Image_Forgery_Detection_Xception_Net.ipynb

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  "metadata": {
    "colab": {
      "name": "Image Forgery Detection Xception Net.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNQbkL2rZQZI15FYuPUDDig",
      "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/Breast-Cancer-Classification-From-Histopathological-Images/blob/master/Image_Forgery_Detection_Xception_Net.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMBBE_uLZ-Sv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42fa046f-112d-4f28-d913-f4b9280fe228"
      },
      "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": "IeSHd6AIk0Sw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96f02c0f-1a36-4e74-c17e-e2f80b899448"
      },
      "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/python3.7/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/python3.7/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/python3.7/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": "dslnl7wWk33J"
      },
      "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": "in3RVQgSlEtP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "a8a769d8-e194-44f8-9be7-2491852b7bf0"
      },
      "source": [
        "\n",
        "\n",
        "dataset\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "\n",
              "[1999 rows x 2 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCO0i5PslHsE"
      },
      "source": [
        "path='/content/drive/My Drive/MICC-F2000/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJ50UpEolR01",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52936d4f-49f0-439f-db3e-6770de5d832b"
      },
      "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": "O1AXum6ulSV2"
      },
      "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": "cyUyXVc3J177"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(X_dataset,Y_dataset,test_size=0.2,shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3nd-mfeltei"
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "folds=list(KFold(n_splits=5,shuffle=True,random_state=1).split(x_train,y_train))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wbl5x5hPl2-B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e32abcb-589a-46aa-b5ed-7427eefd7c64"
      },
      "source": [
        "Xception=tf.keras.applications.Xception(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(1)(x)\n",
        "out=tf.keras.layers.Activation(activation='sigmoid')(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='binary_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "83689472/83683744 [==============================] - 2s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwCuOANiTZ8E"
      },
      "source": [
        "from sklearn.metrics import auc,roc_curve\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-UfKPXkil37K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "27f88a8a-c2a2-4ab4-80ed-e1452911ff56"
      },
      "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=50,validation_data=(x_val,y_val))\n",
        "    y_predict=model.predict(x_val)\n",
        "    Y_predict=np.float32(y_predict>0.5)\n",
        "    l=len(y_val)\n",
        "    Y_predict=np.reshape(Y_predict,(l,1))\n",
        "    prescore=precision_score(y_val,Y_predict,average='binary')\n",
        "    recaller=recall_score(y_val,Y_predict,average='binary')\n",
        "    score=f1_score(y_val,Y_predict,average='binary')\n",
        "    sensitivity=sensitivity_specificity_support(y_val,Y_predict,average='binary')\n",
        "    fpr, tpr, thresholds = roc_curve(y_val,Y_predict)\n",
        "    auc = roc_auc_score(y_val, Y_predict)\n",
        "    print(\"Precision: \",prescore)\n",
        "    print(\"Recall: \",recaller)\n",
        "    print(\"F1-score \",score)\n",
        "    print(\"Sensitivity \",sensitivity)\n",
        "    print(\"AUC score\",auc)\n",
        "    History.append(history)\n",
        "    Prescore.append(prescore)\n",
        "    F1.append(score)\n",
        "    Recall.append(recaller)\n",
        "    plt.plot([0,1],[0,1], linestyle='--', label='No Skill')\n",
        "    plt.plot(fpr,tpr, marker='.', label='XceptionNet')\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel('True Positive Rate')\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fold 1\n",
            "Epoch 1/50\n",
            "40/40 [==============================] - 97s 1s/step - loss: 0.4736 - accuracy: 0.7667 - val_loss: 0.4505 - val_accuracy: 0.7781\n",
            "Epoch 2/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0943 - accuracy: 0.9804 - val_loss: 0.2071 - val_accuracy: 0.9094\n",
            "Epoch 3/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0556 - accuracy: 0.9826 - val_loss: 0.1378 - val_accuracy: 0.9594\n",
            "Epoch 4/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0747 - accuracy: 0.9772 - val_loss: 0.1156 - val_accuracy: 0.9750\n",
            "Epoch 5/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0490 - accuracy: 0.9861 - val_loss: 0.1113 - val_accuracy: 0.9781\n",
            "Epoch 6/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0475 - accuracy: 0.9833 - val_loss: 0.1204 - val_accuracy: 0.9812\n",
            "Epoch 7/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0435 - accuracy: 0.9873 - val_loss: 0.1186 - val_accuracy: 0.9812\n",
            "Epoch 8/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0367 - accuracy: 0.9888 - val_loss: 0.1236 - val_accuracy: 0.9812\n",
            "Epoch 9/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0265 - accuracy: 0.9919 - val_loss: 0.1257 - val_accuracy: 0.9781\n",
            "Epoch 10/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0279 - accuracy: 0.9936 - val_loss: 0.1263 - val_accuracy: 0.9781\n",
            "Epoch 11/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0211 - accuracy: 0.9934 - val_loss: 0.1313 - val_accuracy: 0.9781\n",
            "Epoch 12/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0210 - accuracy: 0.9950 - val_loss: 0.1335 - val_accuracy: 0.9812\n",
            "Epoch 13/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0361 - accuracy: 0.9898 - val_loss: 0.1365 - val_accuracy: 0.9750\n",
            "Epoch 14/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0199 - accuracy: 0.9940 - val_loss: 0.1404 - val_accuracy: 0.9750\n",
            "Epoch 15/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0272 - accuracy: 0.9903 - val_loss: 0.1389 - val_accuracy: 0.9781\n",
            "Epoch 16/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0157 - accuracy: 0.9948 - val_loss: 0.1352 - val_accuracy: 0.9781\n",
            "Epoch 17/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0167 - accuracy: 0.9954 - val_loss: 0.1376 - val_accuracy: 0.9781\n",
            "Epoch 18/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0168 - accuracy: 0.9945 - val_loss: 0.1388 - val_accuracy: 0.9781\n",
            "Epoch 19/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0260 - accuracy: 0.9891 - val_loss: 0.1400 - val_accuracy: 0.9781\n",
            "Epoch 20/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0115 - accuracy: 0.9948 - val_loss: 0.1457 - val_accuracy: 0.9781\n",
            "Epoch 21/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0163 - accuracy: 0.9930 - val_loss: 0.1448 - val_accuracy: 0.9812\n",
            "Epoch 22/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0180 - accuracy: 0.9933 - val_loss: 0.1486 - val_accuracy: 0.9812\n",
            "Epoch 23/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0074 - accuracy: 0.9976 - val_loss: 0.1511 - val_accuracy: 0.9781\n",
            "Epoch 24/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0117 - accuracy: 0.9952 - val_loss: 0.1496 - val_accuracy: 0.9812\n",
            "Epoch 25/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0120 - accuracy: 0.9924 - val_loss: 0.1508 - val_accuracy: 0.9781\n",
            "Epoch 26/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0066 - accuracy: 0.9972 - val_loss: 0.1530 - val_accuracy: 0.9781\n",
            "Epoch 27/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0092 - accuracy: 0.9941 - val_loss: 0.1591 - val_accuracy: 0.9719\n",
            "Epoch 28/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0136 - accuracy: 0.9936 - val_loss: 0.1553 - val_accuracy: 0.9781\n",
            "Epoch 29/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0058 - accuracy: 0.9965 - val_loss: 0.1595 - val_accuracy: 0.9750\n",
            "Epoch 30/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0051 - accuracy: 0.9977 - val_loss: 0.1581 - val_accuracy: 0.9750\n",
            "Epoch 31/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0054 - accuracy: 0.9984 - val_loss: 0.1679 - val_accuracy: 0.9750\n",
            "Epoch 32/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0082 - accuracy: 0.9983 - val_loss: 0.1652 - val_accuracy: 0.9750\n",
            "Epoch 33/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0064 - accuracy: 0.9977 - val_loss: 0.1651 - val_accuracy: 0.9750\n",
            "Epoch 34/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0062 - accuracy: 0.9987 - val_loss: 0.1683 - val_accuracy: 0.9750\n",
            "Epoch 35/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0085 - accuracy: 0.9969 - val_loss: 0.1683 - val_accuracy: 0.9750\n",
            "Epoch 36/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0040 - accuracy: 0.9995 - val_loss: 0.1676 - val_accuracy: 0.9750\n",
            "Epoch 37/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0054 - accuracy: 0.9982 - val_loss: 0.1689 - val_accuracy: 0.9750\n",
            "Epoch 38/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0082 - accuracy: 0.9974 - val_loss: 0.1694 - val_accuracy: 0.9750\n",
            "Epoch 39/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0025 - accuracy: 0.9998 - val_loss: 0.1696 - val_accuracy: 0.9750\n",
            "Epoch 40/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0018 - accuracy: 0.9999 - val_loss: 0.1719 - val_accuracy: 0.9750\n",
            "Epoch 41/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0018 - accuracy: 0.9999 - val_loss: 0.1733 - val_accuracy: 0.9750\n",
            "Epoch 42/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.1696 - val_accuracy: 0.9781\n",
            "Epoch 43/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0035 - accuracy: 0.9989 - val_loss: 0.1716 - val_accuracy: 0.9781\n",
            "Epoch 44/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0039 - accuracy: 0.9990 - val_loss: 0.1792 - val_accuracy: 0.9781\n",
            "Epoch 45/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0054 - accuracy: 0.9994 - val_loss: 0.1844 - val_accuracy: 0.9781\n",
            "Epoch 46/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.1852 - val_accuracy: 0.9781\n",
            "Epoch 47/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.1818 - val_accuracy: 0.9781\n",
            "Epoch 48/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.1832 - val_accuracy: 0.9781\n",
            "Epoch 49/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 7.1908e-04 - accuracy: 1.0000 - val_loss: 0.1828 - val_accuracy: 0.9781\n",
            "Epoch 50/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 5.2019e-04 - accuracy: 1.0000 - val_loss: 0.1818 - val_accuracy: 0.9781\n",
            "Precision:  0.958904109589041\n",
            "Recall:  0.9929078014184397\n",
            "F1-score  0.975609756097561\n",
            "Sensitivity  (0.9929078014184397, 0.9664804469273743, None)\n",
            "AUC score 0.9796941241729071\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l0cBlODWGvlDVZSIyBLgXqAn0DEyILlO1q4ZM2PLk5fPCN6sBuOs8KxJnSlZai2Ai0ApYCDwvIluAFGCMqk4NRHABkXMQUOsaMmHnxw17uGdyGmt2HuSyFCsSZ46ttESQAnRX1QIRiQW2AW1VdXdgQguQwsqjdtWQCRMHPXk8MWMVr89bT/N6NXn92j6c1cFmDTPHVtrlozmqWgCgqtnA2vImAREZJCKrRGS1iIw5xjaXiUi6iCwXkXfKs/9KUTQpjbUITHjYsu8w7yzcyJ9Obs2M28+0JGDKVFqLoJOIpHmfC9DWuyyAqmr30nbsHWN4ERgAZACLRCRVVdN9tmkPjAVOU9W9IhL469gsEZgwsP9QLp8v3coVfZ0icXPu6UcTGww2fiotEXSu4L77AKtVdS2AiLwHDAPSfbb5K/Ciqu4FUNUdFTxm+VnXkAlxXyzbxj8+Wcaegzn0bRNP20a1LQmYcimt6FxFC821ADb5LGcAfYtt0wFARL7HKW09TlW/KL4jERkFjAJITEysYFjFFE1KY4nAhJYdmdmMS13OtKXb6NKsLq+OPIm2jez32JSfX5PXu3z89sDZQEtgtoicoKr7fDdS1fHAeICUlBSt1AiK5iu2riETOvILlMtemseW/dncfV5HRp3ZxorEmePmZiLYjHP5aaGW3nW+MoAFqpoLrBORX3ESwyIX4zpSjo0RmNCxdf9hmtSJdYrEDe1KqwZxViraVJhfHyFEpKaIdCznvhcB7UUkWUSqAyOA1GLbTMVpDSAiCThdRWvLeZyKsa4hEwIKCpTXvl9H/6dm8VZhkbiOjS0JmEpRZiIQkT8AS4AvvMs9RKT4P/SjqGoecDMwA1gBfKCqy0XkQREZ6t1sBrBbRNKB74C7A36fgicTJApi4gJ6WGP8tXpHFpe9PI9xn6aTkhTPOZ2sSJypXP50DY3DuQJoJoCqLhGRZH92rqrTgGnF1j3g81yBO7xfwVFYZ8juuDRV0HsLN/JA6nJqxkTz1KUncnGvFnZ3sKl0fpWhVtX9xX75KnfANpgK5ys2pgpKbBjHuZ0b88+h3WhUp0awwzFhyp9EsFxErgCivTeA3Qr84G5YAeQ5YPcQmCojOzef57/5DYB7BnXi1LYJnNrWisQZd/kzWHwLznzFHuAdnHLU4TMfQY7NRWCqhsXr93D+83P478w17DmYg9Nzaoz7/GkRdFLV+4D73A4mKKxryARZliePJ75YyRvzN9Cifk3euLYPZ1p9IBNA/iSCp0SkKTAZeF9Vl7kcU2B5MqG2XYVhgmfb/sO8t2gT15ySxN3ndaRWjWDf52kiTZldQ6raD2dmsp3AyyKyVETudz2yQLGuIRMEew/m8OZ8536Ado2dInHjhna1JGCCwq8bylR1m6o+D1yPc0/BA2W8JXR4Mi0RmIBRVaYt3cqAZ2bxz9TlrNnp3NBo00aaYCrz44eIdAYuBy4BdgPv40xkH/pUvfcR2BiBcd+OA9n845NlzFi+nRNa1OONa/takThTJfjTDp2E88//PFXd4nI8gZWXDQV5NlhsXJdfoFz68jy27c9m7OBOXHd6MtWsSJypIspMBKp6SiACCYqiOkN1gxuHCVtb9h2maV2nSNyDw7rRqkFN2lgrwFQxx/xIIiIfeB+Xikiaz9dSn5nLQlth5VHrGjKVLL9AebVYkbizOjSyJGCqpNJaBLd5H4cEIpCgKJqm0v44TeVZvSOTeyan8dPGfZzdsRH9OzcJdkjGlKq0Gcq2ep/eqKqjfV8TkceA0Ue/K8QUdQ3ZVUOmcryzYCPjUpdTq0Y0z1x+Ihf2sCJxpurzZ7RqQAnrBld2IEFRNF+xJQJTOZIS4hjYtQlf3XEWF/VsaUnAhIRjtghE5AbgRqBNsTGBOsD3bgcWENY1ZCooOzefZ77+FUEYM9iKxJnQVNoYwTvAdODfwBif9ZmqusfVqALFY9NUmuO3YO1uxkxZyrpdB7mybyKqai0AE5JKSwSqqutF5KbiL4hIfFgkg6KuIWsRGP9lZufy2BcreWv+RhLj43jnL305tZ21AkzoKqtFMAT4EWciGt+POgq0cTGuwPDY5aOm/LYf8DD5xwz+cnoydwzsQFx1qw9kQltpVw0N8T76NS1lSPJkQUwtiLI7PE3p9hzM4fO0LVx9ShLtGtdmzj3n2IxhJmz4U2voNGCJqh4UkauAXsCzqrrR9ejclmMF50zpVJXP0rYyLnU5B7JzOa1dAm0a1bYkYMKKPx+F/wccEpETcYrNrQHedDWqQPFk2hVD5pi2H8jmr2/8yC3v/kyLBjX59JbT7c5gE5b86dzMU1UVkWHAf1R1oohc53ZgAeGxyqOmZPkFymXeInH3nd+ZP5+WZEXiTNjyJxFkishY4GrgDBGJAmLcDStAbFIaU0zG3kM0q1eT6CjhoWHdSIyPIymhVrDDMsZV/nzEuRxn4vprVXUb0BJ4wtWoAsVjicA48guUV+as5dynZ/GWd+awMzs0siRgIoI/Zai3icjbwEkiMgRYqKpvuB9aAHgOWNeQYdW2TO75KI1fNu2jf6fGDOxqReJMZPHnqqHLcFoAM3HuJXhBRO5W1ckux+Y+6xqKeG/N38A/P11OndgYnhvRg6EnNre7g03E8WeM4D7gJFXdASAijYCvgdBPBJ4su2ooQhWWg2jXuDbnn9CMB4Z0oWFtuyTURCZ/EkFUYRLw2o2fk95XaXk5kO+xyqMR5nBOPk9/tYqoKGHs4M6c3KYhJ7dpGOywjAkqfxLBFyIyA3jXu3w5MM29kAKksM6QtQgixrw1uxkzJY0Nuw9x9cmtrUicMV7+DBbfLSIXA6d7V41X1Y/dDSsArPJoxDiQncu/p63k3YUbad0wjnf+2tdKRRvjo7T5CNoDTwJtgaXAXaq6OVCBuc4KzkWMHQc8TP15M6PObMPt53agZvXoYIdkTJVSWl//JOAz4BKcCqQvlHfnIjJIRFaJyGoRGVPKdpeIiIpISnmPcdysayis7c7y8Nr36wBo17g2c0f3497zO1sSMKYEpXUN1VHVCd7nq0Tkp/LsWESigRdxprrMABaJSKqqphfbrg5wG7CgPPuvsKL5iusG9LDGXapK6i9bGJe6nCxPHmd2aESbRrXtiiBjSlFaIogVkZ78Pg9BTd9lVS0rMfQBVqvqWgAReQ8YBqQX2+4h4DHg7nLGXjGeA86jdQ2FjS37DnP/1GV8u3IHPVrV5/Hh3a1InDF+KC0RbAWe9lne5rOswDll7LsFsMlnOQPo67uBiPQCWqnq5yJyzEQgIqOAUQCJiYllHNZP1jUUVvLyCxgxfj47Mz38Y0gXRp6aRHSUXRFkjD9Km5imn5sH9havexoYWda2qjoeGA+QkpKilRJAUdeQXTUUyjbtOUTz+jWpFh3Fvy46gcT4OBIbxgU7LGNCips3hm0GWvkst/SuK1QH6AbMFJH1wMlAasAGjIvmK7ZEEIry8gsYP3sN5z49izfnrQfg9PYJlgSMOQ5uTra6CGgvIsk4CWAEcEXhi6q6Hyi6mFtEZuJcorrYxZh+5zkA1WIh2uabDTUrth5g9EdppGXsZ0CXJgw+oVmwQzImpLn2X1BV80TkZmAGEA1MUtXlIvIgsFhVU906tl+sBHVIenPeev75aTr1asbwnyt6csEJzezuYGMqyJ/qowJcCbRR1QdFJBFoqqoLy3qvqk6jWDkKVX3gGNue7VfElSXHZicLJYXlIDo0qcMfTmzOP4Z0Ib5W9WCHZUxY8KdF8F+gAOcqoQeBTOAj4CQX43KfzVccEg7l5PHkjF+pFi3ce35n+rZpSF8rEmdMpfJnsLivqt4EZAOo6l4g9D+KebJsoLiK+371Ls57djaTvl9HTl4BqpVzwZgx5kj+tAhyvXcJKxTNR1DgalSBkJMJtZsGOwpTgv2Hc/nX5yt4f/EmkhNq8cHfTqFPcnywwzImbPmTCJ4HPgYai8gjwHDgflejCgRPJjRsF+woTAl2ZXn4NG0L15/Vlr+f257YGKsPZIyb/ClD/baI/Aj0xykvcaGqrnA9Mrd5bLC4KtmZ6eHTX7Zw7enJtG1Um7mjz7HBYGMCxJ+rhhKBQ8CnvutUdaObgbnO5iuuElSVqUs2889P0znkyadfp8YkJ9SyJGBMAPnTNfQ5zviAALFAMrAK6OpiXO7Kz4PcQ5YIgmzzvsPc9/FSZq7aSa9Ep0hcckKtYIdlTMTxp2voBN9lb6G4G12LKBCKyktY11CwOEXi5rE7K4dxf+jC1adYkThjgqXcdxar6k8i0rfsLauwHCs4Fywbdx+iRQOnSNyjF3cnMT6OVvFWH8iYYPJnjOAOn8UooBewxbWIAsFjJagDLS+/gAlz1vHM178ydnAn/nxaMqe1s3mDjakK/GkR+H5szsMZM/jInXACpGi+YmsRBMLyLfsZ/VEayzYf4LyuTbjAisQZU6WUmgi8N5LVUdW7AhRPYOR4E4F1Dbnu9R/W89Bn6dSPq87/ruxllUKNqYKOmQhEpJq3guhpgQwoIKxryHWFReI6Na3DsB4t+MeQztSPs0tCjamKSmsRLMQZD1giIqnAh8DBwhdVdYrLsbmnqGvIEkFlO+jJ44kZq4iJFu67oIsViTMmBPgzRhAL7MapPlp4P4ECoZsI7KohV8z+dSdjpyxly/7DXHNKUlGrwBhTtZWWCBp7rxhaxu8JoFBol4H02BhBZdp/KJeHPk9n8o8ZtGnkFIk7KcmKxBkTKkpLBNFAbY5MAIVCPxFExUC1GsGOJCzsOuhh+tKt3Hh2W27tb0XijAk1pSWCrar6YMAiCaScLBsorqAdmdmkLtnCX85oU1QkroHVBzImJJWWCMK3c9fmKz5uqspHP23moc/SOZybT//OTUhOqGVJwJgQVloi6B+wKALNk2k3kx2HTXsOce/HS5nz2y5SWjfg0UusSJwx4eCYiUBV9wQykIDKsfmKyysvv4A/TpjP3oM5PDSsK1f2bU2UFYkzJiyUu+hcWPBkQZxd1eKP9bsO0io+jmrRUTw+3CkS17KBFYkzJpz4M3l9+Mmx2cnKkptfwIvfrWbgM7N5Y956AE5tm2BJwJgwFKEtAusaKs2yzfu5Z3Ia6VsPcMEJzRjSvXmwQzLGuChCE0GWDRYfw6vfr+Phz1cQX6s6L13Vm0HdmgY7JGOMyyIvERQU2HzFJSgsB9G1eT0u7tmC+y/oQr24mGCHZYwJgMhLBLkHAbWuIa8sTx6Pf7GS6tFR3D+kC32S4+mTbAPpxkSSyBss9th8xYVmrtrBec/M5s35G1CcVoExJvJEXougqPJo3eDGEUR7D+bw0OfpTPlpM+0a12by9afSu3WDYIdljAmSyEsEngPOYwR3De09lMOXy7dz6zntuOmcdtSoZkXijIlkrnYNicggEVklIqtFZEwJr98hIukikiYi34hIazfjASK2a2jHgWzGz16DqtKmUW2+H30OdwzsaEnAGONeIvDOd/wiMBjoAvxRRLoU2+xnIEVVuwOTgcfdiqdIhE1Ko6p8sGgT/Z+exVNf/sr63YcA7IogY0wRN7uG+gCrVXUtgIi8BwwD0gs3UNXvfLafD1zlYjyOCJqUZtOeQ4ydspS5q3fRJzmeRy8+wYrEGWOO4mYiaAFs8lnOAPqWsv11wPSSXhCRUcAogMTExIpFFSHzFRcWidt3KJeHL+zGFX0SrUicMaZEVWKwWESuAlKAs0p6XVXHA+MBUlJSKnaNY5h3Da3bdZBEb5G4J4afSOuGcTSvXzPYYRljqjA3B4s3A618llt61x1BRM4F7gOGqqrHxXgcniyQKIgJr3+OufkFvPDNb5z3zGxe/2E9AKe0bWhJwBhTJjdbBIuA9iKSjJMARgBX+G4gIj2Bl4FBqrrDxVh+VzgpjYRPN0laxj7umZzGym2Z/OHE5gztYUXijDH+cy0RqGqeiNwMzACigUmqulxEHgQWq2oq8ARQG/hQnH/MG1V1qFsxAWE3X/Gkuet4+PN0GtWpwYQ/pTCgS5Ngh2SMCTGujhGo6jRgWrF1D/g8P9fN45fIkxkW4wOFReK6t6zH5Se1YszgztSraZeEGmPKr0oMFgeUJzOkrxjKzM7l0ekrqVEtmgf+0IWUpHhSkqxInDHm+MBJaUYAABQiSURBVEVe0bkQ7hr6buUOBj4zm3cXbqRatFiROGNMpYjAFkEW1AmtyVb2HMzhwU+XM3XJFjo0qc1/rzyVnolWJM4YUzkiMBFkhtzsZPsP5/LNih3c1r89N/VrR/VqkdeQM8a4J/ISQU5ozFe8bX82U5ds5m9ntiE5oRZzx5xjg8HGGFdEViJQdbqGqvBVQ6rKe4s28a/PV5BbUMCgrk1JSqhlScAY45rISgS5h0Hzq+xVQxt2H2TMR0uZt3Y3J7eJ59GLu5NkReJMiMnNzSUjI4Ps7OxghxKRYmNjadmyJTEx/n94jKxEUIXrDOXlF3DFhAXsP5zLvy46gREntbIicSYkZWRkUKdOHZKSkpAwuoM/FKgqu3fvJiMjg+TkZL/fF1mJoAqWoF6zM4vW3iJxT13mFIlrVs/qA5nQlZ2dbUkgSESEhg0bsnPnznK9L7IuP8mpOrOT5eQV8OzXvzLo2dm8MW8DACe3aWhJwIQFSwLBczzf+whtEQQ3ESzZtI/Rk9NYtT2TYT2ac2HPFkGNxxgT2SKrRVA0X3HwuoYmzl3Hxf/9nv2Hc5l4TQrPjehJfK3qQYvHmHAjItx5551Fy08++STjxo3z+/3bt29nyJAhnHjiiXTp0oXzzz8fgJkzZzJkyJCjtk9NTeXRRx8FYNy4cTz55JMAjBw5ksmTJ1fgTAInsloEQRwsLiwS16NVPUb0SWTM4E7UjbVLQo2pbDVq1GDKlCmMHTuWhISEcr//gQceYMCAAdx2220ApKWllbr90KFDGTrU3aLJbousROA54DwGsGvoQHYu/562ktiYKP7vD13p3Tqe3q2tSJyJHJe/PO+odUO6N+PqU5I4nJPPyFcXHvX68N4tuTSlFXsO5nDDWz8e8dr7fzul1ONVq1aNUaNG8cwzz/DII48c8dr69eu59tpr2bVrF40aNeLVV189avrbrVu3MnDgwKLl7t27H3WMRYsWMWrUKCZPnsycOXNYvHgx//nPf0qNqyqL0K6hwCSCr9O3M+DpWby/aCPVq0VZkThjAuSmm27i7bffZv/+/Uesv+WWW7jmmmtIS0vjyiuv5NZbby3xvddddx39+vXjkUceYcuWLUe8/sMPP3D99dfzySef0LZtW1fPI1Aiq0UQoKuGdmd5+Oen6aT+soVOTesw/uoUTmxV39VjGlNVlfYJvmb16FJfj69VvcwWQEnq1q3Ln/70J55//nlq1vz9Srx58+YxZcoUAK6++mruueeeo9573nnnsXbtWr744gumT59Oz549WbZsGQArVqxg1KhRfPnllzRvHj4zAUZYi8A7F0GUu6edmZ3Hd6t2cPu5HUi9+XRLAsYEwd///ncmTpzIwYMHy/3e+Ph4rrjiCt58801OOukkZs+eDUCzZs2IjY3l559/ruxwgyoyE4ELtuw7zIvfrUZVSUqoxfdjzuG2c9tbpVBjgiQ+Pp7LLruMiRMnFq079dRTee+99wB4++23OeOMM45637fffsuhQ4cAyMzMZM2aNUXjCPXr1+fzzz9n7NixzJw50/2TCJDI+i+VU/kF5woKlLfmb2DgM7P5z7er2bDb+QWyK4KMCb4777yTXbt2FS2/8MILvPrqq3Tv3p0333yT55577qj3/Pjjj6SkpNC9e3dOOeUU/vKXv3DSSScVvd6kSRM+++wzbrrpJhYsWBCQ83CbhNoAZkpKii5evPj43vzWJXBoN4yaWSmxrNt1kDEfpbFg3R5Oa9eQf1/UncSGcZWyb2NC1YoVK+jcuXOww4hoJf0MRORHVU0pafvIGiz2ZFVa11BefgFXvbKAA9m5PH5Jdy5NaWm31RtjQlJkJYKcLKifWPZ2pVi9I5OkhrWoFh3FM5f3oHXDOJrUja2kAI0xJvAia4zAk3ncYwSevHye/upXBj07h9e9ReL6JMdbEjDGhLzIahEc51VDP23cy+jJafy2I4uLe7bgYisSZ4wJI5GVCHKyyl1eYsLstfxr+gqa1Y3l1T+fRL+OjV0KzhhjgiNyEkGeB/Jz/O4aKihQoqKEXq3rc2XfREYP6kQduyTUGBOGImeMwM8S1PsP53LP5F/456fLAejdOp6HLzzBkoAxIWLTpk0kJyezZ88eAPbu3UtycjLr16+vlP0vWbKEadOmFS37lqEur/Xr1yMivPDCC0Xrbr75Zl577bVS3zd16lTS09OP65gliZxEkFP2pDQzlm9jwNOz+OinzdSqUc2KxBkTKJsWwpynnMcKatWqFTfccANjxowBYMyYMYwaNYqkpKQK7xuOTgRDhw4tOtbxaNy4Mc899xw5OTl+v6eyE0HkdA15jj0Xwa4sD//3yXI+X7qVLs3qMmnkSXRrUS/AARoThqaPgW1LS9/GcwC2LwMtAImCJt2gRt1jb9/0BBhc+ifw22+/nd69e/Pss88yd+7cohLRjz32GG+99RZRUVEMHjyYRx99lDVr1nDTTTexc+dO4uLimDBhAp06dWLkyJHExsayePFiDhw4wNNPP83AgQN54IEHOHz4MHPnzmXs2LEcPny4qAz1scpcjxw5krp167J48WK2bdvG448/zvDhwwFo1KgRp512Gq+//jp//etfjziPkmLbs2cPqampzJo1i4cffpiPPvqowlVQIygReFsEJVw1lJWdx5zfdnL3eR0ZdWYbYqIjp6FkTNBl73eSADiP2ftLTwR+iImJ4YknnmDQoEF8+eWXxMTEMH36dD755BMWLFhAXFxcUdfRqFGjeOmll2jfvj0LFizgxhtv5NtvvwWcrpuFCxeyZs0a+vXrx+rVq3nwwQePmH/AtxunsMz1Nddcw6RJk7j11luZOnUq4MxzMHfuXFauXMnQoUOLEgHA6NGjGTx4MNdee+0R53Gs2IYOHcqQIUOO2EdFRE4iKDY72eZ9h/n4pwxu6teOpIRa/DC2P7VrRM63w5iAKOOTO+B0B70+1LmYI7o6XPIKtOpT4UNPnz6dZs2asWzZMgYMGMDXX3/Nn//8Z+LinDIw8fHxZGVl8cMPP3DppZcWvc/j8RQ9v+yyy4iKiqJ9+/a0adOGlStXlnrM0spcX3jhhURFRdGlSxe2b99+xPvatGlD3759eeedd4rWlRVbZXL1P5+IDAKeA6KBV1T10WKv1wDeAHoDu4HLVXW9K8F4WwQFMbV5e956Hp2+kgKFId2bk5RQy5KAMcHSqg9ckwrr50DSGZWSBJYsWcJXX33F/PnzOf300xkxYkSJ2xUUFFC/fn2WLFlS4uvFy8ZUpIxMjRo1ip6XNP547733Mnz4cM466yy/YqtMrvWBiEg08CIwGOgC/FFEuhTb7Dpgr6q2A54BHnMrHrY6844++d40/vHJcnq1bsCXt59JUkIt1w5pjPFTqz5wxp2VkgRUlRtuuIFnn32WxMRE7r77bu666y4GDBjAq6++WlRies+ePdStW5fk5GQ+/PDDovf+8ssvRfv68MMPKSgoYM2aNaxdu5aOHTtSp04dMjMzSzy2P2Wuj6VTp0506dKFTz/9FKDU2EqL4Xi42RneB1itqmtVNQd4DxhWbJthwOve55OB/uJG5bZNC9F5Tn/erfseZWL/At64tg+t4q1SqDHhZsKECSQmJjJgwAAAbrzxRlasWEHNmjUZOnQoKSkp9OjRgyeffBJw/mFPnDiRE088ka5du/LJJ58U7SsxMZE+ffowePBgXnrpJWJjY+nXrx/p6en06NGD999//4hj+1PmujT33XcfGRkZRcvHim3EiBE88cQT9OzZkzVr1hzX98mXa2WoRWQ4MEhV/+Jdvhroq6o3+2yzzLtNhnd5jXebXcX2NQoYBZCYmNh7w4YN5QtmzlPwzcNAASrRyDn3OZ8+jDGVLlzKUI8cObJSB2QDqbxlqEPi8hhVHa+qKaqa0qhRo/LvIOkMqFYDJBqJru4sG2OMAdwdLN4MtPJZbuldV9I2GSJSDaiHM2hcuVwYjDLGhLey7u4NJ24mgkVAexFJxvmHPwK4otg2qcA1wDxgOPCtutVX1aqPJQBjAkRVbaKmIDmef6GudQ2pah5wMzADWAF8oKrLReRBERnq3Wwi0FBEVgN3AMd/n7YxpkqIjY1l9+7dVqIlCFSV3bt3ExtbvnlSImvOYmOM63Jzc8nIyCA7OzvYoUSk2NhYWrZsSUzMkYUybc5iY0zAxMTEkJycHOwwTDmExFVDxhhj3GOJwBhjIpwlAmOMiXAhN1gsIjuBct5aXCQB2FXmVuHFzjky2DlHhoqcc2tVLfGO3JBLBBUhIouPNWoeruycI4Odc2Rw65yta8gYYyKcJQJjjIlwkZYIxgc7gCCwc44Mds6RwZVzjqgxAmOMMUeLtBaBMcaYYiwRGGNMhAvLRCAig0RklYisFpGjKpqKSA0Red/7+gIRSQp8lJXLj3O+Q0TSRSRNRL4RkdbBiLMylXXOPttdIiIqIiF/qaE/5ywil3l/1stF5J1Ax1jZ/PjdThSR70TkZ+/v9/nBiLOyiMgkEdnhncGxpNdFRJ73fj/SRKRXhQ+qqmH1BUQDa4A2QHXgF6BLsW1uBF7yPh8BvB/suANwzv2AOO/zGyLhnL3b1QFmA/OBlGDHHYCfc3vgZ6CBd7lxsOMOwDmPB27wPu8CrA923BU85zOBXsCyY7x+PjAdEOBkYEFFjxmOLYI+wGpVXauqOcB7wLBi2wwDXvc+nwz0l9CeRaPMc1bV71T1kHdxPs6McaHMn58zwEPAY0A41ET255z/CryoqnsBVHVHgGOsbP6cswJ1vc/rAVsCGF+lU9XZwJ5SNhkGvKGO+UB9EWlWkWOGYyJoAWzyWc7writxG3Um0NkPNAxIdO7w55x9XYfziSKUlXnO3iZzK1X9PJCBucifn3MHoIOIfC8i80VkUMCic4c/5zwOuEpEMoBpwC2BCS1oyvv3XiabjyDCiMhVQApwVrBjcZOIRAFPAyODHEqgVcPpHjobp9U3W0ROUNV9QY3KXX8EXlPVp0TkFOBNEemmqgXBDixUhGOLYDPQyme5pXddiduISDWc5uTugETnDn/OGRE5F7gPGKqqngDF5payzrkO0A2YKSLrcfpSU0N8wNifn3MGkKqquaq6DvgVJzGEKn/O+TrgAwBVnQfE4hRnC1d+/b2XRzgmgkVAexFJFpHqOIPBqcW2SQWu8T4fDnyr3lGYEFXmOYtIT+BlnCQQ6v3GUMY5q+p+VU1Q1SRVTcIZFxmqqqE8z6k/v9tTcVoDiEgCTlfR2kAGWcn8OeeNQH8AEemMkwh2BjTKwEoF/uS9euhkYL+qbq3IDsOua0hV80TkZmAGzhUHk1R1uYg8CCxW1VRgIk7zcTXOoMyI4EVccX6e8xNAbeBD77j4RlUdGrSgK8jPcw4rfp7zDGCgiKQD+cDdqhqyrV0/z/lOYIKI3I4zcDwylD/Yici7OMk8wTvu8X9ADICqvoQzDnI+sBo4BPy5wscM4e+XMcaYShCOXUPGGGPKwRKBMcZEOEsExhgT4SwRGGNMhLNEYIwxEc4SgamSRCRfRJb4fCWVsm1WJRzvNRFZ5z3WT947VMu7j1dEpIv3+b3FXvuhojF691P4fVkmIp+KSP0ytu8R6tU4jfvs8lFTJYlIlqrWruxtS9nHa8BnqjpZRAYCT6pq9wrsr8IxlbVfEXkd+FVVHyll+5E4VVdvruxYTPiwFoEJCSJS2zuPwk8islREjqo0KiLNRGS2zyfmM7zrB4rIPO97PxSRsv5Bzwbaed97h3dfy0Tk7951tUTkcxH5xbv+cu/6mSKSIiKPAjW9cbztfS3L+/ieiFzgE/NrIjJcRKJF5AkRWeStMf83P74t8/AWGxORPt5z/FlEfhCRjt47cR8ELvfGcrk39kkistC7bUkVW02kCXbtbfuyr5K+cO6KXeL9+hjnLvi63tcScO6qLGzRZnkf7wTu8z6Pxqk3lIDzj72Wd/1o4IESjvcaMNz7/FJgAdAbWArUwrkreznQE7gEmODz3nrex5l45zwojMlnm8IYLwJe9z6vjlNFsiYwCrjfu74GsBhILiHOLJ/z+xAY5F2uC1TzPj8X+Mj7fCTwH5/3/wu4yvu8Pk4tolrB/nnbV3C/wq7EhAkbh1W1R+GCiMQA/xKRM4ECnE/CTYBtPu9ZBEzybjtVVZeIyFk4k5V87y2tUR3nk3RJnhCR+3Hq1FyHU7/mY1U96I1hCnAG8AXwlIg8htOdNKcc5zUdeE5EagCDgNmqetjbHdVdRIZ7t6uHUyxuXbH31xSRJd7zXwF85bP96yLSHqfMQswxjj8QGCoid3mXY4FE775MhLJEYELFlUAjoLeq5opTUTTWdwNVne1NFBcAr4nI08Be4CtV/aMfx7hbVScXLohI/5I2UtVfxZnr4HzgYRH5RlUf9OckVDVbRGYC5wGX40y0As5sU7eo6owydnFYVXuISBxO/Z2bgOdxJuD5TlUv8g6szzzG+wW4RFVX+ROviQw2RmBCRT1ghzcJ9AOOmnNZnHmYt6vqBOAVnOn+5gOniUhhn38tEeng5zHnABeKSJyI1MLp1pkjIs2BQ6r6Fk4xv5LmjM31tkxK8j5OobDC1gU4/9RvKHyPiHTwHrNE6sw2dytwp/xeSr2wFPFIn00zcbrICs0AbhFv80icqrQmwlkiMKHibSBFRJYCfwJWlrDN2cAvIvIzzqft51R1J84/xndFJA2nW6iTPwdU1Z9wxg4W4owZvKKqPwMnAAu9XTT/BzxcwtvHA2mFg8XFfIkzMdDX6ky/CE7iSgd+EmfS8pcpo8XujSUNZ2KWx4F/e8/d933fAV0KB4txWg4x3tiWe5dNhLPLR40xJsJZi8AYYyKcJQJjjIlwlgiMMSbCWSIwxpgIZ4nAGGMinCUCY4yJcJYIjDEmwv0/cCuee/L2ZVQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Fold 2\n",
            "Epoch 1/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0412 - accuracy: 0.9945 - val_loss: 0.0025 - val_accuracy: 1.0000\n",
            "Epoch 2/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0321 - accuracy: 0.9937 - val_loss: 0.0045 - val_accuracy: 0.9969\n",
            "Epoch 3/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0192 - accuracy: 0.9945 - val_loss: 0.0092 - val_accuracy: 0.9969\n",
            "Epoch 4/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0129 - accuracy: 0.9961 - val_loss: 0.0042 - val_accuracy: 1.0000\n",
            "Epoch 5/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0101 - accuracy: 0.9969 - val_loss: 0.0049 - val_accuracy: 1.0000\n",
            "Epoch 6/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0128 - accuracy: 0.9953 - val_loss: 0.0041 - val_accuracy: 1.0000\n",
            "Epoch 7/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0103 - accuracy: 0.9953 - val_loss: 0.0039 - val_accuracy: 1.0000\n",
            "Epoch 8/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0089 - accuracy: 0.9969 - val_loss: 0.0045 - val_accuracy: 1.0000\n",
            "Epoch 9/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0064 - accuracy: 0.9977 - val_loss: 0.0023 - val_accuracy: 1.0000\n",
            "Epoch 10/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0105 - accuracy: 0.9977 - val_loss: 0.0030 - val_accuracy: 1.0000\n",
            "Epoch 11/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0102 - accuracy: 0.9969 - val_loss: 0.0025 - val_accuracy: 1.0000\n",
            "Epoch 12/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0074 - accuracy: 0.9961 - val_loss: 0.0039 - val_accuracy: 1.0000\n",
            "Epoch 13/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0059 - accuracy: 0.9984 - val_loss: 0.0075 - val_accuracy: 0.9969\n",
            "Epoch 14/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0044 - accuracy: 0.9992 - val_loss: 0.0031 - val_accuracy: 1.0000\n",
            "Epoch 15/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000\n",
            "Epoch 16/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000\n",
            "Epoch 17/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0031 - val_accuracy: 1.0000\n",
            "Epoch 18/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 1.0000\n",
            "Epoch 19/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "Epoch 20/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 7.8502e-04 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000\n",
            "Epoch 21/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 4.8060e-04 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000\n",
            "Epoch 22/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.3140e-04 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000\n",
            "Epoch 23/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.7475e-04 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000\n",
            "Epoch 24/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.2597e-04 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000\n",
            "Epoch 25/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.7187e-04 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "Epoch 26/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.0957e-04 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 27/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.3866e-04 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 28/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.6061e-04 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 29/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.1686e-04 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
            "Epoch 30/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.3193e-04 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 31/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.9144e-04 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 6.1424e-04 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0087 - accuracy: 0.9992 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 34/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 2.9276e-04 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "Epoch 35/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 3.9821e-04 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0035 - accuracy: 0.9992 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 37/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0013 - accuracy: 0.9992 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0044 - accuracy: 0.9992 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 4.7389e-04 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.9665e-04 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.5841e-04 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.3564e-04 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.2575e-04 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 44/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.7335e-04 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 45/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.0844e-04 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 8.6094e-05 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 6.8664e-05 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 8.2494e-05 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 7.3748e-05 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 6.3315e-05 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Precision:  1.0\n",
            "Recall:  1.0\n",
            "F1-score  1.0\n",
            "Sensitivity  (1.0, 1.0, None)\n",
            "AUC score 1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Fold 3\n",
            "Epoch 1/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0033 - accuracy: 0.9984 - val_loss: 1.9426e-05 - val_accuracy: 1.0000\n",
            "Epoch 2/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 3.3348e-04 - accuracy: 1.0000 - val_loss: 1.9460e-05 - val_accuracy: 1.0000\n",
            "Epoch 3/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0104 - accuracy: 0.9984 - val_loss: 1.9956e-05 - val_accuracy: 1.0000\n",
            "Epoch 4/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0036 - accuracy: 0.9992 - val_loss: 2.0890e-05 - val_accuracy: 1.0000\n",
            "Epoch 5/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 2.4834e-05 - val_accuracy: 1.0000\n",
            "Epoch 6/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 9.5857e-04 - accuracy: 0.9992 - val_loss: 2.6459e-05 - val_accuracy: 1.0000\n",
            "Epoch 7/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.3206e-04 - accuracy: 1.0000 - val_loss: 2.4365e-05 - val_accuracy: 1.0000\n",
            "Epoch 8/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 2.6530e-04 - accuracy: 1.0000 - val_loss: 2.4071e-05 - val_accuracy: 1.0000\n",
            "Epoch 9/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 1.5463e-04 - accuracy: 1.0000 - val_loss: 2.3843e-05 - val_accuracy: 1.0000\n",
            "Epoch 10/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.9770e-04 - accuracy: 1.0000 - val_loss: 2.4171e-05 - val_accuracy: 1.0000\n",
            "Epoch 11/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 0.0013 - accuracy: 0.9992 - val_loss: 2.4629e-05 - val_accuracy: 1.0000\n",
            "Epoch 12/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0016 - accuracy: 0.9992 - val_loss: 2.3845e-05 - val_accuracy: 1.0000\n",
            "Epoch 13/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.0014e-04 - accuracy: 1.0000 - val_loss: 2.3989e-05 - val_accuracy: 1.0000\n",
            "Epoch 14/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 7.7702e-05 - accuracy: 1.0000 - val_loss: 2.3884e-05 - val_accuracy: 1.0000\n",
            "Epoch 15/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 7.7529e-05 - accuracy: 1.0000 - val_loss: 2.3408e-05 - val_accuracy: 1.0000\n",
            "Epoch 16/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 1.0739e-04 - accuracy: 1.0000 - val_loss: 2.3170e-05 - val_accuracy: 1.0000\n",
            "Epoch 17/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 5.5396e-05 - accuracy: 1.0000 - val_loss: 2.2678e-05 - val_accuracy: 1.0000\n",
            "Epoch 18/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 6.8355e-05 - accuracy: 1.0000 - val_loss: 2.2441e-05 - val_accuracy: 1.0000\n",
            "Epoch 19/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 6.3940e-05 - accuracy: 1.0000 - val_loss: 2.1994e-05 - val_accuracy: 1.0000\n",
            "Epoch 20/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 5.5703e-05 - accuracy: 1.0000 - val_loss: 2.1476e-05 - val_accuracy: 1.0000\n",
            "Epoch 21/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 6.1611e-05 - accuracy: 1.0000 - val_loss: 2.0891e-05 - val_accuracy: 1.0000\n",
            "Epoch 22/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 6.3154e-05 - accuracy: 1.0000 - val_loss: 2.0595e-05 - val_accuracy: 1.0000\n",
            "Epoch 23/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.0971e-04 - accuracy: 1.0000 - val_loss: 1.9802e-05 - val_accuracy: 1.0000\n",
            "Epoch 24/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.6249e-05 - accuracy: 1.0000 - val_loss: 1.9644e-05 - val_accuracy: 1.0000\n",
            "Epoch 25/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 7.5975e-05 - accuracy: 1.0000 - val_loss: 1.9243e-05 - val_accuracy: 1.0000\n",
            "Epoch 26/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 5.1913e-05 - accuracy: 1.0000 - val_loss: 1.8711e-05 - val_accuracy: 1.0000\n",
            "Epoch 27/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 4.7528e-05 - accuracy: 1.0000 - val_loss: 1.8339e-05 - val_accuracy: 1.0000\n",
            "Epoch 28/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 4.6106e-05 - accuracy: 1.0000 - val_loss: 1.8391e-05 - val_accuracy: 1.0000\n",
            "Epoch 29/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.9602e-05 - accuracy: 1.0000 - val_loss: 1.8252e-05 - val_accuracy: 1.0000\n",
            "Epoch 30/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 4.4091e-05 - accuracy: 1.0000 - val_loss: 1.8065e-05 - val_accuracy: 1.0000\n",
            "Epoch 31/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 4.4144e-05 - accuracy: 1.0000 - val_loss: 1.7777e-05 - val_accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 4.1721e-05 - accuracy: 1.0000 - val_loss: 1.7230e-05 - val_accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 4.0179e-05 - accuracy: 1.0000 - val_loss: 1.7188e-05 - val_accuracy: 1.0000\n",
            "Epoch 34/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 4.4009e-05 - accuracy: 1.0000 - val_loss: 1.6963e-05 - val_accuracy: 1.0000\n",
            "Epoch 35/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 3.4866e-05 - accuracy: 1.0000 - val_loss: 1.6881e-05 - val_accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.7765e-05 - accuracy: 1.0000 - val_loss: 1.6425e-05 - val_accuracy: 1.0000\n",
            "Epoch 37/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.7659e-05 - accuracy: 1.0000 - val_loss: 1.6080e-05 - val_accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 2.6932e-05 - accuracy: 1.0000 - val_loss: 1.5988e-05 - val_accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.1260e-05 - accuracy: 1.0000 - val_loss: 1.5867e-05 - val_accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.8173e-05 - accuracy: 1.0000 - val_loss: 1.5814e-05 - val_accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 2.4642e-05 - accuracy: 1.0000 - val_loss: 1.5533e-05 - val_accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.6511e-05 - accuracy: 1.0000 - val_loss: 1.5176e-05 - val_accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.2941e-05 - accuracy: 1.0000 - val_loss: 1.4869e-05 - val_accuracy: 1.0000\n",
            "Epoch 44/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.8786e-05 - accuracy: 1.0000 - val_loss: 1.4540e-05 - val_accuracy: 1.0000\n",
            "Epoch 45/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.0972e-05 - accuracy: 1.0000 - val_loss: 1.4443e-05 - val_accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.4275e-05 - accuracy: 1.0000 - val_loss: 1.4423e-05 - val_accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.6226e-05 - accuracy: 1.0000 - val_loss: 1.4251e-05 - val_accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 6.2057e-05 - accuracy: 1.0000 - val_loss: 1.4182e-05 - val_accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0023 - accuracy: 0.9992 - val_loss: 1.5785e-05 - val_accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.8882e-05 - accuracy: 1.0000 - val_loss: 1.5706e-05 - val_accuracy: 1.0000\n",
            "Precision:  1.0\n",
            "Recall:  1.0\n",
            "F1-score  1.0\n",
            "Sensitivity  (1.0, 1.0, None)\n",
            "AUC score 1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Fold 4\n",
            "Epoch 1/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.6805e-04 - accuracy: 1.0000 - val_loss: 3.8603e-06 - val_accuracy: 1.0000\n",
            "Epoch 2/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 4.4111e-05 - accuracy: 1.0000 - val_loss: 3.7500e-06 - val_accuracy: 1.0000\n",
            "Epoch 3/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.6193e-05 - accuracy: 1.0000 - val_loss: 3.6415e-06 - val_accuracy: 1.0000\n",
            "Epoch 4/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.9521e-05 - accuracy: 1.0000 - val_loss: 3.5909e-06 - val_accuracy: 1.0000\n",
            "Epoch 5/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.8284e-05 - accuracy: 1.0000 - val_loss: 3.5056e-06 - val_accuracy: 1.0000\n",
            "Epoch 6/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.4566e-05 - accuracy: 1.0000 - val_loss: 3.4194e-06 - val_accuracy: 1.0000\n",
            "Epoch 7/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.2040e-05 - accuracy: 1.0000 - val_loss: 3.3206e-06 - val_accuracy: 1.0000\n",
            "Epoch 8/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.0374e-05 - accuracy: 1.0000 - val_loss: 3.2574e-06 - val_accuracy: 1.0000\n",
            "Epoch 9/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.1519e-05 - accuracy: 1.0000 - val_loss: 3.1879e-06 - val_accuracy: 1.0000\n",
            "Epoch 10/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.2435e-05 - accuracy: 1.0000 - val_loss: 3.0977e-06 - val_accuracy: 1.0000\n",
            "Epoch 11/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 7.4189e-05 - accuracy: 1.0000 - val_loss: 4.1675e-06 - val_accuracy: 1.0000\n",
            "Epoch 12/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.5259e-05 - accuracy: 1.0000 - val_loss: 4.0351e-06 - val_accuracy: 1.0000\n",
            "Epoch 13/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 1.8684e-04 - accuracy: 1.0000 - val_loss: 2.9178e-06 - val_accuracy: 1.0000\n",
            "Epoch 14/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0035 - accuracy: 0.9992 - val_loss: 1.5487e-05 - val_accuracy: 1.0000\n",
            "Epoch 15/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.5406e-04 - accuracy: 1.0000 - val_loss: 4.0176e-06 - val_accuracy: 1.0000\n",
            "Epoch 16/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.0003e-05 - accuracy: 1.0000 - val_loss: 3.9923e-06 - val_accuracy: 1.0000\n",
            "Epoch 17/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 8.4461e-05 - accuracy: 1.0000 - val_loss: 4.0321e-06 - val_accuracy: 1.0000\n",
            "Epoch 18/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.9213e-05 - accuracy: 1.0000 - val_loss: 4.0639e-06 - val_accuracy: 1.0000\n",
            "Epoch 19/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.2406e-05 - accuracy: 1.0000 - val_loss: 3.9916e-06 - val_accuracy: 1.0000\n",
            "Epoch 20/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.8558e-05 - accuracy: 1.0000 - val_loss: 3.9259e-06 - val_accuracy: 1.0000\n",
            "Epoch 21/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.9664e-05 - accuracy: 1.0000 - val_loss: 3.8180e-06 - val_accuracy: 1.0000\n",
            "Epoch 22/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.5882e-05 - accuracy: 1.0000 - val_loss: 3.7467e-06 - val_accuracy: 1.0000\n",
            "Epoch 23/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.3933e-05 - accuracy: 1.0000 - val_loss: 3.6533e-06 - val_accuracy: 1.0000\n",
            "Epoch 24/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.5606e-05 - accuracy: 1.0000 - val_loss: 3.5738e-06 - val_accuracy: 1.0000\n",
            "Epoch 25/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.6611e-05 - accuracy: 1.0000 - val_loss: 3.4527e-06 - val_accuracy: 1.0000\n",
            "Epoch 26/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.1319e-05 - accuracy: 1.0000 - val_loss: 3.3467e-06 - val_accuracy: 1.0000\n",
            "Epoch 27/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.3916e-05 - accuracy: 1.0000 - val_loss: 3.2391e-06 - val_accuracy: 1.0000\n",
            "Epoch 28/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.9897e-05 - accuracy: 1.0000 - val_loss: 3.1376e-06 - val_accuracy: 1.0000\n",
            "Epoch 29/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.3251e-05 - accuracy: 1.0000 - val_loss: 3.0213e-06 - val_accuracy: 1.0000\n",
            "Epoch 30/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.1023e-05 - accuracy: 1.0000 - val_loss: 2.9544e-06 - val_accuracy: 1.0000\n",
            "Epoch 31/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.7272e-05 - accuracy: 1.0000 - val_loss: 3.9305e-06 - val_accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 1.2013e-05 - accuracy: 1.0000 - val_loss: 3.9180e-06 - val_accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.7876e-05 - accuracy: 1.0000 - val_loss: 3.8305e-06 - val_accuracy: 1.0000\n",
            "Epoch 34/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 1.1365e-05 - accuracy: 1.0000 - val_loss: 3.7713e-06 - val_accuracy: 1.0000\n",
            "Epoch 35/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 9.3973e-06 - accuracy: 1.0000 - val_loss: 3.6637e-06 - val_accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 8.2469e-06 - accuracy: 1.0000 - val_loss: 3.6229e-06 - val_accuracy: 1.0000\n",
            "Epoch 37/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.5274e-05 - accuracy: 1.0000 - val_loss: 3.6122e-06 - val_accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 9.4593e-06 - accuracy: 1.0000 - val_loss: 3.5677e-06 - val_accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.0405e-05 - accuracy: 1.0000 - val_loss: 3.4522e-06 - val_accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.0444e-05 - accuracy: 1.0000 - val_loss: 3.3558e-06 - val_accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 7.6118e-06 - accuracy: 1.0000 - val_loss: 3.3279e-06 - val_accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 1.1512e-05 - accuracy: 1.0000 - val_loss: 3.2903e-06 - val_accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 7.4825e-06 - accuracy: 1.0000 - val_loss: 3.1905e-06 - val_accuracy: 1.0000\n",
            "Epoch 44/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 7.9318e-06 - accuracy: 1.0000 - val_loss: 3.1702e-06 - val_accuracy: 1.0000\n",
            "Epoch 45/50\n",
            "40/40 [==============================] - 55s 1s/step - loss: 8.6101e-06 - accuracy: 1.0000 - val_loss: 3.1322e-06 - val_accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 8.1582e-06 - accuracy: 1.0000 - val_loss: 3.0541e-06 - val_accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 8.8474e-06 - accuracy: 1.0000 - val_loss: 2.9489e-06 - val_accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.1039e-05 - accuracy: 1.0000 - val_loss: 2.9839e-06 - val_accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 6.1964e-06 - accuracy: 1.0000 - val_loss: 2.9807e-06 - val_accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 6.4308e-06 - accuracy: 1.0000 - val_loss: 2.8894e-06 - val_accuracy: 1.0000\n",
            "Precision:  1.0\n",
            "Recall:  1.0\n",
            "F1-score  1.0\n",
            "Sensitivity  (1.0, 1.0, None)\n",
            "AUC score 1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Fold 5\n",
            "Epoch 1/50\n",
            "40/40 [==============================] - 60s 1s/step - loss: 9.4010e-06 - accuracy: 1.0000 - val_loss: 4.8519e-07 - val_accuracy: 1.0000\n",
            "Epoch 2/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.1212e-05 - accuracy: 1.0000 - val_loss: 4.6079e-07 - val_accuracy: 1.0000\n",
            "Epoch 3/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.1054e-05 - accuracy: 1.0000 - val_loss: 4.5437e-07 - val_accuracy: 1.0000\n",
            "Epoch 4/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.5100e-05 - accuracy: 1.0000 - val_loss: 4.6838e-07 - val_accuracy: 1.0000\n",
            "Epoch 5/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 9.4592e-06 - accuracy: 1.0000 - val_loss: 4.3839e-07 - val_accuracy: 1.0000\n",
            "Epoch 6/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.8338e-05 - accuracy: 1.0000 - val_loss: 4.2805e-07 - val_accuracy: 1.0000\n",
            "Epoch 7/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.0707e-05 - accuracy: 1.0000 - val_loss: 3.8999e-07 - val_accuracy: 1.0000\n",
            "Epoch 8/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 7.1922e-06 - accuracy: 1.0000 - val_loss: 3.8253e-07 - val_accuracy: 1.0000\n",
            "Epoch 9/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 5.9941e-06 - accuracy: 1.0000 - val_loss: 3.7069e-07 - val_accuracy: 1.0000\n",
            "Epoch 10/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 4.2540e-06 - accuracy: 1.0000 - val_loss: 3.6617e-07 - val_accuracy: 1.0000\n",
            "Epoch 11/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 5.1407e-06 - accuracy: 1.0000 - val_loss: 3.4925e-07 - val_accuracy: 1.0000\n",
            "Epoch 12/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 8.2720e-06 - accuracy: 1.0000 - val_loss: 3.3789e-07 - val_accuracy: 1.0000\n",
            "Epoch 13/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.5668e-06 - accuracy: 1.0000 - val_loss: 3.2485e-07 - val_accuracy: 1.0000\n",
            "Epoch 14/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.9398e-06 - accuracy: 1.0000 - val_loss: 3.0910e-07 - val_accuracy: 1.0000\n",
            "Epoch 15/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.7668e-05 - accuracy: 1.0000 - val_loss: 3.1692e-07 - val_accuracy: 1.0000\n",
            "Epoch 16/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0040 - accuracy: 0.9992 - val_loss: 2.7771e-07 - val_accuracy: 1.0000\n",
            "Epoch 17/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0123 - accuracy: 0.9992 - val_loss: 4.5547e-06 - val_accuracy: 1.0000\n",
            "Epoch 18/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 0.0032 - accuracy: 0.9992 - val_loss: 1.2479e-04 - val_accuracy: 1.0000\n",
            "Epoch 19/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.3686e-04 - accuracy: 1.0000 - val_loss: 7.9508e-05 - val_accuracy: 1.0000\n",
            "Epoch 20/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.7304e-04 - accuracy: 1.0000 - val_loss: 3.8324e-05 - val_accuracy: 1.0000\n",
            "Epoch 21/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 6.2037e-05 - accuracy: 1.0000 - val_loss: 2.5918e-05 - val_accuracy: 1.0000\n",
            "Epoch 22/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 5.2498e-05 - accuracy: 1.0000 - val_loss: 1.9592e-05 - val_accuracy: 1.0000\n",
            "Epoch 23/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.7017e-05 - accuracy: 1.0000 - val_loss: 1.5144e-05 - val_accuracy: 1.0000\n",
            "Epoch 24/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 4.1855e-05 - accuracy: 1.0000 - val_loss: 1.2789e-05 - val_accuracy: 1.0000\n",
            "Epoch 25/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.3488e-04 - accuracy: 1.0000 - val_loss: 8.4668e-06 - val_accuracy: 1.0000\n",
            "Epoch 26/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.1634e-05 - accuracy: 1.0000 - val_loss: 7.3476e-06 - val_accuracy: 1.0000\n",
            "Epoch 27/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.4465e-05 - accuracy: 1.0000 - val_loss: 6.2521e-06 - val_accuracy: 1.0000\n",
            "Epoch 28/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.2741e-05 - accuracy: 1.0000 - val_loss: 5.3864e-06 - val_accuracy: 1.0000\n",
            "Epoch 29/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.4552e-05 - accuracy: 1.0000 - val_loss: 4.6252e-06 - val_accuracy: 1.0000\n",
            "Epoch 30/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.5958e-05 - accuracy: 1.0000 - val_loss: 4.1783e-06 - val_accuracy: 1.0000\n",
            "Epoch 31/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.0992e-05 - accuracy: 1.0000 - val_loss: 3.6253e-06 - val_accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.7774e-05 - accuracy: 1.0000 - val_loss: 3.1379e-06 - val_accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 1.2513e-05 - accuracy: 1.0000 - val_loss: 2.6366e-06 - val_accuracy: 1.0000\n",
            "Epoch 34/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 9.1075e-06 - accuracy: 1.0000 - val_loss: 2.4684e-06 - val_accuracy: 1.0000\n",
            "Epoch 35/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 7.6224e-06 - accuracy: 1.0000 - val_loss: 2.2203e-06 - val_accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 9.1310e-06 - accuracy: 1.0000 - val_loss: 2.0617e-06 - val_accuracy: 1.0000\n",
            "Epoch 37/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 9.3564e-06 - accuracy: 1.0000 - val_loss: 1.8482e-06 - val_accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 9.7628e-06 - accuracy: 1.0000 - val_loss: 1.7005e-06 - val_accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 9.4899e-06 - accuracy: 1.0000 - val_loss: 1.4723e-06 - val_accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 5.9708e-06 - accuracy: 1.0000 - val_loss: 1.3792e-06 - val_accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 5.9607e-06 - accuracy: 1.0000 - val_loss: 1.3037e-06 - val_accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 5.3608e-06 - accuracy: 1.0000 - val_loss: 1.2177e-06 - val_accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.8930e-06 - accuracy: 1.0000 - val_loss: 1.1238e-06 - val_accuracy: 1.0000\n",
            "Epoch 44/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 4.3455e-06 - accuracy: 1.0000 - val_loss: 1.0671e-06 - val_accuracy: 1.0000\n",
            "Epoch 45/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 6.8030e-06 - accuracy: 1.0000 - val_loss: 9.6636e-07 - val_accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 4.2279e-06 - accuracy: 1.0000 - val_loss: 8.8329e-07 - val_accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 4.3456e-06 - accuracy: 1.0000 - val_loss: 8.2358e-07 - val_accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 6.3100e-06 - accuracy: 1.0000 - val_loss: 7.3281e-07 - val_accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 2.7723e-06 - accuracy: 1.0000 - val_loss: 7.0300e-07 - val_accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "40/40 [==============================] - 56s 1s/step - loss: 3.7116e-06 - accuracy: 1.0000 - val_loss: 6.6247e-07 - val_accuracy: 1.0000\n",
            "Precision:  1.0\n",
            "Recall:  1.0\n",
            "F1-score  1.0\n",
            "Sensitivity  (1.0, 1.0, None)\n",
            "AUC score 1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXgbqkwWs7uL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2faf009-30bd-4329-ecca-64344235abc7"
      },
      "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[49]\n",
        "Accuracyvalid2=val_acc1[49]\n",
        "Accuracyvalid3=val_acc2[49]\n",
        "Accuracyvalid4=val_acc3[49]\n",
        "Accuracyvalid5=val_acc4[49]\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  0.9781249761581421\n",
            "validation Accuracy for Fold-2 1.0\n",
            "Validation Accuracy for Fold-3  1.0\n",
            "Validation Accuracy for Fold-4   1.0\n",
            "Validation Accuracy for Fold-5   1.0\n",
            "Mean Validation Accuracy  0.9956249952316284\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLHaGqL0l73k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a3fbf39-dd99-4293-ceed-0a9152ecd990"
      },
      "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 0.9929078014184397\n",
            "Precision of Fold - 2 1.0\n",
            "Precision of Fold - 3 1.0\n",
            "Precision of Fold - 4 1.0\n",
            "Precision of Fold - 5 1.0\n",
            "Average precision-score  0.9985815602836879\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkWZhzTxtA-y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "366152bd-165a-484a-8a1e-987c02f48270"
      },
      "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 0.975609756097561\n",
            "Precision of Fold - 2 1.0\n",
            "Precision of Fold - 3 1.0\n",
            "Precision of Fold - 4 1.0\n",
            "Precision of Fold - 5 1.0\n",
            "Average f1-score  0.9951219512195122\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sT4-10ttRb_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acfd6bc7-bb32-43d2-e262-de13cb6ce89b"
      },
      "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 0.958904109589041\n",
            "Precision of Fold - 2 1.0\n",
            "Precision of Fold - 3 1.0\n",
            "Precision of Fold - 4 1.0\n",
            "Precision of Fold - 5 1.0\n",
            "Average precision-score  0.9917808219178081\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "faQ2nYkFpRvL"
      },
      "source": [
        "predict=model.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuDfi1rHqHJC",
        "outputId": "742d04ed-8ebd-440c-ddf2-db7c659fc0f7"
      },
      "source": [
        "predict1=np.float32(predict>0.50)\n",
        "predict1=np.reshape(predict1,(400,1))\n",
        "print(predict)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[5.26376651e-11]\n",
            " [1.00000000e+00]\n",
            " [1.76443553e-08]\n",
            " [4.02491338e-08]\n",
            " [4.20739426e-08]\n",
            " [9.99999881e-01]\n",
            " [1.08789383e-04]\n",
            " [5.11722087e-09]\n",
            " [2.19221814e-08]\n",
            " [6.64101885e-10]\n",
            " [9.99999881e-01]\n",
            " [9.99999881e-01]\n",
            " [1.00000000e+00]\n",
            " [3.29202976e-07]\n",
            " [2.34991518e-12]\n",
            " [1.00000000e+00]\n",
            " [1.76542253e-11]\n",
            " [5.81670122e-07]\n",
            " [4.06000765e-07]\n",
            " [1.00000000e+00]\n",
            " [3.24571092e-10]\n",
            " [6.71301859e-06]\n",
            " [1.00000000e+00]\n",
            " [4.45072465e-06]\n",
            " [1.00000000e+00]\n",
            " [1.00000000e+00]\n",
            " [9.99999881e-01]\n",
            " [3.98548714e-08]\n",
            " [9.99999881e-01]\n",
            " [9.96893039e-04]\n",
            " [1.00000000e+00]\n",
            " [4.04001241e-07]\n",
            " [8.99983837e-11]\n",
            " [9.99999523e-01]\n",
            " [5.01387547e-07]\n",
            " [3.28223990e-07]\n",
            " [5.54243479e-06]\n",
            " [9.99999881e-01]\n",
            " [5.21662400e-07]\n",
            " [9.99999762e-01]\n",
            " [1.81982207e-09]\n",
            " [1.00000000e+00]\n",
            " [7.58891815e-07]\n",
            " [9.35948989e-08]\n",
            " [1.00000000e+00]\n",
            " [2.29835386e-08]\n",
            " [2.69442668e-09]\n",
            " [5.92319527e-10]\n",
            " [5.10080298e-08]\n",
            " [1.02369837e-08]\n",
            " [8.75214255e-07]\n",
            " [9.99999881e-01]\n",
            " [4.81443330e-09]\n",
            " [1.00000000e+00]\n",
            " [2.58650573e-07]\n",
            " [1.40302584e-01]\n",
            " [7.15321857e-10]\n",
            " [9.99999881e-01]\n",
            " [1.00000000e+00]\n",
            " [1.00000000e+00]\n",
            " [6.55974741e-09]\n",
            " [4.18176924e-08]\n",
            " [1.00000000e+00]\n",
            " [8.09783174e-08]\n",
            " [1.02118864e-08]\n",
            " [1.75569355e-08]\n",
            " [2.42299247e-09]\n",
            " [1.00000000e+00]\n",
            " [2.50591681e-09]\n",
            " [1.00000000e+00]\n",
            " [7.39535233e-09]\n",
            " [4.30475300e-09]\n",
            " [9.99999881e-01]\n",
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            " [9.89058835e-12]\n",
            " [1.23580060e-06]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vm2YA2eYqM3w"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DD8zlib0qQB6",
        "outputId": "d2547af5-4950-430d-ef42-ed6a47f05671"
      },
      "source": [
        "metrics=confusion_matrix(y_test,predict1)\n",
        "print(metrics)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[251   4]\n",
            " [  0 145]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcGtidPjqSwU"
      },
      "source": [
        "from sklearn.metrics import auc,roc_curve\n",
        "fpr, tpr, thresholds = roc_curve(y_test,predict)\n",
        "auc = roc_auc_score(y_test, predict)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "f17x6SRUqT4N",
        "outputId": "2710546d-1b25-4540-eeb8-521a1c18dbeb"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr,label='ROC curve (area = %0.2f)' % auc)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([-0.1, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver operating characteristic for Breast Cancer Classification')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}