DenseNet_CIFAR10_Final.ipynb

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    "colab": {
      "name": "DenseNet_CIFAR10_Final.ipynb",
      "version": "0.3.2",
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      "collapsed_sections": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/santhavathi/Deep-Learning-MLBLR/blob/master/DenseNet_CIFAR10_Final.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "3uItuewwvMhj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QVkWFcphtdfk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6da31838-e7d2-4107-b95b-fe3fb0202d92"
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.datasets import cifar10\n",
        "from keras.models import Model, Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Input, AveragePooling2D, merge, Activation\n",
        "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
        "from keras.layers import Concatenate\n",
        "from keras.optimizers import Adam\n",
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "BcE2IiPOu1gg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# this part will prevent tensorflow to allocate all the avaliable GPU Memory\n",
        "# backend\n",
        "import tensorflow as tf\n",
        "from keras import backend as k\n",
        "\n",
        "# Don't pre-allocate memory; allocate as-needed\n",
        "config = tf.ConfigProto()\n",
        "config.gpu_options.allow_growth = True\n",
        "\n",
        "# Create a session with the above options specified.\n",
        "k.tensorflow_backend.set_session(tf.Session(config=config))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pLUPad8Qu9uF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Hyperparameters\n",
        "batch_size = 128\n",
        "num_classes = 10\n",
        "epochs = 50\n",
        "l = 40\n",
        "num_filter = 12\n",
        "compression = 0.5\n",
        "dropout_rate = 0.2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wn1AAb1Gu-st",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Load CIFAR10 Data\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "img_height, img_width, channel = x_train.shape[1],x_train.shape[2],x_train.shape[3]\n",
        "\n",
        "# convert to one hot encoing \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": []
    },
    {
      "metadata": {
        "id": "EA1zwg00reNW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#rescale=1./255\n",
        "\n",
        "#train_datagen = ImageDataGenerator(\n",
        "#        featurewise_center=False,  # set input mean to 0 over the dataset\n",
        "#        samplewise_center=False,  # set each sample mean to 0\n",
        "#        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
        "#        samplewise_std_normalization=False,  # divide each input by its std\n",
        "#        zca_whitening=False,  # apply ZCA whitening\n",
        "#        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)\n",
        "#        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
        "#        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
        "#        horizontal_flip=True,  # randomly flip images\n",
        "#        vertical_flip=False,\n",
        "#        rescale=rescale)  # randomly flip images\n",
        "\n",
        "#datagen = ImageDataGenerator(rescale=rescale)\n",
        "#datagen.fit(x_train)\n",
        "#datagen.fit(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JPeipZmmuRAT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "x_train /= 255\n",
        "x_test /= 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WPM9dMxEvcQH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Dense Block\n",
        "def add_denseblock(input, num_filter = 12, dropout_rate = 0.2):\n",
        "    global compression\n",
        "    temp = input\n",
        "    for _ in range(l):\n",
        "        BatchNorm = BatchNormalization()(temp)\n",
        "        relu = Activation('relu')(BatchNorm)\n",
        "        Conv2D_3_3 = Conv2D(int(num_filter*compression), (3,3), use_bias=False ,padding='same')(relu)\n",
        "        if dropout_rate>0:\n",
        "          Conv2D_3_3 = Dropout(dropout_rate)(Conv2D_3_3)\n",
        "        concat = Concatenate(axis=-1)([temp,Conv2D_3_3])\n",
        "        \n",
        "        temp = concat\n",
        "        \n",
        "    return temp"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xYGrwT0v5pjY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def add_transition(input, num_filter = 12, dropout_rate = 0.2):\n",
        "    global compression\n",
        "    BatchNorm = BatchNormalization()(input)\n",
        "    relu = Activation('relu')(BatchNorm)\n",
        "    Conv2D_BottleNeck = Conv2D(int(num_filter*compression), (1,1), use_bias=False ,padding='same')(relu)\n",
        "    if dropout_rate>0:\n",
        "      Conv2D_BottleNeck = Dropout(dropout_rate)(Conv2D_BottleNeck)\n",
        "    avg = AveragePooling2D(pool_size=(2,2))(Conv2D_BottleNeck)\n",
        "    \n",
        "    return avg"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eo5bg61x5sTK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def output_layer(input):\n",
        "    global compression\n",
        "    BatchNorm = BatchNormalization()(input)\n",
        "    relu = Activation('relu')(BatchNorm)\n",
        "    AvgPooling = AveragePooling2D(pool_size=(2,2))(relu)\n",
        "    flat = Flatten()(AvgPooling)\n",
        "    output = Dense(num_classes, activation='softmax')(flat)\n",
        "    \n",
        "    return output"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SYVwwOgT_-uf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 11356
        },
        "outputId": "e0eba221-93cd-4bf1-edd3-81cacad8d101"
      },
      "cell_type": "code",
      "source": [
        "num_filter = 64\n",
        "dropout_rate = 0.4\n",
        "l = 14\n",
        "\n",
        "input = Input(shape=(img_height, img_width, channel,))\n",
        "First_Conv2D = Conv2D(num_filter, (3,3), use_bias=False ,padding='same')(input)\n",
        "\n",
        "First_Block = add_denseblock(First_Conv2D, num_filter, dropout_rate)\n",
        "First_Transition = add_transition(First_Block, num_filter, dropout_rate)\n",
        "\n",
        "Second_Block = add_denseblock(First_Transition, num_filter, dropout_rate)\n",
        "Second_Transition = add_transition(Second_Block, num_filter, dropout_rate)\n",
        "\n",
        "Third_Block = add_denseblock(Second_Transition, num_filter, dropout_rate)\n",
        "Third_Transition = add_transition(Third_Block, num_filter, dropout_rate)\n",
        "\n",
        "num_filter = 32\n",
        "dropout_rate = 0.2\n",
        "l = 14\n",
        "\n",
        "Last_Block = add_denseblock(Third_Transition,  num_filter, dropout_rate)\n",
        "output = output_layer(Last_Block)\n",
        "\n",
        "model = Model(inputs=[input], outputs=[output])\n",
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 32, 32, 3)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 32, 32, 64)   1728        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 32, 32, 64)   256         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 32, 32, 64)   0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 32, 32, 32)   18432       activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 32, 32, 32)   0           conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 32, 32, 96)   0           conv2d_1[0][0]                   \n",
            "                                                                 dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 32, 32, 96)   384         concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 32, 32, 96)   0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 32, 32, 32)   27648       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 32, 32, 32)   0           conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 32, 32, 128)  0           concatenate_1[0][0]              \n",
            "                                                                 dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 32, 32, 128)  512         concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 32, 32, 128)  0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 32, 32, 32)   36864       activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 32, 32, 32)   0           conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_3 (Concatenate)     (None, 32, 32, 160)  0           concatenate_2[0][0]              \n",
            "                                                                 dropout_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 32, 32, 160)  640         concatenate_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 32, 32, 160)  0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 32, 32, 32)   46080       activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_4 (Dropout)             (None, 32, 32, 32)   0           conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_4 (Concatenate)     (None, 32, 32, 192)  0           concatenate_3[0][0]              \n",
            "                                                                 dropout_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 32, 32, 192)  768         concatenate_4[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 32, 32, 192)  0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 32, 32, 32)   55296       activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_5 (Dropout)             (None, 32, 32, 32)   0           conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_5 (Concatenate)     (None, 32, 32, 224)  0           concatenate_4[0][0]              \n",
            "                                                                 dropout_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 32, 32, 224)  896         concatenate_5[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 32, 32, 224)  0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 32, 32, 32)   64512       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_6 (Dropout)             (None, 32, 32, 32)   0           conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_6 (Concatenate)     (None, 32, 32, 256)  0           concatenate_5[0][0]              \n",
            "                                                                 dropout_6[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 32, 32, 256)  1024        concatenate_6[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 32, 32, 256)  0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 32, 32, 32)   73728       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_7 (Dropout)             (None, 32, 32, 32)   0           conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_7 (Concatenate)     (None, 32, 32, 288)  0           concatenate_6[0][0]              \n",
            "                                                                 dropout_7[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 32, 32, 288)  1152        concatenate_7[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 32, 32, 288)  0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 32, 32, 32)   82944       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_8 (Dropout)             (None, 32, 32, 32)   0           conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_8 (Concatenate)     (None, 32, 32, 320)  0           concatenate_7[0][0]              \n",
            "                                                                 dropout_8[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 32, 32, 320)  1280        concatenate_8[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 32, 32, 320)  0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 32, 32, 32)   92160       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_9 (Dropout)             (None, 32, 32, 32)   0           conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_9 (Concatenate)     (None, 32, 32, 352)  0           concatenate_8[0][0]              \n",
            "                                                                 dropout_9[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 32, 32, 352)  1408        concatenate_9[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 32, 32, 352)  0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 32, 32, 32)   101376      activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_10 (Dropout)            (None, 32, 32, 32)   0           conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_10 (Concatenate)    (None, 32, 32, 384)  0           concatenate_9[0][0]              \n",
            "                                                                 dropout_10[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 32, 32, 384)  1536        concatenate_10[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 32, 32, 384)  0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 32, 32, 32)   110592      activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_11 (Dropout)            (None, 32, 32, 32)   0           conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_11 (Concatenate)    (None, 32, 32, 416)  0           concatenate_10[0][0]             \n",
            "                                                                 dropout_11[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 32, 32, 416)  1664        concatenate_11[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 32, 32, 416)  0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 32, 32, 32)   119808      activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_12 (Dropout)            (None, 32, 32, 32)   0           conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_12 (Concatenate)    (None, 32, 32, 448)  0           concatenate_11[0][0]             \n",
            "                                                                 dropout_12[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 32, 32, 448)  1792        concatenate_12[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 32, 32, 448)  0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 32, 32, 32)   129024      activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_13 (Dropout)            (None, 32, 32, 32)   0           conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_13 (Concatenate)    (None, 32, 32, 480)  0           concatenate_12[0][0]             \n",
            "                                                                 dropout_13[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 32, 32, 480)  1920        concatenate_13[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 32, 32, 480)  0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 32, 32, 32)   138240      activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_14 (Dropout)            (None, 32, 32, 32)   0           conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_14 (Concatenate)    (None, 32, 32, 512)  0           concatenate_13[0][0]             \n",
            "                                                                 dropout_14[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 32, 32, 512)  2048        concatenate_14[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 32, 32, 512)  0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 32, 32, 32)   16384       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_15 (Dropout)            (None, 32, 32, 32)   0           conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, 16, 16, 32)   0           dropout_15[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 16, 16, 32)   128         average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 16, 16, 32)   0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 16, 16, 32)   9216        activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_16 (Dropout)            (None, 16, 16, 32)   0           conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_15 (Concatenate)    (None, 16, 16, 64)   0           average_pooling2d_1[0][0]        \n",
            "                                                                 dropout_16[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 16, 16, 64)   256         concatenate_15[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 16, 16, 64)   0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 16, 16, 32)   18432       activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_17 (Dropout)            (None, 16, 16, 32)   0           conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_16 (Concatenate)    (None, 16, 16, 96)   0           concatenate_15[0][0]             \n",
            "                                                                 dropout_17[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 16, 16, 96)   384         concatenate_16[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 16, 16, 96)   0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 16, 16, 32)   27648       activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_18 (Dropout)            (None, 16, 16, 32)   0           conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_17 (Concatenate)    (None, 16, 16, 128)  0           concatenate_16[0][0]             \n",
            "                                                                 dropout_18[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 16, 16, 128)  512         concatenate_17[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 16, 16, 128)  0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 16, 16, 32)   36864       activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_19 (Dropout)            (None, 16, 16, 32)   0           conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_18 (Concatenate)    (None, 16, 16, 160)  0           concatenate_17[0][0]             \n",
            "                                                                 dropout_19[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, 16, 16, 160)  640         concatenate_18[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 16, 16, 160)  0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 16, 16, 32)   46080       activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_20 (Dropout)            (None, 16, 16, 32)   0           conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_19 (Concatenate)    (None, 16, 16, 192)  0           concatenate_18[0][0]             \n",
            "                                                                 dropout_20[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, 16, 16, 192)  768         concatenate_19[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 16, 16, 192)  0           batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 16, 16, 32)   55296       activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_21 (Dropout)            (None, 16, 16, 32)   0           conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_20 (Concatenate)    (None, 16, 16, 224)  0           concatenate_19[0][0]             \n",
            "                                                                 dropout_21[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, 16, 16, 224)  896         concatenate_20[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 16, 16, 224)  0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 16, 16, 32)   64512       activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_22 (Dropout)            (None, 16, 16, 32)   0           conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_21 (Concatenate)    (None, 16, 16, 256)  0           concatenate_20[0][0]             \n",
            "                                                                 dropout_22[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 16, 16, 256)  1024        concatenate_21[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 16, 16, 256)  0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 16, 16, 32)   73728       activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_23 (Dropout)            (None, 16, 16, 32)   0           conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_22 (Concatenate)    (None, 16, 16, 288)  0           concatenate_21[0][0]             \n",
            "                                                                 dropout_23[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 16, 16, 288)  1152        concatenate_22[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 16, 16, 288)  0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 16, 16, 32)   82944       activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_24 (Dropout)            (None, 16, 16, 32)   0           conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_23 (Concatenate)    (None, 16, 16, 320)  0           concatenate_22[0][0]             \n",
            "                                                                 dropout_24[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 16, 16, 320)  1280        concatenate_23[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 16, 16, 320)  0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 16, 16, 32)   92160       activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_25 (Dropout)            (None, 16, 16, 32)   0           conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_24 (Concatenate)    (None, 16, 16, 352)  0           concatenate_23[0][0]             \n",
            "                                                                 dropout_25[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, 16, 16, 352)  1408        concatenate_24[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 16, 16, 352)  0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, 16, 16, 32)   101376      activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_26 (Dropout)            (None, 16, 16, 32)   0           conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_25 (Concatenate)    (None, 16, 16, 384)  0           concatenate_24[0][0]             \n",
            "                                                                 dropout_26[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 16, 16, 384)  1536        concatenate_25[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 16, 16, 384)  0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 16, 16, 32)   110592      activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_27 (Dropout)            (None, 16, 16, 32)   0           conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_26 (Concatenate)    (None, 16, 16, 416)  0           concatenate_25[0][0]             \n",
            "                                                                 dropout_27[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 16, 16, 416)  1664        concatenate_26[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 16, 16, 416)  0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 16, 16, 32)   119808      activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_28 (Dropout)            (None, 16, 16, 32)   0           conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_27 (Concatenate)    (None, 16, 16, 448)  0           concatenate_26[0][0]             \n",
            "                                                                 dropout_28[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 16, 16, 448)  1792        concatenate_27[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 16, 16, 448)  0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 16, 16, 32)   129024      activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_29 (Dropout)            (None, 16, 16, 32)   0           conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_28 (Concatenate)    (None, 16, 16, 480)  0           concatenate_27[0][0]             \n",
            "                                                                 dropout_29[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 16, 16, 480)  1920        concatenate_28[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 16, 16, 480)  0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 16, 16, 32)   15360       activation_30[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_30 (Dropout)            (None, 16, 16, 32)   0           conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_2 (AveragePoo (None, 8, 8, 32)     0           dropout_30[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 8, 8, 32)     128         average_pooling2d_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 8, 8, 32)     0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 8, 8, 32)     9216        activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_31 (Dropout)            (None, 8, 8, 32)     0           conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_29 (Concatenate)    (None, 8, 8, 64)     0           average_pooling2d_2[0][0]        \n",
            "                                                                 dropout_31[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 8, 8, 64)     256         concatenate_29[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 8, 8, 64)     0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 8, 8, 32)     18432       activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_32 (Dropout)            (None, 8, 8, 32)     0           conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_30 (Concatenate)    (None, 8, 8, 96)     0           concatenate_29[0][0]             \n",
            "                                                                 dropout_32[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, 8, 8, 96)     384         concatenate_30[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 8, 8, 96)     0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 8, 8, 32)     27648       activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_33 (Dropout)            (None, 8, 8, 32)     0           conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_31 (Concatenate)    (None, 8, 8, 128)    0           concatenate_30[0][0]             \n",
            "                                                                 dropout_33[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, 8, 8, 128)    512         concatenate_31[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 8, 8, 128)    0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 8, 8, 32)     36864       activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_34 (Dropout)            (None, 8, 8, 32)     0           conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_32 (Concatenate)    (None, 8, 8, 160)    0           concatenate_31[0][0]             \n",
            "                                                                 dropout_34[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, 8, 8, 160)    640         concatenate_32[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 8, 8, 160)    0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 8, 8, 32)     46080       activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_35 (Dropout)            (None, 8, 8, 32)     0           conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_33 (Concatenate)    (None, 8, 8, 192)    0           concatenate_32[0][0]             \n",
            "                                                                 dropout_35[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, 8, 8, 192)    768         concatenate_33[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 8, 8, 192)    0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 8, 8, 32)     55296       activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_36 (Dropout)            (None, 8, 8, 32)     0           conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_34 (Concatenate)    (None, 8, 8, 224)    0           concatenate_33[0][0]             \n",
            "                                                                 dropout_36[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, 8, 8, 224)    896         concatenate_34[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 8, 8, 224)    0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 8, 8, 32)     64512       activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_37 (Dropout)            (None, 8, 8, 32)     0           conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_35 (Concatenate)    (None, 8, 8, 256)    0           concatenate_34[0][0]             \n",
            "                                                                 dropout_37[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, 8, 8, 256)    1024        concatenate_35[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 8, 8, 256)    0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 8, 8, 32)     73728       activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_38 (Dropout)            (None, 8, 8, 32)     0           conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_36 (Concatenate)    (None, 8, 8, 288)    0           concatenate_35[0][0]             \n",
            "                                                                 dropout_38[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, 8, 8, 288)    1152        concatenate_36[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 8, 8, 288)    0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 8, 8, 32)     82944       activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_39 (Dropout)            (None, 8, 8, 32)     0           conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_37 (Concatenate)    (None, 8, 8, 320)    0           concatenate_36[0][0]             \n",
            "                                                                 dropout_39[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, 8, 8, 320)    1280        concatenate_37[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 8, 8, 320)    0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 8, 8, 32)     92160       activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_40 (Dropout)            (None, 8, 8, 32)     0           conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_38 (Concatenate)    (None, 8, 8, 352)    0           concatenate_37[0][0]             \n",
            "                                                                 dropout_40[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, 8, 8, 352)    1408        concatenate_38[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 8, 8, 352)    0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 8, 8, 32)     101376      activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_41 (Dropout)            (None, 8, 8, 32)     0           conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_39 (Concatenate)    (None, 8, 8, 384)    0           concatenate_38[0][0]             \n",
            "                                                                 dropout_41[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 8, 8, 384)    1536        concatenate_39[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 8, 8, 384)    0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 8, 8, 32)     110592      activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_42 (Dropout)            (None, 8, 8, 32)     0           conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_40 (Concatenate)    (None, 8, 8, 416)    0           concatenate_39[0][0]             \n",
            "                                                                 dropout_42[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, 8, 8, 416)    1664        concatenate_40[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 8, 8, 416)    0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 8, 8, 32)     119808      activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_43 (Dropout)            (None, 8, 8, 32)     0           conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_41 (Concatenate)    (None, 8, 8, 448)    0           concatenate_40[0][0]             \n",
            "                                                                 dropout_43[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, 8, 8, 448)    1792        concatenate_41[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 8, 8, 448)    0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 8, 8, 32)     129024      activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_44 (Dropout)            (None, 8, 8, 32)     0           conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_42 (Concatenate)    (None, 8, 8, 480)    0           concatenate_41[0][0]             \n",
            "                                                                 dropout_44[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, 8, 8, 480)    1920        concatenate_42[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 8, 8, 480)    0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 8, 8, 32)     15360       activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_45 (Dropout)            (None, 8, 8, 32)     0           conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_3 (AveragePoo (None, 4, 4, 32)     0           dropout_45[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, 4, 4, 32)     128         average_pooling2d_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 4, 4, 32)     0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 4, 4, 16)     4608        activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_46 (Dropout)            (None, 4, 4, 16)     0           conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_43 (Concatenate)    (None, 4, 4, 48)     0           average_pooling2d_3[0][0]        \n",
            "                                                                 dropout_46[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, 4, 4, 48)     192         concatenate_43[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 4, 4, 48)     0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 4, 4, 16)     6912        activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_47 (Dropout)            (None, 4, 4, 16)     0           conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_44 (Concatenate)    (None, 4, 4, 64)     0           concatenate_43[0][0]             \n",
            "                                                                 dropout_47[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, 4, 4, 64)     256         concatenate_44[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 4, 4, 64)     0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, 4, 4, 16)     9216        activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_48 (Dropout)            (None, 4, 4, 16)     0           conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_45 (Concatenate)    (None, 4, 4, 80)     0           concatenate_44[0][0]             \n",
            "                                                                 dropout_48[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, 4, 4, 80)     320         concatenate_45[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 4, 4, 80)     0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 4, 4, 16)     11520       activation_49[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_49 (Dropout)            (None, 4, 4, 16)     0           conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_46 (Concatenate)    (None, 4, 4, 96)     0           concatenate_45[0][0]             \n",
            "                                                                 dropout_49[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, 4, 4, 96)     384         concatenate_46[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_50 (Activation)      (None, 4, 4, 96)     0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 4, 4, 16)     13824       activation_50[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_50 (Dropout)            (None, 4, 4, 16)     0           conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_47 (Concatenate)    (None, 4, 4, 112)    0           concatenate_46[0][0]             \n",
            "                                                                 dropout_50[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, 4, 4, 112)    448         concatenate_47[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_51 (Activation)      (None, 4, 4, 112)    0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, 4, 4, 16)     16128       activation_51[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_51 (Dropout)            (None, 4, 4, 16)     0           conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_48 (Concatenate)    (None, 4, 4, 128)    0           concatenate_47[0][0]             \n",
            "                                                                 dropout_51[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, 4, 4, 128)    512         concatenate_48[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_52 (Activation)      (None, 4, 4, 128)    0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, 4, 4, 16)     18432       activation_52[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_52 (Dropout)            (None, 4, 4, 16)     0           conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_49 (Concatenate)    (None, 4, 4, 144)    0           concatenate_48[0][0]             \n",
            "                                                                 dropout_52[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, 4, 4, 144)    576         concatenate_49[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_53 (Activation)      (None, 4, 4, 144)    0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "conv2d_54 (Conv2D)              (None, 4, 4, 16)     20736       activation_53[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_53 (Dropout)            (None, 4, 4, 16)     0           conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_50 (Concatenate)    (None, 4, 4, 160)    0           concatenate_49[0][0]             \n",
            "                                                                 dropout_53[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, 4, 4, 160)    640         concatenate_50[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_54 (Activation)      (None, 4, 4, 160)    0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, 4, 4, 16)     23040       activation_54[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_54 (Dropout)            (None, 4, 4, 16)     0           conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_51 (Concatenate)    (None, 4, 4, 176)    0           concatenate_50[0][0]             \n",
            "                                                                 dropout_54[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_55 (BatchNo (None, 4, 4, 176)    704         concatenate_51[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_55 (Activation)      (None, 4, 4, 176)    0           batch_normalization_55[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, 4, 4, 16)     25344       activation_55[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_55 (Dropout)            (None, 4, 4, 16)     0           conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_52 (Concatenate)    (None, 4, 4, 192)    0           concatenate_51[0][0]             \n",
            "                                                                 dropout_55[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_56 (BatchNo (None, 4, 4, 192)    768         concatenate_52[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_56 (Activation)      (None, 4, 4, 192)    0           batch_normalization_56[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, 4, 4, 16)     27648       activation_56[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_56 (Dropout)            (None, 4, 4, 16)     0           conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_53 (Concatenate)    (None, 4, 4, 208)    0           concatenate_52[0][0]             \n",
            "                                                                 dropout_56[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_57 (BatchNo (None, 4, 4, 208)    832         concatenate_53[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_57 (Activation)      (None, 4, 4, 208)    0           batch_normalization_57[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_58 (Conv2D)              (None, 4, 4, 16)     29952       activation_57[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_57 (Dropout)            (None, 4, 4, 16)     0           conv2d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_54 (Concatenate)    (None, 4, 4, 224)    0           concatenate_53[0][0]             \n",
            "                                                                 dropout_57[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_58 (BatchNo (None, 4, 4, 224)    896         concatenate_54[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_58 (Activation)      (None, 4, 4, 224)    0           batch_normalization_58[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_59 (Conv2D)              (None, 4, 4, 16)     32256       activation_58[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_58 (Dropout)            (None, 4, 4, 16)     0           conv2d_59[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_55 (Concatenate)    (None, 4, 4, 240)    0           concatenate_54[0][0]             \n",
            "                                                                 dropout_58[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_59 (BatchNo (None, 4, 4, 240)    960         concatenate_55[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_59 (Activation)      (None, 4, 4, 240)    0           batch_normalization_59[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_60 (Conv2D)              (None, 4, 4, 16)     34560       activation_59[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_59 (Dropout)            (None, 4, 4, 16)     0           conv2d_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_56 (Concatenate)    (None, 4, 4, 256)    0           concatenate_55[0][0]             \n",
            "                                                                 dropout_59[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_60 (BatchNo (None, 4, 4, 256)    1024        concatenate_56[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_60 (Activation)      (None, 4, 4, 256)    0           batch_normalization_60[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_4 (AveragePoo (None, 2, 2, 256)    0           activation_60[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)             (None, 1024)         0           average_pooling2d_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 10)           10250       flatten_1[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 3,421,962\n",
            "Trainable params: 3,393,642\n",
            "Non-trainable params: 28,320\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9T9C6NzxAo51",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# determine Loss function and Optimizer\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-eAoaWTC9lbm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "0dc24df0-ecd2-49ac-b81a-5971f4f8132d"
      },
      "cell_type": "code",
      "source": [
        "!pip install -U -q PyDrive\n",
        "\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# 1. Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "for i in range(1,6):\n",
        "  model.fit(x_train, y_train,\n",
        "                    batch_size=batch_size,\n",
        "                    epochs=10,\n",
        "                    verbose=1,\n",
        "                    validation_data=(x_test, y_test))\n",
        "  \n",
        "  #train_datagen = model.fit_generator(datagen.flow(x_train, y_train,\n",
        "  #                                 batch_size=batch_size),\n",
        "  #                                 epochs=epochs,\n",
        "  #                                 validation_data=(x_test, y_test),\n",
        "  #                                 workers=4)\n",
        "\n",
        "\n",
        "  file_name = 'DenseNet_CIFAR10_' + str(i) + '.h5'\n",
        "\n",
        "  # Save the trained weights in to .h5 format\n",
        "  #model.save(file_name)\n",
        "  model.save_weights(file_name)\n",
        "  print(\"Saved model to disk - {0}\".format(file_name))\n",
        "\n",
        "  # Saving using PyDrive to google drive\n",
        "\n",
        "  # Create & upload a file.\n",
        "  uploaded = drive.CreateFile({'title': file_name})\n",
        "  uploaded.SetContentFile(file_name)\n",
        "  uploaded.Upload()\n",
        "  print('Uploaded file with ID {}'.format(uploaded.get('id')))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "50000/50000 [==============================] - 645s 13ms/step - loss: 1.5357 - acc: 0.4383 - val_loss: 2.6936 - val_acc: 0.3320\n",
            "Epoch 2/10\n",
            "19072/50000 [==========>...................] - ETA: 5:54 - loss: 1.1724 - acc: 0.5749"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 618s 12ms/step - loss: 1.0875 - acc: 0.6073 - val_loss: 1.1640 - val_acc: 0.6289\n",
            "Epoch 3/10\n",
            "45184/50000 [==========================>...] - ETA: 55s - loss: 0.9162 - acc: 0.6717"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.9116 - acc: 0.6732 - val_loss: 1.3001 - val_acc: 0.6247\n",
            "Epoch 4/10\n",
            "50000/50000 [==============================] - 618s 12ms/step - loss: 0.7971 - acc: 0.7140 - val_loss: 2.5836 - val_acc: 0.4419\n",
            "Epoch 5/10\n",
            " 2944/50000 [>.............................] - ETA: 8:59 - loss: 0.7184 - acc: 0.7401"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 618s 12ms/step - loss: 0.7073 - acc: 0.7508 - val_loss: 1.7575 - val_acc: 0.6080\n",
            "Epoch 6/10\n",
            "39040/50000 [======================>.......] - ETA: 2:05 - loss: 0.6420 - acc: 0.7724"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 618s 12ms/step - loss: 0.6398 - acc: 0.7738 - val_loss: 1.2379 - val_acc: 0.6776\n",
            "Epoch 7/10\n",
            "50000/50000 [==============================] - 618s 12ms/step - loss: 0.5961 - acc: 0.7905 - val_loss: 1.4388 - val_acc: 0.6428\n",
            "Epoch 8/10\n",
            " 1536/50000 [..............................] - ETA: 9:15 - loss: 0.5326 - acc: 0.8314"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 618s 12ms/step - loss: 0.5436 - acc: 0.8078 - val_loss: 1.7878 - val_acc: 0.6027\n",
            "Epoch 9/10\n",
            "38528/50000 [======================>.......] - ETA: 2:11 - loss: 0.5193 - acc: 0.8167"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.5207 - acc: 0.8172 - val_loss: 1.3701 - val_acc: 0.6540\n",
            "Epoch 10/10\n",
            "50000/50000 [==============================] - 618s 12ms/step - loss: 0.4869 - acc: 0.8292 - val_loss: 1.4594 - val_acc: 0.6463\n",
            "Saved model to disk - DenseNet_CIFAR10_1.h5\n",
            "Uploaded file with ID 1mVgcnNODIrzm1zAIskBIpbm0d9mMXKIm\n",
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "  768/50000 [..............................] - ETA: 9:31 - loss: 0.4816 - acc: 0.8229"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.4584 - acc: 0.8401 - val_loss: 1.1230 - val_acc: 0.7048\n",
            "Epoch 2/10\n",
            "38272/50000 [=====================>........] - ETA: 2:14 - loss: 0.4401 - acc: 0.8464"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.4384 - acc: 0.8476 - val_loss: 1.2403 - val_acc: 0.7123\n",
            "Epoch 3/10\n",
            "32640/50000 [==================>...........] - ETA: 3:18 - loss: 0.4121 - acc: 0.8559"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WhY-eqgGrJSU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "421e0362-de88-429f-f17d-d71f43d71fdb"
      },
      "cell_type": "code",
      "source": [
        "!ls -l"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 27828\r\n",
            "drwxr-xr-x 1 root root     4096 May 12 10:05 datalab\r\n",
            "-rw-r--r-- 1 root root 14244496 May 12 13:32 DenseNet_CIFAR10_1.h5\r\n",
            "-rw-r--r-- 1 root root 14244496 May 12 15:59 DenseNet_CIFAR10_2.h5\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YDhnrdBrrm87",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.load_weights('DenseNet_CIFAR10_2.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "61m1G4-LrgoT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "8fe8363b-f6ed-40b7-b7ca-7056b5378cb0"
      },
      "cell_type": "code",
      "source": [
        "!pip install -U -q PyDrive\n",
        "\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# 1. Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "for i in range(2,6):\n",
        "  model.fit(x_train, y_train,\n",
        "                    batch_size=batch_size,\n",
        "                    epochs=10,\n",
        "                    verbose=1,\n",
        "                    validation_data=(x_test, y_test))\n",
        "  \n",
        "  #train_datagen = model.fit_generator(datagen.flow(x_train, y_train,\n",
        "  #                                 batch_size=batch_size),\n",
        "  #                                 epochs=epochs,\n",
        "  #                                 validation_data=(x_test, y_test),\n",
        "  #                                 workers=4)\n",
        "\n",
        "\n",
        "  file_name = 'DenseNet_CIFAR10_' + str(i) + '.h5'\n",
        "\n",
        "  # Save the trained weights in to .h5 format\n",
        "  #model.save(file_name)\n",
        "  model.save_weights(file_name)\n",
        "  print(\"Saved model to disk - {0}\".format(file_name))\n",
        "\n",
        "  # Saving using PyDrive to google drive\n",
        "\n",
        "  # Create & upload a file.\n",
        "  uploaded = drive.CreateFile({'title': file_name})\n",
        "  uploaded.SetContentFile(file_name)\n",
        "  uploaded.Upload()\n",
        "  print('Uploaded file with ID {}'.format(uploaded.get('id')))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "50000/50000 [==============================] - 640s 13ms/step - loss: 0.4726 - acc: 0.8339 - val_loss: 0.8854 - val_acc: 0.7628\n",
            "Epoch 2/10\n",
            "19072/50000 [==========>...................] - ETA: 5:53 - loss: 0.4495 - acc: 0.8406"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 617s 12ms/step - loss: 0.4499 - acc: 0.8419 - val_loss: 1.0399 - val_acc: 0.7294\n",
            "Epoch 3/10\n",
            "45184/50000 [==========================>...] - ETA: 55s - loss: 0.4157 - acc: 0.8524"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 617s 12ms/step - loss: 0.4164 - acc: 0.8522 - val_loss: 0.8201 - val_acc: 0.7854\n",
            "Epoch 4/10\n",
            "50000/50000 [==============================] - 617s 12ms/step - loss: 0.3980 - acc: 0.8609 - val_loss: 0.8160 - val_acc: 0.7733\n",
            "Epoch 5/10\n",
            " 2944/50000 [>.............................] - ETA: 8:58 - loss: 0.3736 - acc: 0.8679"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 617s 12ms/step - loss: 0.3818 - acc: 0.8659 - val_loss: 1.0340 - val_acc: 0.7450\n",
            "Epoch 6/10\n",
            "39040/50000 [======================>.......] - ETA: 2:05 - loss: 0.3599 - acc: 0.8744"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 617s 12ms/step - loss: 0.3614 - acc: 0.8730 - val_loss: 1.3355 - val_acc: 0.6973\n",
            "Epoch 7/10\n",
            "50000/50000 [==============================] - 617s 12ms/step - loss: 0.3429 - acc: 0.8800 - val_loss: 1.2650 - val_acc: 0.6960\n",
            "Epoch 8/10\n",
            " 1536/50000 [..............................] - ETA: 9:13 - loss: 0.3274 - acc: 0.8867"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 617s 12ms/step - loss: 0.3254 - acc: 0.8844 - val_loss: 0.7819 - val_acc: 0.8083\n",
            "Epoch 9/10\n",
            "38528/50000 [======================>.......] - ETA: 2:11 - loss: 0.3133 - acc: 0.8897"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 617s 12ms/step - loss: 0.3156 - acc: 0.8893 - val_loss: 1.4855 - val_acc: 0.6664\n",
            "Epoch 10/10\n",
            "36736/50000 [=====================>........] - ETA: 2:31 - loss: 0.3007 - acc: 0.8935"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_RxlEnWE0eG7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###Score after 20 epochs"
      ]
    },
    {
      "metadata": {
        "id": "dSYDrUOzFjmH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d5640629-de79-48ab-ff96-93f851f033dd"
      },
      "cell_type": "code",
      "source": [
        "score = model.evaluate(x_test, y_test, verbose=1)\n",
        "print(score)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 57s 6ms/step\n",
            "[1.0432363877296447, 0.7534]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "d1KaW79DGcJA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2224
        },
        "outputId": "09969b45-fe30-4629-fc88-d5931f456cfa"
      },
      "cell_type": "code",
      "source": [
        "!pip install -U -q PyDrive\n",
        "\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# 1. Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "for i in range(3,6):\n",
        "  model.fit(x_train, y_train,\n",
        "                    batch_size=batch_size,\n",
        "                    epochs=10,\n",
        "                    verbose=1,\n",
        "                    validation_data=(x_test, y_test))\n",
        "  \n",
        "  #train_datagen = model.fit_generator(datagen.flow(x_train, y_train,\n",
        "  #                                 batch_size=batch_size),\n",
        "  #                                 epochs=epochs,\n",
        "  #                                 validation_data=(x_test, y_test),\n",
        "  #                                 workers=4)\n",
        "\n",
        "\n",
        "  file_name = 'DenseNet_CIFAR10_' + str(i) + '.h5'\n",
        "\n",
        "  # Save the trained weights in to .h5 format\n",
        "  #model.save(file_name)\n",
        "  model.save_weights(file_name)\n",
        "  print(\"Saved model to disk - {0}\".format(file_name))\n",
        "\n",
        "  # Saving using PyDrive to google drive\n",
        "\n",
        "  # Create & upload a file.\n",
        "  uploaded = drive.CreateFile({'title': file_name})\n",
        "  uploaded.SetContentFile(file_name)\n",
        "  uploaded.Upload()\n",
        "  print('Uploaded file with ID {}'.format(uploaded.get('id')))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "50000/50000 [==============================] - 642s 13ms/step - loss: 0.2996 - acc: 0.8955 - val_loss: 1.9391 - val_acc: 0.6511\n",
            "Epoch 2/10\n",
            "19072/50000 [==========>...................] - ETA: 5:54 - loss: 0.2849 - acc: 0.8994"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.2875 - acc: 0.8982 - val_loss: 0.6840 - val_acc: 0.8218\n",
            "Epoch 3/10\n",
            "45184/50000 [==========================>...] - ETA: 55s - loss: 0.2721 - acc: 0.9043"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.2727 - acc: 0.9044 - val_loss: 0.8907 - val_acc: 0.8000\n",
            "Epoch 4/10\n",
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.2608 - acc: 0.9087 - val_loss: 0.9232 - val_acc: 0.7938\n",
            "Epoch 5/10\n",
            " 2944/50000 [>.............................] - ETA: 8:59 - loss: 0.2421 - acc: 0.9141"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.2515 - acc: 0.9112 - val_loss: 0.5231 - val_acc: 0.8678\n",
            "Epoch 6/10\n",
            "39168/50000 [======================>.......] - ETA: 2:04 - loss: 0.2388 - acc: 0.9154"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.2414 - acc: 0.9144 - val_loss: 0.7384 - val_acc: 0.8234\n",
            "Epoch 7/10\n",
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.2382 - acc: 0.9146 - val_loss: 0.8782 - val_acc: 0.8093\n",
            "Epoch 8/10\n",
            " 1536/50000 [..............................] - ETA: 9:17 - loss: 0.1832 - acc: 0.9382"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.2271 - acc: 0.9198 - val_loss: 0.7562 - val_acc: 0.8336\n",
            "Epoch 9/10\n",
            "38528/50000 [======================>.......] - ETA: 2:11 - loss: 0.2094 - acc: 0.9259"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.2094 - acc: 0.9259 - val_loss: 1.1928 - val_acc: 0.7535\n",
            "Epoch 10/10\n",
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.2106 - acc: 0.9244 - val_loss: 0.6218 - val_acc: 0.8450\n",
            "Saved model to disk - DenseNet_CIFAR10_3.h5\n",
            "Uploaded file with ID 1m08i0w9Ak8iFuj04KknPTcgKG6Bmh3Vc\n",
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "  768/50000 [..............................] - ETA: 9:35 - loss: 0.2390 - acc: 0.9076"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.2006 - acc: 0.9280 - val_loss: 1.1849 - val_acc: 0.7778\n",
            "Epoch 2/10\n",
            "38272/50000 [=====================>........] - ETA: 2:14 - loss: 0.1932 - acc: 0.9305"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.1943 - acc: 0.9298 - val_loss: 1.3947 - val_acc: 0.7473\n",
            "Epoch 3/10\n",
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.1879 - acc: 0.9327 - val_loss: 0.5917 - val_acc: 0.8592\n",
            "Epoch 4/10\n",
            " 1408/50000 [..............................] - ETA: 9:16 - loss: 0.1652 - acc: 0.9403"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.1836 - acc: 0.9344 - val_loss: 0.6720 - val_acc: 0.8493\n",
            "Epoch 5/10\n",
            "38528/50000 [======================>.......] - ETA: 2:11 - loss: 0.1712 - acc: 0.9390"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.1747 - acc: 0.9383 - val_loss: 1.4080 - val_acc: 0.7403\n",
            "Epoch 6/10\n",
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.1737 - acc: 0.9384 - val_loss: 0.7417 - val_acc: 0.8339\n",
            "Epoch 7/10\n",
            " 1408/50000 [..............................] - ETA: 9:16 - loss: 0.1596 - acc: 0.9411"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.1691 - acc: 0.9396 - val_loss: 0.8341 - val_acc: 0.8308\n",
            "Epoch 8/10\n",
            "38528/50000 [======================>.......] - ETA: 2:11 - loss: 0.1633 - acc: 0.9414"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.1646 - acc: 0.9411 - val_loss: 0.6393 - val_acc: 0.8518\n",
            "Epoch 9/10\n",
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.1560 - acc: 0.9437 - val_loss: 0.7381 - val_acc: 0.8480\n",
            "Epoch 10/10\n",
            " 1408/50000 [..............................] - ETA: 9:18 - loss: 0.1352 - acc: 0.9553"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 619s 12ms/step - loss: 0.1541 - acc: 0.9446 - val_loss: 0.6435 - val_acc: 0.8644\n",
            "Saved model to disk - DenseNet_CIFAR10_4.h5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "InvalidConfigError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/oauth2client/clientsecrets.py\u001b[0m in \u001b[0;36m_loadfile\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'client_secrets.json'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mInvalidClientSecretsError\u001b[0m                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/auth.py\u001b[0m in \u001b[0;36mLoadClientConfigFile\u001b[0;34m(self, client_config_file)\u001b[0m\n\u001b[1;32m    385\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m       \u001b[0mclient_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclient_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclientsecrets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclient_config_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mclientsecrets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidClientSecretsError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/oauth2client/clientsecrets.py\u001b[0m in \u001b[0;36mloadfile\u001b[0;34m(filename, cache)\u001b[0m\n\u001b[1;32m    164\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_loadfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/oauth2client/clientsecrets.py\u001b[0m in \u001b[0;36m_loadfile\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    124\u001b[0m         raise InvalidClientSecretsError('Error opening file', exc.filename,\n\u001b[0;32m--> 125\u001b[0;31m                                         exc.strerror, exc.errno)\n\u001b[0m\u001b[1;32m    126\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_validate_clientsecrets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidClientSecretsError\u001b[0m: ('Error opening file', 'client_secrets.json', 'No such file or directory', 2)",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mInvalidConfigError\u001b[0m                        Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-205b20f2bc60>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     38\u001b[0m   \u001b[0muploaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCreateFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'title'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m   \u001b[0muploaded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSetContentFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m   \u001b[0muploaded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUpload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Uploaded file with ID {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muploaded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/files.py\u001b[0m in \u001b[0;36mUpload\u001b[0;34m(self, param)\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FilesPatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FilesInsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mTrash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/auth.py\u001b[0m in \u001b[0;36m_decorated\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     55\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mServiceAuth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLocalWebserverAuth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;31m# Initialise service if not built yet.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/auth.py\u001b[0m in \u001b[0;36m_decorated\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    111\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLoadCredentials\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflow\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetFlow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    114\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcredentials\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m       \u001b[0mcode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdecoratee\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/auth.py\u001b[0m in \u001b[0;36mGetFlow\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    441\u001b[0m     if not all(config in self.client_config \\\n\u001b[1;32m    442\u001b[0m                for config in self.CLIENT_CONFIGS_LIST):\n\u001b[0;32m--> 443\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLoadClientConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    444\u001b[0m     constructor_kwargs = {\n\u001b[1;32m    445\u001b[0m         \u001b[0;34m'redirect_uri'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient_config\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'redirect_uri'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/auth.py\u001b[0m in \u001b[0;36mLoadClientConfig\u001b[0;34m(self, backend)\u001b[0m\n\u001b[1;32m    364\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mInvalidConfigError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Please specify client config backend'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'file'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 366\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLoadClientConfigFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    367\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'settings'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    368\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLoadClientConfigSettings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/auth.py\u001b[0m in \u001b[0;36mLoadClientConfigFile\u001b[0;34m(self, client_config_file)\u001b[0m\n\u001b[1;32m    386\u001b[0m       \u001b[0mclient_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclient_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclientsecrets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclient_config_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mclientsecrets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidClientSecretsError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mInvalidConfigError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Invalid client secrets file %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m     if not client_type in (clientsecrets.TYPE_WEB,\n\u001b[1;32m    390\u001b[0m                            clientsecrets.TYPE_INSTALLED):\n",
            "\u001b[0;31mInvalidConfigError\u001b[0m: Invalid client secrets file ('Error opening file', 'client_secrets.json', 'No such file or directory', 2)"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "SsS64pO06yql",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "a38544d1-e272-4428-e372-129d6d59746e"
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-fcd34475-a361-41f2-8627-d0471aa054c1\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-fcd34475-a361-41f2-8627-d0471aa054c1\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving DenseNet_CIFAR10_3 (1).h5 to DenseNet_CIFAR10_3 (1).h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BRwmNfoq9ErT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.load_weights('DenseNet_CIFAR10_3 (1).h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "omjR5YWP02T4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###Score after 30 epochs"
      ]
    },
    {
      "metadata": {
        "id": "wSHIJgGvGm1z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "1668aa4e-4a69-4aa1-9061-a0e875f9915d"
      },
      "cell_type": "code",
      "source": [
        "score = model.evaluate(x_test, y_test, verbose=1)\n",
        "print(score)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 58s 6ms/step\n",
            "[0.6218343338787555, 0.845]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EB4MDXyU9WRx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2224
        },
        "outputId": "946b4310-a2f6-406b-b004-acef414cd18a"
      },
      "cell_type": "code",
      "source": [
        "!pip install -U -q PyDrive\n",
        "\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# 1. Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "for i in range(4,6):\n",
        "  model.fit(x_train, y_train,\n",
        "                    batch_size=batch_size,\n",
        "                    epochs=10,\n",
        "                    verbose=1,\n",
        "                    validation_data=(x_test, y_test))\n",
        "  \n",
        "  #train_datagen = model.fit_generator(datagen.flow(x_train, y_train,\n",
        "  #                                 batch_size=batch_size),\n",
        "  #                                 epochs=epochs,\n",
        "  #                                 validation_data=(x_test, y_test),\n",
        "  #                                 workers=4)\n",
        "\n",
        "\n",
        "  file_name = 'DenseNet_CIFAR10_' + str(i) + '.h5'\n",
        "\n",
        "  # Save the trained weights in to .h5 format\n",
        "  #model.save(file_name)\n",
        "  model.save_weights(file_name)\n",
        "  print(\"Saved model to disk - {0}\".format(file_name))\n",
        "\n",
        "  # Saving using PyDrive to google drive\n",
        "\n",
        "  # Create & upload a file.\n",
        "  uploaded = drive.CreateFile({'title': file_name})\n",
        "  uploaded.SetContentFile(file_name)\n",
        "  uploaded.Upload()\n",
        "  print('Uploaded file with ID {}'.format(uploaded.get('id')))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "50000/50000 [==============================] - 651s 13ms/step - loss: 0.2084 - acc: 0.9256 - val_loss: 0.7997 - val_acc: 0.8258\n",
            "Epoch 2/10\n",
            "19072/50000 [==========>...................] - ETA: 5:59 - loss: 0.1921 - acc: 0.9319"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 629s 13ms/step - loss: 0.1972 - acc: 0.9303 - val_loss: 1.0086 - val_acc: 0.7824\n",
            "Epoch 3/10\n",
            "45184/50000 [==========================>...] - ETA: 56s - loss: 0.1848 - acc: 0.9343"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 629s 13ms/step - loss: 0.1864 - acc: 0.9341 - val_loss: 0.9619 - val_acc: 0.8061\n",
            "Epoch 4/10\n",
            "50000/50000 [==============================] - 629s 13ms/step - loss: 0.1872 - acc: 0.9334 - val_loss: 0.6336 - val_acc: 0.8542\n",
            "Epoch 5/10\n",
            " 2944/50000 [>.............................] - ETA: 9:07 - loss: 0.1622 - acc: 0.9406"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 629s 13ms/step - loss: 0.1765 - acc: 0.9367 - val_loss: 0.9155 - val_acc: 0.8085\n",
            "Epoch 6/10\n",
            "39040/50000 [======================>.......] - ETA: 2:07 - loss: 0.1716 - acc: 0.9393"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 629s 13ms/step - loss: 0.1760 - acc: 0.9373 - val_loss: 1.0458 - val_acc: 0.7986\n",
            "Epoch 7/10\n",
            "50000/50000 [==============================] - 625s 12ms/step - loss: 0.1651 - acc: 0.9415 - val_loss: 1.1058 - val_acc: 0.7862\n",
            "Epoch 8/10\n",
            " 1536/50000 [..............................] - ETA: 9:19 - loss: 0.1607 - acc: 0.9479"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 625s 12ms/step - loss: 0.1604 - acc: 0.9427 - val_loss: 0.6882 - val_acc: 0.8461\n",
            "Epoch 9/10\n",
            "38528/50000 [======================>.......] - ETA: 2:12 - loss: 0.1552 - acc: 0.9442"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 625s 13ms/step - loss: 0.1572 - acc: 0.9435 - val_loss: 0.6234 - val_acc: 0.8686\n",
            "Epoch 10/10\n",
            "50000/50000 [==============================] - 625s 13ms/step - loss: 0.1514 - acc: 0.9459 - val_loss: 0.8048 - val_acc: 0.8333\n",
            "Saved model to disk - DenseNet_CIFAR10_4.h5\n",
            "Uploaded file with ID 18TxAe6qjjBiqJsQsSMjpekDhANA48Odk\n",
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "  768/50000 [..............................] - ETA: 9:35 - loss: 0.1252 - acc: 0.9492"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 625s 13ms/step - loss: 0.1462 - acc: 0.9486 - val_loss: 0.6582 - val_acc: 0.8547\n",
            "Epoch 2/10\n",
            "38272/50000 [=====================>........] - ETA: 2:15 - loss: 0.1453 - acc: 0.9481"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 625s 13ms/step - loss: 0.1487 - acc: 0.9472 - val_loss: 0.7540 - val_acc: 0.8411\n",
            "Epoch 3/10\n",
            "50000/50000 [==============================] - 624s 12ms/step - loss: 0.1405 - acc: 0.9505 - val_loss: 0.6961 - val_acc: 0.8578\n",
            "Epoch 4/10\n",
            " 1408/50000 [..............................] - ETA: 9:22 - loss: 0.1219 - acc: 0.9517"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 626s 13ms/step - loss: 0.1356 - acc: 0.9508 - val_loss: 0.6901 - val_acc: 0.8566\n",
            "Epoch 5/10\n",
            "38528/50000 [======================>.......] - ETA: 2:12 - loss: 0.1262 - acc: 0.9550"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 626s 13ms/step - loss: 0.1281 - acc: 0.9542 - val_loss: 0.7224 - val_acc: 0.8562\n",
            "Epoch 6/10\n",
            "50000/50000 [==============================] - 626s 13ms/step - loss: 0.1301 - acc: 0.9537 - val_loss: 0.6436 - val_acc: 0.8650\n",
            "Epoch 7/10\n",
            " 1408/50000 [..............................] - ETA: 9:23 - loss: 0.1262 - acc: 0.9588"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 625s 13ms/step - loss: 0.1284 - acc: 0.9547 - val_loss: 0.7621 - val_acc: 0.8530\n",
            "Epoch 8/10\n",
            "38528/50000 [======================>.......] - ETA: 2:12 - loss: 0.1161 - acc: 0.9583"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 625s 13ms/step - loss: 0.1187 - acc: 0.9576 - val_loss: 0.6790 - val_acc: 0.8564\n",
            "Epoch 9/10\n",
            "50000/50000 [==============================] - 625s 12ms/step - loss: 0.1156 - acc: 0.9578 - val_loss: 0.6604 - val_acc: 0.8600\n",
            "Epoch 10/10\n",
            " 1408/50000 [..............................] - ETA: 9:21 - loss: 0.1184 - acc: 0.9624"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50000/50000 [==============================] - 626s 13ms/step - loss: 0.1151 - acc: 0.9593 - val_loss: 0.5517 - val_acc: 0.8814\n",
            "Saved model to disk - DenseNet_CIFAR10_5.h5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "InvalidConfigError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/oauth2client/clientsecrets.py\u001b[0m in \u001b[0;36m_loadfile\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'client_secrets.json'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mInvalidClientSecretsError\u001b[0m                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/auth.py\u001b[0m in \u001b[0;36mLoadClientConfigFile\u001b[0;34m(self, client_config_file)\u001b[0m\n\u001b[1;32m    385\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m       \u001b[0mclient_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclient_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclientsecrets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclient_config_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mclientsecrets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidClientSecretsError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/oauth2client/clientsecrets.py\u001b[0m in \u001b[0;36mloadfile\u001b[0;34m(filename, cache)\u001b[0m\n\u001b[1;32m    164\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_loadfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/oauth2client/clientsecrets.py\u001b[0m in \u001b[0;36m_loadfile\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    124\u001b[0m         raise InvalidClientSecretsError('Error opening file', exc.filename,\n\u001b[0;32m--> 125\u001b[0;31m                                         exc.strerror, exc.errno)\n\u001b[0m\u001b[1;32m    126\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_validate_clientsecrets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidClientSecretsError\u001b[0m: ('Error opening file', 'client_secrets.json', 'No such file or directory', 2)",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mInvalidConfigError\u001b[0m                        Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-0eafa108ce93>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     38\u001b[0m   \u001b[0muploaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCreateFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'title'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m   \u001b[0muploaded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSetContentFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m   \u001b[0muploaded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUpload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Uploaded file with ID {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muploaded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/files.py\u001b[0m in \u001b[0;36mUpload\u001b[0;34m(self, param)\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FilesPatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FilesInsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mTrash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/auth.py\u001b[0m in \u001b[0;36m_decorated\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     55\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mServiceAuth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLocalWebserverAuth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;31m# Initialise service if not built yet.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/auth.py\u001b[0m in \u001b[0;36m_decorated\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    111\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLoadCredentials\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflow\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetFlow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    114\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcredentials\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m       \u001b[0mcode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdecoratee\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/auth.py\u001b[0m in \u001b[0;36mGetFlow\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    441\u001b[0m     if not all(config in self.client_config \\\n\u001b[1;32m    442\u001b[0m                for config in self.CLIENT_CONFIGS_LIST):\n\u001b[0;32m--> 443\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLoadClientConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    444\u001b[0m     constructor_kwargs = {\n\u001b[1;32m    445\u001b[0m         \u001b[0;34m'redirect_uri'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient_config\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'redirect_uri'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/auth.py\u001b[0m in \u001b[0;36mLoadClientConfig\u001b[0;34m(self, backend)\u001b[0m\n\u001b[1;32m    364\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mInvalidConfigError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Please specify client config backend'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'file'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 366\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLoadClientConfigFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    367\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'settings'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    368\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLoadClientConfigSettings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pydrive/auth.py\u001b[0m in \u001b[0;36mLoadClientConfigFile\u001b[0;34m(self, client_config_file)\u001b[0m\n\u001b[1;32m    386\u001b[0m       \u001b[0mclient_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclient_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclientsecrets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclient_config_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mclientsecrets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidClientSecretsError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mInvalidConfigError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Invalid client secrets file %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m     if not client_type in (clientsecrets.TYPE_WEB,\n\u001b[1;32m    390\u001b[0m                            clientsecrets.TYPE_INSTALLED):\n",
            "\u001b[0;31mInvalidConfigError\u001b[0m: Invalid client secrets file ('Error opening file', 'client_secrets.json', 'No such file or directory', 2)"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "gYIlXNgq08JP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###Score after 50 epochs"
      ]
    },
    {
      "metadata": {
        "id": "wwN_RIee9a54",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "67ca5708-73da-4928-ee42-c0dbfafa0358"
      },
      "cell_type": "code",
      "source": [
        "score = model.evaluate(x_test, y_test, verbose=1)\n",
        "print(score)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 55s 5ms/step\n",
            "[0.5516593681804836, 0.8814]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pfWnFAUOwfAL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9680502d-3b98-46d9-ad0f-b720fef2dccb"
      },
      "cell_type": "code",
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# 1. Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "uploaded = drive.CreateFile({'title': 'DenseNet_CIFAR10_5.h5'})\n",
        "uploaded.SetContentFile('DenseNet_CIFAR10_5.h5')\n",
        "uploaded.Upload()\n",
        "print('Uploaded file with ID {}'.format(uploaded.get('id')))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uploaded file with ID 1vtHTEaPBn_cn6wqJEXFYcBVYhqeFdf71\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}