train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train=pd.read_csv('train_final.csv',index_col=False)\n",
    "labels=df_train[['784']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "     0    1    2    3    4    5    6    7    8    9 ...   774  775  776  777  \\\n",
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       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.drop(df_train.columns[[784]],axis=1,inplace=True)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1212)\n",
    "import keras\n",
    "from keras.models import Model\n",
    "from keras.layers import *\n",
    "from keras import optimizers\n",
    "from keras.layers import Input, Dense\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.utils import np_utils\n",
    "from keras import backend as K\n",
    "K.set_image_dim_ordering('th')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "labels=np.array(labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.np_utils import to_categorical\n",
    "cat=to_categorical(labels,num_classes=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "print(cat[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "     0    1    2    3    4    5    6    7    8    9 ...   774  775  776  777  \\\n",
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     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "l=[]\n",
    "for i in range(47504):\n",
    "    l.append(np.array(df_train[i:i+1]).reshape(1,28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(30, (5, 5), input_shape=(1 , 28, 28), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(15, (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(50, activation='relu'))\n",
    "model.add(Dense(13, activation='softmax'))\n",
    "# Compile model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import model_from_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "47504/47504 [==============================] - 1333s 28ms/step - loss: 1.4781 - acc: 0.6736\n",
      "Epoch 2/10\n",
      "47504/47504 [==============================] - 1327s 28ms/step - loss: 0.2734 - acc: 0.9172\n",
      "Epoch 3/10\n",
      "47504/47504 [==============================] - 1266s 27ms/step - loss: 0.1674 - acc: 0.9493\n",
      "Epoch 4/10\n",
      "47504/47504 [==============================] - 1265s 27ms/step - loss: 0.1253 - acc: 0.9617\n",
      "Epoch 5/10\n",
      "47504/47504 [==============================] - 1270s 27ms/step - loss: 0.0993 - acc: 0.9710\n",
      "Epoch 6/10\n",
      "47504/47504 [==============================] - 1270s 27ms/step - loss: 0.0862 - acc: 0.9741\n",
      "Epoch 7/10\n",
      "47504/47504 [==============================] - 1263s 27ms/step - loss: 0.0720 - acc: 0.9783\n",
      "Epoch 8/10\n",
      "47504/47504 [==============================] - 1252s 26ms/step - loss: 0.0617 - acc: 0.9812\n",
      "Epoch 9/10\n",
      "47504/47504 [==============================] - 1095s 23ms/step - loss: 0.0566 - acc: 0.9830\n",
      "Epoch 10/10\n",
      "47504/47504 [==============================] - 1099s 23ms/step - loss: 0.0524 - acc: 0.9846\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2801ad4d0f0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(np.array(l), cat, epochs=10, batch_size=200,shuffle=True,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model_json = model.to_json()\n",
    "with open(\"model_final.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"model_final.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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