5.+Cifar10_l2_dropout_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "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",
    "from keras.regularizers import l2\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the parameters\n",
    "batch_size = 32\n",
    "num_classes = 10\n",
    "epochs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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": 15,
   "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": 16,
   "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": 17,
   "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": 18,
   "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",
    "model.add(Dropout(0.25))\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",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512,kernel_regularizer=l2(0.01)))\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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_5 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 32, 32, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 30, 30, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 30, 30, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 30, 30, 32)        128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 15, 15, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 15, 15, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 15, 15, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 13, 13, 64)        36928     \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 13, 13, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 2304)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 512)               1180160   \n",
      "_________________________________________________________________\n",
      "activation_11 (Activation)   (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                5130      \n",
      "_________________________________________________________________\n",
      "activation_12 (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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "50000/50000 [==============================] - 37s 747us/step - loss: 7.9267 - acc: 0.4100 - val_loss: 5.7962 - val_acc: 0.5341\n",
      "Epoch 2/50\n",
      "50000/50000 [==============================] - 25s 491us/step - loss: 4.6546 - acc: 0.5375 - val_loss: 3.6013 - val_acc: 0.5868\n",
      "Epoch 3/50\n",
      "50000/50000 [==============================] - 24s 490us/step - loss: 2.9441 - acc: 0.6115 - val_loss: 2.3049 - val_acc: 0.6597\n",
      "Epoch 4/50\n",
      "50000/50000 [==============================] - 24s 489us/step - loss: 2.0057 - acc: 0.6639 - val_loss: 1.6531 - val_acc: 0.6962\n",
      "Epoch 5/50\n",
      "50000/50000 [==============================] - 25s 498us/step - loss: 1.4972 - acc: 0.6959 - val_loss: 1.3457 - val_acc: 0.6943\n",
      "Epoch 6/50\n",
      "50000/50000 [==============================] - 24s 489us/step - loss: 1.2171 - acc: 0.7171 - val_loss: 1.1035 - val_acc: 0.7349\n",
      "Epoch 7/50\n",
      "50000/50000 [==============================] - 25s 490us/step - loss: 1.0586 - acc: 0.7363 - val_loss: 0.9905 - val_acc: 0.7462\n",
      "Epoch 8/50\n",
      "50000/50000 [==============================] - 24s 489us/step - loss: 0.9678 - acc: 0.7505 - val_loss: 0.9896 - val_acc: 0.7379\n",
      "Epoch 9/50\n",
      "50000/50000 [==============================] - 24s 488us/step - loss: 0.9155 - acc: 0.7606 - val_loss: 0.8801 - val_acc: 0.7698\n",
      "Epoch 10/50\n",
      "50000/50000 [==============================] - 24s 480us/step - loss: 0.8823 - acc: 0.7692 - val_loss: 0.9062 - val_acc: 0.7601\n",
      "Epoch 11/50\n",
      "50000/50000 [==============================] - 23s 468us/step - loss: 0.8605 - acc: 0.7770 - val_loss: 0.9483 - val_acc: 0.7489\n",
      "Epoch 12/50\n",
      "50000/50000 [==============================] - 23s 468us/step - loss: 0.8387 - acc: 0.7861 - val_loss: 0.8884 - val_acc: 0.7729\n",
      "Epoch 13/50\n",
      "50000/50000 [==============================] - 23s 464us/step - loss: 0.8282 - acc: 0.7944 - val_loss: 0.8451 - val_acc: 0.7908\n",
      "Epoch 14/50\n",
      "50000/50000 [==============================] - 23s 462us/step - loss: 0.8259 - acc: 0.7964 - val_loss: 0.8515 - val_acc: 0.7945\n",
      "Epoch 15/50\n",
      "50000/50000 [==============================] - 23s 468us/step - loss: 0.8180 - acc: 0.8043 - val_loss: 0.9157 - val_acc: 0.7739\n",
      "Epoch 16/50\n",
      "50000/50000 [==============================] - 23s 461us/step - loss: 0.8162 - acc: 0.8069 - val_loss: 0.8846 - val_acc: 0.7913\n",
      "Epoch 17/50\n",
      "50000/50000 [==============================] - 23s 466us/step - loss: 0.8148 - acc: 0.8101 - val_loss: 0.9240 - val_acc: 0.7724\n",
      "Epoch 18/50\n",
      "50000/50000 [==============================] - 24s 474us/step - loss: 0.8086 - acc: 0.8169 - val_loss: 0.9043 - val_acc: 0.7893\n",
      "Epoch 19/50\n",
      "50000/50000 [==============================] - 23s 469us/step - loss: 0.8034 - acc: 0.8224 - val_loss: 0.8521 - val_acc: 0.8086\n",
      "Epoch 20/50\n",
      "50000/50000 [==============================] - 23s 469us/step - loss: 0.8083 - acc: 0.8221 - val_loss: 0.8789 - val_acc: 0.8054\n",
      "Epoch 21/50\n",
      "50000/50000 [==============================] - 23s 469us/step - loss: 0.8004 - acc: 0.8275 - val_loss: 0.9076 - val_acc: 0.7977\n",
      "Epoch 22/50\n",
      "50000/50000 [==============================] - 23s 469us/step - loss: 0.8013 - acc: 0.8282 - val_loss: 0.9063 - val_acc: 0.8011\n",
      "Epoch 23/50\n",
      "50000/50000 [==============================] - 23s 468us/step - loss: 0.8042 - acc: 0.8309 - val_loss: 0.8820 - val_acc: 0.8095\n",
      "Epoch 24/50\n",
      "50000/50000 [==============================] - 23s 468us/step - loss: 0.7979 - acc: 0.8340 - val_loss: 0.9481 - val_acc: 0.7964\n",
      "Epoch 25/50\n",
      "50000/50000 [==============================] - 23s 469us/step - loss: 0.8033 - acc: 0.8351 - val_loss: 0.9275 - val_acc: 0.8003\n",
      "Epoch 26/50\n",
      "50000/50000 [==============================] - 23s 468us/step - loss: 0.8047 - acc: 0.8367 - val_loss: 0.9591 - val_acc: 0.7956\n",
      "Epoch 27/50\n",
      "50000/50000 [==============================] - 23s 468us/step - loss: 0.8013 - acc: 0.8397 - val_loss: 0.9032 - val_acc: 0.8090\n",
      "Epoch 28/50\n",
      "50000/50000 [==============================] - 24s 471us/step - loss: 0.8022 - acc: 0.8409 - val_loss: 0.9242 - val_acc: 0.8038\n",
      "Epoch 29/50\n",
      "50000/50000 [==============================] - 23s 468us/step - loss: 0.7967 - acc: 0.8452 - val_loss: 0.8969 - val_acc: 0.8177\n",
      "Epoch 30/50\n",
      "50000/50000 [==============================] - 23s 469us/step - loss: 0.8003 - acc: 0.8414 - val_loss: 0.9660 - val_acc: 0.7916\n",
      "Epoch 31/50\n",
      "50000/50000 [==============================] - 24s 473us/step - loss: 0.7948 - acc: 0.8478 - val_loss: 1.0084 - val_acc: 0.7839\n",
      "Epoch 32/50\n",
      "50000/50000 [==============================] - 23s 469us/step - loss: 0.7968 - acc: 0.8490 - val_loss: 0.9184 - val_acc: 0.8140\n",
      "Epoch 33/50\n",
      "50000/50000 [==============================] - 23s 469us/step - loss: 0.7970 - acc: 0.8497 - val_loss: 0.9350 - val_acc: 0.8118\n",
      "Epoch 34/50\n",
      "50000/50000 [==============================] - 23s 467us/step - loss: 0.7919 - acc: 0.8511 - val_loss: 0.9179 - val_acc: 0.8138\n",
      "Epoch 35/50\n",
      "50000/50000 [==============================] - 23s 467us/step - loss: 0.7935 - acc: 0.8517 - val_loss: 0.9037 - val_acc: 0.8184\n",
      "Epoch 36/50\n",
      "50000/50000 [==============================] - 23s 468us/step - loss: 0.7910 - acc: 0.8535 - val_loss: 0.9323 - val_acc: 0.8132\n",
      "Epoch 37/50\n",
      "50000/50000 [==============================] - 23s 467us/step - loss: 0.7913 - acc: 0.8547 - val_loss: 0.9212 - val_acc: 0.8175\n",
      "Epoch 38/50\n",
      "50000/50000 [==============================] - 23s 468us/step - loss: 0.7939 - acc: 0.8542 - val_loss: 0.9119 - val_acc: 0.8230\n",
      "Epoch 39/50\n",
      "50000/50000 [==============================] - 23s 464us/step - loss: 0.7899 - acc: 0.8567 - val_loss: 0.9369 - val_acc: 0.8157\n",
      "Epoch 40/50\n",
      "50000/50000 [==============================] - 23s 464us/step - loss: 0.7836 - acc: 0.8589 - val_loss: 0.9391 - val_acc: 0.8175\n",
      "Epoch 41/50\n",
      "50000/50000 [==============================] - 23s 468us/step - loss: 0.7860 - acc: 0.8578 - val_loss: 0.9118 - val_acc: 0.8232\n",
      "Epoch 42/50\n",
      "50000/50000 [==============================] - 23s 459us/step - loss: 0.7839 - acc: 0.8605 - val_loss: 0.9773 - val_acc: 0.8066\n",
      "Epoch 43/50\n",
      "50000/50000 [==============================] - 23s 470us/step - loss: 0.7845 - acc: 0.8612 - val_loss: 0.9185 - val_acc: 0.8246\n",
      "Epoch 44/50\n",
      "50000/50000 [==============================] - 24s 471us/step - loss: 0.7797 - acc: 0.8599 - val_loss: 0.9224 - val_acc: 0.8251\n",
      "Epoch 45/50\n",
      "50000/50000 [==============================] - 24s 474us/step - loss: 0.7774 - acc: 0.8622 - val_loss: 0.9093 - val_acc: 0.8277\n",
      "Epoch 46/50\n",
      "50000/50000 [==============================] - 24s 470us/step - loss: 0.7816 - acc: 0.8624 - val_loss: 0.9287 - val_acc: 0.8198\n",
      "Epoch 47/50\n",
      "50000/50000 [==============================] - 23s 470us/step - loss: 0.7798 - acc: 0.8654 - val_loss: 0.9193 - val_acc: 0.8248\n",
      "Epoch 48/50\n",
      "50000/50000 [==============================] - 23s 469us/step - loss: 0.7757 - acc: 0.8656 - val_loss: 0.9210 - val_acc: 0.8300\n",
      "Epoch 49/50\n",
      "50000/50000 [==============================] - 23s 469us/step - loss: 0.7784 - acc: 0.8647 - val_loss: 0.9349 - val_acc: 0.8202\n",
      "Epoch 50/50\n",
      "50000/50000 [==============================] - 23s 469us/step - loss: 0.7783 - acc: 0.8634 - val_loss: 0.9395 - val_acc: 0.8178\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "history = 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": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}