3.Cifar 10 notebook.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python [conda env:tensorflow_p36]",
      "language": "python",
      "name": "conda-env-tensorflow_p36-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.4"
    },
    "colab": {
      "name": "3.+Cifar_10_notebook.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dbkCjXrXxGs",
        "colab_type": "code",
        "colab": {},
        "outputId": "ec37dbef-a280-43d6-f279-615116c1e0d8"
      },
      "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"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
            "  from ._conv import register_converters as _register_converters\n",
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cpcUUyyRXxG1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Defining the parameters\n",
        "batch_size = 32\n",
        "num_classes = 10\n",
        "epochs = 50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRVZa7TGXxG8",
        "colab_type": "code",
        "colab": {},
        "outputId": "96bed3b5-3b8e-4256-acc1-4675dba97bc4"
      },
      "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')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (50000, 32, 32, 3)\n",
            "50000 train samples\n",
            "10000 test samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_Zu5yjMXxHE",
        "colab_type": "code",
        "colab": {},
        "outputId": "fb9001e8-a19c-446e-da4b-bd52352758c6"
      },
      "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()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x216 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMwO7kFOXxHM",
        "colab_type": "code",
        "colab": {}
      },
      "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9h2DYjj4XxHT",
        "colab_type": "code",
        "colab": {},
        "outputId": "8c08743c-1a85-49f4-a913-d380f64ca795"
      },
      "source": [
        "# Printing sample data\n",
        "print(y_train[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hXIuD2GXxHa",
        "colab_type": "code",
        "colab": {}
      },
      "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))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes))\n",
        "model.add(Activation('softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opNkDHAeXxHi",
        "colab_type": "code",
        "colab": {},
        "outputId": "3fd713a4-2c03-4296-f04d-67a0c23b190e"
      },
      "source": [
        "# summary of the model\n",
        "print(model.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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",
            "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_3 (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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMAuxbMlXxHp",
        "colab_type": "code",
        "colab": {}
      },
      "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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvq2G9VyXxHv",
        "colab_type": "code",
        "colab": {},
        "outputId": "5b11123d-620f-4449-df9f-2a77e6e1b5e8"
      },
      "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)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/50\n",
            "50000/50000 [==============================] - 22s 431us/step - loss: 1.7133 - acc: 0.3987 - val_loss: 1.2498 - val_acc: 0.5514\n",
            "Epoch 2/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 1.3162 - acc: 0.5301 - val_loss: 1.2677 - val_acc: 0.5573\n",
            "Epoch 3/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 1.1558 - acc: 0.5883 - val_loss: 1.1549 - val_acc: 0.5899\n",
            "Epoch 4/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 1.0529 - acc: 0.6254 - val_loss: 0.9183 - val_acc: 0.6755\n",
            "Epoch 5/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.9781 - acc: 0.6505 - val_loss: 0.8693 - val_acc: 0.6998\n",
            "Epoch 6/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.9252 - acc: 0.6722 - val_loss: 0.8247 - val_acc: 0.7087\n",
            "Epoch 7/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.8678 - acc: 0.6928 - val_loss: 0.8635 - val_acc: 0.6955\n",
            "Epoch 8/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.8329 - acc: 0.7041 - val_loss: 0.7816 - val_acc: 0.7281\n",
            "Epoch 9/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.7858 - acc: 0.7224 - val_loss: 0.7491 - val_acc: 0.7373\n",
            "Epoch 10/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.7608 - acc: 0.7323 - val_loss: 0.7187 - val_acc: 0.7511\n",
            "Epoch 11/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.7364 - acc: 0.7393 - val_loss: 0.7323 - val_acc: 0.7461\n",
            "Epoch 12/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.7095 - acc: 0.7478 - val_loss: 0.6601 - val_acc: 0.7689\n",
            "Epoch 13/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.6829 - acc: 0.7572 - val_loss: 0.6677 - val_acc: 0.7717\n",
            "Epoch 14/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.6625 - acc: 0.7638 - val_loss: 0.6338 - val_acc: 0.7806\n",
            "Epoch 15/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.6383 - acc: 0.7743 - val_loss: 0.6850 - val_acc: 0.7663\n",
            "Epoch 16/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.6265 - acc: 0.7759 - val_loss: 0.6574 - val_acc: 0.7745\n",
            "Epoch 17/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.6070 - acc: 0.7846 - val_loss: 0.6410 - val_acc: 0.7840\n",
            "Epoch 18/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.5868 - acc: 0.7917 - val_loss: 0.6129 - val_acc: 0.7870\n",
            "Epoch 19/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.5780 - acc: 0.7960 - val_loss: 0.6120 - val_acc: 0.7929\n",
            "Epoch 20/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.5538 - acc: 0.8026 - val_loss: 0.5781 - val_acc: 0.8026\n",
            "Epoch 21/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.5494 - acc: 0.8039 - val_loss: 0.5902 - val_acc: 0.7954\n",
            "Epoch 22/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.5358 - acc: 0.8102 - val_loss: 0.5968 - val_acc: 0.8014\n",
            "Epoch 23/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.5176 - acc: 0.8173 - val_loss: 0.5826 - val_acc: 0.8019\n",
            "Epoch 24/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.5120 - acc: 0.8184 - val_loss: 0.6153 - val_acc: 0.7939\n",
            "Epoch 25/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.5005 - acc: 0.8212 - val_loss: 0.6026 - val_acc: 0.7955\n",
            "Epoch 26/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.4875 - acc: 0.8265 - val_loss: 0.5796 - val_acc: 0.8047\n",
            "Epoch 27/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.4713 - acc: 0.8330 - val_loss: 0.6852 - val_acc: 0.7749\n",
            "Epoch 28/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.4651 - acc: 0.8332 - val_loss: 0.5924 - val_acc: 0.8007\n",
            "Epoch 29/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.4522 - acc: 0.8379 - val_loss: 0.5701 - val_acc: 0.8113\n",
            "Epoch 30/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.4480 - acc: 0.8402 - val_loss: 0.5709 - val_acc: 0.8135\n",
            "Epoch 31/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.4342 - acc: 0.8445 - val_loss: 0.5510 - val_acc: 0.8166\n",
            "Epoch 32/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.4218 - acc: 0.8515 - val_loss: 0.6262 - val_acc: 0.7935\n",
            "Epoch 33/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.4095 - acc: 0.8536 - val_loss: 0.5638 - val_acc: 0.8131\n",
            "Epoch 34/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.4056 - acc: 0.8566 - val_loss: 0.5881 - val_acc: 0.8095\n",
            "Epoch 35/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.3959 - acc: 0.8585 - val_loss: 0.5623 - val_acc: 0.8144\n",
            "Epoch 36/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.3875 - acc: 0.8611 - val_loss: 0.5397 - val_acc: 0.8235\n",
            "Epoch 37/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.3826 - acc: 0.8628 - val_loss: 0.5566 - val_acc: 0.8213\n",
            "Epoch 38/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.3714 - acc: 0.8668 - val_loss: 0.5507 - val_acc: 0.8248\n",
            "Epoch 39/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.3662 - acc: 0.8677 - val_loss: 0.5400 - val_acc: 0.8282\n",
            "Epoch 40/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.3554 - acc: 0.8731 - val_loss: 0.5703 - val_acc: 0.8202\n",
            "Epoch 41/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.3549 - acc: 0.8752 - val_loss: 0.5662 - val_acc: 0.8179\n",
            "Epoch 42/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.3432 - acc: 0.8778 - val_loss: 0.5484 - val_acc: 0.8251\n",
            "Epoch 43/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.3362 - acc: 0.8801 - val_loss: 0.5534 - val_acc: 0.8222\n",
            "Epoch 44/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.3305 - acc: 0.8822 - val_loss: 0.5651 - val_acc: 0.8201\n",
            "Epoch 45/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.3245 - acc: 0.8847 - val_loss: 0.5557 - val_acc: 0.8246\n",
            "Epoch 46/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.3168 - acc: 0.8864 - val_loss: 0.5389 - val_acc: 0.8296\n",
            "Epoch 47/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.3148 - acc: 0.8864 - val_loss: 0.6068 - val_acc: 0.8151\n",
            "Epoch 48/50\n",
            "50000/50000 [==============================] - 20s 399us/step - loss: 0.3037 - acc: 0.8911 - val_loss: 0.5786 - val_acc: 0.8240\n",
            "Epoch 49/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.2963 - acc: 0.8943 - val_loss: 0.6158 - val_acc: 0.8168\n",
            "Epoch 50/50\n",
            "50000/50000 [==============================] - 20s 400us/step - loss: 0.2923 - acc: 0.8961 - val_loss: 0.5548 - val_acc: 0.8285\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7efe03489898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    }
  ]
}