2.+Cifar_10_Notebook_with_BN_without_dropout.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.datasets import cifar10\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the parameters\n",
    "batch_size = 32\n",
    "num_classes = 10\n",
    "epochs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
      "170500096/170498071 [==============================] - 12s 0us/step\n",
      "x_train shape: (50000, 32, 32, 3)\n",
      "50000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "# Splitting the data between train and test\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting some random 10 images\n",
    "\n",
    "class_names = ['airplane','automobile','bird','cat','deer',\n",
    "               'dog','frog','horse','ship','truck']\n",
    "\n",
    "fig = plt.figure(figsize=(8,3))\n",
    "for i in range(num_classes):\n",
    "    ax = fig.add_subplot(2, 5, 1 + i, xticks=[], yticks=[])\n",
    "    idx = np.where(y_train[:]==i)[0]\n",
    "    features_idx = x_train[idx,::]\n",
    "    img_num = np.random.randint(features_idx.shape[0])\n",
    "    im = (features_idx[img_num,::])\n",
    "    ax.set_title(class_names[i])\n",
    "    plt.imshow(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert class vectors to binary class matrices.\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# Printing sample data\n",
    "print(y_train[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), padding='same',\n",
    "                 input_shape=x_train.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(32, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(64, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes))\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 30, 30, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 30, 30, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 30, 30, 32)        128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 15, 15, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 15, 15, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 15, 15, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 13, 13, 64)        36928     \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 13, 13, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2304)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               1180160   \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                5130      \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 1,251,626\n",
      "Trainable params: 1,251,242\n",
      "Non-trainable params: 384\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# summary of the model\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='sgd',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "\n",
    "# Normalizing the input image\n",
    "x_train /= 255\n",
    "x_test /= 255\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "50000/50000 [==============================] - 33s 656us/step - loss: 1.5921 - acc: 0.4420 - val_loss: 1.2511 - val_acc: 0.5498\n",
      "Epoch 2/50\n",
      "50000/50000 [==============================] - 22s 430us/step - loss: 1.1806 - acc: 0.5783 - val_loss: 1.1161 - val_acc: 0.6022\n",
      "Epoch 3/50\n",
      "50000/50000 [==============================] - 22s 436us/step - loss: 0.9981 - acc: 0.6459 - val_loss: 0.9491 - val_acc: 0.6645\n",
      "Epoch 4/50\n",
      "50000/50000 [==============================] - 22s 433us/step - loss: 0.8784 - acc: 0.6881 - val_loss: 0.8733 - val_acc: 0.6970\n",
      "Epoch 5/50\n",
      "50000/50000 [==============================] - 21s 430us/step - loss: 0.7905 - acc: 0.7220 - val_loss: 0.7577 - val_acc: 0.7337\n",
      "Epoch 6/50\n",
      "50000/50000 [==============================] - 21s 426us/step - loss: 0.7195 - acc: 0.7457 - val_loss: 0.7262 - val_acc: 0.7446\n",
      "Epoch 7/50\n",
      "50000/50000 [==============================] - 22s 433us/step - loss: 0.6620 - acc: 0.7663 - val_loss: 0.7585 - val_acc: 0.7339\n",
      "Epoch 8/50\n",
      "50000/50000 [==============================] - 22s 431us/step - loss: 0.6135 - acc: 0.7826 - val_loss: 0.7075 - val_acc: 0.7511\n",
      "Epoch 9/50\n",
      "50000/50000 [==============================] - 22s 431us/step - loss: 0.5738 - acc: 0.7960 - val_loss: 0.6972 - val_acc: 0.7593\n",
      "Epoch 10/50\n",
      "50000/50000 [==============================] - 22s 431us/step - loss: 0.5281 - acc: 0.8148 - val_loss: 0.6827 - val_acc: 0.7579\n",
      "Epoch 11/50\n",
      "50000/50000 [==============================] - 22s 434us/step - loss: 0.4909 - acc: 0.8252 - val_loss: 0.7075 - val_acc: 0.7602\n",
      "Epoch 12/50\n",
      "50000/50000 [==============================] - 22s 430us/step - loss: 0.4574 - acc: 0.8379 - val_loss: 0.7028 - val_acc: 0.7593\n",
      "Epoch 13/50\n",
      "50000/50000 [==============================] - 22s 432us/step - loss: 0.4225 - acc: 0.8498 - val_loss: 0.6644 - val_acc: 0.7747\n",
      "Epoch 14/50\n",
      "50000/50000 [==============================] - 22s 450us/step - loss: 0.3894 - acc: 0.8626 - val_loss: 0.6688 - val_acc: 0.7740\n",
      "Epoch 15/50\n",
      "50000/50000 [==============================] - 22s 432us/step - loss: 0.3639 - acc: 0.8702 - val_loss: 0.6822 - val_acc: 0.7735\n",
      "Epoch 16/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.3315 - acc: 0.8804 - val_loss: 0.6942 - val_acc: 0.7752\n",
      "Epoch 17/50\n",
      "50000/50000 [==============================] - 21s 411us/step - loss: 0.3087 - acc: 0.8892 - val_loss: 0.7046 - val_acc: 0.7745\n",
      "Epoch 18/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.2864 - acc: 0.8967 - val_loss: 0.7006 - val_acc: 0.7817\n",
      "Epoch 19/50\n",
      "50000/50000 [==============================] - 20s 406us/step - loss: 0.2654 - acc: 0.9058 - val_loss: 0.6965 - val_acc: 0.7799\n",
      "Epoch 20/50\n",
      "50000/50000 [==============================] - 21s 414us/step - loss: 0.2479 - acc: 0.9109 - val_loss: 0.7314 - val_acc: 0.7816\n",
      "Epoch 21/50\n",
      "50000/50000 [==============================] - 20s 408us/step - loss: 0.2311 - acc: 0.9173 - val_loss: 0.7626 - val_acc: 0.7781\n",
      "Epoch 22/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.2150 - acc: 0.9241 - val_loss: 0.7402 - val_acc: 0.7838\n",
      "Epoch 23/50\n",
      "50000/50000 [==============================] - 20s 406us/step - loss: 0.1932 - acc: 0.9307 - val_loss: 0.7281 - val_acc: 0.7830\n",
      "Epoch 24/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.1827 - acc: 0.9355 - val_loss: 0.7787 - val_acc: 0.7847\n",
      "Epoch 25/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.1736 - acc: 0.9372 - val_loss: 0.7833 - val_acc: 0.7799\n",
      "Epoch 26/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.1607 - acc: 0.9430 - val_loss: 0.8113 - val_acc: 0.7821\n",
      "Epoch 27/50\n",
      "50000/50000 [==============================] - 20s 406us/step - loss: 0.1553 - acc: 0.9457 - val_loss: 0.7793 - val_acc: 0.7883\n",
      "Epoch 28/50\n",
      "50000/50000 [==============================] - 20s 406us/step - loss: 0.1425 - acc: 0.9492 - val_loss: 0.7997 - val_acc: 0.7883\n",
      "Epoch 29/50\n",
      "50000/50000 [==============================] - 20s 406us/step - loss: 0.1337 - acc: 0.9528 - val_loss: 0.7958 - val_acc: 0.7915\n",
      "Epoch 30/50\n",
      "50000/50000 [==============================] - 21s 414us/step - loss: 0.1228 - acc: 0.9573 - val_loss: 0.8147 - val_acc: 0.7849\n",
      "Epoch 31/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.1152 - acc: 0.9599 - val_loss: 0.8674 - val_acc: 0.7800\n",
      "Epoch 32/50\n",
      "50000/50000 [==============================] - 21s 411us/step - loss: 0.1146 - acc: 0.9601 - val_loss: 0.8057 - val_acc: 0.7930\n",
      "Epoch 33/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.1044 - acc: 0.9643 - val_loss: 0.8430 - val_acc: 0.7849\n",
      "Epoch 34/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.1000 - acc: 0.9646 - val_loss: 0.8850 - val_acc: 0.7837\n",
      "Epoch 35/50\n",
      "50000/50000 [==============================] - 20s 406us/step - loss: 0.0948 - acc: 0.9676 - val_loss: 0.8730 - val_acc: 0.7895\n",
      "Epoch 36/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.0868 - acc: 0.9703 - val_loss: 0.8388 - val_acc: 0.7940\n",
      "Epoch 37/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.0860 - acc: 0.9704 - val_loss: 0.9262 - val_acc: 0.7864\n",
      "Epoch 38/50\n",
      "50000/50000 [==============================] - 20s 406us/step - loss: 0.0821 - acc: 0.9719 - val_loss: 0.9022 - val_acc: 0.7913\n",
      "Epoch 39/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.0813 - acc: 0.9715 - val_loss: 0.9007 - val_acc: 0.7900\n",
      "Epoch 40/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.0795 - acc: 0.9725 - val_loss: 0.8877 - val_acc: 0.7863\n",
      "Epoch 41/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.0771 - acc: 0.9735 - val_loss: 0.8988 - val_acc: 0.7851\n",
      "Epoch 42/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.0711 - acc: 0.9752 - val_loss: 0.9784 - val_acc: 0.7780\n",
      "Epoch 43/50\n",
      "50000/50000 [==============================] - 20s 406us/step - loss: 0.0739 - acc: 0.9736 - val_loss: 0.9369 - val_acc: 0.7901\n",
      "Epoch 44/50\n",
      "50000/50000 [==============================] - 20s 406us/step - loss: 0.0660 - acc: 0.9777 - val_loss: 0.9366 - val_acc: 0.7918\n",
      "Epoch 45/50\n",
      "50000/50000 [==============================] - 21s 411us/step - loss: 0.0633 - acc: 0.9780 - val_loss: 0.9273 - val_acc: 0.7887\n",
      "Epoch 46/50\n",
      "50000/50000 [==============================] - 21s 410us/step - loss: 0.0616 - acc: 0.9788 - val_loss: 0.9353 - val_acc: 0.7866\n",
      "Epoch 47/50\n",
      "50000/50000 [==============================] - 21s 410us/step - loss: 0.0593 - acc: 0.9797 - val_loss: 0.9268 - val_acc: 0.7925\n",
      "Epoch 48/50\n",
      "50000/50000 [==============================] - 20s 406us/step - loss: 0.0574 - acc: 0.9801 - val_loss: 0.9666 - val_acc: 0.7872\n",
      "Epoch 49/50\n",
      "50000/50000 [==============================] - 20s 406us/step - loss: 0.0586 - acc: 0.9790 - val_loss: 1.0017 - val_acc: 0.7825\n",
      "Epoch 50/50\n",
      "50000/50000 [==============================] - 20s 407us/step - loss: 0.0545 - acc: 0.9809 - val_loss: 0.9652 - val_acc: 0.7920\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fdfdcd85e80>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the model\n",
    "model.fit(x_train, y_train,\n",
    "              batch_size=batch_size,\n",
    "              epochs=epochs,\n",
    "              validation_data=(x_test, y_test),\n",
    "              shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result: The training accuracy is increasing because of BN but the difference between train accuracy and validation accuracy is low."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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