Digit_Recognizer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST Dataset | Convolutional Neural Networks with Keras\n",
    "If you want to apply machine learning to image recognition, convolutional neural networks (CNN) is the way to go. It has been sweeping the board in competitions for the last several years, but perhaps its first big success came in the late 90's when Yann LeCun used it to solve MNIST with 99.5% accuracy. I will show you how it is done in Keras, which is a user-friendly neural network library for python.\n",
    "\n",
    "Many other notebooks here use a simple fully-connected network (no convolution) to achieve 96-97%, which is a poor result on this dataset. In contrast, what I will show you here is nearly state-of-the-art. In the Kernel with less than 20 minutes training we will achieve 99%, but if you train it overnight (or with a GPU) you should reach 99.5. If you then ensemble over several runs, you should get close to the best published accuracy of 99.77%.\n",
    "\n",
    "## Data Preparation\n",
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Training and Test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set consists of 42000 observations and 785 columns.\n",
      "Test set consists of 28000 observations and 784 columns.\n"
     ]
    }
   ],
   "source": [
    "train_df = pd.read_csv('input/train.csv', header=0)\n",
    "test_df = pd.read_csv('input/test.csv', header=0)\n",
    "print('Training set consists of {} observations and {} columns.'.format(train_df.shape[0], train_df.shape[1]))\n",
    "print('Test set consists of {} observations and {} columns.'.format(test_df.shape[0], test_df.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform Training and Test sets to Numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (42000, 784)\n",
      "y_train shape: (42000, 1)\n",
      "X_test shape: (28000, 784)\n"
     ]
    }
   ],
   "source": [
    "X_train = train_df.iloc[:, 1:].as_matrix()\n",
    "y_train = train_df.iloc[:, 0].values.reshape(-1, 1)\n",
    "X_test = test_df.as_matrix()\n",
    "print('X_train shape: {}\\ny_train shape: {}\\nX_test shape: {}'.format(\n",
    "    X_train.shape, y_train.shape, X_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting some Examples of Digits in Training Set\n",
    "Here I plot the first 25 digits of the Training Set and their respective labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119fe36a0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1000,
       "width": 987
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrows = 5\n",
    "ncols = 5\n",
    "fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(14, 14))\n",
    "for i in range(nrows*ncols):\n",
    "    ax[i//ncols, i%ncols].imshow(X_train[i, :].reshape(28, 28), cmap='Greys')\n",
    "    ax[i//ncols, i%ncols].set_title('Label: {}'.format(int(y_train[i])), fontsize=16)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshaping Training and Test set rows to [28x28x1] sized arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(-1, 28, 28, 1)\n",
    "X_test = X_test.reshape(-1, 28, 28, 1)\n",
    "X_test = X_test.astype('float32')/255."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Training Set into a new Training Set and a Validation Set\n",
    "It is good practice to have a Validation Set to check the model predictions and improvement. Here I decided on a relatively small Validation Set size of 5%. Using Scikit-Learn's [`train_test_split()`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.05, random_state=555)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding Labels into [1x10] sized Arrays\n",
    "The labels are encoded as one digit (0-9) per row. In order to work with the MNIST dataset I will need to one-hot encode them, into [1x10] sized Numpy arrays with one in the position whose index corresponds to the label, and zeros elsewhere. This is very easy to achieve using Scikit-Learn's [`OneHotEncoder()`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (39900, 28, 28, 1)\n",
      "y_train shape: (39900, 10)\n",
      "X_val shape: (2100, 28, 28, 1)\n",
      "y_val shape: (2100, 10)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "y_train = OneHotEncoder(sparse=False).fit_transform(y_train)\n",
    "y_val = OneHotEncoder(sparse=False).fit_transform(y_val)\n",
    "\n",
    "print('X_train shape: {}\\ny_train shape: {}\\nX_val shape: {}\\ny_val shape: {}'.format(\n",
    "    X_train.shape, y_train.shape, X_val.shape, y_val.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the CNN\n",
    "### Importing the Keras Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPool2D, Flatten, BatchNormalization\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import LearningRateScheduler, TerminateOnNaN, EarlyStopping, TensorBoard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constructing the Architecture of the CNN\n",
    "Check:\n",
    "[`Sequential()`](https://keras.io/models/sequential/)\n",
    "[`Conv2D()`](https://keras.io/layers/convolutional/#conv2d)\n",
    "[`MaxPool2D()`](https://keras.io/layers/pooling/#maxpooling2d)\n",
    "[`Flatten()`](https://keras.io/layers/core/#flatten)\n",
    "[`Dense()`](https://keras.io/layers/core/#dense)\n",
    "[`Dropout()`](https://keras.io/layers/core/#dropout)\n",
    "[`BatchNormalization()`](https://keras.io/layers/normalization/#batchnormalization)\n",
    "\n",
    "With the Keras **Sequential Model API** [`Sequential()`](https://keras.io/models/sequential/), where you just add on one layer at a time, starting from the input.\n",
    "\n",
    "The most important part are the **Convolutional layers** [`Conv2D()`](https://keras.io/layers/convolutional/#conv2d). Here they have 32-64 filters that use nine weights each to transform a pixel to a weighted average of itself and its eight neighbors. As the same nine weights are used over the whole image, the net will pick up features that are useful everywhere. As it is only nine weights, we can stack many convolutional layers on top of each other without running out of memory/time.\n",
    "![Convolution](http://d3kbpzbmcynnmx.cloudfront.net/wp-content/uploads/2015/11/Screen-Shot-2015-11-07-at-7.26.20-AM.png)\n",
    "The **Max Pooling layers** [`MaxPool2D()`](https://keras.io/layers/pooling/#maxpooling2d) just look at four neighboring pixels and picks the maximal value. This reduces the size of the image by half, and by combining convolutional and pooling layers, the net be able to combine its features to learn more global features of the image. \n",
    "![Max Pooling](http://cs231n.github.io/assets/cnn/maxpool.jpeg)\n",
    "\n",
    "In the end we use the features in two Fully-Connected [`Dense()`](https://keras.io/layers/core/#dense) layers.\n",
    "\n",
    "Batch Normalization is a technical trick to make training faster. \n",
    "Dropout is a regularization method, where the layer randomly replaces  a proportion of its weights to zero for each training sample. This forces the net to learn features in a distributed way, not relying to much on a particular weight, and therefore improves generalization. \n",
    "![Dropout](http://cs231n.github.io/assets/nn2/dropout.jpeg)\n",
    "'relu' is the activation function x -> max(x,0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 24, 24, 32)        832       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 24, 24, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 20, 20, 32)        25632     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 20, 20, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 16, 16, 32)        25632     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 16, 16, 32)        128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 6, 6, 64)          18496     \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 6, 6, 64)          256       \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 4, 4, 64)          36928     \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 4, 4, 64)          256       \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 2, 2, 64)          36928     \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 2, 2, 64)          256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 1, 1, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 1, 1, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               33280     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1024)              525312    \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                10250     \n",
      "=================================================================\n",
      "Total params: 714,442\n",
      "Trainable params: 713,866\n",
      "Non-trainable params: 576\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Initialize CNN\n",
    "classifier = Sequential()\n",
    "\n",
    "# Add 1st, 2nd & 3rd convolution layers\n",
    "classifier.add(Conv2D(filters=32, kernel_size=(5, 5), input_shape=X_train.shape[1:], activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "classifier.add(Conv2D(filters=32, kernel_size=(5, 5), activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "classifier.add(Conv2D(filters=32, kernel_size=(5, 5), activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "\n",
    "# Add 1st pooling layer\n",
    "classifier.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "classifier.add(Dropout(0.25))\n",
    "\n",
    "# Add 4th, 5th & 6th convolution layers\n",
    "classifier.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "classifier.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "classifier.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "\n",
    "# Add 2nd pooling layer\n",
    "classifier.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "classifier.add(Dropout(0.25))\n",
    "\n",
    "# Flatten features\n",
    "classifier.add(Flatten())\n",
    "\n",
    "# Add 1st hidden layer\n",
    "classifier.add(Dense(units=512, activation='relu'))\n",
    "classifier.add(Dropout(0.25))\n",
    "\n",
    "# Add 2nd hidden layer\n",
    "classifier.add(Dense(units=1024, activation='relu'))\n",
    "classifier.add(Dropout(0.5))\n",
    "\n",
    "# Add output layer\n",
    "classifier.add(Dense(units=y_train.shape[1], activation='softmax'))\n",
    "\n",
    "# Compile classifier: categorical cross entropy for multiclass classification\n",
    "classifier.compile(optimizer=Adam(lr=1e-4), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Print model summary\n",
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Initialize CNN\n",
    "classifier = Sequential()\n",
    "\n",
    "# Add 1st, 2nd convolution layers\n",
    "classifier.add(Conv2D(filters=32, kernel_size=(3, 3), input_shape=X_train.shape[1:], \n",
    "                      activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "classifier.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "\n",
    "# Add 3rd, 4th convolution layers\n",
    "classifier.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "classifier.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "\n",
    "# Add 1st pooling layer\n",
    "classifier.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2))\n",
    "classifier.add(Dropout(0.25))\n",
    "\n",
    "# Add 5th, 6th convolution layers\n",
    "classifier.add(Conv2D(filters=128, kernel_size=(3, 3), activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "classifier.add(Conv2D(filters=128, kernel_size=(3, 3), activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "\n",
    "# Add 7th, 8th convolution layers\n",
    "classifier.add(Conv2D(filters=256, kernel_size=(3, 3), activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "classifier.add(Conv2D(filters=256, kernel_size=(3, 3), activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "\n",
    "# Add 2nd pooling layer\n",
    "classifier.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "classifier.add(Dropout(0.25))\n",
    "\n",
    "# Flatten features\n",
    "classifier.add(Flatten())\n",
    "\n",
    "# Add 1st hidden layer\n",
    "classifier.add(Dense(units=128, activation='relu'))\n",
    "classifier.add(Dropout(0.25))\n",
    "\n",
    "# Add 2nd hidden layer\n",
    "classifier.add(Dense(units=128, activation='relu'))\n",
    "classifier.add(Dropout(0.25))\n",
    "\n",
    "# Add output layer\n",
    "classifier.add(Dense(units=y_train.shape[1], activation='softmax'))\n",
    "\n",
    "# Compile classifier: categorical cross entropy for multiclass classification\n",
    "classifier.compile(optimizer=Adam(lr=1e-4), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Print model summary\n",
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Initialize CNN\n",
    "classifier = Sequential()\n",
    "\n",
    "# Add 1st, 2nd convolution layers\n",
    "classifier.add(Conv2D(filters=32, kernel_size=(3, 3), padding='same', input_shape=X_train.shape[1:], \n",
    "                      activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "classifier.add(Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "\n",
    "# Add 1st pooling layer\n",
    "classifier.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))\n",
    "classifier.add(Dropout(0.25))\n",
    "\n",
    "# Add 3rd, 4th convolution layers\n",
    "classifier.add(Conv2D(filters=128, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "classifier.add(Conv2D(filters=256, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "\n",
    "# Add 2nd pooling layer\n",
    "classifier.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))\n",
    "classifier.add(Dropout(0.25))\n",
    "\n",
    "# Add 5th, 6th convolution layers\n",
    "classifier.add(Conv2D(filters=512, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "classifier.add(Conv2D(filters=1024, kernel_size=(3, 3), padding='same', activation='relu'))\n",
    "classifier.add(BatchNormalization())\n",
    "\n",
    "# Add 3rd pooling layer\n",
    "classifier.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))\n",
    "classifier.add(Dropout(0.25))\n",
    "\n",
    "# Flatten features\n",
    "classifier.add(Flatten())\n",
    "\n",
    "# Add 1st hidden layer\n",
    "classifier.add(Dense(units=128, activation='relu'))\n",
    "classifier.add(Dropout(0.25))\n",
    "\n",
    "# # Add 2nd hidden layer\n",
    "# classifier.add(Dense(units=128, activation='relu'))\n",
    "# classifier.add(Dropout(0.25))\n",
    "\n",
    "# Add output layer\n",
    "classifier.add(Dense(units=y_train.shape[1], activation='softmax'))\n",
    "\n",
    "# Compile classifier: categorical cross entropy for multiclass classification\n",
    "classifier.compile(optimizer=Adam(lr=1e-4), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Print model summary\n",
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit CNN to Training Set and Validation Sets\n",
    "### Image Augmentation\n",
    "Need to perform to avoid overfitting. It will create many batches of our images and on each batch it will apply some random transformations on a random selection of our images, by rotating, flipping, shearing, shifting, etc. The model will never find the same images in the training set and increase the size of the set.\n",
    "\n",
    "See: https://keras.io/preprocessing/image/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# Select batch size\n",
    "batch_size = 32\n",
    "\n",
    "# Define parameters for Image Augmentation to be done on the Training set\n",
    "train_datagen = ImageDataGenerator(rescale=1./255,\n",
    "                                   width_shift_range=0.10, \n",
    "                                   height_shift_range=0.10,\n",
    "                                   shear_range=0.10,\n",
    "                                   zoom_range=0.10, \n",
    "                                   rotation_range=10)\n",
    "\n",
    "# Perform rescaling only on the Validation Set\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# Create Training and Validation Set generators\n",
    "train_datagen = train_datagen.flow(x=X_train, y=y_train, batch_size=batch_size)\n",
    "val_datagen = val_datagen.flow(x=X_val, y=y_val, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Learning Rate Decayer\n",
    "We train once with a smaller learning rate to ensure convergence. We then speed things up, only to reduce the learning rate by 10% every epoch. Keras has a function for this. Check: https://keras.io/callbacks/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "annealer = LearningRateScheduler(lambda e: 1e-3 * 0.9 ** e)\n",
    "early_stopping = EarlyStopping(min_delta=0.001, patience=5, verbose=1)\n",
    "tensorboard = TensorBoard(log_dir='./logs', histogram_freq=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit CNN\n",
    "Check `Sequential()` [Model Methods](https://keras.io/models/sequential/#sequential-model-methods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/64\n",
      " 131/1246 [==>...........................] - ETA: 143s - loss: 1.2491 - acc: 0.5809"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-da28b5196ce8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m                                 \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_datagen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                                 \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mannealer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mearly_stopping\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTerminateOnNaN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensorboard\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m                                 epochs=64, verbose=1, workers=12)\n\u001b[0m",
      "\u001b[0;32m~/anaconda/envs/tensorflow/lib/python3.5/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     85\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[1;32m     86\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 87\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tensorflow/lib/python3.5/site-packages/keras/models.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, initial_epoch)\u001b[0m\n\u001b[1;32m   1115\u001b[0m                                         \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1116\u001b[0m                                         \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1117\u001b[0;31m                                         initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1119\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tensorflow/lib/python3.5/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     85\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[1;32m     86\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 87\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tensorflow/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, initial_epoch)\u001b[0m\n\u001b[1;32m   1838\u001b[0m                     outs = self.train_on_batch(x, y,\n\u001b[1;32m   1839\u001b[0m                                                \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1840\u001b[0;31m                                                class_weight=class_weight)\n\u001b[0m\u001b[1;32m   1841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1842\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tensorflow/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1563\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1565\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1566\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1567\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tensorflow/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2266\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m   2267\u001b[0m                               \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2268\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2269\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    887\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 889\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    890\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1116\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1117\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1118\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1119\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1120\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1313\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1314\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1315\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1316\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1317\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1319\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1320\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1321\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1322\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1298\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1299\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1300\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1302\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "hist = classifier.fit_generator(generator=train_datagen, steps_per_epoch=X_train.shape[0]/batch_size, \n",
    "                                validation_data=val_datagen, validation_steps=X_val.shape[0]/batch_size,\n",
    "                                callbacks=[annealer, early_stopping, TerminateOnNaN(), tensorboard],\n",
    "                                epochs=64, verbose=1, workers=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Output of Intermediate Layer\n",
    "Check [Intermediate Layer](https://keras.io/getting-started/faq/#how-can-i-obtain-the-output-of-an-intermediate-layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "get_21st_layer_output = K.function(inputs=[classifier.layers[0].input, K.learning_phase()], \n",
    "                                   outputs=[classifier.layers[21].output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# output in train mode = 1\n",
    "layer_output = get_21st_layer_output([X_train, 1])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 24)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_output[50][:, :, -1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "len(classifier.layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10f861160>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1lv8iyRWtta9NOdsoqupJSX4myXflPz9ety2zl+fektkrJ2trx1um+Q9Pv7af\nu3yutXbGXo+5MMkrknxPZj9gfTbJnyR5vYsW7dsDeZ6r6gVJnp3Zx8EemmR9kq8k+XCSN7bWPrBU\nyFp3EM9zkry/tXbxXo+zTz8Ia67oAWAtWTPv0QPAWqToAWBgih4ABqboAWBgih4ABqboAWBgih4A\nBqboAWBgih4ABqboAWBgih4ABqboAWBgih4ABqboAWBgih4ABqboAWBgih4ABvb/AerFHG7uIhmp\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f6a2da0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(layer_output[50][:, :, -1], cmap='Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nrows = 5\n",
    "ncols = 5\n",
    "fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(14, 14))\n",
    "for i in range(nrows*ncols):\n",
    "    ax[i//ncols, i%ncols].imshow(X_train[i, :].reshape(28, 28), cmap='Greys')\n",
    "    ax[i//ncols, i%ncols].set_title('Label: {}'.format(int(y_train[i])), fontsize=16)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Loss and Accuracy on Validation Set\n",
    "### Calculate Final Loss and Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final loss: 0.0240\n",
      "Final accuracy: 0.9962\n"
     ]
    }
   ],
   "source": [
    "X_val = X_val.astype('float32')/255.\n",
    "final_loss, final_acc = classifier.evaluate(X_val, y_val, verbose=0)\n",
    "print('Final loss: {0:.4f}\\nFinal accuracy: {1:.4f}'.format(final_loss, final_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Loss and Accuracy vs Number of Epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TLzR/YZJ058HmIT9fd86tL+H7geoLskH1BcmpvkD1BSWh+gKRLZQC5iIi5esAouNj/eic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\n0aJFPPTQQy26RkTwv//7v3z44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w3nnXdekuTAAw9Mkuy2225ZYYUV5nzpYd6/k8MOOyx3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14f351390>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 423,
       "width": 998
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "plt.subplot(121)\n",
    "plt.plot(hist.history['loss'], color='b', label='Train Loss')\n",
    "plt.plot(hist.history['val_loss'], color='r', label='Validation Loss')\n",
    "plt.title('Loss vs Number of Epochs', fontsize=16)\n",
    "plt.xlabel('Number of Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.subplot(122)\n",
    "plt.plot(hist.history['acc'], color='b', label='Train Accuracy')\n",
    "plt.plot(hist.history['val_acc'], color='r', label='Validation Accuracy')\n",
    "plt.title('Accuracy vs Number of Epochs', fontsize=16)\n",
    "plt.xlabel('Number of Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='center right')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix and Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[191   0   0   0   1   0   1   0   0   0]\n",
      " [  0 228   0   0   0   0   1   0   0   0]\n",
      " [  1   0 193   0   0   0   0   0   0   0]\n",
      " [  0   0   0 239   0   0   0   1   0   0]\n",
      " [  0   0   0   0 215   0   0   0   0   0]\n",
      " [  0   0   0   0   0 180   0   0   0   0]\n",
      " [  0   0   0   0   0   0 191   0   0   0]\n",
      " [  0   0   0   0   0   0   0 212   0   0]\n",
      " [  0   0   1   0   0   0   0   0 203   0]\n",
      " [  0   0   0   0   0   0   0   0   2 240]] \n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.99      0.99       193\n",
      "          1       1.00      1.00      1.00       229\n",
      "          2       0.99      0.99      0.99       194\n",
      "          3       1.00      1.00      1.00       240\n",
      "          4       1.00      1.00      1.00       215\n",
      "          5       1.00      1.00      1.00       180\n",
      "          6       0.99      1.00      0.99       191\n",
      "          7       1.00      1.00      1.00       212\n",
      "          8       0.99      1.00      0.99       204\n",
      "          9       1.00      0.99      1.00       242\n",
      "\n",
      "avg / total       1.00      1.00      1.00      2100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "y_pred = classifier.predict_classes(X_val, batch_size=batch_size, verbose=0)\n",
    "y_true = np.argmax(y_val, axis=1)\n",
    "print(confusion_matrix(y_true, y_pred), '\\n')\n",
    "print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the Wrong Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 8 misclassified digits.\n"
     ]
    },
    {
     "data": {
      "image/png": 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kmfsAsClY7LsJ39gtb5q1buZK+R+fY/vPJLk2yf2raqvFbAwAAGCC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\nVbU+yZu7t8dMMicAAIBxUvcAsBJN6wEiT0lyeFV9Mcn3kly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AAHpCMxAAAAAAekIzEAAA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\nAAAABkIxEAAAAAAGQjEQAAAAAAZCMRAAA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GEgAAAAADRCGAgAAAAAjRAGAgAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15e4bf898>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 998,
       "width": 641
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = []\n",
    "for i in range(len(y_true)):\n",
    "    if y_true[i] != y_pred[i]:\n",
    "        diff.append(i)\n",
    "print('There are {} misclassified digits.'.format(len(diff)))\n",
    "\n",
    "ncols = 2\n",
    "nrows = (len(diff)//ncols)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(14, 14))\n",
    "for i in range(len(diff)):\n",
    "    ax[i//ncols, i%ncols].imshow(X_val[diff[i], :, :, -1], cmap='Greys')\n",
    "    ax[i//ncols, i%ncols].set_title('Label: {}\\nPrediction: {}'.format(int(y_true[diff[i]]), \n",
    "                                                                       int(y_pred[diff[i]])), fontsize=12)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see there are quite a few parameters that could be tweaked (number of layers, number of filters, Dropout parameters, learning rate, augmentation settings). This is often done with trial and error, and there is no easy shortcut.\n",
    "\n",
    "Getting convergence should not be a problem, unless you use an extremely large learning rate. It's easy, however, to create a net that overfits, with perfect results on the training set and very poor results on the validation data. If this happens, you could try increasing the Dropout parameters, increase augmentation, or perhaps stop training earlier. If you instead wants to increase accuracy, try adding on two more layers, or increase the number of filters.\n",
    "\n",
    "## Make Predictions on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28000/28000 [==============================] - 101s   \n"
     ]
    }
   ],
   "source": [
    "predictions = classifier.predict_classes(X_test, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Predictions vs Actual Digits on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x14f345390>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1000,
       "width": 959
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrows = 7\n",
    "ncols = 6\n",
    "fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(14, 14))\n",
    "for i in range(nrows*ncols):\n",
    "    ax[i//ncols, i%ncols].imshow(X_test[i, :].reshape(28, 28), cmap='Greys')\n",
    "    ax[i//ncols, i%ncols].set_title('Prediction: {}'.format(int(predictions[i])), fontsize=16)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame({\"ImageId\": list(range(1, len(predictions) + 1)), \n",
    "              \"Label\": predictions}\n",
    "            ).to_csv(\"submission.csv\", index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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