cnn_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'keras'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-2b95ae8d4acc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmnist\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDropout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFlatten\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mConv2D\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMaxPooling2D\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'keras'"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\n",
    "from keras import backend as k\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load mnist dataset\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd4AAAFyCAYAAABbQLtWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRMcLAEBEdLwAAERExwsAQET/H95sGVnigCAGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b4cd506048>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#visualising first 9 data from training dataset\n",
    "fig = plt.figure()\n",
    "for i in range(9):\n",
    "  plt.subplot(3,3,i+1)\n",
    "  plt.tight_layout()\n",
    "  plt.imshow(X_train[i], cmap='gray', interpolation='none')\n",
    "  plt.title(\"Digit: {}\".format(y_train[i]))\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape (60000, 28, 28)\n",
      "y_train shape (60000,)\n",
      "X_test shape (10000, 28, 28)\n",
      "y_test shape (10000,)\n"
     ]
    }
   ],
   "source": [
    "# let's print the actual data shape before we reshape and normalize\n",
    "print(\"X_train shape\", X_train.shape)\n",
    "print(\"y_train shape\", y_train.shape)\n",
    "print(\"X_test shape\", X_test.shape)\n",
    "print(\"y_test shape\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#input image size 28*28\n",
    "img_rows , img_cols = 28, 28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "#reshaping\n",
    "#this assumes our data format\n",
    "#For 3D data, \"channels_last\" assumes (conv_dim1, conv_dim2, conv_dim3, channels) while \n",
    "#\"channels_first\" assumes (channels, conv_dim1, conv_dim2, conv_dim3).\n",
    "if k.image_data_format() == 'channels_first':\n",
    "    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n",
    "    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "#more reshaping\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "X_train /= 255\n",
    "X_test /= 255\n",
    "print('X_train shape:', X_train.shape)\n",
    "print(X_train.shape[0], 'train samples')\n",
    "print(X_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8), array([5923, 6742, 5958, 6131, 5842, 5421, 5918, 6265, 5851, 5949], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(y_train, return_counts=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#set number of categories\n",
    "num_category = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_category)\n",
    "y_test = keras.utils.to_categorical(y_test, num_category)\n",
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##model building\n",
    "model = Sequential()\n",
    "#convolutional layer with rectified linear unit activation\n",
    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
    "                 activation='relu',\n",
    "                 input_shape=input_shape))\n",
    "#32 convolution filters used each of size 3x3\n",
    "#again\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "#64 convolution filters used each of size 3x3\n",
    "#choose the best features via pooling\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "#randomly turn neurons on and off to improve convergence\n",
    "model.add(Dropout(0.25))\n",
    "#flatten since too many dimensions, we only want a classification output\n",
    "model.add(Flatten())\n",
    "#fully connected to get all relevant data\n",
    "model.add(Dense(128, activation='relu'))\n",
    "#one more dropout for convergence' sake :) \n",
    "model.add(Dropout(0.5))\n",
    "#output a softmax to squash the matrix into output probabilities\n",
    "model.add(Dense(num_category, activation='softmax'))\n",
    "#Adaptive learning rate (adaDelta) is a popular form of gradient descent rivaled only by adam and adagrad\n",
    "#categorical ce since we have multiple classes (10) \n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adadelta(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 284s 5ms/step - loss: 0.2120 - acc: 0.9354 - val_loss: 0.0610 - val_acc: 0.9801\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 271s 5ms/step - loss: 0.0894 - acc: 0.9737 - val_loss: 0.0422 - val_acc: 0.9861\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 270s 4ms/step - loss: 0.0677 - acc: 0.9800 - val_loss: 0.0402 - val_acc: 0.9870\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 276s 5ms/step - loss: 0.0567 - acc: 0.9834 - val_loss: 0.0332 - val_acc: 0.9891\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 271s 5ms/step - loss: 0.0472 - acc: 0.9860 - val_loss: 0.0322 - val_acc: 0.9887\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 274s 5ms/step - loss: 0.0417 - acc: 0.9870 - val_loss: 0.0297 - val_acc: 0.9899\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 264s 4ms/step - loss: 0.0371 - acc: 0.9887 - val_loss: 0.0312 - val_acc: 0.9906\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 264s 4ms/step - loss: 0.0355 - acc: 0.9890 - val_loss: 0.0302 - val_acc: 0.9894\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 270s 4ms/step - loss: 0.0335 - acc: 0.9896 - val_loss: 0.0387 - val_acc: 0.9886\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 271s 5ms/step - loss: 0.0335 - acc: 0.9897 - val_loss: 0.0296 - val_acc: 0.9904\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "num_epoch = 3\n",
    "#model training\n",
    "model_log = model.fit(X_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=num_epoch,\n",
    "          verbose=1,\n",
    "          validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.0296396646054\n",
      "Test accuracy: 0.9904\n"
     ]
    }
   ],
   "source": [
    "#how well did it do? \n",
    "score = model.evaluate(X_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk4AAAGGCAYAAACNCg6xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b4cc39eac8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "# plotting the metrics\n",
    "fig = plt.figure()\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(model_log.history['accuracy'])\n",
    "plt.plot(model_log.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='lower right')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(model_log.history['loss'])\n",
    "plt.plot(model_log.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "#Save the model\n",
    "# serialize model to JSON\n",
    "model_digit_json = model.to_json()\n",
    "with open(\"model_digit.json\", \"w\") as json_file:\n",
    "    json_file.write(model_digit_json)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"model_digit.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}