TinyImageNet_Network_1.ipynb

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  "metadata": {
    "colab": {
      "name": "Assignment 4_Nishad Rajmalwar",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ZohebAbai/Tiny-ImageNet-Challenge/blob/master/TinyImageNet_Network_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "eFXEYRqIab1C",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Name: Nishad Rajmalwar,\n",
        "Batch: 7,\n",
        "Email: nrajmalwar@gmail.com"
      ]
    },
    {
      "metadata": {
        "id": "sZQ1u9HB-XZu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Model Architecture Builder Code"
      ]
    },
    {
      "metadata": {
        "id": "8edSiHPi2N5l",
        "colab_type": "code",
        "outputId": "362db7f8-401c-4cf8-c9cf-9a05f177af7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# https://keras.io/\n",
        "!pip install -q keras\n",
        "import keras\n",
        "\n",
        "import keras\n",
        "from keras.models import Model, Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Input, AveragePooling2D, merge, Activation, GlobalAveragePooling2D\n",
        "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
        "from keras.layers import Concatenate\n",
        "from keras.optimizers import Adam\n",
        "from keras.layers import Reshape, Activation, Conv2D, Input, MaxPooling2D, BatchNormalization, Flatten, Dense, Lambda\n",
        "from keras.layers.merge import concatenate\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.models import load_model\n",
        "\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.utils import np_utils\n",
        "from keras.callbacks import ReduceLROnPlateau, CSVLogger, EarlyStopping\n",
        "from keras.models import load_model\n",
        "from keras.callbacks import *\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# 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))\n",
        "\n",
        "# Hyperparameters\n",
        "\n",
        "# number of classes\n",
        "num_classes = 200\n",
        "# input image dimensions\n",
        "img_height, img_width = 32, 32\n",
        "# The images are RGB.\n",
        "img_channels = 3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "ztoDypc63gEs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def space_to_depth_x2(x):\n",
        "    import tensorflow as tf\n",
        "    return tf.space_to_depth(x, block_size=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SNnGm8Tv2fR1",
        "colab_type": "code",
        "outputId": "15a41aa7-fd10-47c4-b018-ce4d264bfb13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "input = Input(shape=(None, None, img_channels))\n",
        "\n",
        "# Block 1\n",
        "\n",
        "layer1 = Conv2D(32, (3,3), strides=(1,1), padding='same', name='conv_1', use_bias=False)(input)\n",
        "layer1 = BatchNormalization(name='norm_1')(layer1)\n",
        "layer1 = Activation(\"relu\")(layer1)\n",
        "\n",
        "layer2 = Conv2D(64, (3,3), strides=(1,1), padding='same', name='conv_2', use_bias=False)(layer1)\n",
        "layer2 = BatchNormalization(name='norm_2')(layer2)\n",
        "layer2 = Activation(\"relu\")(layer2)\n",
        "\n",
        "layer3 = Conv2D(128, (3,3), strides=(1,1), padding='same', name='conv_3', use_bias=False)(layer2)\n",
        "layer3 = BatchNormalization(name='norm_3')(layer3)\n",
        "layer3 = Activation(\"relu\")(layer3)\n",
        "\n",
        "layer4 = Conv2D(256, (3,3), strides=(1,1), padding='same', name='conv_4', use_bias=False)(layer3)\n",
        "layer4 = BatchNormalization(name='norm_4')(layer4)\n",
        "layer4 = Activation(\"relu\")(layer4)\n",
        "\n",
        "layer5 = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_5', use_bias=False)(layer4)\n",
        "layer5 = BatchNormalization(name='norm_5')(layer5)\n",
        "layer5 = Activation(\"relu\")(layer5)\n",
        "\n",
        "layer6 = MaxPooling2D(pool_size=(2, 2))(layer5)\n",
        "\n",
        "skip_connection_1 = layer6\n",
        "\n",
        "# Block 2\n",
        "\n",
        "layer7 = Conv2D(64, (3,3), strides=(1,1), padding='same', name='conv_7', use_bias=False)(layer6)\n",
        "layer7 = BatchNormalization(name='norm_7')(layer7)\n",
        "layer7 = Activation(\"relu\")(layer7)\n",
        "\n",
        "layer8 = Conv2D(128, (3,3), strides=(1,1), padding='same', name='conv_8', use_bias=False)(layer7)\n",
        "layer8 = BatchNormalization(name='norm_8')(layer8)\n",
        "layer8 = Activation(\"relu\")(layer8)\n",
        "\n",
        "layer9 = Conv2D(256, (3,3), strides=(1,1), padding='same', name='conv_9', use_bias=False)(layer8)\n",
        "layer9 = BatchNormalization(name='norm_9')(layer9)\n",
        "layer9 = Activation(\"relu\")(layer9)\n",
        "\n",
        "layer10 = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_10', use_bias=False)(layer9)\n",
        "layer10 = BatchNormalization(name='norm_10')(layer10)\n",
        "layer10 = Activation(\"relu\")(layer10)\n",
        "\n",
        "layer11 = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_11', use_bias=False)(layer10)\n",
        "layer11 = BatchNormalization(name='norm_11')(layer11)\n",
        "layer11 = Activation(\"relu\")(layer11)\n",
        "\n",
        "layer12 = MaxPooling2D(pool_size=(2, 2))(layer11)\n",
        "\n",
        "skip_connection_1 = Lambda(space_to_depth_x2)(skip_connection_1)\n",
        "\n",
        "layer13 = concatenate([skip_connection_1, layer12])\n",
        "\n",
        "skip_connection_2 = layer13\n",
        "\n",
        "# Block 3\n",
        "\n",
        "layer14 = Conv2D(32, (3,3), strides=(1,1), padding='same', name='conv_14', use_bias=False)(layer13)\n",
        "layer14 = BatchNormalization(name='norm_14')(layer14)\n",
        "layer14 = Activation(\"relu\")(layer14)\n",
        "\n",
        "layer15 = Conv2D(128, (3,3), strides=(1,1), padding='same', name='conv_15', use_bias=False)(layer14)\n",
        "layer15 = BatchNormalization(name='norm_15')(layer15)\n",
        "layer15 = Activation(\"relu\")(layer15)\n",
        "\n",
        "layer16 = Conv2D(256, (3,3), strides=(1,1), padding='same', name='conv_16', use_bias=False)(layer15)\n",
        "layer16 = BatchNormalization(name='norm_16')(layer16)\n",
        "layer16 = Activation(\"relu\")(layer16)\n",
        "\n",
        "layer17 = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_17', use_bias=False)(layer16)\n",
        "layer17 = BatchNormalization(name='norm_17')(layer17)\n",
        "layer17 = Activation(\"relu\")(layer17)\n",
        "\n",
        "layer18 = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_18', use_bias=False)(layer17)\n",
        "layer18 = BatchNormalization(name='norm_18')(layer18)\n",
        "layer18 = Activation(\"relu\")(layer18)\n",
        "\n",
        "layer19 = MaxPooling2D(pool_size=(2, 2))(layer18)x\n",
        "\n",
        "skip_connection_2 = Lambda(space_to_depth_x2)(skip_connection_2)\n",
        "\n",
        "layer20 = concatenate([skip_connection_2, layer19])\n",
        "\n",
        "layer21 = Conv2D(num_classes, (1,1), name='conv_21', use_bias=False)(layer20)\n",
        "layer21 = BatchNormalization(name='norm_21')(layer21)\n",
        "\n",
        "layer22 = GlobalAveragePooling2D(data_format=None)(layer21)\n",
        "\n",
        "layer23 = Activation('softmax')(layer22)\n",
        "\n",
        "output = layer23\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uPFcNRhmd3K4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Import Dataset"
      ]
    },
    {
      "metadata": {
        "id": "Rbga60bNW5qn",
        "colab_type": "code",
        "outputId": "8beb0b23-fa14-4736-e202-30bdb6e8152e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2-uxInsGaQTb",
        "colab_type": "code",
        "outputId": "5c318a80-c08b-44f0-efb5-f9678407baa9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "cell_type": "code",
      "source": [
        "!wget http://cs231n.stanford.edu/tiny-imagenet-200.zip\n",
        "!unzip -qq 'tiny-imagenet-200.zip'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-04-05 04:36:19--  http://cs231n.stanford.edu/tiny-imagenet-200.zip\n",
            "Resolving cs231n.stanford.edu (cs231n.stanford.edu)... 171.64.68.10\n",
            "Connecting to cs231n.stanford.edu (cs231n.stanford.edu)|171.64.68.10|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 248100043 (237M) [application/zip]\n",
            "Saving to: ‘tiny-imagenet-200.zip’\n",
            "\n",
            "tiny-imagenet-200.z 100%[===================>] 236.61M  20.7MB/s    in 14s     \n",
            "\n",
            "2019-04-05 04:36:33 (16.7 MB/s) - ‘tiny-imagenet-200.zip’ saved [248100043/248100043]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "r7SjAhI2Jskn",
        "colab_type": "code",
        "outputId": "d6b0d77b-7ec1-42e9-e70a-48f52d74a626",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "cell_type": "code",
      "source": [
        "val_data = pd.read_csv('./tiny-imagenet-200/val/val_annotations.txt', sep='\\t', header=None, names=['File', 'Class', 'X', 'Y', 'H', 'W'])\n",
        "val_data.drop(['X', 'Y', 'H', 'W'], axis=1, inplace=True)\n",
        "val_data.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>File</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>val_0.JPEG</td>\n",
              "      <td>n03444034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>val_1.JPEG</td>\n",
              "      <td>n04067472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>val_2.JPEG</td>\n",
              "      <td>n04070727</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "         File      Class\n",
              "0  val_0.JPEG  n03444034\n",
              "1  val_1.JPEG  n04067472\n",
              "2  val_2.JPEG  n04070727"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "wRYLyZtwKKDp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "    rescale= 1./255)\n",
        "\n",
        "valid_datagen = ImageDataGenerator(rescale=1./255)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "igYtU_VSKXto",
        "colab_type": "code",
        "outputId": "8878fa72-29e7-4dc5-e07d-b9413e9f6d61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "train_generator = train_datagen.flow_from_directory( r'./tiny-imagenet-200/train/', target_size=(32, 32), color_mode='rgb', \n",
        "                                                    batch_size=256, class_mode='categorical', shuffle=True, seed=42)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 100000 images belonging to 200 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UBRfC4SdKbca",
        "colab_type": "code",
        "outputId": "3a405c74-d2ed-4798-aeac-47a13cd1bac1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "validation_generator = valid_datagen.flow_from_dataframe(val_data, directory='./tiny-imagenet-200/val/images/', x_col='File', y_col='Class', target_size=(64, 64),\n",
        "                                                    color_mode='rgb', class_mode='categorical', batch_size=256, shuffle=True, seed=42)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 10000 images belonging to 200 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dSZCfzq3LctT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Model"
      ]
    },
    {
      "metadata": {
        "id": "Jeh0VAxy26NV",
        "colab_type": "code",
        "outputId": "e20ff033-72b0-465c-8808-83f11a417382",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2149
        }
      },
      "cell_type": "code",
      "source": [
        "model = Model(inputs=[input], outputs=[output])\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, None, None, 3 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv_1 (Conv2D)                 (None, None, None, 3 864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "norm_1 (BatchNormalization)     (None, None, None, 3 128         conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, None, None, 3 0           norm_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_2 (Conv2D)                 (None, None, None, 6 18432       activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "norm_2 (BatchNormalization)     (None, None, None, 6 256         conv_2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, None, None, 6 0           norm_2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_3 (Conv2D)                 (None, None, None, 1 73728       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "norm_3 (BatchNormalization)     (None, None, None, 1 512         conv_3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, None, None, 1 0           norm_3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_4 (Conv2D)                 (None, None, None, 2 294912      activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "norm_4 (BatchNormalization)     (None, None, None, 2 1024        conv_4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, None, None, 2 0           norm_4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_5 (Conv2D)                 (None, None, None, 5 1179648     activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "norm_5 (BatchNormalization)     (None, None, None, 5 2048        conv_5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, None, None, 5 0           norm_5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, None, None, 5 0           activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv_7 (Conv2D)                 (None, None, None, 6 294912      max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "norm_7 (BatchNormalization)     (None, None, None, 6 256         conv_7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, None, None, 6 0           norm_7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_8 (Conv2D)                 (None, None, None, 1 73728       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "norm_8 (BatchNormalization)     (None, None, None, 1 512         conv_8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, None, None, 1 0           norm_8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_9 (Conv2D)                 (None, None, None, 2 294912      activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "norm_9 (BatchNormalization)     (None, None, None, 2 1024        conv_9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, None, None, 2 0           norm_9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_10 (Conv2D)                (None, None, None, 5 1179648     activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "norm_10 (BatchNormalization)    (None, None, None, 5 2048        conv_10[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, None, None, 5 0           norm_10[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_11 (Conv2D)                (None, None, None, 1 4718592     activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "norm_11 (BatchNormalization)    (None, None, None, 1 4096        conv_11[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, None, None, 1 0           norm_11[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_1 (Lambda)               (None, None, None, 2 0           max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, None, None, 1 0           activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, None, None, 3 0           lambda_1[0][0]                   \n",
            "                                                                 max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv_14 (Conv2D)                (None, None, None, 3 884736      concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "norm_14 (BatchNormalization)    (None, None, None, 3 128         conv_14[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, None, None, 3 0           norm_14[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_15 (Conv2D)                (None, None, None, 1 36864       activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "norm_15 (BatchNormalization)    (None, None, None, 1 512         conv_15[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, None, None, 1 0           norm_15[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_16 (Conv2D)                (None, None, None, 2 294912      activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "norm_16 (BatchNormalization)    (None, None, None, 2 1024        conv_16[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, None, None, 2 0           norm_16[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_17 (Conv2D)                (None, None, None, 5 1179648     activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "norm_17 (BatchNormalization)    (None, None, None, 5 2048        conv_17[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, None, None, 5 0           norm_17[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_18 (Conv2D)                (None, None, None, 1 4718592     activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "norm_18 (BatchNormalization)    (None, None, None, 1 4096        conv_18[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, None, None, 1 0           norm_18[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_2 (Lambda)               (None, None, None, 1 0           concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, None, None, 1 0           activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, None, None, 1 0           lambda_2[0][0]                   \n",
            "                                                                 max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv_21 (Conv2D)                (None, None, None, 2 2662400     concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "norm_21 (BatchNormalization)    (None, None, None, 2 800         conv_21[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_1 (Glo (None, 200)          0           norm_21[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 200)          0           global_average_pooling2d_1[0][0] \n",
            "==================================================================================================\n",
            "Total params: 17,927,040\n",
            "Trainable params: 17,916,784\n",
            "Non-trainable params: 10,256\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "apCwOjvZ4Kts",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1), cooldown=0, patience=5, min_lr=0.5e-6)\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UxCBlIY7EBcX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Model Training"
      ]
    },
    {
      "metadata": {
        "id": "tLaFy2AO4TLl",
        "colab_type": "code",
        "outputId": "d90f43d4-4ee3-423d-ed63-bf26dfb2b28d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "cell_type": "code",
      "source": [
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1, callbacks=[lr_reducer])\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_1.h5')  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/15\n",
            "200/200 [==============================] - 719s 4s/step - loss: 4.5179 - acc: 0.0921 - val_loss: 5.2283 - val_acc: 0.0477\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 687s 3s/step - loss: 3.9749 - acc: 0.1672 - val_loss: 4.3596 - val_acc: 0.0938\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 680s 3s/step - loss: 3.6383 - acc: 0.2196 - val_loss: 4.4865 - val_acc: 0.0935\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 680s 3s/step - loss: 3.4201 - acc: 0.2534 - val_loss: 4.1701 - val_acc: 0.1314\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 681s 3s/step - loss: 3.1836 - acc: 0.2940 - val_loss: 4.0022 - val_acc: 0.1568\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 680s 3s/step - loss: 3.0464 - acc: 0.3201 - val_loss: 4.1359 - val_acc: 0.1327\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 681s 3s/step - loss: 2.8370 - acc: 0.3549 - val_loss: 3.9202 - val_acc: 0.1839\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 680s 3s/step - loss: 2.7479 - acc: 0.3728 - val_loss: 3.9752 - val_acc: 0.1682\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 682s 3s/step - loss: 2.5738 - acc: 0.4051 - val_loss: 3.8251 - val_acc: 0.1930\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 680s 3s/step - loss: 2.4878 - acc: 0.4188 - val_loss: 3.8984 - val_acc: 0.1820\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 682s 3s/step - loss: 2.3193 - acc: 0.4530 - val_loss: 3.9706 - val_acc: 0.1753\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 678s 3s/step - loss: 2.2565 - acc: 0.4669 - val_loss: 4.5060 - val_acc: 0.0928\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 680s 3s/step - loss: 2.0922 - acc: 0.5023 - val_loss: 3.9182 - val_acc: 0.1745\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 677s 3s/step - loss: 2.0139 - acc: 0.5208 - val_loss: 3.9520 - val_acc: 0.1696\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 681s 3s/step - loss: 1.6449 - acc: 0.6062 - val_loss: 3.5176 - val_acc: 0.2562\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "KP-SlBZKY4vF",
        "outputId": "e3b07eeb-6a92-49d9-e907-b0225c68988b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "train_generator = train_datagen.flow_from_directory( r'./tiny-imagenet-200/train/', target_size=(64, 64), color_mode='rgb', \n",
        "                                                    batch_size=64, class_mode='categorical', shuffle=True, seed=42)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 100000 images belonging to 200 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Y9OEIKglY4vV",
        "outputId": "e292df0d-2bcb-4de7-ff9f-2a70c95b63ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "validation_generator = valid_datagen.flow_from_dataframe(val_data, directory='./tiny-imagenet-200/val/images/', x_col='File', y_col='Class', target_size=(64, 64),\n",
        "                                                    color_mode='rgb', class_mode='categorical', batch_size=64, shuffle=True, seed=42)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 10000 images belonging to 200 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pTfZIGrf4Uyd",
        "colab_type": "code",
        "outputId": "4b9feb4e-d94e-44af-dacf-2e57a0b3ff3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_1.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1, callbacks=[lr_reducer])\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_2.h5') "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/15\n",
            "200/200 [==============================] - 395s 2s/step - loss: 2.7409 - acc: 0.4071 - val_loss: 2.8456 - val_acc: 0.3434\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 372s 2s/step - loss: 2.5851 - acc: 0.4286 - val_loss: 2.8436 - val_acc: 0.3512\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 373s 2s/step - loss: 2.5120 - acc: 0.4382 - val_loss: 2.5711 - val_acc: 0.4074\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 371s 2s/step - loss: 2.4493 - acc: 0.4444 - val_loss: 2.8408 - val_acc: 0.3536\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 373s 2s/step - loss: 2.3869 - acc: 0.4577 - val_loss: 2.5100 - val_acc: 0.4247\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 372s 2s/step - loss: 2.3580 - acc: 0.4674 - val_loss: 2.5383 - val_acc: 0.4141\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 373s 2s/step - loss: 2.3610 - acc: 0.4598 - val_loss: 2.5741 - val_acc: 0.4052\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 379s 2s/step - loss: 2.2868 - acc: 0.4767 - val_loss: 2.4385 - val_acc: 0.4362\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 373s 2s/step - loss: 2.1375 - acc: 0.5100 - val_loss: 2.5223 - val_acc: 0.4102\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 372s 2s/step - loss: 2.1429 - acc: 0.5081 - val_loss: 2.4010 - val_acc: 0.4424\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 373s 2s/step - loss: 2.1376 - acc: 0.5087 - val_loss: 2.4554 - val_acc: 0.4244\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 372s 2s/step - loss: 2.1385 - acc: 0.5064 - val_loss: 2.4826 - val_acc: 0.4293\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 372s 2s/step - loss: 2.1224 - acc: 0.5095 - val_loss: 2.4316 - val_acc: 0.4317\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 371s 2s/step - loss: 2.1017 - acc: 0.5105 - val_loss: 2.4378 - val_acc: 0.4353\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 370s 2s/step - loss: 2.1063 - acc: 0.5145 - val_loss: 2.3793 - val_acc: 0.4429\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Jiyb9TlVGsZs",
        "colab_type": "code",
        "outputId": "d31c98b6-fee0-4d68-8777-41e3544d550e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_2.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1)\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_3.h5')  # creates a HDF5 file 'my_model.h5'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "200/200 [==============================] - 375s 2s/step - loss: 2.0313 - acc: 0.5311 - val_loss: 2.2556 - val_acc: 0.4666\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 371s 2s/step - loss: 1.9186 - acc: 0.5566 - val_loss: 2.3949 - val_acc: 0.4382\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 371s 2s/step - loss: 1.9166 - acc: 0.5563 - val_loss: 2.2960 - val_acc: 0.4643\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 370s 2s/step - loss: 1.9561 - acc: 0.5463 - val_loss: 2.3387 - val_acc: 0.4475\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 371s 2s/step - loss: 1.9363 - acc: 0.5496 - val_loss: 2.2723 - val_acc: 0.4688\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 372s 2s/step - loss: 1.9214 - acc: 0.5480 - val_loss: 2.2265 - val_acc: 0.4754\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 372s 2s/step - loss: 1.9495 - acc: 0.5410 - val_loss: 2.4182 - val_acc: 0.4413\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 369s 2s/step - loss: 1.9169 - acc: 0.5523 - val_loss: 2.3398 - val_acc: 0.4514\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.8185 - acc: 0.5760 - val_loss: 2.2635 - val_acc: 0.4736\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.7786 - acc: 0.5863 - val_loss: 2.3366 - val_acc: 0.4569\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.7436 - acc: 0.5928 - val_loss: 2.3672 - val_acc: 0.4485\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 1.7651 - acc: 0.5904 - val_loss: 2.2242 - val_acc: 0.4697\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.7753 - acc: 0.5811 - val_loss: 2.2009 - val_acc: 0.4836\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 1.7831 - acc: 0.5816 - val_loss: 2.2450 - val_acc: 0.4729\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.7623 - acc: 0.5804 - val_loss: 2.1495 - val_acc: 0.4893\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "dc51f7ab-522e-4a9b-8e3b-cc5e01eb8078",
        "id": "bQuOD6-q_WBs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "train_generator = train_datagen.flow_from_directory( r'./tiny-imagenet-200/train/', target_size=(16, 16), color_mode='rgb', \n",
        "                                                    batch_size=256, class_mode='categorical', shuffle=True, seed=42)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 100000 images belonging to 200 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "afeea3c8-4656-40a0-8403-746e7fb05672",
        "id": "T4nEVSde_WBx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "validation_generator = valid_datagen.flow_from_dataframe(val_data, directory='./tiny-imagenet-200/val/images/', x_col='File', y_col='Class', target_size=(64, 64),\n",
        "                                                    color_mode='rgb', class_mode='categorical', batch_size=256, shuffle=True, seed=42)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 10000 images belonging to 200 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "g9A3pesKbUJ8",
        "colab_type": "code",
        "outputId": "a5816699-bcc8-4226-f9ff-1eea354e59ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_3.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=10, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1)\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_4.h5')  # creates a HDF5 file 'my_model.h5'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "200/200 [==============================] - 516s 3s/step - loss: 3.4860 - acc: 0.2437 - val_loss: 4.7604 - val_acc: 0.1183\n",
            "Epoch 2/10\n",
            "200/200 [==============================] - 490s 2s/step - loss: 2.9366 - acc: 0.3229 - val_loss: 4.7005 - val_acc: 0.1193\n",
            "Epoch 3/10\n",
            "200/200 [==============================] - 485s 2s/step - loss: 2.5609 - acc: 0.3910 - val_loss: 4.8211 - val_acc: 0.1118\n",
            "Epoch 4/10\n",
            "200/200 [==============================] - 485s 2s/step - loss: 2.5257 - acc: 0.3996 - val_loss: 4.7537 - val_acc: 0.1168\n",
            "Epoch 5/10\n",
            "200/200 [==============================] - 485s 2s/step - loss: 2.2143 - acc: 0.4627 - val_loss: 4.9427 - val_acc: 0.0998\n",
            "Epoch 6/10\n",
            "200/200 [==============================] - 485s 2s/step - loss: 2.1764 - acc: 0.4738 - val_loss: 4.7097 - val_acc: 0.1144\n",
            "Epoch 7/10\n",
            "200/200 [==============================] - 485s 2s/step - loss: 1.8859 - acc: 0.5362 - val_loss: 4.8113 - val_acc: 0.1110\n",
            "Epoch 8/10\n",
            "200/200 [==============================] - 485s 2s/step - loss: 1.8675 - acc: 0.5440 - val_loss: 4.9543 - val_acc: 0.1019\n",
            "Epoch 9/10\n",
            "200/200 [==============================] - 486s 2s/step - loss: 1.5894 - acc: 0.6119 - val_loss: 5.0219 - val_acc: 0.0944\n",
            "Epoch 10/10\n",
            "200/200 [==============================] - 485s 2s/step - loss: 1.5537 - acc: 0.6211 - val_loss: 5.0340 - val_acc: 0.0911\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "7916dbbc-15f2-45bd-d4da-5570710eeae1",
        "id": "GgB7y1Bw_Pyg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "train_generator = train_datagen.flow_from_directory( r'./tiny-imagenet-200/train/', target_size=(64, 64), color_mode='rgb', \n",
        "                                                    batch_size=64, class_mode='categorical', shuffle=True, seed=42)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 100000 images belonging to 200 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "b613a3de-31eb-48fd-ad2c-fa017209197a",
        "id": "IuROfpVM_Pyo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "validation_generator = valid_datagen.flow_from_dataframe(val_data, directory='./tiny-imagenet-200/val/images/', x_col='File', y_col='Class', target_size=(64, 64),\n",
        "                                                    color_mode='rgb', class_mode='categorical', batch_size=64, shuffle=True, seed=42)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 10000 images belonging to 200 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Go-hD8jOiV63",
        "colab_type": "code",
        "outputId": "3916a8c6-d98d-4771-fbbf-87957e63f3c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_4.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1)\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_5.h5')  # creates a HDF5 file 'my_model.h5'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "200/200 [==============================] - 372s 2s/step - loss: 2.0079 - acc: 0.5406 - val_loss: 2.4416 - val_acc: 0.4276\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 1.8085 - acc: 0.5763 - val_loss: 2.3363 - val_acc: 0.4518\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 367s 2s/step - loss: 1.7462 - acc: 0.5863 - val_loss: 2.2683 - val_acc: 0.4579\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 1.7171 - acc: 0.5946 - val_loss: 2.4370 - val_acc: 0.4351\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 1.6746 - acc: 0.5999 - val_loss: 2.2319 - val_acc: 0.4773\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 367s 2s/step - loss: 1.6712 - acc: 0.6012 - val_loss: 2.2274 - val_acc: 0.4707\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 1.6880 - acc: 0.5963 - val_loss: 2.3078 - val_acc: 0.4560\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 364s 2s/step - loss: 1.6217 - acc: 0.6159 - val_loss: 2.1228 - val_acc: 0.4938\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 1.4558 - acc: 0.6572 - val_loss: 2.2523 - val_acc: 0.4685\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 1.4755 - acc: 0.6541 - val_loss: 2.1089 - val_acc: 0.5022\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 1.4876 - acc: 0.6445 - val_loss: 2.1873 - val_acc: 0.4876\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 1.4948 - acc: 0.6470 - val_loss: 2.2288 - val_acc: 0.4788\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 1.4982 - acc: 0.6491 - val_loss: 2.2284 - val_acc: 0.4773\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.4952 - acc: 0.6460 - val_loss: 2.3042 - val_acc: 0.4628\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 1.4960 - acc: 0.6451 - val_loss: 2.1528 - val_acc: 0.4847\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YKbchzczZ5rG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Image Augmentation"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "uY9B6BcJpxuS",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from imgaug import augmenters as iaa\n",
        "\n",
        "seq = iaa.SomeOf((0, None), [iaa.Affine(scale=(0.5, 1.5)), iaa.Affine(rotate=20),\n",
        "    iaa.CoarseDropout((0.0, 0.2), size_percent=(0.05, 0.07)), iaa.AdditiveGaussianNoise(scale=0.05*255), iaa.CropAndPad(percent=(-0.25, 0.25))\n",
        "     ])\n",
        "\n",
        "train_datagen = ImageDataGenerator(preprocessing_function=seq.augment_image, rescale=1./255)\n",
        "\n",
        "valid_datagen = ImageDataGenerator(rescale=1./255)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "81171c3c-6dde-40e1-8275-71c9e5a47fa1",
        "id": "JPxmaJdfpxwj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "train_generator = train_datagen.flow_from_directory( r'./tiny-imagenet-200/train/', target_size=(64, 64), color_mode='rgb', \n",
        "                                                    batch_size=64, class_mode='categorical', shuffle=True, seed=42)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 100000 images belonging to 200 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "9105613d-f2dc-4f71-ce54-00049916a157",
        "id": "i-QiQmF6pxwo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "validation_generator = valid_datagen.flow_from_dataframe(val_data, directory='./tiny-imagenet-200/val/images/', x_col='File', y_col='Class', target_size=(64, 64),\n",
        "                                                    color_mode='rgb', class_mode='categorical', batch_size=64, shuffle=True, seed=42)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 10000 images belonging to 200 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NC8riS_4QbXZ",
        "colab_type": "code",
        "outputId": "b7c7b096-95c1-4bb1-c691-114a6d361567",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "cell_type": "code",
      "source": [
        "x_batch, y_batch = next(train_generator)\n",
        "\n",
        "fig = plt.figure(figsize=(14, 5))\n",
        "\n",
        "for i in range(8):\n",
        "    sub = fig.add_subplot(2, 4, i + 1)\n",
        "    sub.imshow(x_batch[i,:,:], interpolation='bilinear')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YUr7lSiSOPyI9aprmFvE/HA5oo8sC0Bhr8X5gGKTTu+8PKCVNITFGtNLl92aGvmfo+7kL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ODm8xDQpVTUpfAG/rzSvetdFFx2fAi/C4L/sksbHiKlo6pqTGtI05Q4GTKZbJGmDq3SQHmu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\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la0yrOU2TMiLW65LjE0U1DUgJu5cMiIQglTowrQSXnyh55kO7I5K26zq6rk0ZAsD+FUNZ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\n",
            "text/plain": [
              "<Figure size 1008x360 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "yu-V8CS0iWRS",
        "colab_type": "code",
        "outputId": "6d11ac00-eb1c-4ea0-fa89-0a602d2a7545",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_5.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1)\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_6.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/15\n",
            "200/200 [==============================] - 382s 2s/step - loss: 2.8627 - acc: 0.3569 - val_loss: 2.7110 - val_acc: 0.3952\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 360s 2s/step - loss: 2.6453 - acc: 0.3914 - val_loss: 2.4072 - val_acc: 0.4429\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 360s 2s/step - loss: 2.5821 - acc: 0.4111 - val_loss: 2.5396 - val_acc: 0.4261\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 359s 2s/step - loss: 2.5369 - acc: 0.4145 - val_loss: 2.5355 - val_acc: 0.4201\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 360s 2s/step - loss: 2.5000 - acc: 0.4201 - val_loss: 2.4054 - val_acc: 0.4378\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 360s 2s/step - loss: 2.4910 - acc: 0.4248 - val_loss: 2.3955 - val_acc: 0.4479\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 360s 2s/step - loss: 2.5049 - acc: 0.4137 - val_loss: 2.3900 - val_acc: 0.4493\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 368s 2s/step - loss: 2.4515 - acc: 0.4316 - val_loss: 2.3580 - val_acc: 0.4499\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 2.3837 - acc: 0.4459 - val_loss: 2.4884 - val_acc: 0.4257\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 360s 2s/step - loss: 2.3759 - acc: 0.4493 - val_loss: 2.4049 - val_acc: 0.4413\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 360s 2s/step - loss: 2.4008 - acc: 0.4376 - val_loss: 2.4296 - val_acc: 0.4414\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 2.3758 - acc: 0.4391 - val_loss: 2.2801 - val_acc: 0.4724\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 360s 2s/step - loss: 2.3497 - acc: 0.4506 - val_loss: 2.3978 - val_acc: 0.4484\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 359s 2s/step - loss: 2.3477 - acc: 0.4473 - val_loss: 2.2802 - val_acc: 0.4648\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 360s 2s/step - loss: 2.3532 - acc: 0.4524 - val_loss: 2.3235 - val_acc: 0.4621\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ngWSQXS_vWed",
        "colab_type": "code",
        "outputId": "c9c3a3e9-81ce-4e95-aa69-67a6222c3bdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_6.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1)\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_7.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 2.3833 - acc: 0.4430 - val_loss: 2.2280 - val_acc: 0.4722\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 363s 2s/step - loss: 2.2865 - acc: 0.4653 - val_loss: 2.2960 - val_acc: 0.4679\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 363s 2s/step - loss: 2.2779 - acc: 0.4613 - val_loss: 2.3714 - val_acc: 0.4479\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.3223 - acc: 0.4541 - val_loss: 2.2054 - val_acc: 0.4766\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.3297 - acc: 0.4516 - val_loss: 2.2342 - val_acc: 0.4755\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.2897 - acc: 0.4607 - val_loss: 2.2915 - val_acc: 0.4648\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 2.3102 - acc: 0.4630 - val_loss: 2.2837 - val_acc: 0.4729\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 2.3223 - acc: 0.4572 - val_loss: 2.2760 - val_acc: 0.4720\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 2.2406 - acc: 0.4731 - val_loss: 2.1512 - val_acc: 0.4901\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 2.2517 - acc: 0.4632 - val_loss: 2.2970 - val_acc: 0.4637\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 2.2138 - acc: 0.4773 - val_loss: 2.1989 - val_acc: 0.4861\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.2350 - acc: 0.4685 - val_loss: 2.2684 - val_acc: 0.4737\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.2366 - acc: 0.4698 - val_loss: 2.3082 - val_acc: 0.4630\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.2756 - acc: 0.4672 - val_loss: 2.4158 - val_acc: 0.4437\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 363s 2s/step - loss: 2.2313 - acc: 0.4709 - val_loss: 2.2173 - val_acc: 0.4756\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "O4YBiw7UrQrT",
        "colab_type": "code",
        "outputId": "8f98f693-a4a3-4aed-be3b-29ee2dffb1ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_7.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1)\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_8.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "200/200 [==============================] - 367s 2s/step - loss: 2.2663 - acc: 0.4680 - val_loss: 2.2842 - val_acc: 0.4758\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.1773 - acc: 0.4935 - val_loss: 2.2088 - val_acc: 0.4836\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.1583 - acc: 0.4919 - val_loss: 2.2170 - val_acc: 0.4787\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.1747 - acc: 0.4875 - val_loss: 2.2576 - val_acc: 0.4731\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.1979 - acc: 0.4864 - val_loss: 2.3209 - val_acc: 0.4617\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.1850 - acc: 0.4834 - val_loss: 2.2705 - val_acc: 0.4757\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.2168 - acc: 0.4771 - val_loss: 2.1889 - val_acc: 0.4821\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.1966 - acc: 0.4758 - val_loss: 2.1728 - val_acc: 0.4920\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.1975 - acc: 0.4798 - val_loss: 2.1387 - val_acc: 0.4932\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0984 - acc: 0.5016 - val_loss: 2.2164 - val_acc: 0.4796\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.1107 - acc: 0.4950 - val_loss: 2.2932 - val_acc: 0.4707\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.1231 - acc: 0.4950 - val_loss: 2.2895 - val_acc: 0.4667\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.1528 - acc: 0.4869 - val_loss: 2.2601 - val_acc: 0.4714\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 2.1335 - acc: 0.4946 - val_loss: 2.1881 - val_acc: 0.4866\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.1610 - acc: 0.4853 - val_loss: 2.1870 - val_acc: 0.4862\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5BMzXw6A7VG6",
        "colab_type": "code",
        "outputId": "f59c5996-7f27-490e-9534-9b26256fb6a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_8.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1)\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_9.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "200/200 [==============================] - 366s 2s/step - loss: 2.1521 - acc: 0.4890 - val_loss: 2.1257 - val_acc: 0.5005\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 2.0982 - acc: 0.4981 - val_loss: 2.2512 - val_acc: 0.4745\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0412 - acc: 0.5166 - val_loss: 2.4439 - val_acc: 0.4402\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0843 - acc: 0.4982 - val_loss: 2.2887 - val_acc: 0.4633\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0836 - acc: 0.5081 - val_loss: 2.1877 - val_acc: 0.4895\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0756 - acc: 0.5077 - val_loss: 2.1564 - val_acc: 0.4969\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 363s 2s/step - loss: 2.1002 - acc: 0.5034 - val_loss: 2.2122 - val_acc: 0.4800\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0931 - acc: 0.5013 - val_loss: 2.1078 - val_acc: 0.4987\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0962 - acc: 0.5001 - val_loss: 2.2246 - val_acc: 0.4819\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 2.0600 - acc: 0.5054 - val_loss: 2.1572 - val_acc: 0.4998\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0202 - acc: 0.5227 - val_loss: 2.1397 - val_acc: 0.5006\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0146 - acc: 0.5194 - val_loss: 2.0847 - val_acc: 0.5143\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 2.0489 - acc: 0.5073 - val_loss: 2.1758 - val_acc: 0.4939\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0337 - acc: 0.5135 - val_loss: 2.1819 - val_acc: 0.4936\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0462 - acc: 0.5108 - val_loss: 2.1928 - val_acc: 0.4902\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3FhGaSe4JRZV",
        "colab_type": "code",
        "outputId": "1761aa2a-e470-4064-924b-80b4427abf28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_9.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1)\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_10.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "200/200 [==============================] - 367s 2s/step - loss: 1.9811 - acc: 0.5243 - val_loss: 2.2030 - val_acc: 0.4866\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 1.9912 - acc: 0.5199 - val_loss: 2.1993 - val_acc: 0.4882\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0057 - acc: 0.5184 - val_loss: 2.2174 - val_acc: 0.4799\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 2.0149 - acc: 0.5167 - val_loss: 2.3835 - val_acc: 0.4561\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 1.9944 - acc: 0.5245 - val_loss: 2.1740 - val_acc: 0.4949\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 1.9915 - acc: 0.5223 - val_loss: 2.1827 - val_acc: 0.4881\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 1.9763 - acc: 0.5294 - val_loss: 2.1332 - val_acc: 0.5010\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 362s 2s/step - loss: 1.9436 - acc: 0.5328 - val_loss: 2.1748 - val_acc: 0.4954\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 1.9742 - acc: 0.5253 - val_loss: 2.1023 - val_acc: 0.5039\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 1.9667 - acc: 0.5327 - val_loss: 2.0895 - val_acc: 0.5097\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 1.9606 - acc: 0.5292 - val_loss: 2.1037 - val_acc: 0.5047\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 360s 2s/step - loss: 1.9778 - acc: 0.5269 - val_loss: 2.0701 - val_acc: 0.5128\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 1.9615 - acc: 0.5224 - val_loss: 2.1167 - val_acc: 0.5013\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 361s 2s/step - loss: 1.9869 - acc: 0.5188 - val_loss: 2.0950 - val_acc: 0.4992\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 360s 2s/step - loss: 1.9906 - acc: 0.5219 - val_loss: 2.0837 - val_acc: 0.5103\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3WOp_Fh4s2fd",
        "colab_type": "code",
        "outputId": "15677d96-0606-48c0-b41e-9b7a98137b35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_10.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1)\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_11.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/15\n",
            "200/200 [==============================] - 368s 2s/step - loss: 1.8977 - acc: 0.5450 - val_loss: 2.0854 - val_acc: 0.5118\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 348s 2s/step - loss: 1.9261 - acc: 0.5409 - val_loss: 2.0325 - val_acc: 0.5218\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.9013 - acc: 0.5430 - val_loss: 2.0751 - val_acc: 0.5155\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.9180 - acc: 0.5359 - val_loss: 2.0963 - val_acc: 0.5062\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.9230 - acc: 0.5390 - val_loss: 2.0226 - val_acc: 0.5212\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 348s 2s/step - loss: 1.9247 - acc: 0.5320 - val_loss: 2.1358 - val_acc: 0.4959\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 346s 2s/step - loss: 1.9261 - acc: 0.5364 - val_loss: 2.2188 - val_acc: 0.4911\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 355s 2s/step - loss: 1.9244 - acc: 0.5355 - val_loss: 2.0558 - val_acc: 0.5117\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.8396 - acc: 0.5589 - val_loss: 2.0781 - val_acc: 0.5130\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.8489 - acc: 0.5553 - val_loss: 2.0734 - val_acc: 0.5049\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 346s 2s/step - loss: 1.8650 - acc: 0.5465 - val_loss: 2.0496 - val_acc: 0.5203\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.8751 - acc: 0.5524 - val_loss: 2.0129 - val_acc: 0.5285\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.8640 - acc: 0.5517 - val_loss: 2.0945 - val_acc: 0.5095\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.8578 - acc: 0.5489 - val_loss: 2.0315 - val_acc: 0.5208\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 349s 2s/step - loss: 1.8699 - acc: 0.5509 - val_loss: 2.0415 - val_acc: 0.5231\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Yh0K2l76yJvB",
        "colab_type": "code",
        "outputId": "25076bb0-838a-4e99-8841-d2bbcf0d3e0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_11.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1)\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_12.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "200/200 [==============================] - 354s 2s/step - loss: 1.7889 - acc: 0.5675 - val_loss: 2.1402 - val_acc: 0.5017\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.8288 - acc: 0.5596 - val_loss: 2.0741 - val_acc: 0.5140\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 348s 2s/step - loss: 1.8203 - acc: 0.5613 - val_loss: 2.0446 - val_acc: 0.5251\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 348s 2s/step - loss: 1.8302 - acc: 0.5534 - val_loss: 2.1222 - val_acc: 0.5022\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.8313 - acc: 0.5544 - val_loss: 2.0407 - val_acc: 0.5227\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 348s 2s/step - loss: 1.8224 - acc: 0.5617 - val_loss: 2.0687 - val_acc: 0.5167\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 348s 2s/step - loss: 1.8544 - acc: 0.5559 - val_loss: 2.0957 - val_acc: 0.5150\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 346s 2s/step - loss: 1.8123 - acc: 0.5654 - val_loss: 2.0181 - val_acc: 0.5272\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 348s 2s/step - loss: 1.7418 - acc: 0.5766 - val_loss: 2.0364 - val_acc: 0.5218\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 348s 2s/step - loss: 1.7635 - acc: 0.5766 - val_loss: 2.0133 - val_acc: 0.5275\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.7759 - acc: 0.5640 - val_loss: 2.1445 - val_acc: 0.5042\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 348s 2s/step - loss: 1.7879 - acc: 0.5646 - val_loss: 2.1316 - val_acc: 0.5056\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.7782 - acc: 0.5699 - val_loss: 2.0688 - val_acc: 0.5158\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.7860 - acc: 0.5641 - val_loss: 2.0866 - val_acc: 0.5165\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 347s 2s/step - loss: 1.7874 - acc: 0.5715 - val_loss: 2.1434 - val_acc: 0.5062\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "aQK9GFu6-B37",
        "colab_type": "code",
        "outputId": "d68214f4-f1cf-4e1a-ab13-34794851d182",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_12.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1, callbacks=[lr_reducer])\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_13.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/15\n",
            "200/200 [==============================] - 390s 2s/step - loss: 1.6067 - acc: 0.6187 - val_loss: 2.0142 - val_acc: 0.5257\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.6079 - acc: 0.6155 - val_loss: 2.0256 - val_acc: 0.5267\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.5738 - acc: 0.6269 - val_loss: 2.0561 - val_acc: 0.5211\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 364s 2s/step - loss: 1.5915 - acc: 0.6181 - val_loss: 2.0679 - val_acc: 0.5176\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.5954 - acc: 0.6158 - val_loss: 1.9966 - val_acc: 0.5287\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.5931 - acc: 0.6189 - val_loss: 2.1341 - val_acc: 0.5026\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.5928 - acc: 0.6166 - val_loss: 2.1717 - val_acc: 0.5067\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 372s 2s/step - loss: 1.5879 - acc: 0.6202 - val_loss: 2.0135 - val_acc: 0.5321\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.5084 - acc: 0.6443 - val_loss: 2.0583 - val_acc: 0.5209\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.5104 - acc: 0.6399 - val_loss: 2.0645 - val_acc: 0.5143\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.4687 - acc: 0.6458 - val_loss: 1.8322 - val_acc: 0.5686\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.4321 - acc: 0.6569 - val_loss: 1.8244 - val_acc: 0.5728\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.3945 - acc: 0.6673 - val_loss: 1.8388 - val_acc: 0.5628\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.3741 - acc: 0.6696 - val_loss: 1.8338 - val_acc: 0.5669\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 364s 2s/step - loss: 1.3720 - acc: 0.6753 - val_loss: 1.8138 - val_acc: 0.5705\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BK6WZCO4-ELs",
        "colab_type": "code",
        "outputId": "88bce82e-1f7e-4263-9890-4bc4117bd1f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_13.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1, callbacks=[lr_reducer])\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_14.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/15\n",
            "200/200 [==============================] - 380s 2s/step - loss: 1.2164 - acc: 0.7241 - val_loss: 1.8155 - val_acc: 0.5692\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.1886 - acc: 0.7307 - val_loss: 1.8174 - val_acc: 0.5707\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.1492 - acc: 0.7439 - val_loss: 1.8298 - val_acc: 0.5716\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 356s 2s/step - loss: 1.1399 - acc: 0.7462 - val_loss: 1.8449 - val_acc: 0.5670\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.1312 - acc: 0.7476 - val_loss: 1.8500 - val_acc: 0.5663\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.1073 - acc: 0.7575 - val_loss: 1.8488 - val_acc: 0.5644\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.1002 - acc: 0.7579 - val_loss: 1.7843 - val_acc: 0.5773\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.0793 - acc: 0.7595 - val_loss: 1.7738 - val_acc: 0.5833\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.0075 - acc: 0.7849 - val_loss: 1.7758 - val_acc: 0.5812\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 0.9855 - acc: 0.7913 - val_loss: 1.7612 - val_acc: 0.5814\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.1961 - acc: 0.7226 - val_loss: 1.7677 - val_acc: 0.5830\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.1880 - acc: 0.7280 - val_loss: 1.7615 - val_acc: 0.5844\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.1622 - acc: 0.7343 - val_loss: 1.7822 - val_acc: 0.5798\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.1461 - acc: 0.7363 - val_loss: 1.7667 - val_acc: 0.5838\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.1470 - acc: 0.7410 - val_loss: 1.7726 - val_acc: 0.5811\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OfCDWePn-E7T",
        "colab_type": "code",
        "outputId": "514a635b-1758-496f-ecff-dfb40ad1b2a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_14.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1, callbacks=[lr_reducer])\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_15.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "200/200 [==============================] - 360s 2s/step - loss: 1.5404 - acc: 0.6245 - val_loss: 1.7478 - val_acc: 0.5865\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.4405 - acc: 0.6481 - val_loss: 1.7474 - val_acc: 0.5903\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.4057 - acc: 0.6598 - val_loss: 1.7395 - val_acc: 0.5904\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.4201 - acc: 0.6554 - val_loss: 1.7497 - val_acc: 0.5848\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.4346 - acc: 0.6535 - val_loss: 1.7248 - val_acc: 0.5938\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.3880 - acc: 0.6651 - val_loss: 1.7464 - val_acc: 0.5864\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.4110 - acc: 0.6619 - val_loss: 1.7356 - val_acc: 0.5902\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.4199 - acc: 0.6548 - val_loss: 1.7552 - val_acc: 0.5851\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.4006 - acc: 0.6643 - val_loss: 1.7101 - val_acc: 0.5977\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.4241 - acc: 0.6547 - val_loss: 1.7504 - val_acc: 0.5838\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.3774 - acc: 0.6683 - val_loss: 1.7350 - val_acc: 0.5898\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.3867 - acc: 0.6640 - val_loss: 1.7261 - val_acc: 0.5914\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.3926 - acc: 0.6655 - val_loss: 1.7406 - val_acc: 0.5887\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.4231 - acc: 0.6601 - val_loss: 1.7314 - val_acc: 0.5919\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.3878 - acc: 0.6628 - val_loss: 1.7310 - val_acc: 0.5874\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QCaL-x2d-FUH",
        "colab_type": "code",
        "outputId": "5dd7bd80-ca9d-47e7-d7a2-8783d66fbd26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_15.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=15, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1, callbacks=[lr_reducer])\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_16.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "200/200 [==============================] - 363s 2s/step - loss: 1.4273 - acc: 0.6569 - val_loss: 1.7375 - val_acc: 0.5905\n",
            "Epoch 2/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.4040 - acc: 0.6619 - val_loss: 1.7232 - val_acc: 0.5896\n",
            "Epoch 3/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.3838 - acc: 0.6679 - val_loss: 1.7398 - val_acc: 0.5878\n",
            "Epoch 4/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.3816 - acc: 0.6665 - val_loss: 1.7201 - val_acc: 0.5927\n",
            "Epoch 5/15\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.3995 - acc: 0.6627 - val_loss: 1.7309 - val_acc: 0.5943\n",
            "Epoch 6/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.4008 - acc: 0.6628 - val_loss: 1.7266 - val_acc: 0.5897\n",
            "Epoch 7/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.4042 - acc: 0.6602 - val_loss: 1.7276 - val_acc: 0.5918\n",
            "Epoch 8/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.3884 - acc: 0.6645 - val_loss: 1.7265 - val_acc: 0.5936\n",
            "Epoch 9/15\n",
            "200/200 [==============================] - 357s 2s/step - loss: 1.4074 - acc: 0.6635 - val_loss: 1.7293 - val_acc: 0.5921\n",
            "Epoch 10/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.3835 - acc: 0.6664 - val_loss: 1.7358 - val_acc: 0.5893\n",
            "Epoch 11/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.3779 - acc: 0.6660 - val_loss: 1.7230 - val_acc: 0.5938\n",
            "Epoch 12/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.3968 - acc: 0.6645 - val_loss: 1.7454 - val_acc: 0.5864\n",
            "Epoch 13/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.4067 - acc: 0.6584 - val_loss: 1.7193 - val_acc: 0.5940\n",
            "Epoch 14/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.4144 - acc: 0.6581 - val_loss: 1.7227 - val_acc: 0.5902\n",
            "Epoch 15/15\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.4103 - acc: 0.6613 - val_loss: 1.7368 - val_acc: 0.5899\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZdRXCaX914mk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "checkpoint = ModelCheckpoint('/content/drive/My Drive/Colab Notebooks/EIP Models/{epoch:02d}-{val_acc:.2f}.hdf5', monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5mGnCwjbjwLD",
        "colab_type": "code",
        "outputId": "ee883e3d-7939-4373-ce14-e375af814df1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_16.h5')\n",
        "\n",
        "model.fit_generator(train_generator, epochs=10, steps_per_epoch=200, validation_steps=200, validation_data=validation_generator, verbose=1, callbacks=[lr_reducer, checkpoint])\n",
        "\n",
        "model.save('/content/drive/My Drive/Colab Notebooks/EIP Models/Exp_25_17.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "200/200 [==============================] - 365s 2s/step - loss: 1.4030 - acc: 0.6616 - val_loss: 1.7339 - val_acc: 0.5916\n",
            "\n",
            "Epoch 00001: val_acc improved from -inf to 0.59159, saving model to /content/drive/My Drive/Colab Notebooks/EIP Models/01-0.59.hdf5\n",
            "Epoch 2/10\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.3881 - acc: 0.6642 - val_loss: 1.7303 - val_acc: 0.5899\n",
            "\n",
            "Epoch 00002: val_acc did not improve from 0.59159\n",
            "Epoch 3/10\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.3769 - acc: 0.6645 - val_loss: 1.7168 - val_acc: 0.5929\n",
            "\n",
            "Epoch 00003: val_acc improved from 0.59159 to 0.59288, saving model to /content/drive/My Drive/Colab Notebooks/EIP Models/03-0.59.hdf5\n",
            "Epoch 4/10\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.3691 - acc: 0.6767 - val_loss: 1.7424 - val_acc: 0.5885\n",
            "\n",
            "Epoch 00004: val_acc did not improve from 0.59288\n",
            "Epoch 5/10\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.3985 - acc: 0.6605 - val_loss: 1.7172 - val_acc: 0.5920\n",
            "\n",
            "Epoch 00005: val_acc did not improve from 0.59288\n",
            "Epoch 6/10\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.3788 - acc: 0.6691 - val_loss: 1.7337 - val_acc: 0.5904\n",
            "\n",
            "Epoch 00006: val_acc did not improve from 0.59288\n",
            "Epoch 7/10\n",
            "200/200 [==============================] - 358s 2s/step - loss: 1.3509 - acc: 0.6759 - val_loss: 1.7301 - val_acc: 0.5904\n",
            "\n",
            "Epoch 00007: val_acc did not improve from 0.59288\n",
            "Epoch 8/10\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.3402 - acc: 0.6793 - val_loss: 1.7188 - val_acc: 0.5933\n",
            "\n",
            "Epoch 00008: val_acc improved from 0.59288 to 0.59332, saving model to /content/drive/My Drive/Colab Notebooks/EIP Models/08-0.59.hdf5\n",
            "Epoch 9/10\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.3377 - acc: 0.6752 - val_loss: 1.7285 - val_acc: 0.5921\n",
            "\n",
            "Epoch 00009: val_acc did not improve from 0.59332\n",
            "Epoch 10/10\n",
            "200/200 [==============================] - 359s 2s/step - loss: 1.3909 - acc: 0.6650 - val_loss: 1.7367 - val_acc: 0.5871\n",
            "\n",
            "Epoch 00010: val_acc did not improve from 0.59332\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kgq7cgymat1Y",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Model Evaluation"
      ]
    },
    {
      "metadata": {
        "id": "oe98jZ7kaAdJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "valid_datagen = ImageDataGenerator(rescale=1./255)\n",
        "validation_generator = valid_datagen.flow_from_dataframe(val_data, directory='./tiny-imagenet-200/val/images/', x_col='File', y_col='Class', target_size=(64, 64),\n",
        "                                                    color_mode='rgb', class_mode='categorical', batch_size=64, shuffle=True, seed=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "rN0oEHEpY70E",
        "outputId": "bdf38b8a-1699-4b7d-a21d-48f6120da698",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "cell_type": "code",
      "source": [
        "model = load_model('/content/drive/My Drive/Colab Notebooks/EIP Models/08-0.59.hdf5')\n",
        "\n",
        "validation_steps_per_epoch = np.math.ceil(validation_generator.samples / validation_generator.batch_size)\n",
        "\n",
        "score = model.evaluate_generator(validation_generator, verbose=1, steps=validation_steps_per_epoch)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "157/157 [==============================] - 71s 449ms/step\n",
            "Test loss: 1.7169813255310058\n",
            "Test accuracy: 0.595\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}