ConvNets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Convolutional Neural Network on MNIST handwritten digit dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('mnist.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
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       "\n",
       "   pixel8    ...     pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
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       "4         0         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reshaping into 28X28 array\n",
    "data.iloc[3,1:].values.reshape(28,28).astype('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preprocessing data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Storing Pixel array in form length width and channel in df_x\n",
    "df_x = data.iloc[:,1:].values.reshape(len(data),28,28,1)\n",
    "\n",
    "#Storing the labels in y\n",
    "y = data.iloc[:,0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting labels to categorical features\n",
    "\n",
    "df_y = keras.utils.to_categorical(y,num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_x = np.array(df_x)\n",
    "df_y = np.array(df_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, ..., 7, 6, 9], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lables\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  1.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  1.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#categorical labels\n",
    "df_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 28, 28, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#test train split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_x,df_y,test_size=0.2,random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CNN model\n",
    "model = Sequential()\n",
    "model.add(Convolution2D(32,3,data_format='channels_last',activation='relu',input_shape=(28,28,1)))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(100))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer = 'adadelta', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 5408)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 100)               540900    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                1010      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 542,230\n",
      "Trainable params: 542,230\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 33600 samples, validate on 8400 samples\n",
      "Epoch 1/10\n",
      "33600/33600 [==============================] - 96s - loss: 7.7860 - acc: 0.5063 - val_loss: 4.4646 - val_acc: 0.7145\n",
      "Epoch 2/10\n",
      "33600/33600 [==============================] - 107s - loss: 3.8784 - acc: 0.7476 - val_loss: 1.6786 - val_acc: 0.8844\n",
      "Epoch 3/10\n",
      "33600/33600 [==============================] - 117s - loss: 1.7091 - acc: 0.8812 - val_loss: 1.0423 - val_acc: 0.9254\n",
      "Epoch 4/10\n",
      "33600/33600 [==============================] - 115s - loss: 1.0479 - acc: 0.9222 - val_loss: 0.7083 - val_acc: 0.9489\n",
      "Epoch 5/10\n",
      "33600/33600 [==============================] - 112s - loss: 0.7755 - acc: 0.9407 - val_loss: 0.5226 - val_acc: 0.9574\n",
      "Epoch 6/10\n",
      "33600/33600 [==============================] - 114s - loss: 0.6248 - acc: 0.9500 - val_loss: 0.4497 - val_acc: 0.9637\n",
      "Epoch 7/10\n",
      "33600/33600 [==============================] - 113s - loss: 0.5083 - acc: 0.9570 - val_loss: 0.3780 - val_acc: 0.9665\n",
      "Epoch 8/10\n",
      "33600/33600 [==============================] - 117s - loss: 0.4292 - acc: 0.9608 - val_loss: 0.3467 - val_acc: 0.9705\n",
      "Epoch 9/10\n",
      "33600/33600 [==============================] - 117s - loss: 0.3536 - acc: 0.9657 - val_loss: 0.3007 - val_acc: 0.9711\n",
      "Epoch 10/10\n",
      "33600/33600 [==============================] - 118s - loss: 0.3104 - acc: 0.9695 - val_loss: 0.2707 - val_acc: 0.9726\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x24c904ba860>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting it with just 100 images for testing \n",
    "\n",
    "model.fit(x_train,y_train,validation_data=(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8384/8400 [============================>.] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.27073475346692977, 0.97261904761904761]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#imporove accuracy by more epocs till the loss is almost same"
   ]
  }
 ],
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