classes=nb_classes),\n",
    "        steps_per_epoch=batches_per_epoch, epochs=nb_epoch, \n",
    "        validation_data=generator(features=X_val_prepro, labels=Y_val, batch_size=batch_size, patch_size=img_cols, \n",
    "                                  channels=channels, nb_classes=nb_classes),\n",
    "        validation_steps=validation_steps)\n",
    "end = time.time()\n",
    "\n",
    "# merge history until epoch_11 with additional epoch\n",
    "history_epoch11 = {'acc': [0.2156700721153846, 0.3423477564102564, 0.42630709134615385, 0.48645332532051283, 0.5366336137820513, 0.5784505208333334, 0.6115284455128205, 0.6462089342948718, 0.6648888221153846, 0.6934595352564102, 0.7147185496794872], 'loss': [2.089567707135127, 1.7973418403894474, 1.5924712048891263, 1.4378445916450941, 1.3178296906825824, 1.21250919959484, 1.1237694015487647, 1.0532857810075467, 0.9948647719545242, 0.929585806452311, 0.8595956252553524], 'val_acc': [0.3524639423076923, 0.4524238782051282, 0.49689503205128205, 0.5552884615384616, 0.5911458333333334, 0.6274038461538461, 0.6372195512820513, 0.625, 0.6810897435897436, 0.6893028846153846, 0.7086338141025641], 'val_loss': [1.8619595368703206, 1.5888427159725091, 1.4259456885166657, 1.2888002365063398, 1.2032615458353972, 1.1024081195012116, 1.0880448734148955, 1.0802756013014378, 0.982640591951517, 0.9475753979805188, 0.9044325435772921]}\n",
    "for key in history_preprocessed.history.keys():\n",
    "    history_preprocessed.history[key] = history_epoch11[key] + history_preprocessed.history[key]\n",
    "\n",
    "plot_train_val_accuracy_and_loss(history_preprocessed, legend_suffix='preprocessed')\n",
    "\n",
    "#model.save_weights('model_weights/alexnet_prepro_epoch11.h5')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 83us/step\n",
      "Summary: Loss over the test dataset: 0.81, Accuracy: 0.73\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model trained on preprocessed input data\n",
    "evaluation = model.evaluate(X_test_prepro, Y_test, batch_size=256, verbose=1)\n",
    "print('Summary: Loss over the test dataset: %.2f, Accuracy: %.2f' % (evaluation[0], evaluation[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare the performance w/ and w/o preprocessing the input images\n",
    "\n",
    "What is the purpose of preprocessing the input images?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# collect all histories and plot all curves in same figure\n",
    "# comment: we skipt the result on the original data, as the model performance was poor\n",
    "\n",
    "history_list = [history_original, history_preprocessed]\n",
    "legend_suffix_list = ['original', 'preprocessed']\n",
    "\n",
    "# plot accuracy curves\n",
    "acc_legend = []\n",
    "for history, legend_suffix in zip(history_list, legend_suffix_list):\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "    plt.title('Accuracy')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    acc_legend += ['train_'+legend_suffix, 'val_'+legend_suffix]\n",
    "plt.legend(acc_legend, loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "# plot loss curves\n",
    "loss_legend = []\n",
    "for history, legend_suffix in zip(history_list, legend_suffix_list):\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title('loss')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('epoch')\n",
    "    loss_legend += ['train_'+legend_suffix, 'val_'+legend_suffix]\n",
    "plt.legend(loss_legend, loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extend the AlexNet like architecture\n",
    "Copy the AlexNet architecture from above into the template AlexNet_modified() and modify/extend it to improve the performance. \n",
    "\n",
    "Possible modifications:    \n",
    "* higher model capacity\n",
    "* stronger non-linearity (increase model depth/ # layers)\n",
    "* stronger pooling (shrinkage -> global information)\n",
    "* stronger regularization (dropout)\n",
    "    \n",
    "Hint: Try to build a deeper model without increasing the number of parameters too much.\n",
    "\n",
    "* Compare the number of parameters to the previous model. Which layers introduce most of the parameters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_16 (Conv2D)           (None, 32, 32, 96)        2688      \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 32, 32, 96)        83040     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling (None, 16, 16, 96)        0         \n",
      "_________________________________________________________________\n",
      "dropout_13 (Dropout)         (None, 16, 16, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 16, 16, 256)       221440    \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 16, 16, 256)       590080    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "dropout_14 (Dropout)         (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 8, 8, 384)         885120    \n",
      "_________________________________________________________________\n",
      "conv2d_21 (Conv2D)           (None, 8, 8, 384)         1327488   \n",
      "_________________________________________________________________\n",
      "conv2d_22 (Conv2D)           (None, 8, 8, 384)         1327488   \n",
      "_________________________________________________________________\n",
      "conv2d_23 (Conv2D)           (None, 8, 8, 384)         1327488   \n",
      "_________________________________________________________________\n",
      "conv2d_24 (Conv2D)           (None, 8, 8, 256)         884992    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dropout_15 (Dropout)         (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 128)               524416    \n",
      "_________________________________________________________________\n",
      "dropout_16 (Dropout)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout_17 (Dropout)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 7,192,042\n",
      "Trainable params: 7,192,042\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# One possible solution\n",
    "base_learning_rate = 0.0001\n",
    "batch_size = 128\n",
    "\n",
    "pool_size = (3, 3)                  # size of pooling area for max pooling\n",
    "prob_drop_conv = 0.2                # drop probability for dropout @ conv layer\n",
    "prob_drop_hidden = 0.5              # drop probability for dropout @ fc layer\n",
    "\n",
    "def AlexNet_modified():\n",
    "\n",
    "    model = Sequential()\n",
    "\n",
    "    # conv1 layer\n",
    "    conv1 = Conv2D(filters=96, kernel_size=(3, 3), strides=(1,1), padding='same', activation='relu', \n",
    "                   input_shape=input_shape)\n",
    "    model.add(conv1)\n",
    "\n",
    "    # conv1.1 layer\n",
    "    model.add(Conv2D(filters=96, kernel_size=(3, 3), strides=(1,1), padding='same', activation='relu', \n",
    "                   input_shape=input_shape))\n",
    "        \n",
    "    model.add(MaxPooling2D(pool_size=(pool_size), strides=(2, 2), padding='same'))\n",
    "    model.add(Dropout(prob_drop_conv))\n",
    "\n",
    "    # conv2 layer\n",
    "    model.add(Conv2D(filters=256, kernel_size=(3, 3), strides=(1,1), padding='same', activation='relu' ))\n",
    "    \n",
    "    # conv2.1 layer\n",
    "    model.add(Conv2D(filters=256, kernel_size=(3, 3), strides=(1,1), padding='same', activation='relu' ))\n",
    "    \n",
    "    model.add(MaxPooling2D(pool_size=pool_size, strides=(2,2), padding='same'))\n",
    "    model.add(Dropout(prob_drop_conv))\n",
    "\n",
    "    # conv3 layer\n",
    "    model.add(Conv2D(filters=384, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "    \n",
    "    # conv4 layer\n",
    "    model.add(Conv2D(filters=384, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "\n",
    "    # conv4.1 layer\n",
    "    model.add(Conv2D(filters=384, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "    \n",
    "    # conv4.2 layer\n",
    "    model.add(Conv2D(filters=384, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "    \n",
    "    # conv5 layer\n",
    "    model.add(Conv2D(filters=256, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=pool_size, strides=(2,2), padding='same'))\n",
    "\n",
    "    \n",
    "    model.add(Flatten())\n",
    "    model.add(Dropout(prob_drop_conv))\n",
    "\n",
    "    # fc1 layer\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dropout(prob_drop_hidden))\n",
    "\n",
    "    # fc2 layer\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dropout(prob_drop_hidden))\n",
    "\n",
    "    # fc3 layer\n",
    "    model.add(Dense(nb_classes, activation='softmax'))\n",
    "    \n",
    "    return model\n",
    "\n",
    "    \n",
    "model_alexNet_mod = AlexNet_modified()\n",
    "model_alexNet_mod.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the modified AlexNet on preprocessed data\n",
    "\n",
    "* Could you improve the performance?\n",
    "* What changed concerning the convergence time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initialize weights of layer: conv2d_25\n",
      "initialize weights of layer: conv2d_26\n",
      "initialize weights of layer: conv2d_27\n",
      "initialize weights of layer: conv2d_28\n",
      "initialize weights of layer: conv2d_29\n",
      "initialize weights of layer: conv2d_30\n",
      "initialize weights of layer: conv2d_31\n",
      "initialize weights of layer: conv2d_32\n",
      "initialize weights of layer: conv2d_33\n",
      "initialize weights of layer: dense_13\n",
      "initialize weights of layer: dense_14\n",
      "initialize weights of layer: dense_15\n",
      "Epoch 1/12\n",
      "312/312 [==============================] - 14s 44ms/step - loss: 2.0941 - acc: 0.1975 - val_loss: 1.7466 - val_acc: 0.3577\n",
      "Epoch 2/12\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 1.7270 - acc: 0.3434 - val_loss: 1.4761 - val_acc: 0.4501\n",
      "Epoch 3/12\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 1.4993 - acc: 0.4473 - val_loss: 1.2515 - val_acc: 0.5472\n",
      "Epoch 4/12\n",
      "312/312 [==============================] - 13s 41ms/step - loss: 1.2984 - acc: 0.5442 - val_loss: 1.2079 - val_acc: 0.5690\n",
      "Epoch 5/12\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 1.1596 - acc: 0.6073 - val_loss: 0.9990 - val_acc: 0.6603\n",
      "Epoch 6/12\n",
      "312/312 [==============================] - 13s 41ms/step - loss: 1.0313 - acc: 0.6511 - val_loss: 0.8999 - val_acc: 0.6822\n",
      "Epoch 7/12\n",
      "312/312 [==============================] - 13s 41ms/step - loss: 0.9458 - acc: 0.6847 - val_loss: 0.7971 - val_acc: 0.7203\n",
      "Epoch 8/12\n",
      "312/312 [==============================] - 13s 41ms/step - loss: 0.8749 - acc: 0.7120 - val_loss: 0.7838 - val_acc: 0.7296\n",
      "Epoch 9/12\n",
      "312/312 [==============================] - 13s 41ms/step - loss: 0.7814 - acc: 0.7451 - val_loss: 0.7287 - val_acc: 0.7518\n",
      "Epoch 10/12\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.7299 - acc: 0.7700 - val_loss: 0.7642 - val_acc: 0.7400\n",
      "Epoch 11/12\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.6465 - acc: 0.7947 - val_loss: 0.7243 - val_acc: 0.7689\n",
      "Epoch 12/12\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.6167 - acc: 0.8100 - val_loss: 0.6881 - val_acc: 0.7797\n",
      "['acc', 'loss', 'val_acc', 'val_loss']\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TRAIN the model again\n",
    "# NEW: PREPROCESSED INPUT IMAGES\n",
    "\n",
    "nb_epoch = 12 # 12\n",
    "\n",
    "model = AlexNet_modified()\n",
    "opt = Adam(lr=base_learning_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# re-initialize the weights of the model to train again from scratch\n",
    "initialize_weights(model)\n",
    "\n",
    "history_preprocessed_modified = model.fit_generator(\n",
    "        generator=generator(features=X_train_prepro, labels=Y_train, batch_size=batch_size, patch_size=img_cols, \n",
    "                            channels=channels, nb_classes=nb_classes),\n",
    "        steps_per_epoch=batches_per_epoch, epochs=nb_epoch, \n",
    "        validation_data=generator(features=X_val_prepro, labels=Y_val, batch_size=batch_size, patch_size=img_cols, \n",
    "                                  channels=channels, nb_classes=nb_classes),\n",
    "        validation_steps=validation_steps)\n",
    "\n",
    "plot_train_val_accuracy_and_loss(history_preprocessed_modified, legend_suffix='preprocessed')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 116us/step\n",
      "Summary: Loss over the test dataset: 0.69, Accuracy: 0.78\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model trained on preprocessed input data\n",
    "evaluation = model.evaluate(X_test_prepro, Y_test, batch_size=256, verbose=1)\n",
    "print('Summary: Loss over the test dataset: %.2f, Accuracy: %.2f' % (evaluation[0], evaluation[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare the performance of your modified model with the AlexNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "history_list = [history_preprocessed, history_preprocessed_modified]\n",
    "legend_suffix_list = ['AlexNet', 'AlexNet_modified']\n",
    "\n",
    "# plot accuracy curves\n",
    "acc_legend = []\n",
    "for history, legend_suffix in zip(history_list, legend_suffix_list):\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "    plt.title('Accuracy')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    acc_legend += ['train_'+legend_suffix, 'val_'+legend_suffix]\n",
    "plt.legend(acc_legend, loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "# plot loss curves\n",
    "loss_legend = []\n",
    "for history, legend_suffix in zip(history_list, legend_suffix_list):\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title('loss')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('epoch')\n",
    "    loss_legend += ['train_'+legend_suffix, 'val_'+legend_suffix]\n",
    "plt.legend(loss_legend, loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The following is thought as a bonus exercise ... or homework"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Data augmentation: Train the modified AlexNet using data augmentation\n",
    "\n",
    "A common trick to improve performance is to use data augmentation at train time. Let's try:\n",
    "\n",
    "The keras ImageDataGenerator provides an image generator with built in data augmentation: https://keras.io/preprocessing/image/\n",
    "\n",
    "Set meaningful data augmentation parameters.\n",
    "\n",
    "* Did the data augmentation improve performance? Can you explain why?\n",
    "* In what scenario might data augmentation be very useful?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initialize weights of layer: conv2d_34\n",
      "initialize weights of layer: conv2d_35\n",
      "initialize weights of layer: conv2d_36\n",
      "initialize weights of layer: conv2d_37\n",
      "initialize weights of layer: conv2d_38\n",
      "initialize weights of layer: conv2d_39\n",
      "initialize weights of layer: conv2d_40\n",
      "initialize weights of layer: conv2d_41\n",
      "initialize weights of layer: conv2d_42\n",
      "initialize weights of layer: dense_16\n",
      "initialize weights of layer: dense_17\n",
      "initialize weights of layer: dense_18\n",
      "Epoch 1/24\n",
      "312/312 [==============================] - 14s 46ms/step - loss: 2.1310 - acc: 0.1908 - val_loss: 1.8981 - val_acc: 0.2806\n",
      "Epoch 2/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 1.8151 - acc: 0.3155 - val_loss: 1.4834 - val_acc: 0.4510\n",
      "Epoch 3/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 1.5751 - acc: 0.4180 - val_loss: 1.2686 - val_acc: 0.5405\n",
      "Epoch 4/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 1.3988 - acc: 0.5015 - val_loss: 1.1823 - val_acc: 0.5867\n",
      "Epoch 5/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 1.2507 - acc: 0.5617 - val_loss: 0.9666 - val_acc: 0.6571\n",
      "Epoch 6/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 1.1164 - acc: 0.6167 - val_loss: 0.9448 - val_acc: 0.6806\n",
      "Epoch 7/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 1.0266 - acc: 0.6510 - val_loss: 0.8836 - val_acc: 0.6983\n",
      "Epoch 8/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.9606 - acc: 0.6784 - val_loss: 0.8185 - val_acc: 0.7337\n",
      "Epoch 9/24\n",
      "312/312 [==============================] - 13s 43ms/step - loss: 0.8737 - acc: 0.7108 - val_loss: 0.7760 - val_acc: 0.7540\n",
      "Epoch 10/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.8249 - acc: 0.7330 - val_loss: 0.7322 - val_acc: 0.7627\n",
      "Epoch 11/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.7721 - acc: 0.7514 - val_loss: 0.6792 - val_acc: 0.7941\n",
      "Epoch 12/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.7236 - acc: 0.7701 - val_loss: 0.6664 - val_acc: 0.7852\n",
      "Epoch 13/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.6840 - acc: 0.7848 - val_loss: 0.6177 - val_acc: 0.8087\n",
      "Epoch 14/24\n",
      "312/312 [==============================] - 13s 43ms/step - loss: 0.6359 - acc: 0.7997 - val_loss: 0.6752 - val_acc: 0.7905\n",
      "Epoch 15/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.6133 - acc: 0.8105 - val_loss: 0.5989 - val_acc: 0.8072\n",
      "Epoch 16/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.5725 - acc: 0.8215 - val_loss: 0.5405 - val_acc: 0.8346\n",
      "Epoch 17/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.5427 - acc: 0.8317 - val_loss: 0.5755 - val_acc: 0.8303\n",
      "Epoch 18/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.5116 - acc: 0.8412 - val_loss: 0.6044 - val_acc: 0.8267\n",
      "Epoch 19/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.4965 - acc: 0.8457 - val_loss: 0.6236 - val_acc: 0.8229\n",
      "Epoch 20/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.4799 - acc: 0.8532 - val_loss: 0.5724 - val_acc: 0.8360\n",
      "Epoch 21/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.4475 - acc: 0.8630 - val_loss: 0.5115 - val_acc: 0.8485\n",
      "Epoch 22/24\n",
      "312/312 [==============================] - 13s 42ms/step - loss: 0.4269 - acc: 0.8680 - val_loss: 0.5630 - val_acc: 0.8403\n",
      "Epoch 23/24\n",
      "312/312 [==============================] - 13s 43ms/step - loss: 0.3969 - acc: 0.8791 - val_loss: 0.5223 - val_acc: 0.8563\n",
      "Epoch 24/24\n",
      "312/312 [==============================] - 13s 43ms/step - loss: 0.3867 - acc: 0.8794 - val_loss: 0.5336 - val_acc: 0.8426\n",
      "['acc', 'loss', 'val_acc', 'val_loss']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TRAIN the model again with data augmentation\n",
    "\n",
    "nb_epoch = 24 # 26\n",
    "\n",
    "model = AlexNet_modified()\n",
    "opt = Adam(lr=base_learning_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# TODO\n",
    "# image generator for data augmentation. Documentation: https://keras.io/preprocessing/image/\n",
    "image_gen = ImageDataGenerator(\n",
    "    width_shift_range=5,   # pixel\n",
    "    height_shift_range=5,  # pixel\n",
    "    horizontal_flip=True)\n",
    "\n",
    "# re-initialize weights of the model before training again\n",
    "initialize_weights(model)\n",
    "\n",
    "history_augmented = model.fit_generator(\n",
    "        generator=image_gen.flow(X_train_prepro, Y_train, batch_size=batch_size),\n",
    "        steps_per_epoch=batches_per_epoch,\n",
    "        epochs=nb_epoch,\n",
    "        verbose=1,\n",
    "        shuffle=True,\n",
    "        validation_data=generator(features=X_val_prepro, labels=Y_val, batch_size=batch_size, patch_size=img_cols, \n",
    "                                  channels=channels, nb_classes=nb_classes),\n",
    "        validation_steps=validation_steps)\n",
    "\n",
    "plot_train_val_accuracy_and_loss(history_augmented, legend_suffix='augmented')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 114us/step\n",
      "Summary: Loss over the test dataset: 0.51, Accuracy: 0.85\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model trained with augmented training data\n",
    "evaluation = model.evaluate(X_test_prepro, Y_test, batch_size=256, verbose=1)\n",
    "print('Summary: Loss over the test dataset: %.2f, Accuracy: %.2f' % (evaluation[0], evaluation[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Compare the performance w/ and w/o data augmentation\n",
    "\n",
    "Did the data augmentation improve performance? Can you explain why?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9ZMs7hXDgpJQyAUAI8QswHKiYFCRwYRJzTyDVhvEoik2ZiovJXrKE7PnfY8zLw23AAPxfnlg+LYHfiy9QtGsXuStWkL1oMdnfL8Cle3fz3cPgwWgczR28aUVpbErcxI6UHRxKP0RhWSEAAS4BhPl2oW+pBy12JyN27EPqTmAfFITniy/iOex+HJo1u2mfX6kfbDbNhRBiJHC3lPIZy/bjQA8p5cQKZRoCfwHegCtwl5RyfyV1TQAmAAQFBXU7e/bspUUUpdqklDXSyWrS6chZ+gtZ8+ZhzM7GtV9f/F9+BefbOlzxnLL0dPJW/0bur79SlpSE8HTnXJ82/Na+iC3aeABCPEPo3qA7XQI60ynHHfu//iZ/7VqMmZloPD3xuOduPIcNx7lL51rbWazUHnVlmotHgUVSyhlCiNuBH4UQHaSUpoqFpJRzgblgnvvoJsSp1CPSZCJn6VIyvvgSrbs7Tu3b4RgailO7djiFtsMuwN+qL1lTaSm5y1eQ9d13GDIycO11O34vv4xLly5VnmsfEEDh6MFE9BSc3LKaltvP0GNdFM+vgTGtG+IzajRBnQZRsHETeX98R+HJU2Bvj3v/O/AYNgy3O+5AY8PpG5Rbly2TQgrQtMJ2E8u+ip4G7gaQUv4thHAC/IB0G8al3MLKUlNJffttiv/ejcvtPbHz9kZ3LIaCjZvKy2j9/HAqTxKhOLVvh32TJuWJQpaWkrtqNZnffovh/HlcwsJoNOMzXMPDr3ptKSUnc0+y6ewm/jr7FydzTwLQsXVH2g56BE+PbrhuisJhxQpKP5hJAjMBcO7alcD33sPj7iHXNZ++oljDls1HdkAcMBBzMtgHjJFSHqtQZh2wTEq5SAgRCmwGGsurBKVmSVWuhZSSvFWrSfvoI5CSgDffwOvhh8u/6I2FheiPH0cXE4suJgZdbCz6kyfLJ4jTuLvjFBqKY8uWFG7bRllKCs6dO+M/6RVceva84p2FlJLY7Fg2nd3ExrMbOZN/BoGgS0AXBgcPZmDQQAJdAy87p2T/fnQxMbj1749DUJBt/3KUW0KtmDpbCDEU+ALzcNMFUsoPhRDvA1FSyj8sI47mAW6YO51fl1L+dbU6VVJQqqssPZ3z77xLYUQELt270/Djj3Bo0qTK80x6Pfq4eEuSiEEXE4v+xAkcW7fG/+WJuPbtW2kyMJqMHEw/yObEzWxJ3EJqUSpaoSUsMIzBzQYzIGgAfs5qmgblxqoVScEWVFJQqiN/7VrO//N9TDodAVOn4D12LEJz5fV6q3KlzulSYym7z+1mc+JmIpIiyNZl46BxoFejXgwIGkD/pv3xdqp/0ywrdUdd6WhWFJsw5ORw/v33KVi3HqdOHWn08Sc4Nr/6MwHWqJgQisqK2JG8g82Jm9mRsoOisiJc7V3p16QfA4MG0rdxX1zsXa77mopyI6mkoNQ7BVu2cO6ddzHm5eE/eTK+Tz9VY1Mr5+hy2Jq0lc2Jm9mduptSUyk+Tj7cHXw3A4MG0qNhj2qvH6wotYlKCkq9YSwoIO2jj8lbvRrHtm0J+n4+Tm2uf5oHKSVRaVGsOLGCjYkbMZgMNHJtxKi2oxgYNJDO/p1tusKYotxIKiko9UJRZCSpb0/HkJ6O7wvP4//CC9e9DGOePo8/Tv3B8hPLOZN/BncHd0a3Gc39Le4n1CdUPTCm1EsqKSh1ipQSw7lz6GJj0cUeR3c8Fn1MLGWpqTg0b07w0p9x7tjxuuo/lHGIFXEr2HBmA3qjno7+HflX738xOHgwznbONfhpFKX2UUlBqbWkwYA+IQF9eQI4jj42FmNenrmAEDgEB+PcuRM+48fhNWoUGiena7pWQWkBaxLWsDxuOfE58bjauzKi5Qgebv0wbXzUTKPKrUMlBaXWkEYjhdu2Ubh1K7rY4+jj4pClpQAIR0ccW7fGfcgQnELb4ti2LU5t2qBxub7RPccyj7EibgVrT6+lxFBCqE8o797+LkNDhqqRQ8otSSUF5aYzFRWRu2o12T/+SFliIhpPT5zaheI9dixOoW1xCg01L8dYQyOIisuKWXt6LSviVhCTFYOznTP3hNzDI60fob1f+ypOzgYHN7BTI4yU+kklBeWmKUtNJfunJeSuWIGpoADnTp0ImPwq7oMG1VgCqCg2K5YVcStYk7CGYkMxLb1a8o8e/+C+5vfh7uBedQVZp2DJSAjuA8O+rvH4FKU2UElBueFKoqPJWryYgr82AuA+eBC+48fj3LlzjV+ruKyY9WfWs+LECo5mHcVR68iQ4CE83PphOvl3sn4EUeJuWPooCAFdHq/xOBWltlBJQbkhpMFAwcaNZC9aTMmhQ2jc3fEZPx6fsY9h36hRjV/vRPYJVsSt4M+EPykqK6KFZwveDH+T+5rfh6ejZ/UqO7oKVj8Pnk1g7K/g07zG41WU2kIlBcWmjPn55pXGflqC4dw57IOCaPD223g9+AAaV9cavVaJoYT1p9fza9yvHM48jIPGwXxX0OZhOvtfw0I0UsKuL2HTu9C0Jzy6FFx8ajRmRaltVFJQbEKWlpLxzSyyf/oJWVyMS/fuBE5/G7f+/RHamn36NzE/kZ9if+LPU39SUFZAiGcIr3d/nWEthlX/ruACowHWvQZRC6D9gzBiDthf23BXRalLVFJQalzZ+fOkvDqZkuhoPO69F9+nn8KpXbsav87JnJPMPzqfdafXoRVaBgcP5uHWD9M1oOv1PW2sL4Rfn4T4v6D3qzDwXbiOmVUVpS5RSUGpUYW7dpE67TWkXk/jmZ/jcc89NX6NmKwY5h2ex6bETTjbOTO+3XjGtR9XM2sU5J+Dnx+BtGNw30wIe+r661SUOkQlBaVGSJOJzDlzyPxmFo4tW9D4yy9xbF6zHbLR6dHMPTyXHSk7cLd357mOzzE2dCxeTjW0RGXaMVjyMOjyYMwyaDWoZupVlDpEJQXluhlyckh97XWKdu7E4/77afjP9677SeMLpJTsPb+XuYfnsvf8XrwdvZnUdRKj2oyy7tkCa53aCsvHgYMrPLkOGlY+f5KUEoNJYq9VzUlK/aSSgnJdSg4dIvnVyRgzMwl87z28Rj1SI7OHSinZkbKD7w5/x+GMw/g7+/Na2GuMbD2yRqafkFJyNCWf9AId3ieW0fnQP8l2CWZ5y89J3SMo0B2kQGegUGcgX1dGgc5Aga6MQr2B0eFBfPTAbdcdg6LURiopKNdESknOkp9J+/e/sQ8IoNnSpTh3qGKKCCuYpInNiZuZe3gux7OP08i1Ef/X8/8Y3nI4jlrH667fYDSx9uh5vtt2imOpeUyxW8Erdr+x3XgbL2VNQre3CHenUtyd7HB3ssPN0Y6mPi7mbUc73J3s6RJUQ81VilILqaSgVJuxsIjz77xD/tq1uPXvT6NPPkbrdf1flEn5Sby18y0OZRwi2COYD3p/wL3N78VeY3/ddReXGlgRlcy8HQkk55TQw7eYiJCVBJ9bR37oaEKHzGCfqzOOdhq1ToJyS7NpUhBC3A18CWiB+VLKTy45PhO407LpAgRIKdXPsFpMHx9P8qRXKT1zBv8pU/B95mnEdQ7XlFKy+uRq/r3332iFlg96f8D9ze+vkdXMsotKWRx5hh/+PkNOcSnjAhN5qcVWAlI3I4pMcOd0PPpNM09foSiK7ZKCEEILzAIGAcnAPiHEH1LKmAtlpJSTK5R/Gehiq3iU65f33/9y7p130bi6ErRwIa49wq+7zmxdNv+M/CdbkrbQPbA7H/b+kIZuDa+73sSsYubvTGB5VBLasiL+0TiaBz3W45wbD3pv6DXRPNzUO/i6r6Uo9Ykt7xTCgZNSygQAIcQvwHAg5grlHwXetWE8yjUqTUwka948clf8iktYGI0+n4F9QMB117sjeQf/t+v/yC/NZ1rYNB5v9zgacX13HUdT8vh22ynWHjlHa00q3zfYxe0FG9FkFULDznDHbOjwINirFdQUpTK2TAqNgaQK28lAj8oKCiGaASHAliscnwBMAAgKCqrZKJVKmUpKKPjrL3J/XUnxvn2g0eDz9FMETJ583dNalxhKmBE1g2UnltHSqyXfDfruulY3k1Ky82Qm321L4O+TadzvGM0W/20E50dBnoN5morwZ6FxN9VMpChVqC0dzaOBX6WUxsoOSinnAnMBwsLC5I0M7FYipUR35Ai5K1eRv2YNpsJC7JsF4T95Mp4jhmPfoMF1X+NY1jHe3P4mZ/LP8Hi7x5nUddJ1jSo6k1nE//1+lJj4Uzzjsp1vPbfgpk8D0RQGvgNdx4NrDTzprCi3CFsmhRSgaYXtJpZ9lRkNvGTDWJSrMGRnk/fHH+StXIU+Ph7h5ITHkCF4jXwI57CwGhmNYzQZ+f7o98yJnoOPsw/zBs+jZ8Oe11ZZYTqlqYfZ+/cO0k8d4C2RRBvnJLQmAzTuD+EzodUQ0NaW3zyKUnfY8v+afUArIUQI5mQwGhhzaSEhRFvAG/jbhrEol5BGI0W7dpH760oKtm6FsjKcOnUk8J//xOPeoWjd3GrsWskFyfxj5z84mH6Qu4PvZnrP6dbNXlpWAhnHzdNPpMVA2lHz++JMHIA+QK69L85NOqJt+gB0HgN+rWosbkW5FdksKUgpDUKIicAGzENSF0gpjwkh3geipJR/WIqOBn6RUqpmoRvAkJFB9pIl5K3+DUNaGlofH3weewyvhx7EsVXNfqFKKfn91O98vOdjNELDx30/5t6Qe69+55G4G3bPMX/5Z58CaTLvt3PG4NeW/Q7hrM/zJcutFaPuHULvjm1rNGZFudWJuvZdHBYWJqOiom52GHWOITubrPnfk/Pzz8jSUtz69sXzoQdx798f4VDzi9CnFqbyyd5P2Jq0lbAGYXzY50MauVWxwlpGHMwfCHaO0LQHNGgPDdpj8m/Hr6ft+Gh9PEV6AxP6NWfina1wdqjZdRkUpT4TQuyXUoZVVU41utZzxtxcshYuIvvHH5E6HZ7334ffiy/i0KyZTa6nN+pZeHQh84/MRyM0TOk2hXHtxlX9IFpJDiwdDVoHeHYreJm7o+LSCnh75RH2nckhPNiHDx/oQKsGNTgRnqIoF1FJoZ4yFhSQvWgx2YsXYyosxGPoPfi99BKOLVrY5HpSSiKSIvjPvv+QXJjMkOAhTAubRqBroBXBGmDFk5CbCE/8CV5NKSk18tWWeOZtT8DNyY7/PNSRkd2aoNGoIaWKYksqKdQzpqIisn/8iayFCzHl5eE+6C78Jr6MU5vWNrvm2fyzfLL3E3am7KSFZwvmD55Pj4aVPpJSuY3/BwlbYdg3ENSTrSfSeef3oyRllzCyWxP+MTQUH9eab+JSFOVyKinUE6aSEnJ+/pms+d9jzMnBrX9//F6eiHP765+59EqKy4qZd2Qei48txkHrwGthr/Fo6KPVm8DuwI+wezbG8OfZYH8XP83bTeSpLFr4u/LLhJ70bO5rs/gVRbmcSgp1nEmvJ3fZMjLnzsOYmYlrnz74vzwR506dbHZNKSUbzmzgs6jPSCtOY1iLYUzuNrn6y2Em7kb+OZmznuGMPjCA89sP0NjLmbfuacsTvYNxtFMdyYpyo6mkUIcV7d5D6htvYEhLw6VHD/y//AKXbt1ses34nHg+3vsx+87vo61PWz6941O6BFRvHkOjSRK5P5qO60aRbfDhgfRnCGvrw0c9grijdQBa1W+gKDeNSgp1lDEvj9TXXkPj4kLQokW49qxGG/41KCgtYHb0bJYeX4qrvSvTe0xnZOuR1Zre+nyejmX7kvhtbxzf6N5Cqykloutc1vTvR2MvNUGdotQGKinUUWn//g+G7GyC58ypkRXPrmb3ud28teMtskqyGNl6JC93eRlvJ2+rzjWZzJPVLdlzlk2x6RhNJpZ7f0s7TSLGR5fxZJshNo1dUZTqUUmhDircsZO8VavwffZZmyYEg8nAt4e+Ze7huYR4hvDNgG9o72fd9aSU/LQnkXnbE0jMLsbX1YFn+zbneVbgtWcHDPoAO5UQFKXWUUmhjjEWFnLunXdwCAnBb6Lt5hBMK0rjjR1vsD9tPyNajuCt8LdwsXex6tzc4lKmrTjMptg0ugd7M21IG4a0b4Bj3BpY/hl0ehR6vWyz2BVFuXYqKdQx6TNmYDh/nmZLlqBxvP6F7CuzM2Vb+jzEAAAgAElEQVQn/9jxD3RGHR/1+Yj7W9xv9bkHE3OY+PNB0gt0vHt/O57oFWye6+j8EVj9HDQOg/u+UOsaKEotpZJCHVK0dy+5S3/BZ/w4XLrW/MqlZaYyvjn4DQuOLqCVezCfBT1G85O7YdssOH8UfIKheX/zK6gXOPzvzkFKyYJdZ/hkXSwNPJxY8XwvOje1LLddmAFLx4CTF4xeAvZONR67oig1Q02IV0eYSkpIGD4CpKT577+hcbGuKccqJbmcS9jEa0dmc0ifwcMlBl5PO4eTlKB1hIYdIfA2yIyHpD1gLDXPUdS0B4TcQWHjPkzbpWF9bCaD2jXgs5Gd8HSxPMBmKIUfhkPqAXhyHTTuWnNxK4piNTUhXj2T8eVXlCUmErRo0fUlBCnh/GFI2gvJUZCyn60lyUz388EoBP/RO3JPgz7QtSs0CYOA9mBXYYqJ0iJI/BsStkFCBGz9F27Ap9KZt5r2IKjNUESRBzhbpuFeOw0SI+Gh71VCUJQ6QCWFOqAkOprsxYvxGj3q+p5HKCuBPyfDoaXmTdcAPg9szE8e/oS6NuGzOz4jyL+K0UUOrtDyLmSLgSyOPMOcpD0MdolnUkgKzTL+hnWvm8u5N4SAdnBqM/SdCreNvPa4FUW5YVRSqOVMej2pb0/HLjCQgGnTrr2inDOwbKy5b6Df6yS1GcRrBz/nWNYxxrQdw9SwqThorZt0Lq+kjDd+Pcz6Y+e5K7QFUx9+EC8Xy7nZp+G05S7i9HZoNxzunH7tcSuKckOppFDLZc6eQ+mpUzSd+921L5EZvwlWPg1IGLOcvxzg3W2TEAhm9p/JXc3usrqqI8l5vPTzAVJzS3h7aCjP9A25eCU1nxDzq9sT1xaroig3leZmB6BcmS4mhqz58/EcMQK3fv2qX4HJBNs+hSUjwbMJTIhgBXlM3TaVEM8Qlt+/3OqEIKXkh7/P8NCcSAxGE8ueu51n+zW/+tKaiqLUOepOoZaSZWWk/uNttD7eNHjzjepXoMuD1c/DibVw2yNw/5fsyTrCR7s/ok/jPnx151fYa62b4jqvpIx/rDrCmiPnGNA2gBkPd8JbrW+gKPWSTZOCEOJu4EtAC8yXUn5SSZlHgPcACRySUo6xZUx1Rdb8+eiPH6fJN1+j9fKq3slpMeb+g9yzcM9/IHwCZ/LPMjliMsGewXza71OrE8K+M9m8+ks0afk63rynLRP6NlernylKPWazpCCE0AKzgEFAMrBPCPGHlDKmQplWwFtAbylljhAiwFbx1CX6+HgyZs/BY+g9uN9lfXs/AEdXwu8TwdEdxv8JzW4nT5/HxC0TsdfY883Ab3BzqLpvwmA08dWWk3yzJZ6mPi78+kKFh9EURam3rEoKQohVwPfAOimlycq6w4GTUsoESx2/AMOBmAplngVmSSlzAKSU6dYGXl9Jo5HUt6ejdXWlwfRqjNoxGmDTu/D3N9C0JzyyGNwDKTOVMSViCqmFqXw/5HsauzWusqqk7GIm/XKQA4m5PNS1Cf8c3h43R9XSqCi3Amv/T58NPAl8JYRYASyUUp6o4pzGQFKF7WTg0kH2rQGEELswNzG9J6Vcf2lFQogJwASAoKAgK0Oum7IX/4Du8GEaffYZdj4+1p1UmG5e+P7sTgifAIM/BDsHpJR8uPtD9p7fy0d9PrJqMZzfo1OYvvooCPjq0S4M69ToOj+Roih1iVVJQUq5CdgkhPAEHrW8TwLmAT9JKcuu4/qtgP5AE2C7EOI2KWXuJdefC8wF8zQX13itWq/0zBkyvvwStwED8Lh3qHUnJe2D5eOgJAcemAudRpUf+jHmR1bGr+TZ256tclK7Al0Z7/5+jFUHUwhr5s3MUZ1p6lODU2koilInWN0mIITwBcYCjwMHgSVAH2A85i/1S6UATStsN7HsqygZ2GNJKqeFEHGYk8Q+a+OqL6TJROr06QgHBwLffbfqoZ5SQtQCWPcGeDSCp/8yz1FksT15O59FfcagZoOY2GXiVas6mJjDpF+iSc4p5tW7WjHxzpbYadVoZUW5FVnbp7AaaAP8CNwvpTxnObRMCHGl2en2Aa2EECGYk8Fo4NKRRb9hvvNYKITww9yclFC9j1A/5Cz5mZKo/TT88EPsG1TR315aDGummKeraDkIHpwLLv9raorLieO1ba/R1qct/+r9LzSi8i94o0kyJ+IkMzfFE+jhxPLnbics2MomK0VR6iVr7xS+klJurezAlWbdk1IahBATgQ2Y+wsWSCmPCSHeB6KklH9Yjg0WQsQARuA1KWVWtT9FHZe/bh1pn3yCa7++eD74wNULZyfAssch7Rj0fwv6vQ6a/33pZ5Zk8vLml3Gzd+PrAV9fcWGc1NwSXl0Wzd7T2Qzr1Ih/PdABDyfrhqkqilJ/WZsU2gkhDl5o6xdCeAOPSilnX+0kKeVaYO0l+96p8F4CUyyvW1L++vWkTHsN5y6daTJz5tWbjU6sg1XPmReoeexXaHXxcFW9Uc+rW18lW5fNonsW0cC1QaXVrD1yjjdXHsZoknz+SCce6NJYPZmsKApg/TQXz1bs/LUMIX3WNiHdOvLXrydl6jScO3cm6Lvv0Li6Vl7QZITN78PS0eaFbp7bfllCkFLyzq53OJRxiA/7fEh738tnO5VS8umG47y45AAh/m6sndSXB7s2UQlBUZRy1t4paIUQwvLL/sKDaWqeg+uQv36DOSF06kTTqyWEokzzZHYJEdDlcRj6WaUrl809PJe1p9fySpdXGBw8+LLjBqOJt1cfZVlUEo+GN+X94R2wV53JiqJcwtqksB5zp/J3lu3nLPuUa5C/4S9Spk41J4S5c9G6XSEhJO83DzctyoBhX0PXcZUW23BmA99Ef8P9ze/nmdueuey4rszIxJ8Psik2jVcGtGTyoNbq7kBRlEpZmxTewJwIXrBsbwTm2ySiei7/L0tC6NjxygnhouGmDeHpDdCo8gfPjmUeY/rO6XT278x7vd677Ms+r7iMZ37YR9TZHD4Y3p7Hbw+2wadSFKW+sPbhNRMwx/JSrlH+xo2kTJmKc4cONJ13hYRQxXDTis4XneflLS/j6+zLF3d+cdkiOefzdIxfsJfTmUXMGtOVobc1tMXHUhSlHrH2OYVWwMdAO6C8QVtK2dxGcdU7+Rs3kjJ5ijkhzJ9X+YI5VQw3rajMWMbUiKkUlRXx3aDv8HX2vej4yfRCxi/YS15JGYue7E6vln62+FiKotQz1jYfLQTeBWYCd2KeB0n1UlqpYNMmc0Jo3/7KCSF+E/z61BWHm15qxv4ZHM48zIw7ZtDKu9VFxw4m5vDUon1oNRp+mdCTDo09a/LjKIpSj1n7xe4spdwMCCnlWSnle8C9tgur/ijYvJnkVyfj1L7dlRPC+SPm9Q+8gyodbnqpDWc2sCR2CWNDx1420ijiRDpj5u3B3cmelS/crhKCoijVYu2dgl4IoQHiLU8ppwDXuGDwraNgy5byhBA0fz5ad/fLCxVnwy+PgbMXjF0Fblef4uJM3hnejXyXjv4dmdLt4mf+Vh9M5rUVh2kT6M6iJ8Pxd3esyY+jKMotwNo7hUmAC/AK0A3zxHjjbRVUfVCwZSvJk17FKTT0ygnBZISVz0B+KjzyY5UJocRQwpRtU7DX2DPjjhkXrZ42f0cCk5cdIjzEh18m9FQJQVGUa1LlnYLlQbVRUsppQCHm/gTlKswJYRJObdsSNH9e5QkBIOJjOLUZ7psJTbtXWe+Huz/kZM5J5tw1h0DXQMD8lPIn647z3fYE7r2tIZ+P6oSjnbYmP46iKLeQKpOClNIohOhzI4KpD8rS0kiZPNmcEL6fj9bDo/KCx9fA9k+hy1joVnWeXR2/mt9P/c7znZ6nd+Pe5msZTby58ggrDyQz7vZmvHt/e7Rq/WRFUa6DtX0KB4UQfwArgKILO6WUq2wSVR2W+e23SJOJxjNnXjkhZMabJ7Zr1AWGzjCPOLqKE9kn+HDPh/Rs2JPnOz4PmKe9fvWXaNYcOcfUQa2ZOKClekpZUZTrZm1ScAKygAEV9klAJYUKSpOTyf11JV4jH8KhyRXWQtYXmEca2TmY+xEqmceoooLSAqZETMHTwZNP+n6CVqNFSsn//X6UNUfO8fbQUJ7tpx4XURSlZlj7RLPqR7BC5uw5CCHwe/75ygtICb+/BJlx8Phv4NW08nLlxc0zn6YUprDw7oXlD6h9vjGOn/ck8kL/FiohKIpSo6x9onkh5juDi0gpn6rxiOoo/enT5P3+Oz5jx2LfoPJ1DIj8CmJ+h0EfQPM7qqzzp9if2JS4iWlh0+gSYJ77aMHO03y95SSjuzfl9SFtavIjKIqiWN189GeF907AA0BqzYdTd2XOmo1wcMB3whWWmUiIgE3vQbsR0OvlKuuLTo/m86jPGdB0AOPamWdHXX0wmff/jGFI+wb8a0QH1YegKEqNs7b5aGXFbSHEUmCnTSKqg3RxceSvWYPvM89g5+t7eYHcRFjxJPi1huGzquxYztZlM3XbVAJdA/mgzwcIIdh6PJ3XVhzm9ua+fDm6C3ZqLQRFUWzA2juFS7UCqlhd/taR+fU3aFxd8X26kta0Mp15kjuTAUYtAcerPwhuNBl5a8db5Opy+WnoT3g4eBB1JpsXluynbUN35o7rhpO9eg5BURTbsLZPoYCL+xTOY15j4ZZXcvQYBRs34jdxIlovr4sPSglrpsK5aBi9FPxaVlnf3MNziUyN5L3b3yPUN5Tj5/N5atE+Gnk6s+jJcNyd7KusQ1EU5VpZ1QYhpXSXUnpUeLW+tEmpMkKIu4UQJ4QQJ4UQb1Zy/AkhRIYQItryunzZsFou4+uv0Hp64jO+klXR9i+E6J/MU2C3HVplXZGpkcw5NIdhLYbxYKsHScouZtz3e3F20PLD0+H4uampKxRFsS2rkoIQ4gEhhGeFbS8hxIgqztECs4B7MK/D8KgQol0lRZdJKTtbXnVqNbfiAwcp2rYdn2eevnwqi6R9sPZ180I5/S/Lh5c5X3SeN7e/SQuvFrzd420yC0t5/Ps96A0mfny6B028XWz0KRRFUf7H2t7Kd6WUeRc2pJS5mNdXuJpw4KSUMkFKWQr8Agy/tjBrp4yvvkLr64vPY49dfKAwHZY/Dp6NzSunaa7eByClZPqu6eiNej7v/zkGoz1PLNxLWr6eBU90p3WDK8ydpCiKUsOsTQqVlauqP6IxkFRhO9my71IPCSEOCyF+FUJU+jSXEGKCECJKCBGVkZFhXcQ2VrR7N8W7d+P33AQ0LhV+xZcWw/JxUJILo3664lKaFW1L3saec3t4tdurNHQJ4tnFUZw4X8CcsV3p1szbhp9CURTlYtYmhSghxOdCiBaW1+fA/hq4/n+BYCllR2AjsLiyQlLKuVLKMCllmL+/fw1c9vpIKcn48ivsGjTAa9So/x3QF8CSkZC4G0bMgsDbqqzLYDLw+f7PCfYIZkSLB3ll6UH2nM5mxiOd6N9GDfBSFOXGsjYpvAyUAsswNwPpgJeqOCcFqPjLv4llXzkpZZaUUm/ZnI95rYZar2jHDkoOHsTvhRfQOFo6f0ty4ccHzAnhofnQ4SGr6loVv4rTeaeZ3HUy7/5+nL9i0njv/nYM73yFuZMURVFsyNqH14qAqntLL7YPaCWECMGcDEYDYyoWEEI0lFKes2wOA2KreY0bTkpJxhdfYt+kCV4PPmDeWZwNP46AtBh4ZDGE3m9VXUVlRcyKnkXXgK5ExTZkeVQCrwxoyRO9Q2z4CRRFUa7M2tFHG4UQXhW2vYUQG652jpTSAEwENmD+sl8upTwmhHhfCDHMUuwVIcQxIcQhzKu6PXEtH+JGKti0CV1MDH4vvohwcDB3Ki+6F9KPw+ifrU4IAAuPLiRbl00fnyeZHZHA6O5NmTyotQ2jVxRFuToh5WXz3F1eSIiDUsouVe27EcLCwmRUVNSNviwA0mjk9IgRyDIDzf/8L6I4HRYPg/wUeHQpNO9vdV1pRWnct/o+whv0ZfvOIYT4u7Li+dvVqmmKotiEEGK/lDKsqnLW9imYhBBBFSoPppJZU+u7/HXr0cefxO/liYjCVFh4DxSch7Erq5UQAGZFz8IojcQf74tGI5g1pqtKCIqi3HTWzn30NrBTCLENEEBfYILNoqqFpMFA5jff4NiqFR7hrWHBPVBaAON+gyZVJt+LnMg+wW8nfyPY/h4Opziw4IlONPVRD6cpinLzWTvNxXogDDgBLAWmAiU2jKvWyfvjv5SeOYP/Ew8hFt8HZcUw/r/VTggAM/fPxFHryuGjYbx0ZwsGtL3C+guKoig3mLUT4j0DTMI8rDQa6An8zcXLc9ZbsrSUzFmzcGrdHLeT74NGA0+uhYDQatcVmRLJrtRdGDLuo1dIU6YMUgvlKIpSe1jbpzAJ6A6clVLeCXQBcm0WVS2Tu2oVZSkp+AfFIOwd4cl115QQjCYjn+77DI3RFzf9HXw5ugtajVooR1GU2sPapKCTUuoAhBCOUsrjwC3xE9ek05H59Zc4BxhxbeFqvkOwYgrsyvz31H85mRdPSdoQZo0Jx99dzXqqKErtYm1Hc7LlOYXfgI1CiBzgrO3Cqj1yZ3+MISuXRsOdEU+tAc8m11RPiaGET/bMxFjSlKm9HiY8pOo5kRRFUW40a59otjy6y3tCiK2AJ7DeZlHVEqbEw2T+8AsuTexxnb4B3AOvua5PIudSZMymo/NzTOjXogajVBRFqTnVXo5TSrnNFoHURoWL3seo0+D39mfXlRDiMlNZdepH7Es78t24hxFVrNGsKIpys6jV368kL4WiPVFonOxw6XvXNVdjNEkm/PExUpTx7wFv4umsltNUFKX2UknhCuTu2RSdd8AlPBxhV+0bqnL/XL+VTM12uvsMZVCrqqfSVhRFuZlUUqhMSS5lW3+grEiLa/+B11xNxIl0ViR8i71w5LNB02owQEVRFNtQSaEy+xdSmGQAwK1Xr2uqIiW3hFd/W4mdeyzPdXoWX2ffmoxQURTFJq69XaS+Muhh9xyKCppg39gT+2bNql1FqcHEi0uiMHj9gb9TA57o8LgNAlUURal56k7hUoeXIfPTKE424Nqr1zWNFPpobSzH8rYjnJKZEjYJJzsnGwSqKIpS81RSqMhkgl1fUUIopmIdrr2r33QUnZTLor/j8W26iVCfUO5tfq8NAlUURbENlRQqilsHWfEU0RWEwKVHj2qdLqXko7WxeAXupdiUydSwqWiE+itWFKXuUN9YFe36EryCKIrLwalDB+y8vat1+qbYdPaePYedzxb6Nu5Lj4bVSyqKoig3m0oKFyTuhqQ9GDtNoOTIEVyrOerIYDTxybpYAhsfQm8q4sXOL9ooUEVRFNtRSeGCnV+Asw/FpS3AaKx2UlgWlcSpjHwcfHbRNaArHfw62ChQRVEU27FpUhBC3C2EOCGEOCmEePMq5R4SQkghRPWXMasJ6cfN/Qk9nqNoz36EszPOXTpbfXqR3sDMjfG0bn6anNI0xrcfb8NgFUVRbMdmzykIIbTALGAQkAzsE0L8IaWMuaScO+ZFfPbYKpYqRX4Nds7Q/VmKPn4Ml/DuaBwcrD597vYEMgt1NGq3k2aiGf2b9rddrIpSibKyMpKTk9HpdDc7FOUmc3JyokmTJtjbX9s8a7Z8eC0cOCmlTAAQQvwCDAdiLin3AfBv4DUbxnJl+alweBmEPUlZro7Ss2fxHvOo1aen5+uYuz2B3h3yOVxwnOk9pqsRR8oNl5ycjLu7O8HBwWoW3luYlJKsrCySk5MJCQm5pjps+e3VGEiqsJ1s2VdOCNEVaCqlXHO1ioQQE4QQUUKIqIyMjJqNcvcckEa4/SUKIyMBqtWfMHNTPAaTCWe/XXg5ejGs5bCajU9RrKDT6fD19VUJ4RYnhMDX1/e67hhv2k9aIYQG+ByYWlVZKeVcKWWYlDLM39+/5oLQ5UHUQmj/AHgHUxQZiV1AAA4trVtuMz6tgGX7EhkW5sDetB2MajMKZzvnmotPUapBJQQFrv+/A1smhRSgaYXtJpZ9F7gDHYAIIcQZoCfwxw3tbI5aCKUF0OsVpNFIceTf1Zra4t/rj+PqYIez3y7sNfaMbjvaxgEriqLYli2Twj6glRAiRAjhAIwG/rhwUEqZJ6X0k1IGSymDgd3AMClllA1j+h/LxHc07w+NOqOLicWYl2f11Ba7E7LYFJvOk/382XD2T+5rcR9+zn42DVlRFMXWbJYUpJQGYCKwAYgFlkspjwkh3hdC3PyG98PLofA89J4EQNGF/oTbb6/yVJPJPJ1FQ08nHH32ojPqGNdunE3DVZTaLDc3l9mzZ1f7vKFDh5Kbm2uDiOo/Nzc3m9Rr0z4FKeVaKWVrKWULKeWHln3vSCn/qKRs/xt2l2AyQeRXEHgbNL8TMCcFx7ZtsfOr+tf+n0fOcTg5j0l3hbA8bil9GvehhVcLW0etKLXWlZKCwWC46nlr167Fy8vLJjFVdW1rGI3GGoikbrk111OIWw+ZcfDQ9yAEpuJiig8cwOfxqtc90BuMfLrhOG0D3bHzOEi2Lls9rKbUKv/87zFiUvNrtM52jTx49/72Vzz+5ptvcurUKTp37oy9vT1OTk54e3tz/Phx4uLiGDFiBElJSeh0OiZNmsSECRMACA4OJioqisLCQu655x769OlDZGQkjRs35vfff8fZufKBG/3796dTp05s27YNg8HAggULCA8P57333uPUqVMkJCQQFBTETz/9xJtvvklERAR6vZ6XXnqJ5557joiICN555x3c3d05efIkd955J7Nnz0aj0eDm5sZzzz3Hpk2bmDVrFnq9nmnTpmEwGOjevTtz5szB0dGRffv2MWnSJIqKinB0dGTz5s24uLhUer1z584xatQo8vPzMRgMzJkzh169evH0008TFRWFEIKnnnqKyZMnc+rUKV566SUyMjJwcXFh3rx5tG3bltOnTzNmzBgKCwsZPnx4jf77rejWTAq7vgTPIGg3AoDiqCgoK7NqKOqPf58lKbuExU92Z2bs57TxbkOPQDXxnXJr++STTzh69CjR0dFERERw7733cvTo0fKx8gsWLMDHx4eSkhK6d+/OQw89hK/vxasRxsfHs3TpUubNm8cjjzzCypUrGTt27BWvWVxcTHR0NNu3b+epp57i6NGjAMTExLBz506cnZ2ZO3cunp6e7Nu3D71eT+/evRk8eDAAe/fuJSYmhmbNmnH33XezatUqRo4cSVFRET169GDGjBnodDpatWrF5s2bad26NePGjWPOnDm8+OKLjBo1imXLltG9e3fy8/Nxdnbm+++/r/R6q1atYsiQIbz99tsYjcby2FNSUsrjvtCMNmHCBL799ltatWrFnj17ePHFF9myZQuTJk3ihRdeYNy4ccyaNavG/x1ecOslhcTdkLQb7vkPaM0fv2hXJMLBAZewblc9Na+4jK+3nKRvKz80ric4lXeKj/p8pIYCKrXK1X7R3yjh4eEXPTz11VdfsXr1agCSkpKIj4+/LCmEhITQubN5eplu3bpx5syZq17j0UfND5n269eP/Pz88i/VYcOGld9h/PXXXxw+fJhff/0VgLy8POLj43FwcCA8PJzmzZuX17Vz505GjhyJVqvloYceAuDEiROEhITQunVrAMaPH8+sWbMYOHAgDRs2pHv37gB4eHhc9Xrdu3fnqaeeoqysjBEjRtC5c2eaN29OQkICL7/8Mvfeey+DBw+msLCQyMhIHn744fLPqdfrAdi1axcrV64E4PHHH+eNN96w4t9E9d16SWHXV+DsDV3+9wukKDISl7BuaJyuvkLa7IiT5OvKeOueUD4/OoUA5wDuDr7b1hErSp3j6upa/j4iIoJNmzbx999/4+LiQv/+/St9uMrR0bH8vVarpaSk5KrXuPTH2IXtiteWUvL1118zZMiQi8pGRERc8XwnJye0Wu1Vr30lV7oewPbt21mzZg1PPPEEU6ZMYdy4cRw6dIgNGzbw7bffsnz5cr744gu8vLyIjo6utP4b8QP01pqPISMOTqyB8AngYP4Ppyw9HX18fJVNR8k5xSyMPMODXZqgcUplz7k9jAkdg7322uYXUZT6xN3dnYKCgkqP5eXl4e3tjYuLC8ePH2f37t01cs1ly5YBsHPnTjw9PfH09LyszJAhQ5gzZw5lZWUAxMXF/X97dx5XZZ0+fPzzPYAIomEmYqiIVCqIbGqapVKJW24VD6X5yuaJmdyt3/Ro2+RM9qsem7Zp0eaZpMUGk9LMnNFHQx2XSlFzScNUXBAFMQjcWM71++McT6gHRfRwgHO9Xy9ennOvF3enc3F/7/u+Lk6ePAnYho/279+P1Wpl/vz53H777Ret37FjR7Kzs/n5558B+Pjjj+nbty8dO3YkNzeXjRs3AlBcXEx5eXmV+ztw4ACtWrUiJSWFRx99lM2bN3P8+HGsViv33XcfM2fOZPPmzTRr1oywsDAWLFgA2JLMDz/8AEDv3r1JS0sDYN68edfkGDrjWWcK698C78a2pGB3spqlLf66PAsD/FfiLby9/QX8vP24/5b7XRmtUvVGixYt6N27N126dMHPz49WrVo55g0cOJDZs2fTuXNnOnbsSM+ePa/JPhs3bkxsbCxlZWV88MEHTpd59NFHyc7OJi4uDhGhZcuWLFq0CIDu3bszceJEx4XmkSNHOt3H3LlzSUpKclxofuyxx2jUqBHz589n0qRJnD59Gj8/P1asWFHl/latWsWsWbPw8fEhICCAjz76iJycHB555BGsVisAL730EmD7wh83bhwzZ86krKyMBx54gOjoaN58801GjRrFK6+84tILzYhIvfqJj4+XGik6IvKXG0SWPHHe5MNPPik/9bpNrBUVVa66/XChhE5bIi//a5ccLTkqMR/GyEvfvVSzOJRygR9//NHdIdSqvn37ysaNG2u8fkZGhgwZMuQaRlS3OPs8AJukGt+xnjN8tOkfYC2HXhMck7r30WgAABpaSURBVESEkxs20KRXL4zF+aEQe9/l5v4+jOsXzqe7P8WKlYc6V31XhFJK1VeeM3x0++PQpgdc38Ex6WzWHiryj19y6GhVVj7r9xbw/NAIvLxKWfDTAu5qdxdtmrapjaiV8mgTJkxg3bp1502bMmUKq1atuqrt9uvXj379+l3VNhoqz0kKjZrALYnnTTpp/7BVVe+owiq8vHQ3oS38GX1rKJ9lfUpxWbE+rKZULXHl/fjKOc8ZPnLi5Pr1NAoPxyc42On8b3bn8dOxYv6Y2BGLxconuz4hNiiW6JbRtRypUkrVDo9NCtazZzm1adMlh46+2HyYFk0aMbBLMCsPriSnJIeHI/QsQSnVcHlsUji9ZQty5gxNbnNeFbXoVBkrd+UxLOZGvC2Gj3Z+RNumbbX/slKqQfPYpHBy3Trw9sa/ew+n87/enktphZV7Y9uwNX8r245vY0zEGLwsNXvSUSml6gMPTgrr8Y+JwSugidP5X2w+zE1BAXQJacaHOz+kWaNmDA934QMjSnkQV/UCaChmzJjBq6++6pZ9e2RSKP/lF87s2lXlXUcHC06x6cAvjIwN4VDxIb45+A3JHZPx9/Gv5UiVUlW52l4H16LfQkPkObekVnJqwwYQqfIi88IttlbSI2JDSP3xNbwt3jzY6cHaDFGpmvvXdDi6/dpuMzgKBr1c5ezp06fTtm1bJkywPRw6Y8YMvL29ycjI4JdffqGsrIyZM2dWqzzDlfQ68PPz44knnqCkpIQbbriB1NRUWrduXe1+C3PnzmXcuHFs2rQJb29vXnvtNRISEqioqGDatGn8+9//xmKxkJKSwqRJk8jMzHS6v7feeovZs2fj7e1NREQEaWlprF69milTbJ0djTGsWbOGpk2bMmvWLD777DPOnj3LyJEj+fOf/wzAiy++yIcffkhQUBBt27YlPv7SVZtdxSOTQsm6dViaNaNxly4XzRMRFm45TM8O1xPgV8qinxcxOGwwLf1buiFSpeqH5ORkpk6d6kgKn332GcuWLWPy5Mk0a9aM48eP07NnT4YNG1atSp/V6XVQVlZG3759+fLLL2nZsiXz58/nmWeecdRBqk6/hb/+9a8YY9i+fTu7d+8mMTGRrKws5s6dS3Z2Nlu3bsXb25sTJ05QVlbGpEmTnO7v5ZdfZv/+/fj6+jpKeL/66qu888479O7dm5KSEho3bszy5cvZs2cP33//PSLCsGHDWLNmDU2aNCEtLY2tW7dSXl5OXFycJoXaIiKcXL+BJj17YpyUx91yqJDsglOM73cTX+/7mjMVZ3goQktaqHrkEn/Ru0psbCx5eXkcOXKE/Px8mjdvTnBwMI8//jhr1qzBYrGQk5PDsWPHCK7iuaDKqtvrYMeOHfTv3x+wDSe1bt3asY3q9FtYu3YtkyZNAqBTp06EhoaSlZXFihUreOyxx/D2tn1FXn/99ezYsaPK/XXt2pXRo0czYsQIRoywNe/q3bs3TzzxBKNHj+bee++lTZs2LF++nOXLlxMbGwtASUkJe/bsobi4mJEjR+Lv7++I0V1cmhSMMQOBNwEv4P+JyMsXzH8MmABUACXA70XkR1fGVLo/m/LcXJr84Q9O5y/cnIOvt4VBUcGkrFhMp+s70en6Tq4MSakGISkpifT0dI4ePUpycjLz5s0jPz+fzMxMfHx8aN++vdM+Cs5Up9eBiBAZGcmGDRuuaBuV+y1ciUvt7+uvv2bNmjV89dVXvPjii2zfvp3p06czZMgQli5dSu/evVm2bBkiwlNPPcUfLvj+eeONN2oUkyu47EKzMcYLeAcYBEQADxpjIi5Y7FMRiRKRGOD/Aq+5Kp5zHKWynVxkLi238tW2IyRGBpN35iA7C3YytMNQV4ekVIOQnJxMWloa6enpJCUlUVRURFBQED4+PmRkZHDgwIFqb6u6vQ7y8/MdX9JlZWXs3LnTMb86/RbuuOMOR2+CrKwsDh48SMeOHenfvz9z5sxxXIw+ceJElfuzWq0cOnSIhIQEXnnlFYqKiigpKWHv3r1ERUUxbdo0unfvzu7duxkwYAAffPABJSUlAOTk5JCXl0efPn1YtGgRp0+fpri4mK+++qrax+pac+WZQg/gZxHZB2CMSQOGA44zARGp3F28CSAujAewPZ/g064djdq2vWhexk95FJ4q497YEBbvnYeX8WJwh8GuDkmpBiEyMpLi4mJCQkJo3bo1o0ePZujQoURFRdGtWzc6dar+GXd1eh00atSI9PR0Jk+eTFFREeXl5UydOpXISFs70ur0Wxg/fjzjxo0jKioKb29vUlNT8fX15dFHHyUrK4uuXbvi4+NDSkoKEydOdLq/W265hYceeoiioiJEhMmTJxMYGMhzzz1HRkYGFouFyMhIBg0ahK+vL7t27aJXL9tDswEBAXzyySfExcWRnJxMdHQ0QUFBjjafblGd+to1+QHuxzZkdO79GOBtJ8tNAPYCh4CbL7fdGvdTEBFraansjouXI88/73T+Hz7aJPEvLJczpaVy5/w7ZfyK8TXel1K1qSH1U7gWvQ6utt9CfVev+ymIyDsiEg5MA551towx5vfGmE3GmE35+fk13tfpbduwnjzp9FbUolNlfLM7j6HRN5KZv5G803kMC3ffxR6llHIHVw4f5QCVx2ja2KdVJQ14z9kMEXkfeB+gW7duNR5iOrluPVgsNHHSDnDJ9iOOshbz9n5M00ZNtc6RUi60fft2xowZc940X19fvvvuu6vudXC1/RY8mSuTwkbgZmNMGLZk8AAwqvICxpibRWSP/e0QYA8udHLdOvyiovBq1uyieQs353BTUADtW1pYuWolQ8OH4uvl68pwlPJoUVFRbN261d1hqAu4bPhIRMqBicAyYBfwmYjsNMb8xRhzblxmojFmpzFmK/AE4LK61BW//srp7dud3nV0rqzFvXEhrDi4gjMVZ3ToSCnlkVz6nIKILAWWXjDtT5VeT3Hl/is7+d13YLU6vZ6wcEsOxsCImBCe+fZFQpuFaiMdpZRHcvuF5tpSUVCAd3AwftHnf9mLCF9sOUzPsBaI9wk2HdvE0A5Dq/UovlJKNTQekxSaP/AAN2V8g/HxOW/65oOFHCg4xci4EL7aa3tg5J7we9wRolJKuZ3HJAW4+LF3gIVbDuPrbWFgZCu+2vsV3YO7ExIQ4obolKq/CgsLeffdd694vcGDBztqEjU0qampHDly5IrWyc7OpouTQp21yeMK4lVWWm5lybZcEiOD2Vf8IweLD5LSNcXdYSl1VV75/hV2n9h9TbfZ6fpOTOsxrcr555LC+PHjz5teXl7uKCrnzNKlS6ucV9+lpqbSpUsXbrzxRneHckU86kzhQpXLWny590v8vP3oH9rf3WEpVe9Mnz6dvXv3EhMTQ/fu3bnjjjsYNmwYERG2cmcjRowgPj6eyMhI3n//fcd67du35/jx42RnZ9O5c2dSUlKIjIwkMTGR06dPV7m/v//973Tv3p3o6Gjuu+8+Tp06BcDYsWNJT093LHeuw5vVamX8+PF06tSJ/v37M3jwYMdy7du356mnniImJoZu3bqxefNmBgwYQHh4OLNnz3Zsa9asWXTv3p2uXbvy/PPPA1QZd3p6Ops2bWL06NHExMRw+vRpMjMz6du3L/Hx8QwYMIDc3FwAMjMziY6OJjo6mnfeeeda/Oe4OtV57Lku/VxNmYsLnStrUXL2lPSa10umr5l+zbatVG1yd5mL/fv3S2RkpIjYylT4+/vLvn37HPMLCgpEROTUqVMSGRkpx48fFxGR0NBQyc/Pl/3794uXl5ds2bJFRESSkpLk448/rnJ/59YXEXnmmWfkrbfeEhGRhx9+WBYsWOCY16RJExERWbBggQwaNEgqKiokNzdXAgMDHcuFhobKu+++KyIiU6dOlaioKPn1118lLy9PgoKCRERk2bJlkpKSIlarVSoqKmTIkCGyevXqS8ZdudRGaWmp9OrVS/Ly8kREJC0tTR555BEREYmKipLVq1eLiMgf//hHx3G8GldT5sJjh48KT5Xyze48HuoZyn+OrKa4rJih4VoRValroUePHoSFhTnev/XWWyxcuBCAQ4cOsWfPHlq0aHHeOmFhYcTExAAQHx9PdnZ2ldvfsWMHzz77LIWFhZSUlDBgwIBLxrN27VqSkpKwWCwEBweTkJBw3vxz/QuioqIoKSmhadOmNG3a1NE0p6o+CO3atatW3FX1figsLKSwsJA+ffoAMGbMGP71r39d8ndxNY9NCku25drKWsSF8O6utwnyD+LW4FvdHZZSDULlngWrVq1ixYoVbNiwAX9/f/r16+e0r4Kv728VBLy8vC45fDR27FgWLVpEdHQ0qampjrIW3t7eWK1WwDZkVFpaWq14z+3bYrGcF4fFYqG8vLzKPgjZ2dnViluq6MVQFy+ye+w1hYVbcrg5KIBWzUtZf2Q9QzsMxctycSc2pdTlNW3alOLiYqfzioqKaN68Of7+/uzevZtvv/32qvdXXFxM69atKSsrc/RDANv1gczMTAAWL15MWVkZYOuC9vnnn2O1Wjl27NgV10aqqg/CpVQ+JlX1YggMDCQwMJC1a9cCnPe7uItHnikcKDhJ5oFf+D8DO7J0/1IqpELLWih1FVq0aEHv3r3p0qULfn5+tGrVyjFv4MCBzJ49m86dO9OxY0d6OilIeaVeeOEFbr31Vlq2bMmtt97q+PJNSUlh+PDhREdHM3DgQMcZy3333cfKlSuJiIigbdu2xMXFOW26U5XExESnfRC8nLT0PWfs2LE89thj+Pn5sWHDhip7P8ydO5ff/e53GGNITEy8iqNybRjb9Yf6o1u3brJp06ar2sYbK7J4c+Ue1k27k8lrxuBj8eGf9/zzGkWoVO3btWsXnTt3dncYdVpJSQkBAQEUFBTQo0cP1q1bV61+0fWRs8+DMSZTRLpdbl2PO1MQERZuyaFnWAuKrQf56ZefeKrHU+4OSynlYvfccw+FhYWUlpby3HPPNdiEcLU8LimcK2sxMeEmvtz7T7wt3gwO05abStVFEyZMYN26dedNmzJlCo888sgVb0t7LFSPxyWFLzYfprGPhf6RLRm++Gv6tulLYONAd4ellHKiTjzM5WE86u6js+UVtrIWEcFsK/ieE2dO6LMJSilViUclhYzd+RSdLmNkXAiL9y4m0DeQPiF93B2WUkrVGR6VFBZuOcwNAb5Et21ExsEMBoUNwsfL5/IrKqWUh/CYpHCurMWw6BtZcWg5pdZShocPd3dYSilVp3hMUliyLZeyCuFeezOd8OvCiWgR4e6wlPJI56qX1lfZ2dl8+umnV7zehVVc6yKPufuoc+umPHp7GAEBv7A1fytT46Zqy03VIB397//m7K5r20/Bt3Mngp9++ppusz47lxRGjRrl7lCuOZeeKRhjBhpjfjLG/GyMme5k/hPGmB+NMduMMSuNMaGuiiU+9HqevSeCJfuWYDDc00Fbbip1rUyfPv2820dnzJjBzJkzueuuu4iLiyMqKoovv/yyWtsqKSlxut6FXcleffVVZsyYAcDGjRvp2rUrMTExPPnkk47lUlNTGTFiBP3796d9+/a8/fbbvPbaa8TGxtKzZ09OnDgBwN69exk4cCDx8fHccccd7N5tS6pjx45l8uTJ3HbbbXTo0MHxV/706dP5z3/+Q0xMDK+//joVFRU8+eSTjn4Lc+bMAWwPy06cOJGOHTty9913X7ZeUp1QnfraNfkBvIC9QAegEfADEHHBMgmAv/31OGD+5bZ7Nf0UKqwVkrggUVKWpdR4G0rVRe7up7B582bp06eP433nzp3l4MGDUlRUJCIi+fn5Eh4eLlarVUR+63PgTFlZmdP1KvdsEBGZNWuWPP/88yIiEhkZKevXrxcRkWnTpjmWmzt3roSHhzv6IzRr1kzee+89EbH1Tnj99ddFROTOO++UrKwsERH59ttvJSEhQURs/Rnuv/9+qaiokJ07d0p4eLiI2HpGDBkyxBHLnDlz5IUXXhARkTNnzkh8fLzs27dPPv/8c7n77rulvLxccnJy5Lrrrjuv34Or1NV+Cj2An0VkH4AxJg0YDvxYKSFlVFr+W+AhF8ZD5rFMjpw8wqS4Sa7cjVIeJzY2lry8PI4cOUJ+fj7NmzcnODiYxx9/nDVr1mCxWMjJyeHYsWOXLS8hIjz99NMXrVeVwsJCiouLHcXqRo0axZIlSxzzExISHP0RrrvuOoYOtT2bFBUVxbZt2ygpKWH9+vUkJSU51jl79qzj9YgRI7BYLERERFQZx/Lly9m2bZvjTKKoqIg9e/awZs0aHnzwQby8vLjxxhu58847L3Mk3c+VSSEEOFTp/WHgUg0L/jfgtLuEMeb3wO8B2rVrV+OAFu9djL+3P3e1u6vG21BKOZeUlER6ejpHjx4lOTmZefPmkZ+fT2ZmJj4+PrRv395pH4ULVbVe5V4JQLW2BVzUH6Fy74Ty8nKsViuBgYFs3br1sutLFQVERYS//e1vFzX7qY89qOvE3UfGmIeAbsAsZ/NF5H0R6SYi3Vq2bFmjfZwqO8Xy7OUktk/Ez9vvKqJVSjmTnJxMWloa6enpJCUlUVRURFBQED4+PmRkZHDgwIFqbaeq9Vq1akVeXh4FBQWcPXvWcTYQGBhI06ZN+e677wBIS0u7oribNWtGWFgYCxYsAGxf8D/88MMl17mwf8SAAQN47733HP0bsrKyOHnyJH369GH+/PlUVFSQm5tLRkZGVZusM1x5ppADtK30vo192nmMMXcDzwB9ReTshfOvlZUHV3Kq/JT2TVDKRSIjIykuLiYkJITWrVszevRohg4dSlRUFN26daNTp07V2k5V6/n4+PCnP/2JHj16EBISct72/vGPf5CSkoLFYqFv375X1CsBbGcn48aNY+bMmZSVlfHAAw8QHR1d5fJdu3bFy8uL6Ohoxo4dy5QpU8jOziYuLg4RoWXLlixatIiRI0fyzTffEBERQbt27RxDXHWZy/opGGO8gSzgLmzJYCMwSkR2VlomFkgHBorInupst6b9FDIOZrDw54W8kfAGFlMnTpCUumY8vZ/CuV4JAC+//DK5ubm8+eabbo7KfepkPwURKTfGTASWYbsT6QMR2WmM+Qu2q+CLsQ0XBQAL7M8MHBQRl/wpn9AugYR2CZdfUClV73z99de89NJLlJeXExoaSmpqqrtDqrdc+vCaiCwFll4w7U+VXt/tyv0rpequ7du3M2bMmPOm+fr6Oq4NXInk5GSSk5OvVWgezWOeaFaqoRORevWUflRUVJV3/Kiau9pLAjq4rlQD0LhxYwoKCq76C0HVbyJCQUEBjRs3rvE29ExBqQagTZs2HD58mPz8fHeHotyscePGtGnTpsbra1JQqgHw8fEhLCzM3WGoBkCHj5RSSjloUlBKKeWgSUEppZSDy55odhVjTD5QvSIqF7sBOH4Nw6mv9Dj8Ro+FjR4Hm4Z8HEJF5LLF4+pdUrgaxphN1XnMu6HT4/AbPRY2ehxs9Djo8JFSSqlKNCkopZRy8LSk8L67A6gj9Dj8Ro+FjR4HG48/Dh51TUEppdSledqZglJKqUvQpKCUUsrBY5KCMWagMeYnY8zPxpjp7o7HXYwx2caY7caYrcaYK29hV08ZYz4wxuQZY3ZUmna9Meb/G2P22P9t7s4Ya0sVx2KGMSbH/rnYaowZ7M4YXc0Y09YYk2GM+dEYs9MYM8U+3SM/E5V5RFIwxngB7wCDgAjgQWNMhHujcqsEEYnxsPuxU4GBF0ybDqwUkZuBlfb3niCVi48FwOv2z0WMvUFWQ1YO/JeIRAA9gQn27wRP/Uw4eERSAHoAP4vIPhEpBdKA4W6OSdUiEVkDnLhg8nDgQ/vrD4ERtRqUm1RxLDyKiOSKyGb762JgFxCCh34mKvOUpBACHKr0/rB9micSYLkxJtMY83t3B+NmrUQk1/76KNDKncHUARONMdvsw0seM2xijGkPxALfoZ8Jj0kK6je3i0gctqG0CcaYPu4OqC4Q273Znnx/9ntAOBAD5AJ/dW84tcMYEwB8DkwVkV8rz/PUz4SnJIUcoG2l923s0zyOiOTY/80DFmIbWvNUx4wxrQHs/+a5OR63EZFjIlIhIlbg73jA58IY44MtIcwTkS/skz3+M+EpSWEjcLMxJswY0wh4AFjs5phqnTGmiTGm6bnXQCKw49JrNWiLgYftrx8GvnRjLG517ovQbiQN/HNhjDHAP4BdIvJapVke/5nwmCea7bfYvQF4AR+IyItuDqnWGWM6YDs7AFsr1k895TgYY/4J9MNWGvkY8DywCPgMaIetHPv/EpEGfwG2imPRD9vQkQDZwB8qja03OMaY24H/ANsBq33y09iuK3jcZ6Iyj0kKSimlLs9Tho+UUkpVgyYFpZRSDpoUlFJKOWhSUEop5aBJQSmllIMmBaVqkTGmnzFmibvjUKoqmhSUUko5aFJQygljzEPGmO/tvQXmGGO8jDElxpjX7fX3VxpjWtqXjTHGfGsvJrfwXDE5Y8xNxpgVxpgfjDGbjTHh9s0HGGPSjTG7jTHz7E/XKlUnaFJQ6gLGmM5AMtBbRGKACmA00ATYJCKRwGpsTwIDfARME5Gu2J6QPTd9HvCOiEQDt2ErNAe2ipxTsfX26AD0dvkvpVQ1ebs7AKXqoLuAeGCj/Y94P2yF0azAfPsynwBfGGOuAwJFZLV9+ofAAnuNqRARWQggImcA7Nv7XkQO299vBdoDa13/ayl1eZoUlLqYAT4UkafOm2jMcxcsV9MaMWcrva5A/z9UdYgOHyl1sZXA/caYIHD07Q3F9v/L/fZlRgFrRaQI+MUYc4d9+hhgtb2b12FjzAj7NnyNMf61+lsoVQP6F4pSFxCRH40xz2LrUGcByoAJwEmgh31eHrbrDmArsTzb/qW/D3jEPn0MMMcY8xf7NpJq8ddQqka0SqpS1WSMKRGRAHfHoZQr6fCRUkopBz1TUEop5aBnCkoppRw0KSillHLQpKCUUspBk4JSSikHTQpKKaUc/gcLpQDpP9MMNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# collect all histories and plot all curves in same figure\n",
    "# comment: we skipt the result on the original data, as the model performance was poor\n",
    "\n",
    "history_list = [history_preprocessed, history_augmented]\n",
    "legend_suffix_list = ['preprocessed', 'augmented']\n",
    "\n",
    "# plot accuracy curves\n",
    "acc_legend = []\n",
    "for history, legend_suffix in zip(history_list, legend_suffix_list):\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "    plt.title('Accuracy')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    acc_legend += ['train_'+legend_suffix, 'val_'+legend_suffix]\n",
    "plt.legend(acc_legend, loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "# plot loss curves\n",
    "loss_legend = []\n",
    "for history, legend_suffix in zip(history_list, legend_suffix_list):\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title('loss')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('epoch')\n",
    "    loss_legend += ['train_'+legend_suffix, 'val_'+legend_suffix]\n",
    "plt.legend(loss_legend, loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: ResNet50\n",
    "\n",
    "The remaining code trains the ResNet50 model for the cifar10 dataset. We use a pre-trained model on ImageNet to initialize our weights and finetune the model.\n",
    "\n",
    "As we are working with small input images (32x32 pixels) this version of the ResNet50 has modified strides, such that the spatial extent of the feature maps is not reduced too much.\n",
    "\n",
    "code source: https://github.com/flyyufelix/cnn_finetune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nlang/.local/lib/python2.7/site-packages/ipykernel_launcher.py:75: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "/home/nlang/.local/lib/python2.7/site-packages/keras/legacy/layers.py:465: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "  name=name)\n",
      "/home/nlang/.local/lib/python2.7/site-packages/ipykernel_launcher.py:36: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 32, 32, 3)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 16, 16, 64)   9472        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 16, 16, 64)   256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 16, 16, 64)   0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_19 (MaxPooling2D) (None, 7, 7, 64)     0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 7, 7, 64)     4160        max_pooling2d_19[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 7, 7, 64)     256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 7, 7, 64)     0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 7, 7, 64)     36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 7, 7, 64)     256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 7, 7, 64)     0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 7, 7, 256)    16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 7, 7, 256)    16640       max_pooling2d_19[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 7, 7, 256)    1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 7, 7, 256)    1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "merge_1 (Merge)                 (None, 7, 7, 256)    0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 7, 7, 256)    0           merge_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 7, 7, 64)     16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 7, 7, 64)     256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 7, 7, 64)     0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 7, 7, 64)     36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 7, 7, 64)     256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 7, 7, 64)     0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 7, 7, 256)    16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 7, 7, 256)    1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "merge_2 (Merge)                 (None, 7, 7, 256)    0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 7, 7, 256)    0           merge_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 7, 7, 64)     16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 7, 7, 64)     256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 7, 7, 64)     0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 7, 7, 64)     36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 7, 7, 64)     256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 7, 7, 64)     0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 7, 7, 256)    16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 7, 7, 256)    1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "merge_3 (Merge)                 (None, 7, 7, 256)    0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 7, 7, 256)    0           merge_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 7, 7, 128)    32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 7, 7, 128)    512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 7, 7, 128)    0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 7, 7, 128)    147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 7, 7, 128)    512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 7, 7, 128)    0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 7, 7, 512)    66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 7, 7, 512)    131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 7, 7, 512)    2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 7, 7, 512)    2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "merge_4 (Merge)                 (None, 7, 7, 512)    0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 7, 7, 512)    0           merge_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 7, 7, 128)    65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 7, 7, 128)    512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 7, 7, 128)    0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 7, 7, 128)    147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 7, 7, 128)    512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 7, 7, 128)    0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 7, 7, 512)    66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 7, 7, 512)    2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "merge_5 (Merge)                 (None, 7, 7, 512)    0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 7, 7, 512)    0           merge_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 7, 7, 128)    65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 7, 7, 128)    512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 7, 7, 128)    0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 7, 7, 128)    147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 7, 7, 128)    512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 7, 7, 128)    0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 7, 7, 512)    66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 7, 7, 512)    2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "merge_6 (Merge)                 (None, 7, 7, 512)    0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 7, 7, 512)    0           merge_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 7, 7, 128)    65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 7, 7, 128)    512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 7, 7, 128)    0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 7, 7, 128)    147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 7, 7, 128)    512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 7, 7, 128)    0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 7, 7, 512)    66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 7, 7, 512)    2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "merge_7 (Merge)                 (None, 7, 7, 512)    0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 7, 7, 512)    0           merge_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 7, 7, 256)    131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 7, 7, 256)    1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 7, 7, 256)    0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 7, 7, 256)    590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 7, 7, 256)    1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 7, 7, 256)    0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 7, 7, 1024)   263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 7, 7, 1024)   525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 7, 7, 1024)   4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 7, 7, 1024)   4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "merge_8 (Merge)                 (None, 7, 7, 1024)   0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 7, 7, 1024)   0           merge_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 7, 7, 256)    262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 7, 7, 256)    1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 7, 7, 256)    0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 7, 7, 256)    590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 7, 7, 256)    1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 7, 7, 256)    0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 7, 7, 1024)   263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 7, 7, 1024)   4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "merge_9 (Merge)                 (None, 7, 7, 1024)   0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 7, 7, 1024)   0           merge_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 7, 7, 256)    262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 7, 7, 256)    1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 7, 7, 256)    0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 7, 7, 256)    590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 7, 7, 256)    1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 7, 7, 256)    0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 7, 7, 1024)   263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 7, 7, 1024)   4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "merge_10 (Merge)                (None, 7, 7, 1024)   0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 7, 7, 1024)   0           merge_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 7, 7, 256)    262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 7, 7, 256)    1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 7, 7, 256)    0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 7, 7, 256)    590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 7, 7, 256)    1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 7, 7, 256)    0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 7, 7, 1024)   263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 7, 7, 1024)   4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "merge_11 (Merge)                (None, 7, 7, 1024)   0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 7, 7, 1024)   0           merge_11[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 7, 7, 256)    262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 7, 7, 256)    1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 7, 7, 256)    0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 7, 7, 256)    590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 7, 7, 256)    1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 7, 7, 256)    0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 7, 7, 1024)   263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 7, 7, 1024)   4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "merge_12 (Merge)                (None, 7, 7, 1024)   0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 7, 7, 1024)   0           merge_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 7, 7, 256)    262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 7, 7, 256)    1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 7, 7, 256)    0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 7, 7, 256)    590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 7, 7, 256)    1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 7, 7, 256)    0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 7, 7, 1024)   263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 7, 7, 1024)   4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "merge_13 (Merge)                (None, 7, 7, 1024)   0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 7, 7, 1024)   0           merge_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "merge_14 (Merge)                (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 2048)   0           merge_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "merge_15 (Merge)                (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 2048)   0           merge_15[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "merge_16 (Merge)                (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 2048)   0           merge_16[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (AveragePooling2D)     (None, 2, 2, 2048)   0           activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_7 (Flatten)             (None, 8192)         0           avg_pool[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "cifar10_logits (Dense)          (None, 10)           81930       flatten_7[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 23,669,642\n",
      "Trainable params: 23,616,522\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.optimizers import SGD\n",
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.models import Model\n",
    "from keras import backend as K\n",
    "\n",
    "def identity_block(input_tensor, kernel_size, filters, stage, block):\n",
    "    \"\"\"\n",
    "    The identity_block is the block that has no conv layer at shortcut\n",
    "    Arguments\n",
    "        input_tensor: input tensor\n",
    "        kernel_size: defualt 3, the kernel size of middle conv layer at main path\n",
    "        filters: list of integers, the nb_filters of 3 conv layer at main path\n",
    "        stage: integer, current stage label, used for generating layer names\n",
    "        block: 'a','b'..., current block label, used for generating layer names\n",
    "    \"\"\"\n",
    "\n",
    "    nb_filter1, nb_filter2, nb_filter3 = filters\n",
    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "\n",
    "    x = Conv2D(filters=nb_filter1, kernel_size=(1, 1), name=conv_name_base + '2a')(input_tensor)\n",
    "\n",
    "    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    x = Conv2D(filters=nb_filter2, kernel_size=(kernel_size, kernel_size), \n",
    "               padding='same', name=conv_name_base + '2b')(x)\n",
    "    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    x = Conv2D(filters=nb_filter3, kernel_size=(1, 1), name=conv_name_base + '2c')(x)\n",
    "    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\n",
    "\n",
    "    x = merge([x, input_tensor], mode='sum')\n",
    "    x = Activation('relu')(x)\n",
    "    return x\n",
    "\n",
    "\n",
    "def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):\n",
    "    \"\"\"\n",
    "    conv_block is the block that has a conv layer at shortcut\n",
    "    # Arguments\n",
    "        input_tensor: input tensor\n",
    "        kernel_size: defualt 3, the kernel size of middle conv layer at main path\n",
    "        filters: list of integers, the nb_filters of 3 conv layer at main path\n",
    "        stage: integer, current stage label, used for generating layer names\n",
    "        block: 'a','b'..., current block label, used for generating layer names\n",
    "    Note that from stage 3, the first conv layer at main path is with subsample=(2,2)\n",
    "    And the shortcut should have subsample=(2,2) as well\n",
    "    \"\"\"\n",
    "\n",
    "    nb_filter1, nb_filter2, nb_filter3 = filters\n",
    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "\n",
    "    x = Conv2D(filters=nb_filter1, kernel_size=(1, 1), strides=strides,\n",
    "                      name=conv_name_base + '2a')(input_tensor)\n",
    "    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    x = Conv2D(filters=nb_filter2, kernel_size= (kernel_size, kernel_size), padding='same',\n",
    "                      name=conv_name_base + '2b')(x)\n",
    "    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    x = Conv2D(filters=nb_filter3, kernel_size=(1, 1), name=conv_name_base + '2c')(x)\n",
    "    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\n",
    "\n",
    "    shortcut = Conv2D(filters=nb_filter3, kernel_size=(1, 1), strides=strides,\n",
    "                             name=conv_name_base + '1')(input_tensor)\n",
    "    shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)\n",
    "\n",
    "    x = merge([x, shortcut], mode='sum')\n",
    "    x = Activation('relu')(x)\n",
    "    return x\n",
    "\n",
    "\n",
    "\n",
    "def ResNet50(img_input):\n",
    "    model = Sequential()\n",
    "\n",
    "    x = Conv2D(64, kernel_size=(7, 7), strides=(2, 2), name='conv1', padding='same')(img_input)\n",
    "    x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)\n",
    "    x = Activation('relu')(x)\n",
    "    x = MaxPooling2D((3, 3), strides=(2, 2))(x)\n",
    "\n",
    "\n",
    "    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))\n",
    "    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')\n",
    "    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')\n",
    "    \n",
    "    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', strides=(1, 1))\n",
    "    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')\n",
    "    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')\n",
    "    x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')\n",
    "    \n",
    "    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', strides=(1, 1))\n",
    "    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')\n",
    "    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')\n",
    "    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')\n",
    "    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')\n",
    "    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')\n",
    "\n",
    "    x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', strides=(1, 1))\n",
    "    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')\n",
    "    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')\n",
    "\n",
    "    # Fully Connected Softmax Layer\n",
    "    x_fc = AveragePooling2D((3, 3), name='avg_pool')(x)\n",
    "    x_fc = Flatten()(x_fc)\n",
    "    x_fc = Dense(nb_classes, activation='softmax', name='cifar10_logits')(x_fc)\n",
    "\n",
    "    model = Model(img_input, x_fc)\n",
    "    model.summary()\n",
    "    return model\n",
    "\n",
    "\n",
    "if K.image_dim_ordering() == 'tf':\n",
    "  bn_axis = 3\n",
    "  img_input = Input(shape=(img_rows, img_cols, channels))\n",
    "else:\n",
    "  bn_axis = 1\n",
    "  img_input = Input(shape=(color_type, img_rows, channels))\n",
    "\n",
    "my_model = ResNet50(img_input)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transfer learning: Initialize the model with pre-trained weights from a model trained on ImageNet dataset\n",
    "ImageNet: http://image-net.org/about-overview\n",
    "\n",
    "ResNet50 weights pre-trained on ImageNet can be downloaded here: https://github.com/flyyufelix/cnn_finetune\n",
    "\n",
    "Save the weights file (.h5) in the following directory: pretrained_models_imageNet (or change the `weights_path` accordingly)\n",
    "\n",
    "We initialize the layer weights from the pre-trained model by layer name. In the final dense layer, we set the number of classes to 10 to solve our cifar10 task. Therefore, the number of parameters has changed compared to the pre-trained model with 1000 classes from another task. By changing the layer name to 'cifar10_logits', the final layer is not initialized from the pre-trained model, but with random weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "base_learning_rate = 0.001\n",
    "opt = Adam(lr=base_learning_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
    "\n",
    "weights_path='pretrained_models_imageNet/resnet50_weights_tf_dim_ordering_tf_kernels.h5'\n",
    "\n",
    "my_model.load_weights(filepath=weights_path, by_name=True, skip_mismatch=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fix all layers except the final dense layer (and batch_norm layers)\n",
    "\n",
    "For the final dense layer, we do not have a good initialization yet. This layer will be initialized randomly. If we would train the new final layer as well as all ohter layers with good initialization together, the random weights from the new layer could deteriorate the already good featrues from our pre-trained model. One way to avoid this, is to fix all layer weights during the training and update only the weights for the last dense layer. Once we have a decent classifier, we allow also all other weights to update."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('total number of layers:', 176)\n",
      "input_1\n",
      "conv1\n",
      "activation_1\n",
      "max_pooling2d_19\n",
      "res2a_branch2a\n",
      "activation_2\n",
      "res2a_branch2b\n",
      "activation_3\n",
      "res2a_branch2c\n",
      "res2a_branch1\n",
      "merge_1\n",
      "activation_4\n",
      "res2b_branch2a\n",
      "activation_5\n",
      "res2b_branch2b\n",
      "activation_6\n",
      "res2b_branch2c\n",
      "merge_2\n",
      "activation_7\n",
      "res2c_branch2a\n",
      "activation_8\n",
      "res2c_branch2b\n",
      "activation_9\n",
      "res2c_branch2c\n",
      "merge_3\n",
      "activation_10\n",
      "res3a_branch2a\n",
      "activation_11\n",
      "res3a_branch2b\n",
      "activation_12\n",
      "res3a_branch2c\n",
      "res3a_branch1\n",
      "merge_4\n",
      "activation_13\n",
      "res3b_branch2a\n",
      "activation_14\n",
      "res3b_branch2b\n",
      "activation_15\n",
      "res3b_branch2c\n",
      "merge_5\n",
      "activation_16\n",
      "res3c_branch2a\n",
      "activation_17\n",
      "res3c_branch2b\n",
      "activation_18\n",
      "res3c_branch2c\n",
      "merge_6\n",
      "activation_19\n",
      "res3d_branch2a\n",
      "activation_20\n",
      "res3d_branch2b\n",
      "activation_21\n",
      "res3d_branch2c\n",
      "merge_7\n",
      "activation_22\n",
      "res4a_branch2a\n",
      "activation_23\n",
      "res4a_branch2b\n",
      "activation_24\n",
      "res4a_branch2c\n",
      "res4a_branch1\n",
      "merge_8\n",
      "activation_25\n",
      "res4b_branch2a\n",
      "activation_26\n",
      "res4b_branch2b\n",
      "activation_27\n",
      "res4b_branch2c\n",
      "merge_9\n",
      "activation_28\n",
      "res4c_branch2a\n",
      "activation_29\n",
      "res4c_branch2b\n",
      "activation_30\n",
      "res4c_branch2c\n",
      "merge_10\n",
      "activation_31\n",
      "res4d_branch2a\n",
      "activation_32\n",
      "res4d_branch2b\n",
      "activation_33\n",
      "res4d_branch2c\n",
      "merge_11\n",
      "activation_34\n",
      "res4e_branch2a\n",
      "activation_35\n",
      "res4e_branch2b\n",
      "activation_36\n",
      "res4e_branch2c\n",
      "merge_12\n",
      "activation_37\n",
      "res4f_branch2a\n",
      "activation_38\n",
      "res4f_branch2b\n",
      "activation_39\n",
      "res4f_branch2c\n",
      "merge_13\n",
      "activation_40\n",
      "res5a_branch2a\n",
      "activation_41\n",
      "res5a_branch2b\n",
      "activation_42\n",
      "res5a_branch2c\n",
      "res5a_branch1\n",
      "merge_14\n",
      "activation_43\n",
      "res5b_branch2a\n",
      "activation_44\n",
      "res5b_branch2b\n",
      "activation_45\n",
      "res5b_branch2c\n",
      "merge_15\n",
      "activation_46\n",
      "res5c_branch2a\n",
      "activation_47\n",
      "res5c_branch2b\n",
      "activation_48\n",
      "res5c_branch2c\n",
      "merge_16\n",
      "activation_49\n",
      "avg_pool\n",
      "flatten_7\n"
     ]
    }
   ],
   "source": [
    "# train only the final dense layer and keep all other layers fixed (except the batch_norm)\n",
    "print('total number of layers:', len(my_model.layers))\n",
    "for layer in my_model.layers[:-1]:\n",
    "    if not 'bn' in layer.name: # we need to update the batch_norm layers anyways\n",
    "        print(layer.name)\n",
    "        layer.trainable = False # can be set to False to fix the weights during training\n",
    "\n",
    "my_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initialize weights of layer: cifar10_logits\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the weights of the final dense layer (before training)\n",
    "initialize_weights(my_model, layer_name='cifar10_logits')\n",
    "w = my_model.get_layer(name='cifar10_logits').get_weights()\n",
    "plt.figure()\n",
    "n, bins, patches = plt.hist(w, bins=501, facecolor='green', alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the ResNet50 model with data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "312/312 [==============================] - 43s 139ms/step - loss: 1.2952 - acc: 0.6038 - val_loss: 0.9819 - val_acc: 0.7005\n",
      "Epoch 2/4\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.8956 - acc: 0.7062 - val_loss: 0.9085 - val_acc: 0.7257\n",
      "Epoch 3/4\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.7318 - acc: 0.7514 - val_loss: 0.8393 - val_acc: 0.7382\n",
      "Epoch 4/4\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.6505 - acc: 0.7773 - val_loss: 0.7405 - val_acc: 0.7706\n",
      "['acc', 'loss', 'val_acc', 'val_loss']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# image generator for data augmentation. Documentation: https://keras.io/preprocessing/image/\n",
    "image_gen = ImageDataGenerator(\n",
    "    width_shift_range=5,   # pixel\n",
    "    height_shift_range=5,  # pixel\n",
    "    horizontal_flip=True)\n",
    "\n",
    "\n",
    "nb_epoch = 4 # 26\n",
    "\n",
    "history_augmented = my_model.fit_generator(\n",
    "        generator=image_gen.flow(X_train_prepro, Y_train, batch_size=batch_size),\n",
    "        steps_per_epoch=batches_per_epoch,\n",
    "        epochs=nb_epoch,\n",
    "        verbose=1,\n",
    "        shuffle=True,\n",
    "        validation_data=generator(features=X_val_prepro, labels=Y_val, batch_size=batch_size, patch_size=img_cols, \n",
    "                                  channels=channels, nb_classes=nb_classes),\n",
    "        validation_steps=validation_steps)\n",
    "\n",
    "plot_train_val_accuracy_and_loss(history_augmented, legend_suffix='augmented')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 5s 524us/step\n",
      "Summary: Loss over the test dataset: 0.67, Accuracy: 0.77\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model trained with augmented training data\n",
    "evaluation = my_model.evaluate(X_test_prepro, Y_test, batch_size=256, verbose=1)\n",
    "print('Summary: Loss over the test dataset: %.2f, Accuracy: %.2f' % (evaluation[0], evaluation[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the learned weights of the final dense layer (after training)\n",
    "w = my_model.get_layer(name='cifar10_logits').get_weights()\n",
    "plt.figure()\n",
    "n, bins, patches = plt.hist(w, bins=500, facecolor='green', alpha=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Allow all layers to update the weights during training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('total number of layers:', 176)\n",
      "input_1\n",
      "conv1\n",
      "bn_conv1\n",
      "activation_1\n",
      "max_pooling2d_19\n",
      "res2a_branch2a\n",
      "bn2a_branch2a\n",
      "activation_2\n",
      "res2a_branch2b\n",
      "bn2a_branch2b\n",
      "activation_3\n",
      "res2a_branch2c\n",
      "res2a_branch1\n",
      "bn2a_branch2c\n",
      "bn2a_branch1\n",
      "merge_1\n",
      "activation_4\n",
      "res2b_branch2a\n",
      "bn2b_branch2a\n",
      "activation_5\n",
      "res2b_branch2b\n",
      "bn2b_branch2b\n",
      "activation_6\n",
      "res2b_branch2c\n",
      "bn2b_branch2c\n",
      "merge_2\n",
      "activation_7\n",
      "res2c_branch2a\n",
      "bn2c_branch2a\n",
      "activation_8\n",
      "res2c_branch2b\n",
      "bn2c_branch2b\n",
      "activation_9\n",
      "res2c_branch2c\n",
      "bn2c_branch2c\n",
      "merge_3\n",
      "activation_10\n",
      "res3a_branch2a\n",
      "bn3a_branch2a\n",
      "activation_11\n",
      "res3a_branch2b\n",
      "bn3a_branch2b\n",
      "activation_12\n",
      "res3a_branch2c\n",
      "res3a_branch1\n",
      "bn3a_branch2c\n",
      "bn3a_branch1\n",
      "merge_4\n",
      "activation_13\n",
      "res3b_branch2a\n",
      "bn3b_branch2a\n",
      "activation_14\n",
      "res3b_branch2b\n",
      "bn3b_branch2b\n",
      "activation_15\n",
      "res3b_branch2c\n",
      "bn3b_branch2c\n",
      "merge_5\n",
      "activation_16\n",
      "res3c_branch2a\n",
      "bn3c_branch2a\n",
      "activation_17\n",
      "res3c_branch2b\n",
      "bn3c_branch2b\n",
      "activation_18\n",
      "res3c_branch2c\n",
      "bn3c_branch2c\n",
      "merge_6\n",
      "activation_19\n",
      "res3d_branch2a\n",
      "bn3d_branch2a\n",
      "activation_20\n",
      "res3d_branch2b\n",
      "bn3d_branch2b\n",
      "activation_21\n",
      "res3d_branch2c\n",
      "bn3d_branch2c\n",
      "merge_7\n",
      "activation_22\n",
      "res4a_branch2a\n",
      "bn4a_branch2a\n",
      "activation_23\n",
      "res4a_branch2b\n",
      "bn4a_branch2b\n",
      "activation_24\n",
      "res4a_branch2c\n",
      "res4a_branch1\n",
      "bn4a_branch2c\n",
      "bn4a_branch1\n",
      "merge_8\n",
      "activation_25\n",
      "res4b_branch2a\n",
      "bn4b_branch2a\n",
      "activation_26\n",
      "res4b_branch2b\n",
      "bn4b_branch2b\n",
      "activation_27\n",
      "res4b_branch2c\n",
      "bn4b_branch2c\n",
      "merge_9\n",
      "activation_28\n",
      "res4c_branch2a\n",
      "bn4c_branch2a\n",
      "activation_29\n",
      "res4c_branch2b\n",
      "bn4c_branch2b\n",
      "activation_30\n",
      "res4c_branch2c\n",
      "bn4c_branch2c\n",
      "merge_10\n",
      "activation_31\n",
      "res4d_branch2a\n",
      "bn4d_branch2a\n",
      "activation_32\n",
      "res4d_branch2b\n",
      "bn4d_branch2b\n",
      "activation_33\n",
      "res4d_branch2c\n",
      "bn4d_branch2c\n",
      "merge_11\n",
      "activation_34\n",
      "res4e_branch2a\n",
      "bn4e_branch2a\n",
      "activation_35\n",
      "res4e_branch2b\n",
      "bn4e_branch2b\n",
      "activation_36\n",
      "res4e_branch2c\n",
      "bn4e_branch2c\n",
      "merge_12\n",
      "activation_37\n",
      "res4f_branch2a\n",
      "bn4f_branch2a\n",
      "activation_38\n",
      "res4f_branch2b\n",
      "bn4f_branch2b\n",
      "activation_39\n",
      "res4f_branch2c\n",
      "bn4f_branch2c\n",
      "merge_13\n",
      "activation_40\n",
      "res5a_branch2a\n",
      "bn5a_branch2a\n",
      "activation_41\n",
      "res5a_branch2b\n",
      "bn5a_branch2b\n",
      "activation_42\n",
      "res5a_branch2c\n",
      "res5a_branch1\n",
      "bn5a_branch2c\n",
      "bn5a_branch1\n",
      "merge_14\n",
      "activation_43\n",
      "res5b_branch2a\n",
      "bn5b_branch2a\n",
      "activation_44\n",
      "res5b_branch2b\n",
      "bn5b_branch2b\n",
      "activation_45\n",
      "res5b_branch2c\n",
      "bn5b_branch2c\n",
      "merge_15\n",
      "activation_46\n",
      "res5c_branch2a\n",
      "bn5c_branch2a\n",
      "activation_47\n",
      "res5c_branch2b\n",
      "bn5c_branch2b\n",
      "activation_48\n",
      "res5c_branch2c\n",
      "bn5c_branch2c\n",
      "merge_16\n",
      "activation_49\n",
      "avg_pool\n",
      "flatten_7\n",
      "cifar10_logits\n",
      "Epoch 1/12\n",
      "\r",
      "  1/312 [..............................] - ETA: 40s - loss: 0.5932 - acc: 0.8281"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nlang/.local/lib/python2.7/site-packages/keras/engine/training.py:975: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "312/312 [==============================] - 39s 124ms/step - loss: 0.6006 - acc: 0.7930 - val_loss: 0.7787 - val_acc: 0.7649\n",
      "Epoch 2/12\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.5516 - acc: 0.8094 - val_loss: 0.6957 - val_acc: 0.7922\n",
      "Epoch 3/12\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.5150 - acc: 0.8217 - val_loss: 0.7137 - val_acc: 0.7913\n",
      "Epoch 4/12\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.4882 - acc: 0.8289 - val_loss: 0.7047 - val_acc: 0.7967\n",
      "Epoch 5/12\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.4638 - acc: 0.8376 - val_loss: 0.6616 - val_acc: 0.8067\n",
      "Epoch 6/12\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.4456 - acc: 0.8426 - val_loss: 0.6686 - val_acc: 0.8132\n",
      "Epoch 7/12\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.4337 - acc: 0.8481 - val_loss: 0.6510 - val_acc: 0.8174\n",
      "Epoch 8/12\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.4056 - acc: 0.8582 - val_loss: 0.6486 - val_acc: 0.8204\n",
      "Epoch 9/12\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.3992 - acc: 0.8587 - val_loss: 0.6886 - val_acc: 0.8156\n",
      "Epoch 10/12\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.3789 - acc: 0.8666 - val_loss: 0.6775 - val_acc: 0.8090\n",
      "Epoch 11/12\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.3662 - acc: 0.8698 - val_loss: 0.6508 - val_acc: 0.8248\n",
      "Epoch 12/12\n",
      "312/312 [==============================] - 39s 124ms/step - loss: 0.3616 - acc: 0.8728 - val_loss: 0.6918 - val_acc: 0.8145\n",
      "['acc', 'loss', 'val_acc', 'val_loss']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train all the other layers as well\n",
    "print('total number of layers:', len(my_model.layers))\n",
    "for layer in my_model.layers[:]:\n",
    "    print(layer.name)\n",
    "    layer.trainable = True # can be set to False to fix the weights during training\n",
    "    \n",
    "nb_epoch = 12     \n",
    "    \n",
    "history_augmented = my_model.fit_generator(\n",
    "        generator=image_gen.flow(X_train_prepro, Y_train, batch_size=batch_size),\n",
    "        steps_per_epoch=batches_per_epoch,\n",
    "        epochs=nb_epoch,\n",
    "        verbose=1,\n",
    "        shuffle=True,\n",
    "        validation_data=generator(features=X_val_prepro, labels=Y_val, batch_size=batch_size, patch_size=img_cols, \n",
    "                                  channels=channels, nb_classes=nb_classes),\n",
    "        validation_steps=validation_steps)\n",
    "\n",
    "plot_train_val_accuracy_and_loss(history_augmented, legend_suffix='augmented')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 5s 493us/step\n",
      "Summary: Loss over the test dataset: 0.55, Accuracy: 0.83\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model trained with augmented training data\n",
    "evaluation = my_model.evaluate(X_test_prepro, Y_test, batch_size=256, verbose=1)\n",
    "print('Summary: Loss over the test dataset: %.2f, Accuracy: %.2f' % (evaluation[0], evaluation[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras already provides many CNN architectures including pre-trained weights\n",
    "\n",
    "For an easy start, check out: https://keras.io/applications/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}