6.+Cifar10_morelayer_notebook.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",
    "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": 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": [
      "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",
    "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(Conv2D(128, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(128, (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": 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",
      "dropout_1 (Dropout)          (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",
      "dropout_2 (Dropout)          (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 6, 6, 128)         73856     \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 6, 6, 128)         512       \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 4, 4, 128)         147584    \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 4, 4, 128)         512       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 2, 2, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 2, 2, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                5130      \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 556,586\n",
      "Trainable params: 555,690\n",
      "Non-trainable params: 896\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 [==============================] - 29s 577us/step - loss: 5.5388 - acc: 0.3792 - val_loss: 4.1704 - val_acc: 0.4807\n",
      "Epoch 2/50\n",
      "50000/50000 [==============================] - 27s 538us/step - loss: 3.3233 - acc: 0.5329 - val_loss: 3.7416 - val_acc: 0.3482\n",
      "Epoch 3/50\n",
      "50000/50000 [==============================] - 26s 525us/step - loss: 2.2278 - acc: 0.6013 - val_loss: 1.8622 - val_acc: 0.6174\n",
      "Epoch 4/50\n",
      "50000/50000 [==============================] - 27s 530us/step - loss: 1.6283 - acc: 0.6380 - val_loss: 1.4909 - val_acc: 0.6325\n",
      "Epoch 5/50\n",
      "50000/50000 [==============================] - 27s 533us/step - loss: 1.2762 - acc: 0.6733 - val_loss: 1.1086 - val_acc: 0.7016\n",
      "Epoch 6/50\n",
      "50000/50000 [==============================] - 27s 533us/step - loss: 1.0830 - acc: 0.6943 - val_loss: 1.0579 - val_acc: 0.6886\n",
      "Epoch 7/50\n",
      "50000/50000 [==============================] - 26s 518us/step - loss: 0.9593 - acc: 0.7120 - val_loss: 0.9005 - val_acc: 0.7230\n",
      "Epoch 8/50\n",
      "50000/50000 [==============================] - 26s 528us/step - loss: 0.8827 - acc: 0.7261 - val_loss: 0.8001 - val_acc: 0.7539\n",
      "Epoch 9/50\n",
      "50000/50000 [==============================] - 26s 527us/step - loss: 0.8309 - acc: 0.7396 - val_loss: 0.9398 - val_acc: 0.7009\n",
      "Epoch 10/50\n",
      "50000/50000 [==============================] - 26s 527us/step - loss: 0.7876 - acc: 0.7529 - val_loss: 0.7518 - val_acc: 0.7661\n",
      "Epoch 11/50\n",
      "50000/50000 [==============================] - 26s 522us/step - loss: 0.7570 - acc: 0.7608 - val_loss: 0.7283 - val_acc: 0.7722\n",
      "Epoch 12/50\n",
      "50000/50000 [==============================] - 27s 533us/step - loss: 0.7312 - acc: 0.7703 - val_loss: 0.8270 - val_acc: 0.7417\n",
      "Epoch 13/50\n",
      "50000/50000 [==============================] - 27s 531us/step - loss: 0.7156 - acc: 0.7734 - val_loss: 0.7468 - val_acc: 0.7655\n",
      "Epoch 14/50\n",
      "50000/50000 [==============================] - 27s 541us/step - loss: 0.6881 - acc: 0.7838 - val_loss: 0.7958 - val_acc: 0.7528\n",
      "Epoch 15/50\n",
      "50000/50000 [==============================] - 26s 530us/step - loss: 0.6697 - acc: 0.7913 - val_loss: 0.6470 - val_acc: 0.8018\n",
      "Epoch 16/50\n",
      "50000/50000 [==============================] - 26s 529us/step - loss: 0.6530 - acc: 0.7960 - val_loss: 0.6797 - val_acc: 0.7922\n",
      "Epoch 17/50\n",
      "50000/50000 [==============================] - 27s 531us/step - loss: 0.6367 - acc: 0.8042 - val_loss: 0.6672 - val_acc: 0.7934\n",
      "Epoch 18/50\n",
      "50000/50000 [==============================] - 27s 532us/step - loss: 0.6221 - acc: 0.8086 - val_loss: 0.6859 - val_acc: 0.7920\n",
      "Epoch 19/50\n",
      "50000/50000 [==============================] - 27s 534us/step - loss: 0.6154 - acc: 0.8116 - val_loss: 0.6823 - val_acc: 0.7935\n",
      "Epoch 20/50\n",
      "50000/50000 [==============================] - 27s 532us/step - loss: 0.5925 - acc: 0.8209 - val_loss: 0.6927 - val_acc: 0.7900\n",
      "Epoch 21/50\n",
      "50000/50000 [==============================] - 27s 533us/step - loss: 0.5839 - acc: 0.8230 - val_loss: 0.6146 - val_acc: 0.8107\n",
      "Epoch 22/50\n",
      "50000/50000 [==============================] - 27s 538us/step - loss: 0.5703 - acc: 0.8263 - val_loss: 0.6168 - val_acc: 0.8167\n",
      "Epoch 23/50\n",
      "50000/50000 [==============================] - 27s 534us/step - loss: 0.5641 - acc: 0.8289 - val_loss: 0.6436 - val_acc: 0.8066\n",
      "Epoch 24/50\n",
      "50000/50000 [==============================] - 27s 534us/step - loss: 0.5491 - acc: 0.8358 - val_loss: 0.6060 - val_acc: 0.8213\n",
      "Epoch 25/50\n",
      "50000/50000 [==============================] - 27s 534us/step - loss: 0.5389 - acc: 0.8395 - val_loss: 0.6158 - val_acc: 0.8174\n",
      "Epoch 26/50\n",
      "50000/50000 [==============================] - 27s 541us/step - loss: 0.5302 - acc: 0.8412 - val_loss: 0.6134 - val_acc: 0.8178\n",
      "Epoch 27/50\n",
      "50000/50000 [==============================] - 27s 535us/step - loss: 0.5289 - acc: 0.8418 - val_loss: 0.5783 - val_acc: 0.8295\n",
      "Epoch 28/50\n",
      "50000/50000 [==============================] - 27s 534us/step - loss: 0.5172 - acc: 0.8469 - val_loss: 0.6054 - val_acc: 0.8235\n",
      "Epoch 29/50\n",
      "50000/50000 [==============================] - 27s 533us/step - loss: 0.5056 - acc: 0.8493 - val_loss: 0.6519 - val_acc: 0.8109\n",
      "Epoch 30/50\n",
      "50000/50000 [==============================] - 27s 531us/step - loss: 0.5002 - acc: 0.8530 - val_loss: 0.5953 - val_acc: 0.8261\n",
      "Epoch 31/50\n",
      "50000/50000 [==============================] - 26s 524us/step - loss: 0.4939 - acc: 0.8564 - val_loss: 0.5893 - val_acc: 0.8311\n",
      "Epoch 32/50\n",
      "50000/50000 [==============================] - 27s 532us/step - loss: 0.4816 - acc: 0.8594 - val_loss: 0.6398 - val_acc: 0.8152\n",
      "Epoch 33/50\n",
      "50000/50000 [==============================] - 26s 525us/step - loss: 0.4764 - acc: 0.8639 - val_loss: 0.5685 - val_acc: 0.8380\n",
      "Epoch 34/50\n",
      "50000/50000 [==============================] - 27s 532us/step - loss: 0.4741 - acc: 0.8618 - val_loss: 0.6196 - val_acc: 0.8257\n",
      "Epoch 35/50\n",
      "50000/50000 [==============================] - 27s 532us/step - loss: 0.4610 - acc: 0.8670 - val_loss: 0.5727 - val_acc: 0.8383\n",
      "Epoch 36/50\n",
      "50000/50000 [==============================] - 27s 532us/step - loss: 0.4557 - acc: 0.8687 - val_loss: 0.5649 - val_acc: 0.8413\n",
      "Epoch 37/50\n",
      "50000/50000 [==============================] - 27s 544us/step - loss: 0.4536 - acc: 0.8709 - val_loss: 0.6375 - val_acc: 0.8226\n",
      "Epoch 38/50\n",
      "50000/50000 [==============================] - 27s 536us/step - loss: 0.4474 - acc: 0.8728 - val_loss: 0.5765 - val_acc: 0.8399\n",
      "Epoch 39/50\n",
      "50000/50000 [==============================] - 27s 537us/step - loss: 0.4397 - acc: 0.8745 - val_loss: 0.6074 - val_acc: 0.8325\n",
      "Epoch 40/50\n",
      "50000/50000 [==============================] - 27s 537us/step - loss: 0.4339 - acc: 0.8783 - val_loss: 0.6246 - val_acc: 0.8232\n",
      "Epoch 41/50\n",
      "50000/50000 [==============================] - 27s 537us/step - loss: 0.4309 - acc: 0.8782 - val_loss: 0.6021 - val_acc: 0.8326\n",
      "Epoch 42/50\n",
      "50000/50000 [==============================] - 27s 535us/step - loss: 0.4240 - acc: 0.8820 - val_loss: 0.5949 - val_acc: 0.8372\n",
      "Epoch 43/50\n",
      "50000/50000 [==============================] - 27s 535us/step - loss: 0.4190 - acc: 0.8823 - val_loss: 0.5711 - val_acc: 0.8444\n",
      "Epoch 44/50\n",
      "50000/50000 [==============================] - 27s 537us/step - loss: 0.4124 - acc: 0.8856 - val_loss: 0.5619 - val_acc: 0.8480\n",
      "Epoch 45/50\n",
      "50000/50000 [==============================] - 27s 538us/step - loss: 0.4130 - acc: 0.8844 - val_loss: 0.5603 - val_acc: 0.8458\n",
      "Epoch 46/50\n",
      "50000/50000 [==============================] - 27s 535us/step - loss: 0.4040 - acc: 0.8893 - val_loss: 0.5725 - val_acc: 0.8449\n",
      "Epoch 47/50\n",
      "50000/50000 [==============================] - 27s 535us/step - loss: 0.3986 - acc: 0.8901 - val_loss: 0.5857 - val_acc: 0.8417\n",
      "Epoch 48/50\n",
      "50000/50000 [==============================] - 27s 535us/step - loss: 0.4003 - acc: 0.8896 - val_loss: 0.5626 - val_acc: 0.8469\n",
      "Epoch 49/50\n",
      "50000/50000 [==============================] - 27s 540us/step - loss: 0.3907 - acc: 0.8929 - val_loss: 0.6225 - val_acc: 0.8322\n",
      "Epoch 50/50\n",
      "50000/50000 [==============================] - 27s 534us/step - loss: 0.3869 - acc: 0.8953 - val_loss: 0.5858 - val_acc: 0.8416\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": 12,
   "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
}