Handwritten Digit Recognition by using CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot ad hoc mnist instances\n",
    "from keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "#load(downloaded if needed)the MNIST dataset\n",
    "(X_train,y_train),(X_test,y_test)=mnist.load_data()\n",
    "#plot 4 images as gray scale\n",
    "plt.subplot(221)\n",
    "plt.imshow(X_train[0],cmap=plt.get_cmap('gray'))\n",
    "plt.subplot(222)\n",
    "plt.imshow(X_train[1],cmap=plt.get_cmap('gray'))\n",
    "plt.subplot(223)\n",
    "plt.imshow(X_train[2],cmap=plt.get_cmap('gray'))\n",
    "plt.subplot(224)\n",
    "plt.imshow(X_train[3],cmap=plt.get_cmap('gray'))\n",
    "#show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Baseline MLP for MNIST dataset\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.utils import np_utils\n",
    "#load data\n",
    "(X_train,y_train),(X_test,y_test)=mnist.load_data()\n",
    "#flatten 28*28 images to a 784 vector for each image\n",
    "num_pixels=X_train.shape[1]*X_train.shape[2]\n",
    "X_train=X_train.reshape((X_train.shape[0],num_pixels)).astype('float32')\n",
    "X_test=X_test.reshape((X_test.shape[0],num_pixels)).astype('float32')\n",
    "#normalize inputs from 0-255 to 0-1\n",
    "X_train=X_train/255\n",
    "X_test=X_test/255\n",
    "#one hot encode outputs\n",
    "y_train=np_utils.to_categorical(y_train)\n",
    "y_test=np_utils.to_categorical(y_test)\n",
    "num_classes=y_test.shape[1]\n",
    "#define baseline model\n",
    "def baseline_model():\n",
    "    #create model\n",
    "    model=Sequential()\n",
    "    model.add(Dense(num_pixels,input_dim=num_pixels,kernel_initializer='normal',activation='relu'))\n",
    "    model.add(Dense(num_classes,kernel_initializer='normal',activation='softmax'))\n",
    "    #compile model\n",
    "    model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "    return model\n",
    "#build the model\n",
    "model=baseline_model()\n",
    "#Fit the model\n",
    "model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=10,batch_size=200,verbose=2)\n",
    "#final evaluation of the model\n",
    "scores=model.evaluate(X_test,y_test,verbose=0)\n",
    "print(\"Baseline error:%.2f%%\"%(100-scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dropout\n",
    "from keras.layers import Flatten\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers.convolutional import MaxPooling1D\n",
    "#load data\n",
    "(X_train,y_train),(X_test,y_test)=mnist.load_data()\n",
    "#reshape to be [samples][width][height][channels]\n",
    "X_train=X_train.reshape((X_train.shape[0],28,28,1)).astype('float32')\n",
    "X_test=X_test.reshape((X_test.shape[0],28,28,1)).astype('float32')\n",
    "#normalize inputs from 0-255 to 0-1\n",
    "X_train=X_train/255\n",
    "X_test=X_test/255\n",
    "#one hot encode outputs\n",
    "y_train=np_utils.to_categorical(y_train)\n",
    "y_test=np_utils.to_categorical(y_test)\n",
    "num_classes=y_test.shape[1]\n",
    "#define a simple CNN model\n",
    "def baseline_model():\n",
    "    #create model\n",
    "    model=Sequential()\n",
    "    model.add(Conv1D(32,(5,5),input_shape=(28,28,1),activation='relu'))\n",
    "    model.add(MaxPooling1D())\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(128,activation='relu'))\n",
    "    model.add(Dense(num_classes,activation='softmax'))\n",
    "    #compile model\n",
    "    model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "    return model\n",
    "#build the model\n",
    "model=baseline_model()\n",
    "#fit the model\n",
    "model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=10,batch_size=200)\n",
    "#final evaluation of the model\n",
    "scores=model.evaluate(X_test,y_test,verbose=0)\n",
    "#final evaluation of the model\n",
    "scores=model.evaluate(X_test,y_test,verbose=0)\n",
    "print(\"CNN Error:%.2f%%\"%(100-scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "#load data\n",
    "(X_train,y_train),(X_test,y_test)=mnist.load_data()\n",
    "#reshape to be [samples][width][height][channels]\n",
    "X_train=X_train.reshape((X_train.shape[0],28,28,1)).astype('float32')\n",
    "X_test=X_test.reshape((X_test.shape[0],28,28,1)).astype('float32')\n",
    "#normalize inputs from 0-255 to 0-1\n",
    "X_train=X_train/255\n",
    "X_test=X_test/255\n",
    "#one hot encode outputs\n",
    "y_train=np_utils.to_categorical(y_train)\n",
    "y_test=np_utils.to_categorical(y_test)\n",
    "num_classes=y_test.shape[1]\n",
    "#define a simple CNN model\n",
    "def baseline_model():\n",
    "    #create model\n",
    "    model=Sequential()\n",
    "    model.add(Conv1D(30,(5,5),input_shape=(28,28,1),activation='relu'))\n",
    "    model.add(MaxPooling1D())\n",
    "    model.add(Conv1D(15,(3,3),activation='relu'))\n",
    "    model.add(MaxPooling1D())\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(128,activation='relu'))\n",
    "    model.add(Dense(50,activation='relu'))\n",
    "    model.add(Dense(num_classes,activation='softmax'))\n",
    "    #compile model\n",
    "    model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "    return model\n",
    "#build the model\n",
    "model=baseline_model()\n",
    "#fit the model\n",
    "model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=10,batch_size=200)\n",
    "#final evaluation of the model\n",
    "scores=model.evaluate(X_test,y_test,verbose=0)\n",
    "#final evaluation of the model\n",
    "scores=model.evaluate(X_test,y_test,verbose=0)\n",
    "print(\" Large CNN Error:%.2f%%\"%(100-scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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