index.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Flatten\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshaping to format which CNN expects (batch, height, width, channels)\n",
    "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2], 1).astype('float32')\n",
    "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2], 1).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize inputs from 0-255 to 0-1\n",
    "X_train/=255\n",
    "X_test/=255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one hot encode\n",
    "number_of_classes = 10\n",
    "y_train = np_utils.to_categorical(y_train, number_of_classes)\n",
    "y_test = np_utils.to_categorical(y_test, number_of_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create model\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (5, 5), input_shape=(X_train.shape[1], X_train.shape[2], 1), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(number_of_classes, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 24, 24, 32)        832       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 10, 10, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 32)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 5, 5, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 800)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               102528    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 113,898\n",
      "Trainable params: 113,898\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile model\n",
    "model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 44s 732us/step - loss: 0.3005 - accuracy: 0.9110 - val_loss: 0.0647 - val_accuracy: 0.9788\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 50s 826us/step - loss: 0.0780 - accuracy: 0.9763 - val_loss: 0.0426 - val_accuracy: 0.9854\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 44s 738us/step - loss: 0.0546 - accuracy: 0.9831 - val_loss: 0.0344 - val_accuracy: 0.9884\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 55s 919us/step - loss: 0.0452 - accuracy: 0.9859 - val_loss: 0.0351 - val_accuracy: 0.9880\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 50s 834us/step - loss: 0.0372 - accuracy: 0.9888 - val_loss: 0.0249 - val_accuracy: 0.9915\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 58s 975us/step - loss: 0.0329 - accuracy: 0.9902 - val_loss: 0.0247 - val_accuracy: 0.9918\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 52s 860us/step - loss: 0.0283 - accuracy: 0.9910 - val_loss: 0.0254 - val_accuracy: 0.9900\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 53s 888us/step - loss: 0.0268 - accuracy: 0.9909 - val_loss: 0.0262 - val_accuracy: 0.9914\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 54s 900us/step - loss: 0.0217 - accuracy: 0.9930 - val_loss: 0.0236 - val_accuracy: 0.9921\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 54s 901us/step - loss: 0.0200 - accuracy: 0.9932 - val_loss: 0.0249 - val_accuracy: 0.9908\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7fe21f741110>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model\n",
    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model\n",
    "model.save('models/mnistCNN.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics(Test loss & Test Accuracy): \n",
      "[0.024913757571813768, 0.9908000230789185]\n"
     ]
    }
   ],
   "source": [
    "# Final evaluation of the model\n",
    "metrics = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"Metrics(Test loss & Test Accuracy): \")\n",
    "print(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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