7. Digit Recognizer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Importing the Keras libraries and packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import Conv2D,MaxPool2D,Dense,Flatten,Dropout\n",
    "from keras.models import Sequential\n",
    "from keras.utils.np_utils import to_categorical # to convert one hot encoding\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ad hoc mnist instances\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot 4 images as gray scale\n",
    "plt.subplot(221)\n",
    "plt.imshow(x_test[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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28) (60000,)\n",
      "(10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape,y_train.shape)\n",
    "print(x_test.shape,y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0    1    2    3    4    5    6    7    8    9   ...   774  775  776  777  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0 ...     0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0 ...     0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0 ...     0    0    0    0   \n",
       "3    0    0    0    0    0    0    0    0    0    0 ...     0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0 ...     0    0    0    0   \n",
       "\n",
       "   778  779  780  781  782  783  \n",
       "0    0    0    0    0    0    0  \n",
       "1    0    0    0    0    0    0  \n",
       "2    0    0    0    0    0    0  \n",
       "3    0    0    0    0    0    0  \n",
       "4    0    0    0    0    0    0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I am fan of seeing data in Dataframe ;)\n",
    "import pandas as pd\n",
    "pd.DataFrame(x_train.reshape(60000,784)).head() # since it is 28*28 image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a43d80f28>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y_train)\n",
    "# it is balanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.max()\n",
    "\n",
    "# since the pixels are in black and white 1D "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalisation it between 0 to 1\n",
    "x_train = x_train/255.0\n",
    "x_test = x_test/255.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "784\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# flatten 28*28 images to a 784 vector for each image\n",
    "num_pixels = x_train.shape[1] * x_train.shape[2]\n",
    "print(num_pixels)\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784) (60000,)\n",
      "(10000, 784) (10000,)\n"
     ]
    }
   ],
   "source": [
    "# (60000, 28, 28) ---> (60000, 784)\n",
    "\n",
    "print(x_train.shape,y_train.shape)\n",
    "print(x_test.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reshape\n",
    "x_train = x_train.reshape(-1,28,28,1)\n",
    "x_test = x_test.reshape(-1,28,28,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1) (60000,)\n",
      "(10000, 28, 28, 1) (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape,y_train.shape)\n",
    "print(x_test.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes=10\n",
    "\n",
    "y_train = to_categorical(y_train,num_classes = 10)\n",
    "y_test = to_categorical(y_test,num_classes = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create model\n",
    "model = Sequential()\n",
    "model.add(Conv2D(128, (3, 3), input_shape=(28, 28,1), activation='relu'))\n",
    "\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "# Compile model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "\n",
    "model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, batch_size=200, verbose=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_pred=model.predict(x_test)\n",
    "y_pred[0].max()\n",
    "y_pred[0].argmax()\n",
    "\n",
    "\n",
    "\n",
    "scores = model.evaluate(x_test, y_test, verbose=0)\n",
    "print(\"CNN Error: %.2f%%\" % (100-scores[1]*100))"
   ]
  }
 ],
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