Iris_Subsystem.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Iris_Subsystem.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/EriProject/Multimodal_Biometrics/blob/master/Iris_Subsystem.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jnDovlZ5b0o5",
        "colab_type": "code",
        "outputId": "a43f534c-db4f-46d4-8441-ca3a644a9785",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oi3tGraDi2v8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline \n",
        "import cv2\n",
        "import os\n",
        "import tensorflow as tf\n",
        "\n",
        "NUM_CLASSES = 40\n",
        "CHANNELS = 3\n",
        "IMAGE_RESIZE = 224\n",
        "RESNET50_POOLING_AVERAGE = 'avg'\n",
        "DENSE_LAYER_ACTIVATION = 'softmax'\n",
        "OBJECTIVE_FUNCTION = 'categorical_crossentropy'\n",
        "LOSS_METRICS = ['accuracy']\n",
        "EARLY_STOP_PATIENCE = 3\n",
        "\n",
        "BATCH_SIZE_TESTING = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kIXqu48yp68m",
        "colab_type": "text"
      },
      "source": [
        "STEPS_PER_EPOCH_TRAINING = training set/ batch size"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwglxhJtpNcx",
        "colab_type": "code",
        "outputId": "4699d18d-6ffa-4c28-cfb4-3f724444a940",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from keras.applications import ResNet50\n",
        "from keras.models import Sequential\n",
        "from keras.applications import imagenet_utils\n",
        "from keras.layers.core import Dense, Flatten, Dropout"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P88Z7_ypltTR",
        "colab_type": "code",
        "outputId": "2121c923-a535-4d4c-9260-6503c09ccf97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "modelNew = Sequential()\n",
        "vgg16_model = Sequential()\n",
        "vgg16_model = ResNet50(include_top = False,  weights = 'imagenet')\n",
        "vgg16_model.summary()\n",
        "type(vgg16_model)\n",
        "i=0\n",
        "for layer in vgg16_model.layers:   # just exclude last layer from copying\n",
        "  i=i+1\n",
        "print('layers: ',i)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
            "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"resnet50\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, None, None, 3 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, None, None, 3 0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1 (Conv2D)                  (None, None, None, 6 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn_conv1 (BatchNormalization)   (None, None, None, 6 256         conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, None, None, 6 0           bn_conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, None, None, 6 0           activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, None, None, 6 0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch2a (Conv2D)         (None, None, None, 6 4160        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch2a (BatchNormalizati (None, None, None, 6 256         res2a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, None, None, 6 0           bn2a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch2b (Conv2D)         (None, None, None, 6 36928       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch2b (BatchNormalizati (None, None, None, 6 256         res2a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, None, None, 6 0           bn2a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch2c (Conv2D)         (None, None, None, 2 16640       activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch1 (Conv2D)          (None, None, None, 2 16640       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch2c (BatchNormalizati (None, None, None, 2 1024        res2a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch1 (BatchNormalizatio (None, None, None, 2 1024        res2a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, None, None, 2 0           bn2a_branch2c[0][0]              \n",
            "                                                                 bn2a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, None, None, 2 0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res2b_branch2a (Conv2D)         (None, None, None, 6 16448       activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2b_branch2a (BatchNormalizati (None, None, None, 6 256         res2b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, None, None, 6 0           bn2b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2b_branch2b (Conv2D)         (None, None, None, 6 36928       activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2b_branch2b (BatchNormalizati (None, None, None, 6 256         res2b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, None, None, 6 0           bn2b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2b_branch2c (Conv2D)         (None, None, None, 2 16640       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2b_branch2c (BatchNormalizati (None, None, None, 2 1024        res2b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, None, None, 2 0           bn2b_branch2c[0][0]              \n",
            "                                                                 activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, None, None, 2 0           add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res2c_branch2a (Conv2D)         (None, None, None, 6 16448       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2c_branch2a (BatchNormalizati (None, None, None, 6 256         res2c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, None, None, 6 0           bn2c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2c_branch2b (Conv2D)         (None, None, None, 6 36928       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2c_branch2b (BatchNormalizati (None, None, None, 6 256         res2c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, None, None, 6 0           bn2c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2c_branch2c (Conv2D)         (None, None, None, 2 16640       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2c_branch2c (BatchNormalizati (None, None, None, 2 1024        res2c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, None, None, 2 0           bn2c_branch2c[0][0]              \n",
            "                                                                 activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, None, None, 2 0           add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch2a (Conv2D)         (None, None, None, 1 32896       activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch2a (BatchNormalizati (None, None, None, 1 512         res3a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, None, None, 1 0           bn3a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch2b (Conv2D)         (None, None, None, 1 147584      activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch2b (BatchNormalizati (None, None, None, 1 512         res3a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, None, None, 1 0           bn3a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch2c (Conv2D)         (None, None, None, 5 66048       activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch1 (Conv2D)          (None, None, None, 5 131584      activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch2c (BatchNormalizati (None, None, None, 5 2048        res3a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch1 (BatchNormalizatio (None, None, None, 5 2048        res3a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, None, None, 5 0           bn3a_branch2c[0][0]              \n",
            "                                                                 bn3a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, None, None, 5 0           add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3b_branch2a (Conv2D)         (None, None, None, 1 65664       activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3b_branch2a (BatchNormalizati (None, None, None, 1 512         res3b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, None, None, 1 0           bn3b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3b_branch2b (Conv2D)         (None, None, None, 1 147584      activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3b_branch2b (BatchNormalizati (None, None, None, 1 512         res3b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, None, None, 1 0           bn3b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3b_branch2c (Conv2D)         (None, None, None, 5 66048       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3b_branch2c (BatchNormalizati (None, None, None, 5 2048        res3b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, None, None, 5 0           bn3b_branch2c[0][0]              \n",
            "                                                                 activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, None, None, 5 0           add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3c_branch2a (Conv2D)         (None, None, None, 1 65664       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3c_branch2a (BatchNormalizati (None, None, None, 1 512         res3c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, None, None, 1 0           bn3c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3c_branch2b (Conv2D)         (None, None, None, 1 147584      activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3c_branch2b (BatchNormalizati (None, None, None, 1 512         res3c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, None, None, 1 0           bn3c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3c_branch2c (Conv2D)         (None, None, None, 5 66048       activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3c_branch2c (BatchNormalizati (None, None, None, 5 2048        res3c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, None, None, 5 0           bn3c_branch2c[0][0]              \n",
            "                                                                 activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, None, None, 5 0           add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3d_branch2a (Conv2D)         (None, None, None, 1 65664       activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3d_branch2a (BatchNormalizati (None, None, None, 1 512         res3d_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, None, None, 1 0           bn3d_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3d_branch2b (Conv2D)         (None, None, None, 1 147584      activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3d_branch2b (BatchNormalizati (None, None, None, 1 512         res3d_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, None, None, 1 0           bn3d_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3d_branch2c (Conv2D)         (None, None, None, 5 66048       activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3d_branch2c (BatchNormalizati (None, None, None, 5 2048        res3d_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, None, None, 5 0           bn3d_branch2c[0][0]              \n",
            "                                                                 activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, None, None, 5 0           add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res4a_branch2a (Conv2D)         (None, None, None, 2 131328      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4a_branch2a (BatchNormalizati (None, None, None, 2 1024        res4a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, None, None, 2 0           bn4a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4a_branch2b (Conv2D)         (None, None, None, 2 590080      activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4a_branch2b (BatchNormalizati (None, None, None, 2 1024        res4a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, None, None, 2 0           bn4a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4a_branch2c (Conv2D)         (None, None, None, 1 263168      activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4a_branch1 (Conv2D)          (None, None, None, 1 525312      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4a_branch2c (BatchNormalizati (None, None, None, 1 4096        res4a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn4a_branch1 (BatchNormalizatio (None, None, None, 1 4096        res4a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, None, None, 1 0           bn4a_branch2c[0][0]              \n",
            "                                                                 bn4a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, None, None, 1 0           add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res4b_branch2a (Conv2D)         (None, None, None, 2 262400      activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4b_branch2a (BatchNormalizati (None, None, None, 2 1024        res4b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, None, None, 2 0           bn4b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4b_branch2b (Conv2D)         (None, None, None, 2 590080      activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4b_branch2b (BatchNormalizati (None, None, None, 2 1024        res4b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, None, None, 2 0           bn4b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4b_branch2c (Conv2D)         (None, None, None, 1 263168      activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4b_branch2c (BatchNormalizati (None, None, None, 1 4096        res4b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, None, None, 1 0           bn4b_branch2c[0][0]              \n",
            "                                                                 activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, None, None, 1 0           add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res4c_branch2a (Conv2D)         (None, None, None, 2 262400      activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4c_branch2a (BatchNormalizati (None, None, None, 2 1024        res4c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, None, None, 2 0           bn4c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4c_branch2b (Conv2D)         (None, None, None, 2 590080      activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4c_branch2b (BatchNormalizati (None, None, None, 2 1024        res4c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, None, None, 2 0           bn4c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4c_branch2c (Conv2D)         (None, None, None, 1 263168      activation_30[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4c_branch2c (BatchNormalizati (None, None, None, 1 4096        res4c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, None, None, 1 0           bn4c_branch2c[0][0]              \n",
            "                                                                 activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, None, None, 1 0           add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res4d_branch2a (Conv2D)         (None, None, None, 2 262400      activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4d_branch2a (BatchNormalizati (None, None, None, 2 1024        res4d_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, None, None, 2 0           bn4d_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4d_branch2b (Conv2D)         (None, None, None, 2 590080      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4d_branch2b (BatchNormalizati (None, None, None, 2 1024        res4d_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, None, None, 2 0           bn4d_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4d_branch2c (Conv2D)         (None, None, None, 1 263168      activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4d_branch2c (BatchNormalizati (None, None, None, 1 4096        res4d_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, None, None, 1 0           bn4d_branch2c[0][0]              \n",
            "                                                                 activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, None, None, 1 0           add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res4e_branch2a (Conv2D)         (None, None, None, 2 262400      activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4e_branch2a (BatchNormalizati (None, None, None, 2 1024        res4e_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, None, None, 2 0           bn4e_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4e_branch2b (Conv2D)         (None, None, None, 2 590080      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4e_branch2b (BatchNormalizati (None, None, None, 2 1024        res4e_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, None, None, 2 0           bn4e_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4e_branch2c (Conv2D)         (None, None, None, 1 263168      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4e_branch2c (BatchNormalizati (None, None, None, 1 4096        res4e_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, None, None, 1 0           bn4e_branch2c[0][0]              \n",
            "                                                                 activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, None, None, 1 0           add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res4f_branch2a (Conv2D)         (None, None, None, 2 262400      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4f_branch2a (BatchNormalizati (None, None, None, 2 1024        res4f_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, None, None, 2 0           bn4f_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4f_branch2b (Conv2D)         (None, None, None, 2 590080      activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4f_branch2b (BatchNormalizati (None, None, None, 2 1024        res4f_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, None, None, 2 0           bn4f_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4f_branch2c (Conv2D)         (None, None, None, 1 263168      activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4f_branch2c (BatchNormalizati (None, None, None, 1 4096        res4f_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, None, None, 1 0           bn4f_branch2c[0][0]              \n",
            "                                                                 activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, None, None, 1 0           add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res5a_branch2a (Conv2D)         (None, None, None, 5 524800      activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5a_branch2a (BatchNormalizati (None, None, None, 5 2048        res5a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, None, None, 5 0           bn5a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5a_branch2b (Conv2D)         (None, None, None, 5 2359808     activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5a_branch2b (BatchNormalizati (None, None, None, 5 2048        res5a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, None, None, 5 0           bn5a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5a_branch2c (Conv2D)         (None, None, None, 2 1050624     activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5a_branch1 (Conv2D)          (None, None, None, 2 2099200     activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5a_branch2c (BatchNormalizati (None, None, None, 2 8192        res5a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn5a_branch1 (BatchNormalizatio (None, None, None, 2 8192        res5a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_14 (Add)                    (None, None, None, 2 0           bn5a_branch2c[0][0]              \n",
            "                                                                 bn5a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, None, None, 2 0           add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res5b_branch2a (Conv2D)         (None, None, None, 5 1049088     activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5b_branch2a (BatchNormalizati (None, None, None, 5 2048        res5b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, None, None, 5 0           bn5b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5b_branch2b (Conv2D)         (None, None, None, 5 2359808     activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5b_branch2b (BatchNormalizati (None, None, None, 5 2048        res5b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, None, None, 5 0           bn5b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5b_branch2c (Conv2D)         (None, None, None, 2 1050624     activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5b_branch2c (BatchNormalizati (None, None, None, 2 8192        res5b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_15 (Add)                    (None, None, None, 2 0           bn5b_branch2c[0][0]              \n",
            "                                                                 activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, None, None, 2 0           add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res5c_branch2a (Conv2D)         (None, None, None, 5 1049088     activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5c_branch2a (BatchNormalizati (None, None, None, 5 2048        res5c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, None, None, 5 0           bn5c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5c_branch2b (Conv2D)         (None, None, None, 5 2359808     activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5c_branch2b (BatchNormalizati (None, None, None, 5 2048        res5c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, None, None, 5 0           bn5c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5c_branch2c (Conv2D)         (None, None, None, 2 1050624     activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5c_branch2c (BatchNormalizati (None, None, None, 2 8192        res5c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_16 (Add)                    (None, None, None, 2 0           bn5c_branch2c[0][0]              \n",
            "                                                                 activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, None, None, 2 0           add_16[0][0]                     \n",
            "==================================================================================================\n",
            "Total params: 23,587,712\n",
            "Trainable params: 23,534,592\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n",
            "layers:  175\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75ybkLI2we6s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import os\n",
        "import tempfile\n",
        "\n",
        "def add_regularization(reg_model, regularizer=tf.keras.regularizers.l2(0.001)):\n",
        "\n",
        "    if not isinstance(regularizer, tf.keras.regularizers.Regularizer):\n",
        "      print(\"Regularizer must be a subclass of tf.keras.regularizers.Regularizer\")\n",
        "      return reg_model\n",
        "\n",
        "    for layer in reg_model.layers:\n",
        "        for attr in ['kernel_regularizer']:\n",
        "            if hasattr(layer, attr):\n",
        "              setattr(layer, attr, regularizer)\n",
        "\n",
        "    # When we change the layers attributes, the change only happens in the model config file\n",
        "    model_json = reg_model.to_json()\n",
        "\n",
        "    # Save the weights before reloading the model.\n",
        "    tmp_weights_path = os.path.join(tempfile.gettempdir(), 'tmp_weights.h5')\n",
        "    reg_model.save_weights(tmp_weights_path)\n",
        "\n",
        "    # load the model from the config\n",
        "    reg_model = tf.keras.models.model_from_json(model_json)\n",
        "    \n",
        "    # Reload the model weights\n",
        "    reg_model.load_weights(tmp_weights_path, by_name=True)\n",
        "    return reg_model\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EchmfQcFp2K6",
        "colab_type": "code",
        "outputId": "c94e1ab9-8d64-41c1-9e08-db94cb77310e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        }
      },
      "source": [
        "#Still not talking about our train/test data or any pre-processing.\n",
        "#from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense,Conv2D, MaxPooling2D\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D, SeparableConv2D\n",
        "model = Sequential()\n",
        "# 1st layer as the lumpsum weights from resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
        "# NOTE that this layer will be set below as NOT TRAINABLE, i.e., use it as is\n",
        "Resnet_Model=ResNet50(include_top = False, pooling = RESNET50_POOLING_AVERAGE, weights = 'imagenet') #resnet_weights_path)\n",
        "model.add(Resnet_Model)\n",
        "# 2nd layer as Dense for 40-class classification using SoftMax activation\n",
        "model.add(Dense(NUM_CLASSES,activation = DENSE_LAYER_ACTIVATION)) #,  kernel_initializer='random_normal'\n",
        "# Say not to train first layer (ResNet) model as it is already trained\n",
        "model.layers[0].trainable = False\n",
        "model.summary()\n",
        "type(model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "resnet50 (Model)             (None, 2048)              23587712  \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 40)                81960     \n",
            "=================================================================\n",
            "Total params: 23,669,672\n",
            "Trainable params: 81,960\n",
            "Non-trainable params: 23,587,712\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "keras.engine.sequential.Sequential"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JL7Li38n4Vnx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.optimizers import Adam \n",
        "from tensorflow.keras.optimizers import SGD\n",
        "model=add_regularization(model)\n",
        "#sgdd= optimizers.SGD(lr = 0.01, momentum = 0.9, clipvalue=0.5 ,decay = 1e-6,nesterov = True) #, clipvalue=0.5 #,decay = 1e-6\n",
        "# model.compile(optimizer =sgd, loss = loss='categorical_crossentropy', metrics = ['accuracy']) #sgd\n",
        "model.compile(Adam(lr= 0.0001),loss='mean_squared_error', metrics=['accuracy']) #\n",
        "img_width, img_height = 224, 224\n",
        "from keras import backend as K  \n",
        "if K.image_data_format() == 'channels_first':\n",
        "    input_shape = (3, img_width, img_height)\n",
        "else:\n",
        "    input_shape = (img_width, img_height, 3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DhqJVw7e5tP-",
        "colab_type": "code",
        "outputId": "9e9a1ca5-5395-4e88-aaed-cf2b8f233d4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "from keras.applications.resnet50 import preprocess_input\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "image_size = IMAGE_RESIZE\n",
        "\n",
        "NUM_EPOCHS = 50\n",
        "BATCH_SIZE_TRAINING = 13 #100\n",
        "BATCH_SIZE_VALIDATION =13 #100\n",
        "\n",
        "# These steps value should be proper FACTOR of no.-of-images in train & valid folders respectively\n",
        "# Training images processed in each step would be no.-of-train-images / STEPS_PER_EPOCH_TRAINING\n",
        "STEPS_PER_EPOCH_TRAINING =  160 // BATCH_SIZE_TRAINING #10\n",
        "STEPS_PER_EPOCH_VALIDATION = 40 // BATCH_SIZE_VALIDATION\n",
        "\n",
        "# Train data generator\n",
        "train_datagen = ImageDataGenerator( width_shift_range=0.1,\n",
        "    preprocessing_function=preprocess_input,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.1,\n",
        "    horizontal_flip=False)\n",
        "Data_classes=[]\n",
        "for i in range (14,225):\n",
        "   Data_classes.append(i)\n",
        "\n",
        "#validation data generator\n",
        "validate_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)\n",
        "# read the images from respective directories\n",
        "# read training images\n",
        "# train_batches= ImageDataGenerator.flow_from_directory(train_path,target_size=(224,224), Data_classes, )\n",
        "\n",
        "train_data_dir = '/content/drive/My Drive/Final_Fusion/Iris_Data/Train' \n",
        "#/content/drive/My Drive/Indian Database/Forty/Train'\n",
        "validation_data_dir = '/content/drive/My Drive/Final_Fusion/Iris_Data/Test'\n",
        "#'/content/drive/My Drive/Indian Database/Forty/Test'\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_data_dir,\n",
        "    target_size=(img_width, img_height),\n",
        "    batch_size=BATCH_SIZE_TRAINING,\n",
        "    shuffle =True,\n",
        "    class_mode='categorical')\n",
        "\n",
        "# read validation images\n",
        "validation_generator = validate_datagen.flow_from_directory(\n",
        "    validation_data_dir,\n",
        "    target_size=(img_width, img_height),\n",
        "    shuffle=True,\n",
        "    batch_size=BATCH_SIZE_VALIDATION,\n",
        "    class_mode='categorical')\n",
        "\n",
        " #/content/drive/My Drive/OUR SDP/Iris Images/IIT_Left_Normalized/Test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 160 images belonging to 40 classes.\n",
            "Found 40 images belonging to 40 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sQzXoDLI-fHg",
        "colab_type": "code",
        "outputId": "59641b2d-f60f-47fd-eae1-d7d12c53f602",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "(BATCH_SIZE_TRAINING, len(train_generator), BATCH_SIZE_VALIDATION, len(validation_generator))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13, 13, 13, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-DpN0rF5APDx",
        "colab_type": "code",
        "outputId": "0711b775-2320-4ec4-e6a3-8b5ced0cf941",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Early stopping & checkpointing the best model in ../working dir & restoring that as our model for prediction\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "cb_early_stopper = EarlyStopping(monitor = 'val_loss', patience = EARLY_STOP_PATIENCE)\n",
        "cb_checkpointer = ModelCheckpoint(filepath = 'BestChoice.hdf5', monitor = 'val_loss', save_best_only = True, mode = 'auto')\n",
        "print(STEPS_PER_EPOCH_TRAINING)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzypWRurAtCp",
        "colab_type": "code",
        "outputId": "b0da4fa5-60a2-4a80-bc72-bd798f9d06f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import tensorflow\n",
        "from tensorflow.keras.models import model_from_json\n",
        "fit_history = model.fit_generator(\n",
        "        train_generator,\n",
        "        steps_per_epoch=STEPS_PER_EPOCH_TRAINING, #\n",
        "        epochs = NUM_EPOCHS,\n",
        "        validation_data=validation_generator,\n",
        "        validation_steps=STEPS_PER_EPOCH_VALIDATION,\n",
        "        shuffle=True,\n",
        "        #callbacks=[cb_checkpointer, cb_early_stopper]\n",
        ")\n",
        "# serialize model to JSON\n",
        "model_json = model.to_json()\n",
        "with open(\"/content/drive/My Drive/Final_Fusion/SAVED MODELS/Iris Model/model_1.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "    print(\"[INFO] the model is saved into jason file.\")\n",
        "# serialize weights to HDF5\n",
        "print(\"[INFO] Saving the model weights...\")\n",
        "model.save_weights('/content/drive/My Drive/Final_Fusion/SAVED MODELS/Iris Model/adam_50epoch_iris.h5')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-12-fd7e912b9b83>:9: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/50\n",
            "12/12 [==============================] - 5s 385ms/step - loss: 0.0995 - accuracy: 0.1156 - val_loss: 0.0968 - val_accuracy: 0.2564\n",
            "Epoch 2/50\n",
            "12/12 [==============================] - 3s 219ms/step - loss: 0.0876 - accuracy: 0.6259 - val_loss: 0.0887 - val_accuracy: 0.4359\n",
            "Epoch 3/50\n",
            "12/12 [==============================] - 3s 209ms/step - loss: 0.0762 - accuracy: 0.8231 - val_loss: 0.0794 - val_accuracy: 0.5897\n",
            "Epoch 4/50\n",
            "12/12 [==============================] - 3s 208ms/step - loss: 0.0697 - accuracy: 0.8980 - val_loss: 0.0715 - val_accuracy: 0.8462\n",
            "Epoch 5/50\n",
            "12/12 [==============================] - 2s 207ms/step - loss: 0.0643 - accuracy: 0.9456 - val_loss: 0.0644 - val_accuracy: 0.9231\n",
            "Epoch 6/50\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0603 - accuracy: 0.9864 - val_loss: 0.0592 - val_accuracy: 0.9744\n",
            "Epoch 7/50\n",
            "12/12 [==============================] - 2s 208ms/step - loss: 0.0565 - accuracy: 1.0000 - val_loss: 0.0555 - val_accuracy: 1.0000\n",
            "Epoch 8/50\n",
            "12/12 [==============================] - 3s 209ms/step - loss: 0.0533 - accuracy: 1.0000 - val_loss: 0.0525 - val_accuracy: 1.0000\n",
            "Epoch 9/50\n",
            "12/12 [==============================] - 2s 207ms/step - loss: 0.0504 - accuracy: 1.0000 - val_loss: 0.0495 - val_accuracy: 1.0000\n",
            "Epoch 10/50\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0477 - accuracy: 1.0000 - val_loss: 0.0464 - val_accuracy: 1.0000\n",
            "Epoch 11/50\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0450 - accuracy: 1.0000 - val_loss: 0.0439 - val_accuracy: 1.0000\n",
            "Epoch 12/50\n",
            "12/12 [==============================] - 3s 208ms/step - loss: 0.0426 - accuracy: 1.0000 - val_loss: 0.0415 - val_accuracy: 1.0000\n",
            "Epoch 13/50\n",
            "12/12 [==============================] - 2s 207ms/step - loss: 0.0401 - accuracy: 1.0000 - val_loss: 0.0393 - val_accuracy: 1.0000\n",
            "Epoch 14/50\n",
            "12/12 [==============================] - 3s 211ms/step - loss: 0.0379 - accuracy: 1.0000 - val_loss: 0.0370 - val_accuracy: 1.0000\n",
            "Epoch 15/50\n",
            "12/12 [==============================] - 3s 208ms/step - loss: 0.0356 - accuracy: 1.0000 - val_loss: 0.0349 - val_accuracy: 1.0000\n",
            "Epoch 16/50\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.0328 - val_accuracy: 1.0000\n",
            "Epoch 17/50\n",
            "12/12 [==============================] - 2s 202ms/step - loss: 0.0316 - accuracy: 1.0000 - val_loss: 0.0308 - val_accuracy: 1.0000\n",
            "Epoch 18/50\n",
            "12/12 [==============================] - 2s 207ms/step - loss: 0.0298 - accuracy: 1.0000 - val_loss: 0.0289 - val_accuracy: 1.0000\n",
            "Epoch 19/50\n",
            "12/12 [==============================] - 2s 208ms/step - loss: 0.0279 - accuracy: 1.0000 - val_loss: 0.0271 - val_accuracy: 1.0000\n",
            "Epoch 20/50\n",
            "12/12 [==============================] - 2s 203ms/step - loss: 0.0262 - accuracy: 1.0000 - val_loss: 0.0256 - val_accuracy: 1.0000\n",
            "Epoch 21/50\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0246 - accuracy: 1.0000 - val_loss: 0.0241 - val_accuracy: 1.0000\n",
            "Epoch 22/50\n",
            "12/12 [==============================] - 2s 207ms/step - loss: 0.0231 - accuracy: 1.0000 - val_loss: 0.0232 - val_accuracy: 1.0000\n",
            "Epoch 23/50\n",
            "12/12 [==============================] - 3s 209ms/step - loss: 0.0217 - accuracy: 1.0000 - val_loss: 0.0216 - val_accuracy: 1.0000\n",
            "Epoch 24/50\n",
            "12/12 [==============================] - 2s 208ms/step - loss: 0.0204 - accuracy: 1.0000 - val_loss: 0.0200 - val_accuracy: 1.0000\n",
            "Epoch 25/50\n",
            "12/12 [==============================] - 2s 208ms/step - loss: 0.0191 - accuracy: 1.0000 - val_loss: 0.0198 - val_accuracy: 0.9744\n",
            "Epoch 26/50\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0181 - accuracy: 1.0000 - val_loss: 0.0178 - val_accuracy: 1.0000\n",
            "Epoch 27/50\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0171 - accuracy: 1.0000 - val_loss: 0.0166 - val_accuracy: 1.0000\n",
            "Epoch 28/50\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0159 - accuracy: 1.0000 - val_loss: 0.0154 - val_accuracy: 1.0000\n",
            "Epoch 29/50\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0150 - accuracy: 1.0000 - val_loss: 0.0144 - val_accuracy: 1.0000\n",
            "Epoch 30/50\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0141 - accuracy: 1.0000 - val_loss: 0.0138 - val_accuracy: 1.0000\n",
            "Epoch 31/50\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0134 - accuracy: 1.0000 - val_loss: 0.0129 - val_accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "12/12 [==============================] - 2s 207ms/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.0124 - val_accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0118 - accuracy: 1.0000 - val_loss: 0.0121 - val_accuracy: 0.9744\n",
            "Epoch 34/50\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0111 - accuracy: 1.0000 - val_loss: 0.0106 - val_accuracy: 1.0000\n",
            "Epoch 35/50\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0105 - accuracy: 1.0000 - val_loss: 0.0102 - val_accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 1.0000\n",
            "Epoch 37/50\n",
            "12/12 [==============================] - 3s 209ms/step - loss: 0.0093 - accuracy: 1.0000 - val_loss: 0.0090 - val_accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "12/12 [==============================] - 2s 207ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.0085 - val_accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0083 - accuracy: 1.0000 - val_loss: 0.0080 - val_accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.0075 - val_accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "12/12 [==============================] - 2s 207ms/step - loss: 0.0075 - accuracy: 1.0000 - val_loss: 0.0070 - val_accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "12/12 [==============================] - 2s 208ms/step - loss: 0.0071 - accuracy: 0.9932 - val_loss: 0.0067 - val_accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "12/12 [==============================] - 3s 213ms/step - loss: 0.0069 - accuracy: 0.9936 - val_loss: 0.0064 - val_accuracy: 1.0000\n",
            "Epoch 44/50\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 1.0000\n",
            "Epoch 45/50\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "12/12 [==============================] - 3s 212ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.0048 - val_accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "12/12 [==============================] - 2s 207ms/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.0045 - val_accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "12/12 [==============================] - 3s 211ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.0043 - val_accuracy: 1.0000\n",
            "[INFO] the model is saved into jason file.\n",
            "[INFO] Saving the model weights...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-_dJQbAPNyiC",
        "colab_type": "code",
        "outputId": "aecf7424-c183-418d-83ee-d263ad70ab7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "plt.figure(1, figsize = (15,8)) \n",
        "    \n",
        "plt.subplot(221)  \n",
        "plt.plot(fit_history.history['accuracy'])  \n",
        "plt.plot(fit_history.history['val_accuracy'])  \n",
        "plt.title('model accuracy')  \n",
        "plt.ylabel('accuracy')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'valid']) \n",
        "    \n",
        "plt.subplot(222)  \n",
        "plt.plot(fit_history.history['loss'])  \n",
        "plt.plot(fit_history.history['val_loss'])  \n",
        "plt.title('model loss')  \n",
        "plt.ylabel('loss')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'valid']) \n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IPrpkTRkP3HJ",
        "colab_type": "code",
        "outputId": "1df0fe5e-71bc-4bab-b0da-1fd41ca855c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# NOTE that flow_from_directory treats each sub-folder as a class which works fine for training data\n",
        "# Actually class_mode=None is a kind of workaround for test data which too must be kept in a subfolder\n",
        "# batch_size can be 1 or any factor of test dataset size to ensure that test dataset is samples just once, i.e., no data is left out\n",
        "test_generator= validate_datagen.flow_from_directory(\n",
        "    directory ='/content/drive/My Drive/Final_Fusion/Iris_Data/Test',\n",
        "    # '/content/drive/My Drive/Face_D/Test',\n",
        "    target_size = (image_size, image_size),\n",
        "    batch_size = 1,#BATCH_SIZE_TESTING,\n",
        "    class_mode = None,\n",
        "    shuffle = False,\n",
        "    seed = 123\n",
        ")\n",
        "\n",
        "test_generator_random=validate_datagen.flow_from_directory(\n",
        "    directory ='/content/drive/My Drive/Final_Fusion/Iris_Data/Iris_Outsider',\n",
        "    # '/content/drive/My Drive/Face_D/Test',\n",
        "    target_size = (image_size, image_size),\n",
        "    batch_size = 1,#BATCH_SIZE_TESTING,\n",
        "    class_mode = None,\n",
        "    shuffle = False,\n",
        "    seed = 123\n",
        ")\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 40 images belonging to 40 classes.\n",
            "Found 40 images belonging to 1 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Il-eDRB5-87c",
        "colab_type": "code",
        "outputId": "bd99d29b-e4b3-4173-cd43-d02550287a60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "import numpy as np\n",
        "test_generator.reset()\n",
        "from sklearn.metrics import confusion_matrix \n",
        "\n",
        "actual_labels = test_generator.classes\n",
        "print(actual_labels)\n",
        "print(test_generator.filenames)\n",
        "pred = model.predict_generator(test_generator, steps = len(test_generator), verbose = 1)\n",
        "random_pred=model.predict_generator(test_generator_random, steps = len(test_generator_random), verbose = 1)\n",
        "predicted_class_indices = np.argmax(pred, axis = 1)\n",
        " \n",
        " # confusion matrix, all corrected classified classes are in the diagonal entries of the matrix\n",
        " # the colomuns are actual labels vs Rows as predicted Labels\n",
        "\n",
        "cm = confusion_matrix(actual_labels,predicted_class_indices)\n",
        "print(cm)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
            " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39]\n",
            "['Person_01/Person_1_5_L.bmp', 'Person_02/Person_2_5_L.bmp', 'Person_03/Person_3_5_L.bmp', 'Person_04/Person_4_5_L.bmp', 'Person_05/Person_5_5_L.bmp', 'Person_06/Person_6_5_L.bmp', 'Person_07/Person_7_5_L.bmp', 'Person_08/Person_8_5_L.bmp', 'Person_09/Person_9_5_L.bmp', 'Person_10/Person_10_5_L.bmp', 'Person_11/Person_11_5_L.bmp', 'Person_12/Person_12_5_L.bmp', 'Person_13/Person_13_4_L.bmp', 'Person_14/Person_14_5_L.bmp', 'Person_15/Person_15_5_L.bmp', 'Person_16/Person_16_5_L.bmp', 'Person_17/Person_17_5_L.bmp', 'Person_18/Person_18_5_L.bmp', 'Person_19/Person_19_5_L.bmp', 'Person_20/Person_20_5_L.bmp', 'Person_21/Person_21_5_L.bmp', 'Person_22/Person_22_5_L.bmp', 'Person_23/Person_23_5_L.bmp', 'Person_24/Person_24_5_L.bmp', 'Person_25/Person_25_5_L.bmp', 'Person_26/Person_26_5_L.bmp', 'Person_27/Person_27_5_L.bmp', 'Person_28/Person_28_5_L.bmp', 'Person_29/Person_29_5_L.bmp', 'Person_30/Person_30_5_L.bmp', 'Person_31/Person_31_5_L.bmp', 'Person_32/Person_32_5_L.bmp', 'Person_33/Person_33_5_L.bmp', 'Person_34/Person_34_5_L.bmp', 'Person_35/Person_35_5_L.bmp', 'Person_36/Person_36_5_L.bmp', 'Person_37/Person_37_5_L.bmp', 'Person_38/Person_38_5_L.bmp', 'Person_39/Person_39_5_L.bmp', 'Person_40/Person_40_5_L.bmp']\n",
            "WARNING:tensorflow:From <ipython-input-15-62d72a91ca89>:8: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.predict, which supports generators.\n",
            "40/40 [==============================] - 0s 9ms/step\n",
            "40/40 [==============================] - 0s 9ms/step\n",
            "[[1 0 0 ... 0 0 0]\n",
            " [0 1 0 ... 0 0 0]\n",
            " [0 0 1 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 1 0 0]\n",
            " [0 0 0 ... 0 1 0]\n",
            " [0 0 0 ... 0 0 1]]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}