Phase2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "mydata=pd.read_csv(r\"fer2013.csv\")\n",
    "\n",
    "X_train,Y_train,X_test,Y_test=[],[],[],[]\n",
    "\n",
    "for index, row in mydata.iterrows():\n",
    "    val=row['pixels'].split(\" \")\n",
    "    if 'Training' in row['Usage']:\n",
    "        X_train.append(np.array(val,'float32'))\n",
    "        Y_train.append(row['emotion'])\n",
    "    elif 'PublicTest' in row['Usage']:\n",
    "        X_test.append(np.array(val,'float32'))\n",
    "        Y_test.append(row['emotion'])\n",
    "'''\n",
    "#this is phase1 code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#phase2 starts here\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization,AveragePooling2D\n",
    "from keras.losses import categorical_crossentropy\n",
    "from keras.optimizers import Adam\n",
    "from keras.regularizers import l2\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_features = 64\n",
    "num_labels = 7\n",
    "batch_size = 64\n",
    "epochs = 30\n",
    "width, height = 48, 48\n",
    "\n",
    "\n",
    "X_train = np.array(X_train,'float32')\n",
    "Y_train = np.array(Y_train,'float32')\n",
    "X_test = np.array(X_test,'float32')\n",
    "Y_test = np.array(Y_test,'float32')\n",
    "\n",
    "Y_train=np_utils.to_categorical(Y_train, num_classes=num_labels)\n",
    "Y_test=np_utils.to_categorical(Y_test, num_classes=num_labels)\n",
    "\n",
    "#normalizing data between oand 1\n",
    "X_train -= np.mean(X_train, axis=0)\n",
    "X_train /= np.std(X_train, axis=0)\n",
    "\n",
    "X_test -= np.mean(X_test, axis=0)\n",
    "X_test /= np.std(X_test, axis=0)\n",
    "\n",
    "X_train = X_train.reshape(X_train.shape[0], 48, 48, 1)\n",
    "\n",
    "X_test = X_test.reshape(X_test.shape[0], 48, 48, 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "##designing the neural net\n",
    "#1st convolution layer\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=(X_train.shape[1:])))\n",
    "model.add(Conv2D(64,kernel_size= (3, 3), activation='relu'))\n",
    "\n",
    "model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2)))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "#2nd convolution layer\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "\n",
    "model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2)))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "#3rd convolution layer\n",
    "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "\n",
    "model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "#fully connected neural networks\n",
    "model.add(Dense(1024, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1024, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(num_labels, activation='softmax'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 46, 46, 64)        640       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 44, 44, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 22, 22, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 22, 22, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 20, 20, 64)        36928     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 18, 18, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 9, 9, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 9, 9, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 7, 7, 128)         73856     \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 5, 5, 128)         147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 2, 2, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1024)              525312    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 7)                 7175      \n",
      "=================================================================\n",
      "Total params: 1,914,951\n",
      "Trainable params: 1,914,951\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compliling the model\n",
    "model.compile(loss=categorical_crossentropy,\n",
    "              optimizer=Adam(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 28709 samples, validate on 3589 samples\n",
      "Epoch 1/30\n",
      " 6016/28709 [=====>........................] - ETA: 4:59 - loss: 1.7946 - acc: 0.2445"
     ]
    }
   ],
   "source": [
    "#Training the model\n",
    "model.fit(X_train, Y_train,batch_size=batch_size,epochs=epochs,verbose=1,validation_data=(X_test, Y_test),shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the  model to  use it later on\n",
    "fer_json = model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(fer_json)\n",
    "model.save_weights(\"model.h5\")"
   ]
  }
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