Hybrid_SVM.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1IJ6LR2e9fmDzPylybN4dRlaLaTbFlhZv",
      "authorship_tag": "ABX9TyNwWlmss3DRL8bNgLX2gP9m",
      "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/aislam388/Hybrid-Technique/blob/main/Hybrid_SVM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmCicaoF1RmC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "fe625dfe-27de-4864-853e-f4e2737a7017"
      },
      "source": [
        "import os\n",
        "import shutil\n",
        "from os.path import isfile, join, abspath, exists, isdir, expanduser\n",
        "from os import listdir, makedirs, getcwd, remove\n",
        "from pathlib import Path\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "sns.set_style('darkgrid')\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mimg\n",
        "# plotly\n",
        "import plotly as py\n",
        "from plotly.offline import init_notebook_mode, iplot\n",
        "init_notebook_mode(connected=True)\n",
        "import plotly.graph_objs as go\n",
        "import plotly.express as px\n",
        "\n",
        "import tensorflow as tf\n",
        "from plotly.graph_objs import *\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from keras.preprocessing.image import ImageDataGenerator,load_img, img_to_array\n",
        "from keras import layers\n",
        "from keras import models\n",
        "from keras import optimizers\n",
        "import keras"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5emEaaQhfad"
      },
      "source": [
        "from keras.utils import plot_model\n",
        "from keras.models import Model\n",
        "from keras.layers import Input\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.pooling import MaxPooling2D\n",
        "from keras.layers.merge import concatenate"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7Wta6WeZLx3"
      },
      "source": [
        "input_path = \"/content/drive/My Drive/LIDC-IDRI-0001/01-01-2000-30178/3000566-03192/\"\n",
        "# train and valid = flowr_path\n",
        "train = input_path+\"/Training/\"\n",
        "valid = input_path+\"/Validation/\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v1hXAkmXupnl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "5bede8ee-df9c-4db8-ff64-5938611ff589"
      },
      "source": [
        "# classes = flower_types\n",
        "classes = os.listdir(train)\n",
        "print(\"No of classes: \", len(classes))\n",
        "print(\"Class Labels: \", classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of classes:  2\n",
            "Class Labels:  ['Positive', 'Negative']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M1HMxaMfxxg_"
      },
      "source": [
        "from keras.applications import VGG16\n",
        "from keras.applications.vgg16 import preprocess_input\n",
        "weights_vgg16 = \"/content/drive/My Drive/LIDC-IDRI-0001/01-01-2000-30178/3000566-03192/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\"\n",
        "conv_base_16= VGG16(weights=weights_vgg16,\n",
        "                 include_top= False,\n",
        "                 input_shape = (224,224,3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1FsmfbeQNCdm"
      },
      "source": [
        "from keras.applications import VGG19\n",
        "from keras.applications.vgg16 import preprocess_input\n",
        "weights_vgg19 = \"/content/drive/My Drive/LIDC-IDRI-0001/01-01-2000-30178/3000566-03192/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\"\n",
        "weights_vgg16 = \"/content/drive/My Drive/LIDC-IDRI-0001/01-01-2000-30178/3000566-03192/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\"\n",
        "conv_base_19= VGG19(weights=weights_vgg19,\n",
        "                 include_top= False,\n",
        "                 input_shape = (224,224,3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Fyr44HUN0qt"
      },
      "source": [
        "from keras.layers import Input, Lambda, Dense, Flatten\n",
        "from keras.models import Model\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "import numpy as np\n",
        "from glob import glob\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from PIL import Image\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5JkQJLfN6l-"
      },
      "source": [
        "def _get_features_16(img_path):\n",
        "  img = image.load_img(img_path, target_size=(224,224))\n",
        "  img_data = image.img_to_array(img)\n",
        "  img_data = np.expand_dims(img_data, axis=0)\n",
        "  img_data = preprocess_input(img_data)\n",
        "  vgg16_features = conv_base_16.predict(img_data)\n",
        "  return vgg16_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgNwn8isN6uT"
      },
      "source": [
        "def _get_features_19(img_path):\n",
        "  img = image.load_img(img_path, target_size=(224,224))\n",
        "  img_data = image.img_to_array(img)\n",
        "  img_data = np.expand_dims(img_data, axis=0)\n",
        "  img_data = preprocess_input(img_data)\n",
        "  vgg19_features = conv_base_19.predict(img_data)\n",
        "  return vgg19_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Esqrr9CAy_tH"
      },
      "source": [
        "# datagen = ImageDataGenerator(rescale=1./255)\n",
        "# batch_size = 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KuizrJ0zzV1T"
      },
      "source": [
        "# # for vgg\n",
        "# def extract_features(directory, sample_count):\n",
        "#     features = [np.zeros((sample_count, 7,7, 512)),np.zeros((sample_count,7,7,512)),np.zeros((sample_count,7,7,512))]\n",
        "#     labels = [np.zeros((sample_count, 2))]\n",
        "\n",
        "#     generator = ImageDataGenerator(rescale=1./255).flow_from_directory(directory,\n",
        "#         target_size=(240, 240),\n",
        "#         batch_size = batch_size, \n",
        "#         class_mode='categorical')\n",
        "\n",
        "#     i = 0\n",
        "\n",
        "#     print('Entering for loop...');\n",
        "\n",
        "    \n",
        "#     for inputs_batch, labels_batch in generator:\n",
        "#         features_batch = conv_base.predict(inputs_batch)\n",
        "#         features[i * batch_size : (i + 1) * batch_size] = features_batch\n",
        "#         labels[i * batch_size : (i + 1) * batch_size] = labels_batch\n",
        "#         i += 1\n",
        "#         if i * batch_size >= sample_count:\n",
        "#             break\n",
        "#     return features, labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNC16pJFOojH"
      },
      "source": [
        "# # for SVM\n",
        "# def _get_features(img_path):\n",
        "#   img = image.load_img(img_path, target_size=(224,224))\n",
        "#   img_data = image.img_to_array(img)\n",
        "#   img_data = np.expand_dims(img_data, axis=0)\n",
        "#   img_data = preprocess_input(img_data)\n",
        "#   vgg16_features = conv_base.predict(img_data)\n",
        "#   return vgg16_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwLUwN-izdV2"
      },
      "source": [
        "# train_features, train_labels = extract_features(train, 962)\n",
        "# validation_features, validation_labels = extract_features(valid, 206)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sllaU26V0ZDj"
      },
      "source": [
        "# train_features = np.reshape(train_features, (962, 7 * 7 * 512))\n",
        "# validation_features = np.reshape(validation_features, (206, 7 * 7 * 512))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTpxUtuhoYDX"
      },
      "source": [
        "# print(train_features.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ezuzL6jWJct"
      },
      "source": [
        "SVM Attempt"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLJefrQ5KKAO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "78feaa5b-11d2-4827-cabe-89ebacab5974"
      },
      "source": [
        "import pandas as pd\n",
        "train_list = []\n",
        "\n",
        "# images = species_num, labels = species, train_list = train, train = train_dir\n",
        "for images, labels in enumerate(classes):\n",
        "  for file in os.listdir(os.path.join(train+ labels)):\n",
        "      train_list.append(['/content/drive/My Drive/LIDC-IDRI-0001/01-01-2000-30178/3000566-03192/Training/{}/{}'.format(labels, file), images, labels])\n",
        "        \n",
        "train_list = pd.DataFrame(train_list, columns=['file', 'images', 'labels'])\n",
        "\n",
        "print('Training Data: ',train_list.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data:  (962, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1RxcSarbDe0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "1cb3be77-f056-4cf5-8515-61bd4f0650c2"
      },
      "source": [
        "print(train_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                                                  file  images    labels\n",
            "0    /content/drive/My Drive/LIDC-IDRI-0001/01-01-2...       0  Positive\n",
            "1    /content/drive/My Drive/LIDC-IDRI-0001/01-01-2...       0  Positive\n",
            "2    /content/drive/My Drive/LIDC-IDRI-0001/01-01-2...       0  Positive\n",
            "3    /content/drive/My Drive/LIDC-IDRI-0001/01-01-2...       0  Positive\n",
            "4    /content/drive/My Drive/LIDC-IDRI-0001/01-01-2...       0  Positive\n",
            "..                                                 ...     ...       ...\n",
            "957  /content/drive/My Drive/LIDC-IDRI-0001/01-01-2...       1  Negative\n",
            "958  /content/drive/My Drive/LIDC-IDRI-0001/01-01-2...       1  Negative\n",
            "959  /content/drive/My Drive/LIDC-IDRI-0001/01-01-2...       1  Negative\n",
            "960  /content/drive/My Drive/LIDC-IDRI-0001/01-01-2...       1  Negative\n",
            "961  /content/drive/My Drive/LIDC-IDRI-0001/01-01-2...       1  Negative\n",
            "\n",
            "[962 rows x 3 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kt9PNOJXWI-H"
      },
      "source": [
        "features_16 = []\n",
        "for i in range(len(train_list)):\n",
        "  features_16.append(_get_features_16(train_list['file'][i]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxGgGXZxf_RN"
      },
      "source": [
        "features_19 = []\n",
        "for i in range(len(train_list)):\n",
        "  features_19.append(_get_features_19(train_list['file'][i]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bMBV531DbuVi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e917d0fd-9719-429c-a8f0-562dc20d716f"
      },
      "source": [
        "print(features_16[1],features_16[1].shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[[ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   ...\n",
            "   [ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          9.906739\n",
            "     0.        ]]\n",
            "\n",
            "  [[ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [11.288255    0.         11.650242   ...  0.          0.\n",
            "     0.        ]\n",
            "   ...\n",
            "   [ 0.          7.3503003   3.5132656  ...  3.4352694   0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          9.771076\n",
            "     0.        ]]\n",
            "\n",
            "  [[ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.6071333   0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [24.092909    7.35752     0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   ...\n",
            "   [25.423975    2.1251438   1.7129931  ...  0.3549373   0.\n",
            "     0.        ]\n",
            "   [11.266139    0.06288332  0.         ...  0.          1.0717361\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.         27.85956\n",
            "     0.        ]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.838211    1.2501779  ...  0.          0.\n",
            "     0.        ]\n",
            "   [30.798822   30.708067   15.1695385  ...  0.          0.\n",
            "     0.        ]\n",
            "   ...\n",
            "   [26.924702    0.         23.814587   ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          1.781405   ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]]\n",
            "\n",
            "  [[ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [15.962405    0.         14.030701   ...  0.          0.\n",
            "     0.        ]\n",
            "   ...\n",
            "   [13.039324    0.         27.11674    ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          6.248825   ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]]\n",
            "\n",
            "  [[ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          1.1439161\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   ...\n",
            "   [ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.          0.\n",
            "     0.        ]\n",
            "   [ 0.          0.          0.         ...  0.         10.382173\n",
            "     0.        ]]]] (1, 7, 7, 512)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BslawiKTC4XZ"
      },
      "source": [
        "tensor_16=[]\n",
        "for i in range(len(features_16)):\n",
        "  tensor_16.append(tf.convert_to_tensor(features_16[i]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVuONXFgePLX"
      },
      "source": [
        "flat1=[]\n",
        "for i in range(len(features_16)):\n",
        "  tensor = tf.convert_to_tensor(features_16[i])\n",
        "  flat1.append(Flatten()(tensor))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BRBkVzJoggl-"
      },
      "source": [
        "flat2=[]\n",
        "for i in range(len(features_19)):\n",
        "  tensor = tf.convert_to_tensor(features_19[i])\n",
        "  flat2.append(Flatten()(tensor))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-A4D3Ozg9hb"
      },
      "source": [
        "merge = []\n",
        "for i in range(len(features_16)):\n",
        "  merge.append(concatenate([flat1[i],flat2[i]]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rVmwBe0figuQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "c7ce69bb-4ee9-44e2-bbf2-451aeed79306"
      },
      "source": [
        "print(merge[1],merge[1].shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor([[0. 0. 0. ... 0. 0. 0.]], shape=(1, 50176), dtype=float32) (1, 50176)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QvmOhVBLAUdD"
      },
      "source": [
        "# _input= keras.Input(shape=(1,50176))\n",
        "# output_list = []\n",
        "# # for i in range(len(merge)):\n",
        "# dense1 = Dense(10,activation=\"relu\")(merge[i])\n",
        "# dense2 = Dense(10,activation=\"relu\")(dense1)\n",
        "# output = Dense(2,activation=\"softmax\")(dense2)\n",
        "# output_list.append(output)\n",
        "\n",
        "# # model = Model(inputs=_input,outputs=output)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y1vORhDhCtDu"
      },
      "source": [
        "# print(output)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rhjAFEjuiwwL"
      },
      "source": [
        "# model = models.Sequential()\n",
        "# model.add(layers.Dense(2048, activation='relu')(tensor_16[1]))\n",
        "# model.add(layers.Dropout(0.5))\n",
        "# model.add(layers.Dense(512, activation='relu', input_dim=7 * 7 * 512))\n",
        "# model.add(layers.Dropout(0.5))\n",
        "# model.add(layers.Dense(128, activation='relu', input_dim=7 * 7 * 512))\n",
        "# model.add(layers.Dropout(0.5))\n",
        "# model.add(layers.Dense(2, activation='softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOvLxSqZTPfQ"
      },
      "source": [
        "SVM\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atWx0PuZsP2B"
      },
      "source": [
        "basepath = \"/content/drive/My Drive/LIDC-IDRI-0001/01-01-2000-30178/3000566-03192/\"\n",
        "class1 = os.listdir(basepath + \"Positivepng/\")\n",
        "class2 = os.listdir(basepath + \"Negativepng/\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jMoHTxMk6R-"
      },
      "source": [
        "data = {'Positivepng': class1[:680], \n",
        "        'Negativepng': class2[:680], \n",
        "        'test': [class1[681], class2[681]]}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H16CG1zdnOJ3"
      },
      "source": [
        "features = {\"Positivepng\":[], \"Negativepng\":[], \"test\":[]}\n",
        "test_features = []\n",
        "# test_features2 for features from vgg19\n",
        "test_features2 =[]\n",
        "test_imgs = []\n",
        "lables=[]\n",
        "\n",
        "for label, val in data.items():\n",
        "  # print(label)\n",
        "  for k, each in enumerate(val):\n",
        "    # print(k,each)\n",
        "    if label == \"test\" and k == 0:\n",
        "      img_path = basepath + \"/Positivepng/\" + each \n",
        "      # print(img_path)\n",
        "      test_imgs.append(img_path)\n",
        "    elif label == \"test\" and k == 1:\n",
        "      img_path = basepath + \"/Negativepng/\" + each\n",
        "      # print(img_path)\n",
        "      test_imgs.append(img_path)\n",
        "    else: \n",
        "      img_path = basepath + label.title() + \"/\" + each\n",
        "      # print(img_path)\n",
        "      # print(label.title())\n",
        "      feats = _get_features_16(img_path)\n",
        "      feats2 = _get_features_19(img_path)\n",
        "    # feats = np.resize(feats,(224,224,3))\n",
        "      test_features.append(feats)\n",
        "      test_features2.append(feats2)\n",
        "    # labels.append(labels)\n",
        "    # print(\"dim: \",feats.ndim, feats.shape, feats.dtype)\n",
        "    # print(type(feats))\n",
        "\n",
        "      features[label].append(feats.flatten())\n",
        "      features[label].append(feats2.flatten())\n",
        "    # print(feats,labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjGm1W6GtlYv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f1fef8c0-d3ec-4db8-bbc9-5b88e888c4e3"
      },
      "source": [
        "print(test_features)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "IOPub data rate exceeded.\n",
            "The notebook server will temporarily stop sending output\n",
            "to the client in order to avoid crashing it.\n",
            "To change this limit, set the config variable\n",
            "`--NotebookApp.iopub_data_rate_limit`.\n",
            "\n",
            "Current values:\n",
            "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
            "NotebookApp.rate_limit_window=3.0 (secs)\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WRyf-wMmtYEL"
      },
      "source": [
        "# implemnting dtaframe for vgg16\n",
        "import pandas as pd\n",
        "test_label =[]\n",
        "dataset = pd.DataFrame(\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTHvfrw_TOyk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "bc3e5491-3539-452e-b646-255b098a3b74"
      },
      "source": [
        "import pandas as pd\n",
        "test_label=[]\n",
        "dataset = pd.DataFrame()\n",
        "\n",
        "for label, feats in features.items():\n",
        "    # print(label,feats)\n",
        "    test_label.append(label)\n",
        "    temp_df = pd.DataFrame(feats)\n",
        "    temp_df['label'] = label\n",
        "    dataset = dataset.append(temp_df, ignore_index=True)\n",
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.536201</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.272427</td>\n",
              "      <td>0.277553</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Positivepng</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.797628</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33.471375</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.080626</td>\n",
              "      <td>0.676197</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.279336</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Positivepng</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 25089 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     0    1    2    3    4  ...  25084  25085      25086  25087        label\n",
              "0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0   9.253778    0.0  Positivepng\n",
              "1  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0   0.000000    0.0  Positivepng\n",
              "2  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0   9.421498    0.0  Positivepng\n",
              "3  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0   0.000000    0.0  Positivepng\n",
              "4  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0  10.279336    0.0  Positivepng\n",
              "\n",
              "[5 rows x 25089 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLs6cQ03ntP6"
      },
      "source": [
        "y = dataset[dataset.label != 'test'].label\n",
        "X = dataset[dataset.label != 'test'].drop('label', axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxYG1SO3nvxR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "b058762d-2d94-44a1-9a55-8b29a3698115"
      },
      "source": [
        "print(y.shape, y.ndim)\n",
        "print(X.shape, X.ndim)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2720,) 1\n",
            "(2720, 25088) 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3feKi8rP_RP"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uc_D2qKF-Fe4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "9fe2e186-0c03-4d36-a706-2332a0928172"
      },
      "source": [
        "print(Xtrain[:-1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      0      1      2      3      4      ...  25083  25084  25085      25086  25087\n",
            "1331    0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0   0.000000    0.0\n",
            "2396    0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0   7.262002    0.0\n",
            "2043    0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0   0.000000    0.0\n",
            "1899    0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0   0.000000    0.0\n",
            "784     0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0   7.496643    0.0\n",
            "...     ...    ...    ...    ...    ...  ...    ...    ...    ...        ...    ...\n",
            "1100    0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0  13.796495    0.0\n",
            "1388    0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0  13.566526    0.0\n",
            "261     0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0   0.000000    0.0\n",
            "1296    0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0   6.118498    0.0\n",
            "1291    0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0   0.000000    0.0\n",
            "\n",
            "[2175 rows x 25088 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PS2WQ1_n0LD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "355799f2-0249-4641-870f-7be0d2c17a2b"
      },
      "source": [
        "import time\n",
        "start = time.time()\n",
        "from sklearn import svm\n",
        "from sklearn.svm import SVC\n",
        "clf = SVC()\n",
        "clf.fit(Xtrain, ytrain)\n",
        "preds = clf.predict(Xtest)\n",
        "\n",
        "print('Running time: %.4f seconds' % (time.time()-start))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running time: 116.2748 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftWGzkjZn4mq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "a804229f-9408-46d2-9de6-79f20c6f1aaf"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "print(\"Accuracy on y_test:\",accuracy_score(ytest, preds))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on y_test: 0.8786764705882353\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uI6fr8_On880",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "4d85ec4a-1ce2-425f-fcf5-b0bdf8d3c846"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "cm = confusion_matrix(ytest, preds)\n",
        "sns.heatmap(cm, annot=True, cbar=False, fmt='g')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.ylabel('True label')\n",
        "plt.title('Confusion Matrix')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Confusion Matrix')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rogJFnNvoAv0"
      },
      "source": [
        "Fine Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TpO54qXf3Mn5"
      },
      "source": [
        "# model = models.Sequential()\n",
        "# model.add(layers.Dense(2048, activation='relu', input_dim=7 * 7 * 512))\n",
        "# model.add(layers.Dropout(0.5))\n",
        "# model.add(layers.Dense(512, activation='relu', input_dim=7 * 7 * 512))\n",
        "# model.add(layers.Dropout(0.5))\n",
        "# model.add(layers.Dense(128, activation='relu', input_dim=7 * 7 * 512))\n",
        "# model.add(layers.Dropout(0.5))\n",
        "# model.add(layers.Dense(2, activation='softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZaceED0T3PEd"
      },
      "source": [
        "# model.compile(loss='categorical_crossentropy',\n",
        "#               optimizer=optimizers.RMSprop(lr=1e-4),\n",
        "#               metrics=['acc'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1911y4jT3Tur"
      },
      "source": [
        "# history = model.fit(train_features, train_labels,\n",
        "#                     epochs=25,\n",
        "#                     batch_size=16,\n",
        "#                     validation_data=(validation_features, validation_labels))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UsjnVnka9F3W"
      },
      "source": [
        "With Augemnatation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G5OGJh7q9Fk3"
      },
      "source": [
        "# model = models.Sequential()\n",
        "# model.add(conv_base)\n",
        "# model.add(layers.Flatten())\n",
        "# model.add(layers.Dense(512, activation='relu'))\n",
        "# model.add(layers.Dropout(0.5))\n",
        "# model.add(layers.Dense(128, activation='relu'))\n",
        "# model.add(layers.Dropout(0.3))\n",
        "# model.add(layers.Dense(2, activation='softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqv1UwYs9QVN"
      },
      "source": [
        "# train_datagen = ImageDataGenerator(\n",
        "#         rescale=1./255,\n",
        "#         rotation_range=15,\n",
        "#         width_shift_range=0.05,\n",
        "#         height_shift_range=0.05,\n",
        "#         shear_range=0.05,\n",
        "#         # zoom_range=0.2,\n",
        "#         horizontal_flip=True,\n",
        "#         vertical_flip=True)\n",
        "\n",
        "# test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "# train_generator = train_datagen.flow_from_directory(\n",
        "#         train,\n",
        "#         target_size=(240, 240),  # all images will be resized to 240x240\n",
        "#         batch_size=batch_size,\n",
        "#         class_mode='categorical')  # more than two classes\n",
        "\n",
        "# validation_generator = test_datagen.flow_from_directory(\n",
        "#         valid,\n",
        "#         target_size=(240, 240),\n",
        "#         batch_size=batch_size,\n",
        "#         class_mode='categorical',\n",
        "#         shuffle = False\n",
        "# )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZtGn6jca9iJo"
      },
      "source": [
        "# model.compile(loss='categorical_crossentropy',\n",
        "#               optimizer=optimizers.Adam(lr=2e-5),\n",
        "#               metrics=['acc'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7PKFiSC9ka0"
      },
      "source": [
        "# history = model.fit_generator(\n",
        "#           train_generator,\n",
        "#           epochs=30,\n",
        "#           validation_data=validation_generator)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XaTHamLaHBuH"
      },
      "source": [
        "Fine Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xI1QW_UUHDUo"
      },
      "source": [
        "# conv_base.trainable = True\n",
        "\n",
        "# set_trainable = False\n",
        "# for layer in conv_base.layers:\n",
        "#     if layer.name == 'block5_conv1':\n",
        "#         set_trainable = True\n",
        "#     if set_trainable:\n",
        "#         layer.trainable = True\n",
        "#     else:\n",
        "#         layer.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWmImYC6HLh_"
      },
      "source": [
        "# model.compile(loss='binary_crossentropy',\n",
        "#               optimizer=optimizers.Adam(lr=2e-5),\n",
        "#               metrics=['acc'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBVwVDk1HNdr"
      },
      "source": [
        "# history = model.fit_generator(\n",
        "#     train_generator,\n",
        "#     steps_per_epoch=100,\n",
        "#     epochs=50,\n",
        "#     validation_data=validation_generator,\n",
        "#     validation_steps=50)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fVEMNzzrHrLY"
      },
      "source": [
        "# acc = history.history['acc']\n",
        "# val_acc = history.history['val_acc']\n",
        "# loss = history.history['loss']\n",
        "# val_loss = history.history['val_loss']\n",
        "# epochs_range = range(1, len(history.epoch) + 1)\n",
        "\n",
        "# plt.figure(figsize=(15,5))\n",
        "\n",
        "# plt.subplot(1, 2, 1)\n",
        "# plt.plot(epochs_range, acc, label='Train Set')\n",
        "# plt.plot(epochs_range, val_acc, label='Val Set')\n",
        "# plt.legend(loc=\"best\")\n",
        "# plt.xlabel('Epochs')\n",
        "# plt.ylabel('Accuracy')\n",
        "# plt.title('Model Accuracy')\n",
        "\n",
        "# plt.subplot(1, 2, 2)\n",
        "# plt.plot(epochs_range, loss, label='Train Set')\n",
        "# plt.plot(epochs_range, val_loss, label='Val Set')\n",
        "# plt.legend(loc=\"best\")\n",
        "# plt.xlabel('Epochs')\n",
        "# plt.ylabel('Loss')\n",
        "# plt.title('Model Loss')\n",
        "\n",
        "# plt.tight_layout()\n",
        "# plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YBfY1OQNENJC"
      },
      "source": [
        "Wrong below this"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hpGghdsv6sf"
      },
      "source": [
        "# # A list that is going to contain tuples: (species of the flower, corresponding image path)\n",
        "# lst_scans = []\n",
        "# # species = categories\n",
        "# for categories in classes:\n",
        "#     # Get all the file names, all_scans = all_flowers\n",
        "#     all_scans = os.listdir(train +\"/\"+ categories )\n",
        "#     # Add then to the list\n",
        "#     for scan in all_scans:\n",
        "#         lst_scans.append((categories, (train /categories + \"/\" + scan)))\n",
        "\n",
        "# # Build a dataframe        \n",
        "# scans = pd.DataFrame(data=scans, columns=['category', 'image'], index=None)\n",
        "# scans.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMUSLJoj3sik"
      },
      "source": [
        "# Parameters for Image Generator\n",
        "# params = {'rescale':1./255, 'rotation_range':40, 'zoom_range':0.15, 'width_shift_range':0.2, 'height_shift_range':0.2, 'shear_range':0.05, 'horizontal_flip':True, 'featurewise_std_normalization':True, 'featurewise_center':True, 'fill_mode':'nearest'}\n",
        "\n",
        "# # Image Data Generator configuration\n",
        "# image_gen = ImageDataGenerator(**params)\n",
        "    \n",
        "# # Train Split\n",
        "# train_generator  = image_gen.flow_from_directory(\n",
        "# directory= '/mnt/4A34DB4C34DB39A1/Miscellaneous/MSDS Datasets/Finalized/Train/',\n",
        "# batch_size=16,\n",
        "# target_size=(224, 224),\n",
        "# color_mode=\"rgb\",\n",
        "# class_mode=\"binary\",\n",
        "# shuffle=True,\n",
        "# seed=42)\n",
        "    \n",
        "# # Validation Split\n",
        "# valid_generator  = image_gen.flow_from_directory(\n",
        "# directory= '/mnt/4A34DB4C34DB39A1/Miscellaneous/MSDS Datasets/Finalized/Validate/',\n",
        "# batch_size=16,\n",
        "# target_size=(224, 224),\n",
        "# color_mode=\"rgb\",\n",
        "# class_mode=\"binary\",\n",
        "# shuffle=True,\n",
        "# seed=42)\n",
        "    \n",
        "# # Test Split\n",
        "# test_generator  = image_gen.flow_from_directory(\n",
        "# directory= '/mnt/4A34DB4C34DB39A1/Miscellaneous/MSDS Datasets/Finalized/Test/',\n",
        "# batch_size=16,\n",
        "# target_size=(224, 224),\n",
        "# color_mode=\"rgb\",\n",
        "# class_mode=\"binary\",\n",
        "# shuffle=True,\n",
        "# seed=42)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}