model1-Conv_2-batchnormalisation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.callbacks import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "traindata = pd.read_csv('sign_mnist_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainlabel=traindata['label'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "traindata.drop('label',inplace=True,axis=1)\n",
    "trainimages = traindata.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainimages=trainimages.reshape(-1,28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "testdata = pd.read_csv('sign_mnist_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "testlabel=testdata['label'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "testdata.drop('label',inplace=True,axis=1)\n",
    "testimages = testdata.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "testimages=testimages.reshape(-1,28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential([])\n",
    "\n",
    "model.add(Conv2D(64,(3,3),activation=\"relu\",input_shape=(28,28,1)))\n",
    "model.add(BatchNormalization())\n",
    "#model.add(Conv2D(64,(3,3),activation=\"relu\"))\n",
    "#model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(2,2))\n",
    "#model.add(Dropout(0.4))\n",
    "\n",
    "model.add(Conv2D(128,(3,3),activation=\"relu\"))\n",
    "model.add(BatchNormalization())\n",
    "#model.add(Conv2D(128,(3,3),activation=\"relu\"))\n",
    "#model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(2,2))\n",
    "#model.add(Dropout(0.4))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(BatchNormalization())\n",
    "#model.add(Dropout(0.4))\n",
    "model.add(Dense(256,activation=\"relu\"))\n",
    "model.add(BatchNormalization())\n",
    "#model.add(Dropout(0.4))\n",
    "model.add(Dense(26,activation=\"softmax\"))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\",optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 26, 26, 64)        640       \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 26, 26, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 11, 11, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 11, 11, 128)       512       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 5, 5, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 3200)              0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 3200)              12800     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 256)               819456    \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 26)                6682      \n",
      "=================================================================\n",
      "Total params: 915,226\n",
      "Trainable params: 907,930\n",
      "Non-trainable params: 7,296\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a Callback class that stops training once accuracy reaches 97.0%\n",
    "class myCallback(Callback):\n",
    "  def on_epoch_end(self, epoch, logs={}):\n",
    "    if(logs.get('accuracy')==1.0):\n",
    "      print(\"\\nReached 100.0% accuracy so cancelling training!\")\n",
    "      self.model.stop_training = True\n",
    "callback=myCallback()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 21964 samples, validate on 5491 samples\n",
      "Epoch 1/5\n",
      "21964/21964 [==============================] - 8s 367us/step - loss: 0.1423 - accuracy: 0.9640 - val_loss: 0.0018 - val_accuracy: 1.0000\n",
      "Epoch 2/5\n",
      "21964/21964 [==============================] - 7s 336us/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 3.8766e-04 - val_accuracy: 1.0000\n",
      "\n",
      "Reached 100.0% accuracy so cancelling training!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x24d32d24b48>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(trainimages,trainlabel,epochs=5,validation_split=0.2,callbacks=[callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7172/7172 [==============================] - 1s 114us/step\n",
      "test accuracy:95.60791850090027\n"
     ]
    }
   ],
   "source": [
    "print(\"test accuracy: \"+ str(model.evaluate(testimages,testlabel)[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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