Image_Forgery_Detection_Using_VGG19Net.ipynb

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  "metadata": {
    "colab": {
      "name": "Image Forgery Detection Using VGG19Net.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP+VM1s0ydo7genMB2O4JFt",
      "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/farhan1503001/Image-Forgery-Detection/blob/main/Image_Forgery_Detection_Using_VGG19Net.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9oFCE-7ebMqq",
        "outputId": "43efbd69-1a22-42bf-9115-961aa3a102e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdFaCh5wU9Fc",
        "outputId": "0a00bb0f-6ce5-4d20-9241-392a622f9cb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import h5py\n",
        "import numpy as np\n",
        "import os\n",
        "import cv2\n",
        "from sklearn.metrics import precision_score,recall_score,f1_score,roc_auc_score\n",
        "from imblearn.metrics import sensitivity_specificity_support\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OsmMsh4rVJ-n"
      },
      "source": [
        "import pandas as pd\n",
        "x_dataset=[]\n",
        "y_dataset=[]\n",
        "dataset=pd.read_csv(filepath_or_buffer=\"/content/drive/My Drive/Filer.txt\",delim_whitespace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZmPrwbHVNoI",
        "outputId": "941b8fd5-afc2-4c7f-b1aa-7717d9bbefa4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
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              "          00007tamp1.jpg  1\n",
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              "1        00007tamp11.jpg  1\n",
              "2        00007tamp12.jpg  1\n",
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              "...                  ... ..\n",
              "1994  PC050594_scale.jpg  0\n",
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              "1997  PC190612_scale.jpg  0\n",
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              "\n",
              "[1999 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txY6I_-pVQoR"
      },
      "source": [
        "path='/content/drive/My Drive/MICC-F2000/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fuU-DrWrVTSh",
        "outputId": "2f7a4cc0-2b61-4417-d050-d255d36966ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "i=0\n",
        "for index, row in dataset.iterrows():\n",
        "  full_file_name=path+row[0]\n",
        "  x=cv2.imread(full_file_name)\n",
        "  x=np.array(x,dtype='float32')\n",
        "  y=np.array(row[1],dtype='float32')\n",
        "  x=x/255.0\n",
        "  x=cv2.resize(x,(224,224))\n",
        "  x_dataset.append(x)\n",
        "  y_dataset.append(y)\n",
        "  i=i+1\n",
        "  print(i)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPA2L_TzVhN-"
      },
      "source": [
        "X_dataset=np.array(x_dataset)\n",
        "Y_dataset=np.array(y_dataset)\n",
        "Y_dataset=Y_dataset.reshape(1999,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYNL3pI1VtuT"
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "folds=list(KFold(n_splits=5,shuffle=True,random_state=1).split(X_dataset,Y_dataset))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWGrDZI0Wbnm",
        "outputId": "9113ef7d-50aa-4fab-8d4b-a9d54ae77e51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "Xception=tf.keras.applications.VGG19(include_top=False,input_shape=(224,224,3))\n",
        "input_image=tf.keras.layers.Input((224,224,3))\n",
        "x=Xception (input_image)\n",
        "x=tf.keras.layers.Flatten()(x)\n",
        "x=tf.keras.layers.Dense(2)(x)\n",
        "out=tf.keras.layers.Activation(activation='softmax')(x)\n",
        "\n",
        "model=tf.keras.Model(inputs=input_image,outputs=out)\n",
        "\n",
        "model.compile(optimizer=tf.keras.optimizers.Adamax(lr=0.0001),loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80142336/80134624 [==============================] - 2s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MWNE_jPZavQ",
        "outputId": "c1cd256b-293c-4d55-f792-ab933a2495c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "History=[]\n",
        "Prescore=[]\n",
        "Recall=[]\n",
        "F1=[]\n",
        "Sensspeci=[]\n",
        "Roc_auc=[]\n",
        "for j,(train_idx,val_idx) in enumerate(folds):\n",
        "    print(\"Fold \"+str(j+1))\n",
        "    \n",
        "    x_train=X_dataset[train_idx]\n",
        "    y_train=Y_dataset[train_idx]\n",
        "    x_val=X_dataset[val_idx]\n",
        "    y_val=Y_dataset[val_idx]\n",
        "    history=model.fit(x_train,y_train,batch_size=32,epochs=10,validation_data=(x_val,y_val))\n",
        "    y_predict=model.predict(x_val)\n",
        "    Y_predict=np.argmax(y_predict,axis=1)\n",
        "    l=len(y_val)\n",
        "    Y_predict=np.reshape(Y_predict,(l,1))\n",
        "    prescore=precision_score(y_val,Y_predict)\n",
        "    recaller=recall_score(y_val,Y_predict)\n",
        "    score=f1_score(y_val,Y_predict)\n",
        "    sensitivity=sensitivity_specificity_support(y_val,Y_predict,average='binary')\n",
        "    print(\"Precision: \",prescore)\n",
        "    print(\"Recall: \",recaller)\n",
        "    print(\"F1-score \",score)\n",
        "    print(\"Sensitivity \",sensitivity)\n",
        "    \n",
        "    History.append(history)\n",
        "    Prescore.append(prescore)\n",
        "    F1.append(score)\n",
        "    Recall.append(recaller)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fold 1\n",
            "Epoch 1/10\n",
            " 2/50 [>.............................] - ETA: 6s - loss: 0.5554 - accuracy: 0.7500WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0859s vs `on_train_batch_end` time: 0.1633s). Check your callbacks.\n",
            "50/50 [==============================] - 15s 302ms/step - loss: 0.3919 - accuracy: 0.8462 - val_loss: 0.2437 - val_accuracy: 0.8925\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.1516 - accuracy: 0.9593 - val_loss: 0.1643 - val_accuracy: 0.9500\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.1230 - accuracy: 0.9650 - val_loss: 0.1916 - val_accuracy: 0.9575\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.1109 - accuracy: 0.9744 - val_loss: 0.1897 - val_accuracy: 0.9700\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.1103 - accuracy: 0.9719 - val_loss: 0.1526 - val_accuracy: 0.9700\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.0963 - accuracy: 0.9750 - val_loss: 0.1580 - val_accuracy: 0.9700\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0897 - accuracy: 0.9762 - val_loss: 0.1613 - val_accuracy: 0.9700\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.0927 - accuracy: 0.9744 - val_loss: 0.1750 - val_accuracy: 0.9575\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.0857 - accuracy: 0.9769 - val_loss: 0.1670 - val_accuracy: 0.9700\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.0758 - accuracy: 0.9781 - val_loss: 0.1643 - val_accuracy: 0.9625\n",
            "Precision:  0.9215686274509803\n",
            "Recall:  0.9791666666666666\n",
            "F1-score  0.9494949494949494\n",
            "Sensitivity  (0.9791666666666666, 0.953125, None)\n",
            "Fold 2\n",
            "Epoch 1/10\n",
            " 2/50 [>.............................] - ETA: 9s - loss: 0.0285 - accuracy: 1.0000WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0788s vs `on_train_batch_end` time: 0.1643s). Check your callbacks.\n",
            "50/50 [==============================] - 13s 262ms/step - loss: 0.1058 - accuracy: 0.9725 - val_loss: 0.0510 - val_accuracy: 0.9850\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0997 - accuracy: 0.9706 - val_loss: 0.0550 - val_accuracy: 0.9850\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.0884 - accuracy: 0.9731 - val_loss: 0.0831 - val_accuracy: 0.9850\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0823 - accuracy: 0.9762 - val_loss: 0.0744 - val_accuracy: 0.9850\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0880 - accuracy: 0.9756 - val_loss: 0.0775 - val_accuracy: 0.9850\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0730 - accuracy: 0.9756 - val_loss: 0.0843 - val_accuracy: 0.9775\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0806 - accuracy: 0.9769 - val_loss: 0.0793 - val_accuracy: 0.9825\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0656 - accuracy: 0.9800 - val_loss: 0.0739 - val_accuracy: 0.9825\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0594 - accuracy: 0.9831 - val_loss: 0.0801 - val_accuracy: 0.9825\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.0628 - accuracy: 0.9794 - val_loss: 0.1047 - val_accuracy: 0.9700\n",
            "Precision:  0.950354609929078\n",
            "Recall:  0.9640287769784173\n",
            "F1-score  0.9571428571428572\n",
            "Sensitivity  (0.9640287769784173, 0.9731800766283525, None)\n",
            "Fold 3\n",
            "Epoch 1/10\n",
            " 2/50 [>.............................] - ETA: 9s - loss: 0.1026 - accuracy: 0.9688WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0787s vs `on_train_batch_end` time: 0.1633s). Check your callbacks.\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0684 - accuracy: 0.9831 - val_loss: 0.0595 - val_accuracy: 0.9825\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.0583 - accuracy: 0.9831 - val_loss: 0.0636 - val_accuracy: 0.9800\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.0467 - accuracy: 0.9862 - val_loss: 0.0713 - val_accuracy: 0.9825\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0464 - accuracy: 0.9844 - val_loss: 0.0810 - val_accuracy: 0.9750\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.0514 - accuracy: 0.9869 - val_loss: 0.0727 - val_accuracy: 0.9725\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0405 - accuracy: 0.9881 - val_loss: 0.0785 - val_accuracy: 0.9750\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0373 - accuracy: 0.9881 - val_loss: 0.0864 - val_accuracy: 0.9750\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0391 - accuracy: 0.9887 - val_loss: 0.0717 - val_accuracy: 0.9750\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.0300 - accuracy: 0.9875 - val_loss: 0.0693 - val_accuracy: 0.9800\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.0294 - accuracy: 0.9925 - val_loss: 0.0762 - val_accuracy: 0.9750\n",
            "Precision:  0.9395973154362416\n",
            "Recall:  0.9929078014184397\n",
            "F1-score  0.9655172413793103\n",
            "Sensitivity  (0.9929078014184397, 0.9652509652509652, None)\n",
            "Fold 4\n",
            "Epoch 1/10\n",
            " 2/50 [>.............................] - ETA: 9s - loss: 0.0441 - accuracy: 0.9844WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0783s vs `on_train_batch_end` time: 0.1638s). Check your callbacks.\n",
            "50/50 [==============================] - 13s 262ms/step - loss: 0.0459 - accuracy: 0.9856 - val_loss: 0.0411 - val_accuracy: 0.9875\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0347 - accuracy: 0.9912 - val_loss: 0.0512 - val_accuracy: 0.9800\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0353 - accuracy: 0.9894 - val_loss: 0.0407 - val_accuracy: 0.9850\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0300 - accuracy: 0.9919 - val_loss: 0.0588 - val_accuracy: 0.9800\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 13s 262ms/step - loss: 0.0226 - accuracy: 0.9931 - val_loss: 0.0578 - val_accuracy: 0.9750\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0213 - accuracy: 0.9944 - val_loss: 0.0648 - val_accuracy: 0.9775\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0167 - accuracy: 0.9956 - val_loss: 0.0700 - val_accuracy: 0.9775\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 13s 262ms/step - loss: 0.0215 - accuracy: 0.9931 - val_loss: 0.0524 - val_accuracy: 0.9825\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 13s 261ms/step - loss: 0.0162 - accuracy: 0.9956 - val_loss: 0.0636 - val_accuracy: 0.9800\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 13s 260ms/step - loss: 0.0129 - accuracy: 0.9962 - val_loss: 0.0666 - val_accuracy: 0.9750\n",
            "Precision:  0.9645390070921985\n",
            "Recall:  0.9645390070921985\n",
            "F1-score  0.9645390070921985\n",
            "Sensitivity  (0.9645390070921985, 0.9806949806949807, None)\n",
            "Fold 5\n",
            "Epoch 1/10\n",
            " 2/50 [>.............................] - ETA: 11s - loss: 0.0210 - accuracy: 0.9844WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0152s vs `on_train_batch_end` time: 0.2257s). Check your callbacks.\n",
            "50/50 [==============================] - 14s 271ms/step - loss: 0.0312 - accuracy: 0.9912 - val_loss: 0.0302 - val_accuracy: 0.9950\n",
            "Epoch 2/10\n",
            "50/50 [==============================] - 13s 263ms/step - loss: 0.0278 - accuracy: 0.9925 - val_loss: 0.0214 - val_accuracy: 0.9950\n",
            "Epoch 3/10\n",
            "50/50 [==============================] - 13s 263ms/step - loss: 0.0242 - accuracy: 0.9919 - val_loss: 0.0146 - val_accuracy: 0.9975\n",
            "Epoch 4/10\n",
            "50/50 [==============================] - 13s 262ms/step - loss: 0.0181 - accuracy: 0.9956 - val_loss: 0.0149 - val_accuracy: 0.9950\n",
            "Epoch 5/10\n",
            "50/50 [==============================] - 13s 263ms/step - loss: 0.0148 - accuracy: 0.9956 - val_loss: 0.0165 - val_accuracy: 0.9975\n",
            "Epoch 6/10\n",
            "50/50 [==============================] - 13s 262ms/step - loss: 0.0185 - accuracy: 0.9931 - val_loss: 0.0197 - val_accuracy: 0.9925\n",
            "Epoch 7/10\n",
            "50/50 [==============================] - 13s 263ms/step - loss: 0.0223 - accuracy: 0.9937 - val_loss: 0.0192 - val_accuracy: 0.9950\n",
            "Epoch 8/10\n",
            "50/50 [==============================] - 13s 263ms/step - loss: 0.0146 - accuracy: 0.9931 - val_loss: 0.0206 - val_accuracy: 0.9925\n",
            "Epoch 9/10\n",
            "50/50 [==============================] - 13s 263ms/step - loss: 0.0119 - accuracy: 0.9969 - val_loss: 0.0229 - val_accuracy: 0.9925\n",
            "Epoch 10/10\n",
            "50/50 [==============================] - 13s 262ms/step - loss: 0.0105 - accuracy: 0.9969 - val_loss: 0.0180 - val_accuracy: 0.9925\n",
            "Precision:  0.9924812030075187\n",
            "Recall:  0.9850746268656716\n",
            "F1-score  0.9887640449438201\n",
            "Sensitivity  (0.9850746268656716, 0.9962264150943396, None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wj5Kk1Ofp89j",
        "outputId": "a4397a45-cbc4-44c7-8271-222ac136c722",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "source": [
        "val_acc=History[0].history['val_accuracy']\n",
        "val_acc1=History[1].history['val_accuracy']\n",
        "val_acc2=History[2].history['val_accuracy']\n",
        "val_acc3=History[3].history['val_accuracy']\n",
        "val_acc4=History[4].history['val_accuracy']\n",
        "Accuracvalid1=val_acc[7]\n",
        "Accuracyvalid2=val_acc1[7]\n",
        "Accuracyvalid3=val_acc2[7]\n",
        "Accuracyvalid4=val_acc3[7]\n",
        "Accuracyvalid5=val_acc4[7]\n",
        "Mean_valid_Accuracy=(Accuracvalid1+Accuracyvalid2+Accuracyvalid3+Accuracyvalid4+Accuracyvalid5)/5.00\n",
        "print(\"validation Accuracy for Fold-1 \",Accuracvalid1)\n",
        "print(\"validation Accuracy for Fold-2\",Accuracyvalid2)\n",
        "print(\"Validation Accuracy for Fold-3 \",Accuracyvalid3)\n",
        "print(\"Validation Accuracy for Fold-4  \",Accuracyvalid4)\n",
        "print(\"Validation Accuracy for Fold-5  \",Accuracyvalid5)\n",
        "print(\"Mean Validation Accuracy \",Mean_valid_Accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "validation Accuracy for Fold-1  0.9574999809265137\n",
            "validation Accuracy for Fold-2 0.9825000166893005\n",
            "Validation Accuracy for Fold-3  0.9750000238418579\n",
            "Validation Accuracy for Fold-4   0.9825000166893005\n",
            "Validation Accuracy for Fold-5   0.9924812316894531\n",
            "Mean Validation Accuracy  0.9779962539672852\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVVe6gPCqCcB",
        "outputId": "d75bb3a5-16e3-4040-d29f-2a0ce8c884fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "print(\"Printing the recall scores and average of them\")\n",
        "avgr=0.0\n",
        "for i in range(5):\n",
        "  print(\"Precision of Fold -\",i+1,Recall[i])\n",
        "  avgr=avgr+Recall[i]\n",
        "\n",
        "print(\"Average precision-score \",avgr/5.00)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Printing the recall scores and average of them\n",
            "Precision of Fold - 1 0.9791666666666666\n",
            "Precision of Fold - 2 0.9640287769784173\n",
            "Precision of Fold - 3 0.9929078014184397\n",
            "Precision of Fold - 4 0.9645390070921985\n",
            "Precision of Fold - 5 0.9850746268656716\n",
            "Average precision-score  0.9771433758042788\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kn8upAfOqGSl",
        "outputId": "d796add5-ac75-48fb-b229-7cd4cd5c7cd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "print(\"Printing the F-1 scores and average of them\")\n",
        "avgf=0.0\n",
        "for i in range(5):\n",
        "  print(\"Precision of Fold -\",i+1,F1[i])\n",
        "  avgf=avgf+F1[i]\n",
        "\n",
        "print(\"Average f1-score \",avgf/5.00)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Printing the F-1 scores and average of them\n",
            "Precision of Fold - 1 0.9494949494949494\n",
            "Precision of Fold - 2 0.9571428571428572\n",
            "Precision of Fold - 3 0.9655172413793103\n",
            "Precision of Fold - 4 0.9645390070921985\n",
            "Precision of Fold - 5 0.9887640449438201\n",
            "Average f1-score  0.9650916200106272\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "By0p0jH-qJsr",
        "outputId": "131ff277-149d-45af-a11a-35aa3b28592e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "print(\"Printing the precision scores and average of them\")\n",
        "avg=0.0\n",
        "for i in range(5):\n",
        "  print(\"Precision of Fold -\",i+1,Prescore[i])\n",
        "  avg=avg+Prescore[i]\n",
        "\n",
        "print(\"Average precision-score \",avg/5.00)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Printing the precision scores and average of them\n",
            "Precision of Fold - 1 0.9215686274509803\n",
            "Precision of Fold - 2 0.950354609929078\n",
            "Precision of Fold - 3 0.9395973154362416\n",
            "Precision of Fold - 4 0.9645390070921985\n",
            "Precision of Fold - 5 0.9924812030075187\n",
            "Average precision-score  0.9537081525832034\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}