CAP_phase_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CAP phase detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 1-D CNN model for CAP classification \n",
        "- Authors - Shruti Murarka, Aditya Wadichar"
      ],
      "metadata": {
        "id": "w6x9vtElDk9P"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2rx7aAlB1CF",
        "outputId": "68709708-7055-4c45-a400-846413c86288"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "import pandas as pd\n",
        "import scipy.io\n",
        "import numpy as np\n",
        "import copy\n",
        "\n",
        "import h5py\n",
        "import scipy\n",
        "from PIL import Image\n",
        "from scipy import ndimage\n",
        "\n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "import tensorflow as tf\n",
        "from keras.callbacks import EarlyStopping\n",
        "import tensorflow.keras.layers as tfl\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import Dropout\n",
        "from keras.utils.vis_utils import plot_model\n",
        "from keras.layers.merge import concatenate\n",
        "from tensorflow import keras\n",
        "from time import time\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import auc\n",
        "\n",
        "%matplotlib inline\n",
        "%load_ext autoreload\n",
        "%autoreload 2"
      ],
      "metadata": {
        "id": "S6OsLA9bCkXf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be20f705-37a3-4894-8861-8b84158ab951"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The autoreload extension is already loaded. To reload it, use:\n",
            "  %reload_ext autoreload\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext watermark\n",
        "\n",
        "%watermark --iversions "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBgPoHPabMl1",
        "outputId": "231911a2-5f91-425f-c4f2-13c8872cfdf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The watermark extension is already loaded. To reload it, use:\n",
            "  %reload_ext watermark\n",
            "google    : 2.0.3\n",
            "pandas    : 1.3.5\n",
            "tensorflow: 2.8.0+zzzcolab20220506162203\n",
            "IPython   : 5.5.0\n",
            "PIL       : 7.1.2\n",
            "scipy     : 1.4.1\n",
            "matplotlib: 3.2.2\n",
            "numpy     : 1.21.6\n",
            "h5py      : 3.1.0\n",
            "csv       : 1.0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Healthy Dataset"
      ],
      "metadata": {
        "id": "I6GwZb9bEuer"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# preparing dataset\n",
        "bal_all = np.loadtxt('/content/drive/MyDrive/FYP/A1_A2_A3_B_data/Data_csv/bal_healthy.csv', delimiter=',')\n",
        "x_bal = bal_all[:,0:1024]\n",
        "y_bal = bal_all[:,1024]\n",
        "y_bal=y_bal.reshape(-1,1 )\n",
        "\n",
        "enc = OneHotEncoder(handle_unknown='ignore')\n",
        "enc.fit(y_bal)\n",
        "onehotlabels = enc.transform(y_bal).toarray()\n",
        "onelabel=np.absolute(1-onehotlabels[:,0])\n",
        "onelabel=(onelabel.reshape(-1,1)).astype(np.int)\n",
        "x_train1, x_test1, y_train1, y_test1 = train_test_split(x_bal, onelabel, test_size = 0.2, shuffle = True)"
      ],
      "metadata": {
        "id": "fmJfkDNEDCjo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a906e2cd-8b2b-4cc1-d267-4bc41bedc860"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hyperparameter Optimization"
      ],
      "metadata": {
        "id": "SSZxc_ZzVEQh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow import keras\n",
        "import tensorflow.keras.layers as tfl\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.utils import *\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers.convolutional import Conv1D\n",
        "\n",
        "param = 16\n",
        "kfold = KFold(n_splits=5, shuffle=True, random_state=1) \n",
        "fold_no = 1\n",
        "acc_per_fold1 = []\n",
        "loss_per_fold1 = []\n",
        "for train, test in kfold.split(x_train1,  y_train1):\n",
        "    model = Sequential()\n",
        "    model.add(tfl.Conv1D(filters=32,\n",
        "                    kernel_size=7,\n",
        "                    strides=1,\n",
        "                    padding='same',\n",
        "            input_shape=(1024,1)))\n",
        "   \n",
        "    model.add(tfl.Conv1D(filters=8,\n",
        "                    kernel_size=3,\n",
        "                    strides=1,\n",
        "                    padding='same', activation='relu'))\n",
        "    model.add(tfl.MaxPool1D(pool_size= 16,\n",
        "                       padding='same'))\n",
        "    model.add(tfl.Conv1D(filters=16,\n",
        "                    kernel_size=2,\n",
        "                    strides=1,\n",
        "                    padding='same'))\n",
        "    model.add(tfl.Dropout(0.4))\n",
        "    model.add(tfl.Conv1D(filters=8,\n",
        "                    kernel_size=2,\n",
        "                    strides=1,\n",
        "                    padding='same',activation='relu'))\n",
        "    model.add(tfl.MaxPool1D(pool_size= 4,\n",
        "                       padding='same'))\n",
        "    model.add(tfl.Flatten())\n",
        "    model.add(tfl.Dense(param, activation='relu'))    ## parameter change\n",
        "    model.add(tfl.Dense(1, activation='sigmoid'))\n",
        "    model.compile(optimizer = 'adam',\n",
        "                  loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "    \n",
        "\n",
        "    # Generate a print\n",
        "    print('------------------------------------------------------------------------')\n",
        "    print(f'Training for fold {fold_no} ...')\n",
        "\n",
        "    \n",
        "    # Fit data to model\n",
        "    history1 = model.fit(x_train1[train], y_train1[train],\n",
        "                batch_size=75,\n",
        "                epochs=50)\n",
        "\n",
        "    # Generate generalization metrics\n",
        "    scores1 = model.evaluate(x_train1[test], y_train1[test], verbose=0)\n",
        "    print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores1[0]}; {model.metrics_names[1]} of {scores1[1]*100}%')\n",
        "    acc_per_fold1.append(scores1[1] * 100)\n",
        "    loss_per_fold1.append(scores1[0])\n",
        "\n",
        "    # Increase fold number\n",
        "    fold_no = fold_no + 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DCrCj7xLU1PY",
        "outputId": "ab7f4b8f-403a-4447-84e3-57f0a23d717a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for fold 1 ...\n",
            "Epoch 1/50\n",
            "80/80 [==============================] - 8s 76ms/step - loss: 0.8609 - accuracy: 0.5009\n",
            "Epoch 2/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6932 - accuracy: 0.5080\n",
            "Epoch 3/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6918 - accuracy: 0.5120\n",
            "Epoch 4/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6924 - accuracy: 0.5085\n",
            "Epoch 5/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6940 - accuracy: 0.5051\n",
            "Epoch 6/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6897 - accuracy: 0.5276\n",
            "Epoch 7/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6770 - accuracy: 0.5636\n",
            "Epoch 8/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6491 - accuracy: 0.6099\n",
            "Epoch 9/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6139 - accuracy: 0.6803\n",
            "Epoch 10/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5953 - accuracy: 0.7058\n",
            "Epoch 11/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.5826 - accuracy: 0.7270\n",
            "Epoch 12/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.5731 - accuracy: 0.7298\n",
            "Epoch 13/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.5619 - accuracy: 0.7385\n",
            "Epoch 14/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.5633 - accuracy: 0.7343\n",
            "Epoch 15/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.5558 - accuracy: 0.7387\n",
            "Epoch 16/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.5468 - accuracy: 0.7437\n",
            "Epoch 17/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5492 - accuracy: 0.7414\n",
            "Epoch 18/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.5412 - accuracy: 0.7466\n",
            "Epoch 19/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5392 - accuracy: 0.7459\n",
            "Epoch 20/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5382 - accuracy: 0.7437\n",
            "Epoch 21/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5316 - accuracy: 0.7518\n",
            "Epoch 22/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5355 - accuracy: 0.7484\n",
            "Epoch 23/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5268 - accuracy: 0.7582\n",
            "Epoch 24/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5224 - accuracy: 0.7582\n",
            "Epoch 25/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5219 - accuracy: 0.7540\n",
            "Epoch 26/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5207 - accuracy: 0.7548\n",
            "Epoch 27/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5185 - accuracy: 0.7577\n",
            "Epoch 28/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5175 - accuracy: 0.7526\n",
            "Epoch 29/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5220 - accuracy: 0.7520\n",
            "Epoch 30/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5166 - accuracy: 0.7520\n",
            "Epoch 31/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.5166 - accuracy: 0.7520\n",
            "Epoch 32/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5175 - accuracy: 0.7565\n",
            "Epoch 33/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5054 - accuracy: 0.7619\n",
            "Epoch 34/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5067 - accuracy: 0.7622\n",
            "Epoch 35/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5047 - accuracy: 0.7617\n",
            "Epoch 36/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5018 - accuracy: 0.7639\n",
            "Epoch 37/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5073 - accuracy: 0.7572\n",
            "Epoch 38/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5028 - accuracy: 0.7595\n",
            "Epoch 39/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5043 - accuracy: 0.7577\n",
            "Epoch 40/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5035 - accuracy: 0.7617\n",
            "Epoch 41/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.4987 - accuracy: 0.7627\n",
            "Epoch 42/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.4985 - accuracy: 0.7669\n",
            "Epoch 43/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.4969 - accuracy: 0.7688\n",
            "Epoch 44/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.4944 - accuracy: 0.7696\n",
            "Epoch 45/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.4954 - accuracy: 0.7656\n",
            "Epoch 46/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.4931 - accuracy: 0.7671\n",
            "Epoch 47/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.4951 - accuracy: 0.7688\n",
            "Epoch 48/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4928 - accuracy: 0.7679\n",
            "Epoch 49/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4891 - accuracy: 0.7735\n",
            "Epoch 50/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.4880 - accuracy: 0.7693\n",
            "Score for fold 1: loss of 0.5087489485740662; accuracy of 75.08394718170166%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 2 ...\n",
            "Epoch 1/50\n",
            "80/80 [==============================] - 7s 77ms/step - loss: 0.7230 - accuracy: 0.4950\n",
            "Epoch 2/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.6931 - accuracy: 0.5036\n",
            "Epoch 3/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.6923 - accuracy: 0.5009\n",
            "Epoch 4/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.6931 - accuracy: 0.5071\n",
            "Epoch 5/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.6889 - accuracy: 0.5142\n",
            "Epoch 6/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.6623 - accuracy: 0.5448\n",
            "Epoch 7/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.6216 - accuracy: 0.6526\n",
            "Epoch 8/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.6026 - accuracy: 0.7013\n",
            "Epoch 9/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5917 - accuracy: 0.7085\n",
            "Epoch 10/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5768 - accuracy: 0.7233\n",
            "Epoch 11/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5706 - accuracy: 0.7300\n",
            "Epoch 12/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5685 - accuracy: 0.7259\n",
            "Epoch 13/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5725 - accuracy: 0.7160\n",
            "Epoch 14/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5520 - accuracy: 0.7360\n",
            "Epoch 15/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5483 - accuracy: 0.7404\n",
            "Epoch 16/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5396 - accuracy: 0.7490\n",
            "Epoch 17/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5417 - accuracy: 0.7441\n",
            "Epoch 18/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5383 - accuracy: 0.7468\n",
            "Epoch 19/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5330 - accuracy: 0.7451\n",
            "Epoch 20/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5260 - accuracy: 0.7505\n",
            "Epoch 21/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5269 - accuracy: 0.7500\n",
            "Epoch 22/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5284 - accuracy: 0.7461\n",
            "Epoch 23/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5246 - accuracy: 0.7508\n",
            "Epoch 24/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5230 - accuracy: 0.7525\n",
            "Epoch 25/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5193 - accuracy: 0.7535\n",
            "Epoch 26/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5160 - accuracy: 0.7563\n",
            "Epoch 27/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5156 - accuracy: 0.7568\n",
            "Epoch 28/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5179 - accuracy: 0.7530\n",
            "Epoch 29/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5138 - accuracy: 0.7567\n",
            "Epoch 30/50\n",
            "80/80 [==============================] - 6s 80ms/step - loss: 0.5135 - accuracy: 0.7567\n",
            "Epoch 31/50\n",
            "80/80 [==============================] - 6s 80ms/step - loss: 0.5281 - accuracy: 0.7451\n",
            "Epoch 32/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5109 - accuracy: 0.7537\n",
            "Epoch 33/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5133 - accuracy: 0.7557\n",
            "Epoch 34/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5066 - accuracy: 0.7639\n",
            "Epoch 35/50\n",
            "80/80 [==============================] - 6s 80ms/step - loss: 0.5027 - accuracy: 0.7639\n",
            "Epoch 36/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5093 - accuracy: 0.7567\n",
            "Epoch 37/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5012 - accuracy: 0.7629\n",
            "Epoch 38/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4999 - accuracy: 0.7673\n",
            "Epoch 39/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5000 - accuracy: 0.7676\n",
            "Epoch 40/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5029 - accuracy: 0.7605\n",
            "Epoch 41/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5069 - accuracy: 0.7573\n",
            "Epoch 42/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5006 - accuracy: 0.7656\n",
            "Epoch 43/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4945 - accuracy: 0.7703\n",
            "Epoch 44/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4946 - accuracy: 0.7649\n",
            "Epoch 45/50\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.5044 - accuracy: 0.7615\n",
            "Epoch 46/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4911 - accuracy: 0.7678\n",
            "Epoch 47/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4956 - accuracy: 0.7652\n",
            "Epoch 48/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4962 - accuracy: 0.7644\n",
            "Epoch 49/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4950 - accuracy: 0.7684\n",
            "Epoch 50/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4935 - accuracy: 0.7699\n",
            "Score for fold 2: loss of 0.48164159059524536; accuracy of 78.84486317634583%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 3 ...\n",
            "Epoch 1/50\n",
            "80/80 [==============================] - 7s 76ms/step - loss: 0.7298 - accuracy: 0.4934\n",
            "Epoch 2/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6926 - accuracy: 0.5008\n",
            "Epoch 3/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6925 - accuracy: 0.5018\n",
            "Epoch 4/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6927 - accuracy: 0.5024\n",
            "Epoch 5/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6922 - accuracy: 0.5123\n",
            "Epoch 6/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6992 - accuracy: 0.5115\n",
            "Epoch 7/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6934 - accuracy: 0.4915\n",
            "Epoch 8/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6937 - accuracy: 0.4971\n",
            "Epoch 9/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6933 - accuracy: 0.4887\n",
            "Epoch 10/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6931 - accuracy: 0.4903\n",
            "Epoch 11/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6932 - accuracy: 0.4919\n",
            "Epoch 12/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6931 - accuracy: 0.5004\n",
            "Epoch 13/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6933 - accuracy: 0.5003\n",
            "Epoch 14/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6931 - accuracy: 0.4882\n",
            "Epoch 15/50\n",
            "80/80 [==============================] - 6s 73ms/step - loss: 0.6931 - accuracy: 0.4982\n",
            "Epoch 16/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6931 - accuracy: 0.4947\n",
            "Epoch 17/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6930 - accuracy: 0.4908\n",
            "Epoch 18/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6931 - accuracy: 0.4917\n",
            "Epoch 19/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6931 - accuracy: 0.5003\n",
            "Epoch 20/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6931 - accuracy: 0.4950\n",
            "Epoch 21/50\n",
            "80/80 [==============================] - 6s 73ms/step - loss: 0.6931 - accuracy: 0.4897\n",
            "Epoch 22/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6930 - accuracy: 0.5018\n",
            "Epoch 23/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6930 - accuracy: 0.5006\n",
            "Epoch 24/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6931 - accuracy: 0.4930\n",
            "Epoch 25/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6930 - accuracy: 0.4939\n",
            "Epoch 26/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6934 - accuracy: 0.5016\n",
            "Epoch 27/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6930 - accuracy: 0.5006\n",
            "Epoch 28/50\n",
            "80/80 [==============================] - 6s 74ms/step - loss: 0.6930 - accuracy: 0.4944\n",
            "Epoch 29/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6930 - accuracy: 0.4890\n",
            "Epoch 30/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6930 - accuracy: 0.4929\n",
            "Epoch 31/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6929 - accuracy: 0.4977\n",
            "Epoch 32/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6934 - accuracy: 0.4987\n",
            "Epoch 33/50\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.6930 - accuracy: 0.5004\n",
            "Epoch 34/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6931 - accuracy: 0.4964\n",
            "Epoch 35/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6930 - accuracy: 0.4942\n",
            "Epoch 36/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6935 - accuracy: 0.5048\n",
            "Epoch 37/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6932 - accuracy: 0.4999\n",
            "Epoch 38/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6931 - accuracy: 0.5003\n",
            "Epoch 39/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6924 - accuracy: 0.5036\n",
            "Epoch 40/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6928 - accuracy: 0.5013\n",
            "Epoch 41/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6924 - accuracy: 0.5029\n",
            "Epoch 42/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6771 - accuracy: 0.5473\n",
            "Epoch 43/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6308 - accuracy: 0.6445\n",
            "Epoch 44/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.5872 - accuracy: 0.6999\n",
            "Epoch 45/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5345 - accuracy: 0.7364\n",
            "Epoch 46/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5215 - accuracy: 0.7437\n",
            "Epoch 47/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.5219 - accuracy: 0.7444\n",
            "Epoch 48/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.5143 - accuracy: 0.7495\n",
            "Epoch 49/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5230 - accuracy: 0.7411\n",
            "Epoch 50/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.5077 - accuracy: 0.7531\n",
            "Score for fold 3: loss of 0.5251858234405518; accuracy of 73.67360591888428%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 4 ...\n",
            "Epoch 1/50\n",
            "80/80 [==============================] - 7s 77ms/step - loss: 1.0169 - accuracy: 0.5029\n",
            "Epoch 2/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.6886 - accuracy: 0.5271\n",
            "Epoch 3/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.6822 - accuracy: 0.5310\n",
            "Epoch 4/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.6712 - accuracy: 0.5555\n",
            "Epoch 5/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.6494 - accuracy: 0.6057\n",
            "Epoch 6/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.6290 - accuracy: 0.6499\n",
            "Epoch 7/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.6185 - accuracy: 0.6831\n",
            "Epoch 8/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.6074 - accuracy: 0.6984\n",
            "Epoch 9/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5891 - accuracy: 0.7076\n",
            "Epoch 10/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5803 - accuracy: 0.7224\n",
            "Epoch 11/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5709 - accuracy: 0.7335\n",
            "Epoch 12/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5711 - accuracy: 0.7261\n",
            "Epoch 13/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5682 - accuracy: 0.7263\n",
            "Epoch 14/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5618 - accuracy: 0.7306\n",
            "Epoch 15/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5506 - accuracy: 0.7422\n",
            "Epoch 16/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5511 - accuracy: 0.7377\n",
            "Epoch 17/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5541 - accuracy: 0.7330\n",
            "Epoch 18/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5448 - accuracy: 0.7427\n",
            "Epoch 19/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5432 - accuracy: 0.7439\n",
            "Epoch 20/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5398 - accuracy: 0.7448\n",
            "Epoch 21/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5385 - accuracy: 0.7409\n",
            "Epoch 22/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5319 - accuracy: 0.7503\n",
            "Epoch 23/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5326 - accuracy: 0.7426\n",
            "Epoch 24/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5297 - accuracy: 0.7453\n",
            "Epoch 25/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5367 - accuracy: 0.7424\n",
            "Epoch 26/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5226 - accuracy: 0.7483\n",
            "Epoch 27/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5249 - accuracy: 0.7481\n",
            "Epoch 28/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5299 - accuracy: 0.7397\n",
            "Epoch 29/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5189 - accuracy: 0.7535\n",
            "Epoch 30/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5197 - accuracy: 0.7446\n",
            "Epoch 31/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5225 - accuracy: 0.7476\n",
            "Epoch 32/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5208 - accuracy: 0.7575\n",
            "Epoch 33/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5183 - accuracy: 0.7511\n",
            "Epoch 34/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5167 - accuracy: 0.7537\n",
            "Epoch 35/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5097 - accuracy: 0.7607\n",
            "Epoch 36/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5183 - accuracy: 0.7449\n",
            "Epoch 37/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5160 - accuracy: 0.7558\n",
            "Epoch 38/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5058 - accuracy: 0.7560\n",
            "Epoch 39/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5058 - accuracy: 0.7557\n",
            "Epoch 40/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5059 - accuracy: 0.7585\n",
            "Epoch 41/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5049 - accuracy: 0.7590\n",
            "Epoch 42/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5032 - accuracy: 0.7626\n",
            "Epoch 43/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5117 - accuracy: 0.7506\n",
            "Epoch 44/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4964 - accuracy: 0.7649\n",
            "Epoch 45/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5074 - accuracy: 0.7602\n",
            "Epoch 46/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5016 - accuracy: 0.7637\n",
            "Epoch 47/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4992 - accuracy: 0.7637\n",
            "Epoch 48/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5013 - accuracy: 0.7631\n",
            "Epoch 49/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.4958 - accuracy: 0.7651\n",
            "Epoch 50/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.4953 - accuracy: 0.7679\n",
            "Score for fold 4: loss of 0.490994393825531; accuracy of 77.63599753379822%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 5 ...\n",
            "Epoch 1/50\n",
            "80/80 [==============================] - 7s 75ms/step - loss: 0.7285 - accuracy: 0.4992\n",
            "Epoch 2/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6930 - accuracy: 0.5005\n",
            "Epoch 3/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6944 - accuracy: 0.4990\n",
            "Epoch 4/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6931 - accuracy: 0.5000\n",
            "Epoch 5/50\n",
            "80/80 [==============================] - 6s 75ms/step - loss: 0.6932 - accuracy: 0.5005\n",
            "Epoch 6/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6930 - accuracy: 0.4966\n",
            "Epoch 7/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6931 - accuracy: 0.5000\n",
            "Epoch 8/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6930 - accuracy: 0.4975\n",
            "Epoch 9/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6871 - accuracy: 0.5277\n",
            "Epoch 10/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6539 - accuracy: 0.6137\n",
            "Epoch 11/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.6252 - accuracy: 0.6691\n",
            "Epoch 12/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5994 - accuracy: 0.7043\n",
            "Epoch 13/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5874 - accuracy: 0.7147\n",
            "Epoch 14/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5789 - accuracy: 0.7184\n",
            "Epoch 15/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5765 - accuracy: 0.7171\n",
            "Epoch 16/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5643 - accuracy: 0.7322\n",
            "Epoch 17/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5553 - accuracy: 0.7362\n",
            "Epoch 18/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5533 - accuracy: 0.7424\n",
            "Epoch 19/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5500 - accuracy: 0.7470\n",
            "Epoch 20/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5405 - accuracy: 0.7471\n",
            "Epoch 21/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5427 - accuracy: 0.7451\n",
            "Epoch 22/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5409 - accuracy: 0.7455\n",
            "Epoch 23/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5330 - accuracy: 0.7500\n",
            "Epoch 24/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5260 - accuracy: 0.7530\n",
            "Epoch 25/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5255 - accuracy: 0.7554\n",
            "Epoch 26/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5188 - accuracy: 0.7601\n",
            "Epoch 27/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5224 - accuracy: 0.7532\n",
            "Epoch 28/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5231 - accuracy: 0.7485\n",
            "Epoch 29/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5150 - accuracy: 0.7607\n",
            "Epoch 30/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5195 - accuracy: 0.7554\n",
            "Epoch 31/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5252 - accuracy: 0.7478\n",
            "Epoch 32/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5146 - accuracy: 0.7607\n",
            "Epoch 33/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5099 - accuracy: 0.7614\n",
            "Epoch 34/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5108 - accuracy: 0.7658\n",
            "Epoch 35/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5086 - accuracy: 0.7576\n",
            "Epoch 36/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5049 - accuracy: 0.7646\n",
            "Epoch 37/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5061 - accuracy: 0.7639\n",
            "Epoch 38/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4979 - accuracy: 0.7685\n",
            "Epoch 39/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.5011 - accuracy: 0.7656\n",
            "Epoch 40/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5028 - accuracy: 0.7596\n",
            "Epoch 41/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.4981 - accuracy: 0.7683\n",
            "Epoch 42/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.4992 - accuracy: 0.7639\n",
            "Epoch 43/50\n",
            "80/80 [==============================] - 6s 76ms/step - loss: 0.4982 - accuracy: 0.7690\n",
            "Epoch 44/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4982 - accuracy: 0.7705\n",
            "Epoch 45/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4964 - accuracy: 0.7695\n",
            "Epoch 46/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4921 - accuracy: 0.7718\n",
            "Epoch 47/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4922 - accuracy: 0.7666\n",
            "Epoch 48/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4931 - accuracy: 0.7685\n",
            "Epoch 49/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4946 - accuracy: 0.7681\n",
            "Epoch 50/50\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4890 - accuracy: 0.7725\n",
            "Score for fold 5: loss of 0.49671509861946106; accuracy of 77.1505355834961%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# == Provide average scores ==\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Score per fold')\n",
        "for i in range(0, len(acc_per_fold1)):\n",
        "  print('------------------------------------------------------------------------')\n",
        "  print(f'> Fold {i+1} - Loss: {loss_per_fold1[i]} - Accuracy: {acc_per_fold1[i]}%')  \n",
        "print('------------------------------------------------------------------------')\n",
        "print('Average scores for all folds:')\n",
        "print(f'> Accuracy: {np.mean(acc_per_fold1)} (+- {np.std(acc_per_fold1)})')\n",
        "print(f'> Loss: {np.mean(loss_per_fold1)}')\n",
        "print('------------------------------------------------------------------------')\n",
        "\n",
        "#np.savetxt('/content/drive/MyDrive/FYP/Hyper_opti/dense_param4.csv', acc_per_fold1, delimiter=',')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAU-DiU2Zxpa",
        "outputId": "4ac4e347-1eda-45c6-98c4-35b171b40129"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------------------------------------------------\n",
            "Score per fold\n",
            "------------------------------------------------------------------------\n",
            "> Fold 1 - Loss: 0.5087489485740662 - Accuracy: 75.08394718170166%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 2 - Loss: 0.48164159059524536 - Accuracy: 78.84486317634583%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 3 - Loss: 0.5251858234405518 - Accuracy: 73.67360591888428%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 4 - Loss: 0.490994393825531 - Accuracy: 77.63599753379822%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 5 - Loss: 0.49671509861946106 - Accuracy: 77.1505355834961%\n",
            "------------------------------------------------------------------------\n",
            "Average scores for all folds:\n",
            "> Accuracy: 76.47778987884521 (+- 1.8549023099096609)\n",
            "> Loss: 0.5006571710109711\n",
            "------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc_per_fold4 = np.loadtxt('/content/drive/MyDrive/FYP/Hyper_opti/dense_param4.csv', delimiter=',')\n",
        "acc_per_fold6 = np.loadtxt('/content/drive/MyDrive/FYP/Hyper_opti/dense_param6.csv', delimiter=',')\n",
        "acc_per_fold16 = np.loadtxt('/content/drive/MyDrive/FYP/Hyper_opti/dense_param16.csv', delimiter=',')\n",
        "acc_per_fold64 = np.loadtxt('/content/drive/MyDrive/FYP/Hyper_opti/dense_param64.csv', delimiter=',')"
      ],
      "metadata": {
        "id": "qNFUO3RFal6j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        " \n",
        " \n",
        "# Creating dataset\n",
        "data_1 = np.array(acc_per_fold4)\n",
        "data_2 = np.array(acc_per_fold6)\n",
        "data_3 = np.array(acc_per_fold16)\n",
        "data_4 = np.array(acc_per_fold64)\n",
        "data = [data_1.flatten(),data_2.flatten(), data_3.flatten(), data_4.flatten()]\n",
        "print(data)\n",
        "fig = plt.figure()\n",
        " \n",
        "# Creating axes instance\n",
        "ax = fig.add_axes([0, 0, 1, 1])\n",
        "#plt.ylim([60,85])\n",
        "\n",
        "bp = ax.boxplot(data, labels=[4, 6, 16, 64], showmeans=True)\n",
        "plt.xlabel('Parameter')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.grid()\n",
        "\n",
        "plt.legend([f'param 4 acc = {round(np.mean(acc_per_fold4),2)} (+- {round(np.std(acc_per_fold4),2)})' ,\n",
        "            f'param 6 acc = {round(np.mean(acc_per_fold6),2)} (+- {round(np.std(acc_per_fold6),2)})' , \n",
        "            f'param 16 acc = {round(np.mean(acc_per_fold16),2)} (+- {round(np.std(acc_per_fold16),2)})', \n",
        "            f'param 64 acc = {round(np.mean(acc_per_fold64),2)} (+- {round(np.std(acc_per_fold64),2)})'], loc='lower left')\n",
        "\n",
        "\n",
        "plt.savefig(\"hyper_opti.pdf\", bbox_inches = 'tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "tofz_uEjawdd",
        "outputId": "2d9ce5e5-37c7-41d2-aeec-efa33d4f754f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([79.34640646, 82.66841173, 80.37933111, 83.06082487, 82.53760338]), array([83.00653696, 83.64944458, 82.8646183 , 80.51013947, 83.19162726]), array([85.42483449, 75.34336448, 82.60300756, 70.04578114, 79.26749587]), array([50.84967613, 74.95094538, 48.7246573 , 84.1072619 , 78.74427438])]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model"
      ],
      "metadata": {
        "id": "sSKallU6dnVN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def CAP_phase_model(input_shape, x_train,y_train,test_data):\n",
        "    \"\"\"\n",
        "    \n",
        "    Arguments:\n",
        "    input_shape, training_input, training_output, test_data\n",
        "\n",
        "    Returns:\n",
        "    model -- TF Keras model (object containing the information for the entire training process) \n",
        "    history\n",
        "    \"\"\"\n",
        "    start = time()\n",
        "    input_img = tf.keras.Input(shape=input_shape)\n",
        "   \n",
        "    Z1 = tfl.Conv1D(filters=32,\n",
        "                    kernel_size=3,\n",
        "                    strides=1,\n",
        "                    padding='same')(input_img)\n",
        "    \n",
        "    Z11 = tfl.Conv1D(filters=8,\n",
        "                    kernel_size=3,\n",
        "                    strides=1,\n",
        "                    padding='same')(Z1)\n",
        "    \n",
        "    A1 = tfl.ReLU()(Z11)\n",
        "    \n",
        "    P1 = tfl.MaxPooling1D(pool_size= 16,\n",
        "                       padding='same')(A1)\n",
        "    \n",
        "   \n",
        "    \n",
        "    Z2 = tfl.Conv1D(filters=16,\n",
        "                    kernel_size=2,\n",
        "                    strides=1,\n",
        "                    padding='same')(P1)\n",
        "    Z2=tf.nn.dropout(Z2, rate = 0.4)\n",
        "    \n",
        "    A2 = tfl.ReLU()(Z2)\n",
        "    \n",
        "    P2 = tfl.MaxPooling1D(pool_size=4,\n",
        "                       padding='same')(A2)\n",
        "\n",
        "\n",
        "\n",
        "    F = tfl.Flatten()(P2)\n",
        "\n",
        "    op2 = tfl.Dense(units=6,\n",
        "                        activation='relu')(F)\n",
        "          \n",
        "    outputs = tfl.Dense(units=1,\n",
        "                        activation='sigmoid')(op2)\n",
        "\n",
        "    model = tf.keras.Model(inputs=input_img, outputs=outputs)\n",
        "\n",
        "    #compile\n",
        "    model.compile(optimizer = 'adam',\n",
        "                  loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "    model.summary()\n",
        "\n",
        "    early_stopping = EarlyStopping(monitor='val_loss', patience=20, verbose=2)\n",
        "    history_m = model.fit(x_train,y_train, epochs=150, validation_split =0.2, callbacks = [early_stopping], batch_size = 75, shuffle = True)\n",
        "    \n",
        "    t=time()-start\n",
        "    print('Total training time:', t)\n",
        "    history_m.history['training_time']=t\n",
        "    \n",
        "    return model, history_m"
      ],
      "metadata": {
        "id": "7uPkMlNYGi9h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conv_model, history = CAP_phase_model((1024, 1), x_train1, y_train1, (x_test1,y_test1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VeJWIqEI1aq",
        "outputId": "992cd05d-4916-48a7-b78d-98006ad38359"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 1024, 1)]         0         \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, 1024, 32)          128       \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 1024, 8)           776       \n",
            "                                                                 \n",
            " re_lu (ReLU)                (None, 1024, 8)           0         \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 64, 8)            0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, 64, 16)            272       \n",
            "                                                                 \n",
            " tf.nn.dropout (TFOpLambda)  (None, 64, 16)            0         \n",
            "                                                                 \n",
            " re_lu_1 (ReLU)              (None, 64, 16)            0         \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 16, 16)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 6)                 1542      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 7         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,725\n",
            "Trainable params: 2,725\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/150\n",
            "80/80 [==============================] - 8s 85ms/step - loss: 0.8311 - accuracy: 0.5051 - val_loss: 0.6851 - val_accuracy: 0.4990\n",
            "Epoch 2/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.6846 - accuracy: 0.5113 - val_loss: 0.6819 - val_accuracy: 0.5232\n",
            "Epoch 3/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.6764 - accuracy: 0.5424 - val_loss: 0.6698 - val_accuracy: 0.5662\n",
            "Epoch 4/150\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.6663 - accuracy: 0.5652 - val_loss: 0.6634 - val_accuracy: 0.5970\n",
            "Epoch 5/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.6513 - accuracy: 0.5970 - val_loss: 0.6472 - val_accuracy: 0.6158\n",
            "Epoch 6/150\n",
            "80/80 [==============================] - 7s 81ms/step - loss: 0.6400 - accuracy: 0.6163 - val_loss: 0.6370 - val_accuracy: 0.6541\n",
            "Epoch 7/150\n",
            "80/80 [==============================] - 6s 81ms/step - loss: 0.6319 - accuracy: 0.6349 - val_loss: 0.6229 - val_accuracy: 0.6367\n",
            "Epoch 8/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.6142 - accuracy: 0.6631 - val_loss: 0.5988 - val_accuracy: 0.6924\n",
            "Epoch 9/150\n",
            "80/80 [==============================] - 6s 81ms/step - loss: 0.5964 - accuracy: 0.6903 - val_loss: 0.6004 - val_accuracy: 0.6770\n",
            "Epoch 10/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.5913 - accuracy: 0.6955 - val_loss: 0.5908 - val_accuracy: 0.6931\n",
            "Epoch 11/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.5936 - accuracy: 0.6897 - val_loss: 0.5975 - val_accuracy: 0.7032\n",
            "Epoch 12/150\n",
            "80/80 [==============================] - 8s 107ms/step - loss: 0.5748 - accuracy: 0.7197 - val_loss: 0.5835 - val_accuracy: 0.7206\n",
            "Epoch 13/150\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5667 - accuracy: 0.7270 - val_loss: 0.6076 - val_accuracy: 0.6891\n",
            "Epoch 14/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.5672 - accuracy: 0.7228 - val_loss: 0.5744 - val_accuracy: 0.7240\n",
            "Epoch 15/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.5672 - accuracy: 0.7211 - val_loss: 0.5824 - val_accuracy: 0.6991\n",
            "Epoch 16/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.5643 - accuracy: 0.7201 - val_loss: 0.5733 - val_accuracy: 0.7233\n",
            "Epoch 17/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.5568 - accuracy: 0.7315 - val_loss: 0.5683 - val_accuracy: 0.7132\n",
            "Epoch 18/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.5597 - accuracy: 0.7264 - val_loss: 0.5648 - val_accuracy: 0.7253\n",
            "Epoch 19/150\n",
            "80/80 [==============================] - 7s 87ms/step - loss: 0.5560 - accuracy: 0.7275 - val_loss: 0.5879 - val_accuracy: 0.7052\n",
            "Epoch 20/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.5550 - accuracy: 0.7254 - val_loss: 0.5722 - val_accuracy: 0.7280\n",
            "Epoch 21/150\n",
            "80/80 [==============================] - 6s 80ms/step - loss: 0.5461 - accuracy: 0.7380 - val_loss: 0.5558 - val_accuracy: 0.7347\n",
            "Epoch 22/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.5457 - accuracy: 0.7377 - val_loss: 0.5701 - val_accuracy: 0.7253\n",
            "Epoch 23/150\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.5398 - accuracy: 0.7367 - val_loss: 0.5633 - val_accuracy: 0.7340\n",
            "Epoch 24/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.5385 - accuracy: 0.7342 - val_loss: 0.5582 - val_accuracy: 0.7374\n",
            "Epoch 25/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.5336 - accuracy: 0.7399 - val_loss: 0.5649 - val_accuracy: 0.7287\n",
            "Epoch 26/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.5346 - accuracy: 0.7404 - val_loss: 0.5458 - val_accuracy: 0.7327\n",
            "Epoch 27/150\n",
            "80/80 [==============================] - 7s 87ms/step - loss: 0.5335 - accuracy: 0.7350 - val_loss: 0.5564 - val_accuracy: 0.7220\n",
            "Epoch 28/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.5329 - accuracy: 0.7303 - val_loss: 0.5443 - val_accuracy: 0.7347\n",
            "Epoch 29/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.5248 - accuracy: 0.7385 - val_loss: 0.5483 - val_accuracy: 0.7132\n",
            "Epoch 30/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.5218 - accuracy: 0.7469 - val_loss: 0.5454 - val_accuracy: 0.7226\n",
            "Epoch 31/150\n",
            "80/80 [==============================] - 6s 81ms/step - loss: 0.5229 - accuracy: 0.7427 - val_loss: 0.5484 - val_accuracy: 0.7253\n",
            "Epoch 32/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.5224 - accuracy: 0.7411 - val_loss: 0.5209 - val_accuracy: 0.7408\n",
            "Epoch 33/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.5201 - accuracy: 0.7444 - val_loss: 0.5282 - val_accuracy: 0.7475\n",
            "Epoch 34/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.5175 - accuracy: 0.7436 - val_loss: 0.5271 - val_accuracy: 0.7374\n",
            "Epoch 35/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.5148 - accuracy: 0.7484 - val_loss: 0.5196 - val_accuracy: 0.7475\n",
            "Epoch 36/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.5078 - accuracy: 0.7491 - val_loss: 0.5473 - val_accuracy: 0.7381\n",
            "Epoch 37/150\n",
            "80/80 [==============================] - 6s 81ms/step - loss: 0.5199 - accuracy: 0.7469 - val_loss: 0.5259 - val_accuracy: 0.7354\n",
            "Epoch 38/150\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.5136 - accuracy: 0.7491 - val_loss: 0.5229 - val_accuracy: 0.7361\n",
            "Epoch 39/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.5104 - accuracy: 0.7521 - val_loss: 0.5208 - val_accuracy: 0.7461\n",
            "Epoch 40/150\n",
            "80/80 [==============================] - 7s 87ms/step - loss: 0.5049 - accuracy: 0.7580 - val_loss: 0.5108 - val_accuracy: 0.7347\n",
            "Epoch 41/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.5020 - accuracy: 0.7567 - val_loss: 0.5266 - val_accuracy: 0.7428\n",
            "Epoch 42/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.5087 - accuracy: 0.7557 - val_loss: 0.5131 - val_accuracy: 0.7482\n",
            "Epoch 43/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.5051 - accuracy: 0.7572 - val_loss: 0.5051 - val_accuracy: 0.7502\n",
            "Epoch 44/150\n",
            "80/80 [==============================] - 6s 81ms/step - loss: 0.4997 - accuracy: 0.7570 - val_loss: 0.5086 - val_accuracy: 0.7555\n",
            "Epoch 45/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.5013 - accuracy: 0.7543 - val_loss: 0.5191 - val_accuracy: 0.7435\n",
            "Epoch 46/150\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.4988 - accuracy: 0.7612 - val_loss: 0.5060 - val_accuracy: 0.7488\n",
            "Epoch 47/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4961 - accuracy: 0.7582 - val_loss: 0.5278 - val_accuracy: 0.7448\n",
            "Epoch 48/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.4990 - accuracy: 0.7587 - val_loss: 0.5009 - val_accuracy: 0.7522\n",
            "Epoch 49/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4955 - accuracy: 0.7602 - val_loss: 0.5120 - val_accuracy: 0.7576\n",
            "Epoch 50/150\n",
            "80/80 [==============================] - 6s 77ms/step - loss: 0.4954 - accuracy: 0.7641 - val_loss: 0.5106 - val_accuracy: 0.7555\n",
            "Epoch 51/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4932 - accuracy: 0.7636 - val_loss: 0.5138 - val_accuracy: 0.7529\n",
            "Epoch 52/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4908 - accuracy: 0.7681 - val_loss: 0.5137 - val_accuracy: 0.7502\n",
            "Epoch 53/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4928 - accuracy: 0.7634 - val_loss: 0.5121 - val_accuracy: 0.7616\n",
            "Epoch 54/150\n",
            "80/80 [==============================] - 6s 81ms/step - loss: 0.4892 - accuracy: 0.7639 - val_loss: 0.5076 - val_accuracy: 0.7535\n",
            "Epoch 55/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4953 - accuracy: 0.7552 - val_loss: 0.4997 - val_accuracy: 0.7555\n",
            "Epoch 56/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4949 - accuracy: 0.7592 - val_loss: 0.5030 - val_accuracy: 0.7482\n",
            "Epoch 57/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4949 - accuracy: 0.7637 - val_loss: 0.5095 - val_accuracy: 0.7428\n",
            "Epoch 58/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4895 - accuracy: 0.7641 - val_loss: 0.5062 - val_accuracy: 0.7515\n",
            "Epoch 59/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4863 - accuracy: 0.7696 - val_loss: 0.5023 - val_accuracy: 0.7562\n",
            "Epoch 60/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4845 - accuracy: 0.7713 - val_loss: 0.4905 - val_accuracy: 0.7616\n",
            "Epoch 61/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.4877 - accuracy: 0.7664 - val_loss: 0.4972 - val_accuracy: 0.7535\n",
            "Epoch 62/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.4928 - accuracy: 0.7644 - val_loss: 0.5034 - val_accuracy: 0.7596\n",
            "Epoch 63/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4934 - accuracy: 0.7669 - val_loss: 0.4983 - val_accuracy: 0.7555\n",
            "Epoch 64/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4898 - accuracy: 0.7666 - val_loss: 0.5052 - val_accuracy: 0.7636\n",
            "Epoch 65/150\n",
            "80/80 [==============================] - 6s 80ms/step - loss: 0.4873 - accuracy: 0.7736 - val_loss: 0.5008 - val_accuracy: 0.7676\n",
            "Epoch 66/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4817 - accuracy: 0.7738 - val_loss: 0.5015 - val_accuracy: 0.7576\n",
            "Epoch 67/150\n",
            "80/80 [==============================] - 6s 81ms/step - loss: 0.4794 - accuracy: 0.7763 - val_loss: 0.5006 - val_accuracy: 0.7602\n",
            "Epoch 68/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4902 - accuracy: 0.7632 - val_loss: 0.4971 - val_accuracy: 0.7582\n",
            "Epoch 69/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4827 - accuracy: 0.7701 - val_loss: 0.5018 - val_accuracy: 0.7589\n",
            "Epoch 70/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4767 - accuracy: 0.7782 - val_loss: 0.4901 - val_accuracy: 0.7602\n",
            "Epoch 71/150\n",
            "80/80 [==============================] - 6s 80ms/step - loss: 0.4864 - accuracy: 0.7699 - val_loss: 0.5051 - val_accuracy: 0.7690\n",
            "Epoch 72/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4820 - accuracy: 0.7735 - val_loss: 0.5065 - val_accuracy: 0.7589\n",
            "Epoch 73/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4819 - accuracy: 0.7669 - val_loss: 0.4974 - val_accuracy: 0.7629\n",
            "Epoch 74/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4814 - accuracy: 0.7713 - val_loss: 0.5217 - val_accuracy: 0.7542\n",
            "Epoch 75/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4857 - accuracy: 0.7664 - val_loss: 0.5030 - val_accuracy: 0.7522\n",
            "Epoch 76/150\n",
            "80/80 [==============================] - 6s 80ms/step - loss: 0.4820 - accuracy: 0.7703 - val_loss: 0.4962 - val_accuracy: 0.7508\n",
            "Epoch 77/150\n",
            "80/80 [==============================] - 6s 81ms/step - loss: 0.4783 - accuracy: 0.7773 - val_loss: 0.4940 - val_accuracy: 0.7703\n",
            "Epoch 78/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4782 - accuracy: 0.7755 - val_loss: 0.4933 - val_accuracy: 0.7730\n",
            "Epoch 79/150\n",
            "80/80 [==============================] - 6s 80ms/step - loss: 0.4772 - accuracy: 0.7787 - val_loss: 0.4956 - val_accuracy: 0.7569\n",
            "Epoch 80/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4836 - accuracy: 0.7757 - val_loss: 0.4978 - val_accuracy: 0.7616\n",
            "Epoch 81/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4770 - accuracy: 0.7740 - val_loss: 0.4987 - val_accuracy: 0.7643\n",
            "Epoch 82/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4777 - accuracy: 0.7765 - val_loss: 0.5049 - val_accuracy: 0.7569\n",
            "Epoch 83/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4726 - accuracy: 0.7767 - val_loss: 0.4909 - val_accuracy: 0.7602\n",
            "Epoch 84/150\n",
            "80/80 [==============================] - 7s 87ms/step - loss: 0.4770 - accuracy: 0.7736 - val_loss: 0.5003 - val_accuracy: 0.7582\n",
            "Epoch 85/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4784 - accuracy: 0.7730 - val_loss: 0.5029 - val_accuracy: 0.7515\n",
            "Epoch 86/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4748 - accuracy: 0.7770 - val_loss: 0.5041 - val_accuracy: 0.7636\n",
            "Epoch 87/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4751 - accuracy: 0.7763 - val_loss: 0.4985 - val_accuracy: 0.7629\n",
            "Epoch 88/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4740 - accuracy: 0.7760 - val_loss: 0.4992 - val_accuracy: 0.7629\n",
            "Epoch 89/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4813 - accuracy: 0.7731 - val_loss: 0.5121 - val_accuracy: 0.7649\n",
            "Epoch 90/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4758 - accuracy: 0.7797 - val_loss: 0.4898 - val_accuracy: 0.7643\n",
            "Epoch 91/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4746 - accuracy: 0.7787 - val_loss: 0.4925 - val_accuracy: 0.7743\n",
            "Epoch 92/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4717 - accuracy: 0.7736 - val_loss: 0.4941 - val_accuracy: 0.7562\n",
            "Epoch 93/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.4712 - accuracy: 0.7758 - val_loss: 0.4992 - val_accuracy: 0.7643\n",
            "Epoch 94/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4753 - accuracy: 0.7795 - val_loss: 0.4972 - val_accuracy: 0.7636\n",
            "Epoch 95/150\n",
            "80/80 [==============================] - 6s 79ms/step - loss: 0.4721 - accuracy: 0.7797 - val_loss: 0.4890 - val_accuracy: 0.7649\n",
            "Epoch 96/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4701 - accuracy: 0.7788 - val_loss: 0.4910 - val_accuracy: 0.7589\n",
            "Epoch 97/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4784 - accuracy: 0.7780 - val_loss: 0.5020 - val_accuracy: 0.7461\n",
            "Epoch 98/150\n",
            "80/80 [==============================] - 6s 81ms/step - loss: 0.4717 - accuracy: 0.7856 - val_loss: 0.4916 - val_accuracy: 0.7582\n",
            "Epoch 99/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.4665 - accuracy: 0.7837 - val_loss: 0.4889 - val_accuracy: 0.7710\n",
            "Epoch 100/150\n",
            "80/80 [==============================] - 6s 80ms/step - loss: 0.4735 - accuracy: 0.7790 - val_loss: 0.4935 - val_accuracy: 0.7723\n",
            "Epoch 101/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4723 - accuracy: 0.7797 - val_loss: 0.5020 - val_accuracy: 0.7629\n",
            "Epoch 102/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4734 - accuracy: 0.7750 - val_loss: 0.4926 - val_accuracy: 0.7656\n",
            "Epoch 103/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4657 - accuracy: 0.7809 - val_loss: 0.4921 - val_accuracy: 0.7582\n",
            "Epoch 104/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4714 - accuracy: 0.7783 - val_loss: 0.5003 - val_accuracy: 0.7643\n",
            "Epoch 105/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4723 - accuracy: 0.7805 - val_loss: 0.5027 - val_accuracy: 0.7690\n",
            "Epoch 106/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4676 - accuracy: 0.7775 - val_loss: 0.5096 - val_accuracy: 0.7582\n",
            "Epoch 107/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4739 - accuracy: 0.7715 - val_loss: 0.5089 - val_accuracy: 0.7441\n",
            "Epoch 108/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4669 - accuracy: 0.7817 - val_loss: 0.4860 - val_accuracy: 0.7723\n",
            "Epoch 109/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4679 - accuracy: 0.7847 - val_loss: 0.5043 - val_accuracy: 0.7663\n",
            "Epoch 110/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4644 - accuracy: 0.7810 - val_loss: 0.5060 - val_accuracy: 0.7623\n",
            "Epoch 111/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.4697 - accuracy: 0.7832 - val_loss: 0.5315 - val_accuracy: 0.7522\n",
            "Epoch 112/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4725 - accuracy: 0.7807 - val_loss: 0.4920 - val_accuracy: 0.7730\n",
            "Epoch 113/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4631 - accuracy: 0.7846 - val_loss: 0.4980 - val_accuracy: 0.7629\n",
            "Epoch 114/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4633 - accuracy: 0.7812 - val_loss: 0.4908 - val_accuracy: 0.7730\n",
            "Epoch 115/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4685 - accuracy: 0.7777 - val_loss: 0.4984 - val_accuracy: 0.7623\n",
            "Epoch 116/150\n",
            "80/80 [==============================] - 6s 78ms/step - loss: 0.4649 - accuracy: 0.7849 - val_loss: 0.5014 - val_accuracy: 0.7670\n",
            "Epoch 117/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4686 - accuracy: 0.7795 - val_loss: 0.4908 - val_accuracy: 0.7656\n",
            "Epoch 118/150\n",
            "80/80 [==============================] - 7s 87ms/step - loss: 0.4674 - accuracy: 0.7829 - val_loss: 0.4999 - val_accuracy: 0.7522\n",
            "Epoch 119/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4671 - accuracy: 0.7790 - val_loss: 0.4982 - val_accuracy: 0.7676\n",
            "Epoch 120/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4611 - accuracy: 0.7852 - val_loss: 0.5282 - val_accuracy: 0.7569\n",
            "Epoch 121/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4611 - accuracy: 0.7874 - val_loss: 0.5144 - val_accuracy: 0.7609\n",
            "Epoch 122/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4635 - accuracy: 0.7815 - val_loss: 0.4859 - val_accuracy: 0.7703\n",
            "Epoch 123/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4647 - accuracy: 0.7840 - val_loss: 0.4938 - val_accuracy: 0.7549\n",
            "Epoch 124/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4627 - accuracy: 0.7817 - val_loss: 0.4993 - val_accuracy: 0.7609\n",
            "Epoch 125/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4624 - accuracy: 0.7864 - val_loss: 0.5133 - val_accuracy: 0.7616\n",
            "Epoch 126/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4638 - accuracy: 0.7839 - val_loss: 0.5052 - val_accuracy: 0.7690\n",
            "Epoch 127/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4613 - accuracy: 0.7893 - val_loss: 0.4969 - val_accuracy: 0.7582\n",
            "Epoch 128/150\n",
            "80/80 [==============================] - 7s 82ms/step - loss: 0.4613 - accuracy: 0.7846 - val_loss: 0.5013 - val_accuracy: 0.7555\n",
            "Epoch 129/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4595 - accuracy: 0.7856 - val_loss: 0.5089 - val_accuracy: 0.7502\n",
            "Epoch 130/150\n",
            "80/80 [==============================] - 7s 87ms/step - loss: 0.4589 - accuracy: 0.7820 - val_loss: 0.5036 - val_accuracy: 0.7623\n",
            "Epoch 131/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4632 - accuracy: 0.7810 - val_loss: 0.4934 - val_accuracy: 0.7656\n",
            "Epoch 132/150\n",
            "80/80 [==============================] - 7s 83ms/step - loss: 0.4613 - accuracy: 0.7839 - val_loss: 0.5037 - val_accuracy: 0.7596\n",
            "Epoch 133/150\n",
            "80/80 [==============================] - 7s 88ms/step - loss: 0.4642 - accuracy: 0.7830 - val_loss: 0.4921 - val_accuracy: 0.7636\n",
            "Epoch 134/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4585 - accuracy: 0.7861 - val_loss: 0.5012 - val_accuracy: 0.7616\n",
            "Epoch 135/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4600 - accuracy: 0.7864 - val_loss: 0.5053 - val_accuracy: 0.7670\n",
            "Epoch 136/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4604 - accuracy: 0.7842 - val_loss: 0.4855 - val_accuracy: 0.7730\n",
            "Epoch 137/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4596 - accuracy: 0.7835 - val_loss: 0.4992 - val_accuracy: 0.7602\n",
            "Epoch 138/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4576 - accuracy: 0.7874 - val_loss: 0.4992 - val_accuracy: 0.7616\n",
            "Epoch 139/150\n",
            "80/80 [==============================] - 7s 90ms/step - loss: 0.4576 - accuracy: 0.7797 - val_loss: 0.4996 - val_accuracy: 0.7670\n",
            "Epoch 140/150\n",
            "80/80 [==============================] - 7s 89ms/step - loss: 0.4576 - accuracy: 0.7874 - val_loss: 0.5038 - val_accuracy: 0.7676\n",
            "Epoch 141/150\n",
            "80/80 [==============================] - 7s 85ms/step - loss: 0.4512 - accuracy: 0.7894 - val_loss: 0.4983 - val_accuracy: 0.7831\n",
            "Epoch 142/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4568 - accuracy: 0.7893 - val_loss: 0.4989 - val_accuracy: 0.7683\n",
            "Epoch 143/150\n",
            "80/80 [==============================] - 7s 87ms/step - loss: 0.4563 - accuracy: 0.7869 - val_loss: 0.4923 - val_accuracy: 0.7730\n",
            "Epoch 144/150\n",
            "80/80 [==============================] - 7s 87ms/step - loss: 0.4560 - accuracy: 0.7884 - val_loss: 0.4983 - val_accuracy: 0.7696\n",
            "Epoch 145/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4505 - accuracy: 0.7928 - val_loss: 0.5190 - val_accuracy: 0.7549\n",
            "Epoch 146/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4595 - accuracy: 0.7879 - val_loss: 0.5198 - val_accuracy: 0.7408\n",
            "Epoch 147/150\n",
            "80/80 [==============================] - 7s 88ms/step - loss: 0.4537 - accuracy: 0.7896 - val_loss: 0.4923 - val_accuracy: 0.7710\n",
            "Epoch 148/150\n",
            "80/80 [==============================] - 7s 86ms/step - loss: 0.4592 - accuracy: 0.7844 - val_loss: 0.4963 - val_accuracy: 0.7643\n",
            "Epoch 149/150\n",
            "80/80 [==============================] - 7s 87ms/step - loss: 0.4559 - accuracy: 0.7913 - val_loss: 0.5021 - val_accuracy: 0.7609\n",
            "Epoch 150/150\n",
            "80/80 [==============================] - 7s 84ms/step - loss: 0.4542 - accuracy: 0.7842 - val_loss: 0.4923 - val_accuracy: 0.7690\n",
            "Total training time: 1043.4917905330658\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save model, history and training and test dataset\n",
        "conv_model.save('/content/drive/MyDrive/FYP/CNN/Un-healthy/model_v2.h5')\n",
        "np.save('/content/drive/MyDrive/FYP/CNN/Un-healthy/ver2_history.npy', history)\n",
        "\n",
        "# save testdata to csv file\n",
        "np.savetxt('/content/drive/MyDrive/FYP/CNN/Un-healthy/xtest_data_ver2.csv', x_test1, delimiter=',')\n",
        "np.savetxt('/content/drive/MyDrive/FYP/CNN/Un-healthy/ytest_data_ver2.csv', y_test1, delimiter=',')\n",
        "np.savetxt('/content/drive/MyDrive/FYP/CNN/Un-healthy/xtrain_data_ver2.csv', x_train1, delimiter=',')\n",
        "np.savetxt('/content/drive/MyDrive/FYP/CNN/Un-healthy/ytrain_data_ver2.csv', y_train1, delimiter=',')"
      ],
      "metadata": {
        "id": "lpyIucUIJCOo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Results"
      ],
      "metadata": {
        "id": "zrot2y2OJaEG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Healthy"
      ],
      "metadata": {
        "id": "VVHczewfEU7m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load model, history and dataset\n",
        "conv_model = tf.keras.models.load_model('/content/drive/MyDrive/GUI/GUI/healthy_cap.h5')\n",
        "history=np.load('/content/drive/MyDrive/GUI/Data/healthy_cap_hist.npy',allow_pickle='TRUE').item()\n",
        "x_test1 = np.loadtxt('/content/drive/MyDrive/GUI/Data/healthy_cap_xtest.csv', delimiter=',')\n",
        "y_test1 = np.loadtxt('/content/drive/MyDrive/GUI/Data/healthy_cap_ytest.csv', delimiter=',')\n",
        "disease='Healthy'   # disorder of loded dataset"
      ],
      "metadata": {
        "id": "x1VwooWsJt6A"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "    ## GRAPHS FROM RESTORE MODEL\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "print('Patient:',disease)\n",
        "prob = conv_model.predict(x_test1,verbose = 2)\n",
        "pred = (prob > 0.5).astype(\"int32\")\n",
        "\n",
        "# reduce to 1d array\n",
        "yhat_probs = prob[:, 0]\n",
        "yhat_classes = pred[:, 0]\n",
        "\n",
        "cm=metrics.confusion_matrix(y_test1,pred)\n",
        "# accuracy: (tp + tn) / (p + n)\n",
        "accuracy = metrics.accuracy_score(y_test1,pred)\n",
        "print('Accuracy: %f' % accuracy)\n",
        "# specificity = tn(tn+fp)\n",
        "specificity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "print('Specificity: %f' % specificity)\n",
        "# precision tp / (tp + fp)\n",
        "precision = metrics.precision_score(y_test1,pred)\n",
        "print('Precision: %f' % precision)\n",
        "# recall: tp / (tp + fn)\n",
        "recall = metrics.recall_score(y_test1, pred)\n",
        "print('Sensitivity: %f' % recall)\n",
        "# f1: 2 tp / (2 tp + fp + fn)\n",
        "f1 = metrics.f1_score(y_test1, pred)\n",
        "print('F1 score: %f' % f1)\n",
        "# kappa\n",
        "kappa = metrics.cohen_kappa_score(y_test1, pred)\n",
        "print('Cohens kappa: %f' % kappa)\n",
        "\n",
        "labels=[\"B\", \"A\"]\n",
        "cm=metrics.confusion_matrix(y_test1,pred)\n",
        "disp = metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "id": "Nx_93bdvJb4c",
        "outputId": "1596cab9-e691-4c53-ea83-c3b482206637"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient: Healthy\n",
            "59/59 - 1s - 552ms/epoch - 9ms/step\n",
            "Accuracy: 0.772288\n",
            "Specificity: 0.745614\n",
            "Precision: 0.805024\n",
            "Sensitivity: 0.720557\n",
            "F1 score: 0.760452\n",
            "Cohens kappa: 0.544725\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fe08c003110>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels=[\"B\", \"A\"]\n",
        "index=4\n",
        "#pred = new_model.predict(x_test1)\n",
        "real=y_test1\n",
        "prediction=0\n",
        "if (pred[index]>=0.5): prediction=1\n",
        "act=labels[int(real[index])]\n",
        "pr=labels[prediction]\n",
        "print(\"Patient:\", disease)\n",
        "print (\"Actual = \" + act + \", Predection = \" + pr)\n",
        "plt.plot(np.arange(1024)/512 ,x_test1[index,:])\n",
        "plt.xlabel(\"time duration in sec\")\n",
        "plt.ylabel(\"Amplitude of signal\")\n",
        "plt.ylim([-100, 100])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "bd_qCWaaJ7En",
        "outputId": "b805abf6-eee2-4d0c-d69c-4b6bcc97a572"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient: Healthy\n",
            "Actual = B, Predection = B\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_keras = conv_model.predict(x_test1).ravel()\n",
        "fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test1, y_pred_keras)\n",
        "auc_keras = auc(fpr_keras, tpr_keras)\n",
        "plt.title(disease)\n",
        "plt.plot(fpr_keras, tpr_keras, label='ROC curve')\n",
        "plt.fill_between(fpr_keras, tpr_keras, 0, color='blue', alpha=.1, label='AUC = {:.3f}'.format(auc_keras))\n",
        "plt.axis([0, 1, 0, 1])\n",
        "plt.grid()\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.xlabel('False positive rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "XHR0HfTtLHH1",
        "outputId": "462e95d3-64da-4202-cf74-2d0f5865e2b3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'False positive rate')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Insomnia"
      ],
      "metadata": {
        "id": "VCU7l1geEY3m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load model, history and dataset\n",
        "conv_model = tf.keras.models.load_model('/content/drive/MyDrive/GUI/GUI/ins_cap.h5')\n",
        "history=np.load('/content/drive/MyDrive/GUI/Data/ins_cap_hist.npy',allow_pickle='TRUE').item()\n",
        "x_test1 = np.loadtxt('/content/drive/MyDrive/GUI/Data/ins_cap_xtest.csv', delimiter=',')\n",
        "y_test1 = np.loadtxt('/content/drive/MyDrive/GUI/Data/ins_cap_ytest.csv', delimiter=',')\n",
        "disease='Insomnia'   # disorder of loded dataset"
      ],
      "metadata": {
        "id": "OSsbsD3mER1H"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "    ## GRAPHS FROM RESTORE MODEL\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "print('Patient:',disease)\n",
        "prob = conv_model.predict(x_test1,verbose = 2)\n",
        "pred = (prob > 0.5).astype(\"int32\")\n",
        "\n",
        "# reduce to 1d array\n",
        "yhat_probs = prob[:, 0]\n",
        "yhat_classes = pred[:, 0]\n",
        "\n",
        "cm=metrics.confusion_matrix(y_test1,pred)\n",
        "# accuracy: (tp + tn) / (p + n)\n",
        "accuracy = metrics.accuracy_score(y_test1,pred)\n",
        "print('Accuracy: %f' % accuracy)\n",
        "# specificity = tn(tn+fp)\n",
        "specificity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "print('Specificity: %f' % specificity)\n",
        "# precision tp / (tp + fp)\n",
        "precision = metrics.precision_score(y_test1,pred)\n",
        "print('Precision: %f' % precision)\n",
        "# recall: tp / (tp + fn)\n",
        "recall = metrics.recall_score(y_test1, pred)\n",
        "print('Sensitivity: %f' % recall)\n",
        "# f1: 2 tp / (2 tp + fp + fn)\n",
        "f1 = metrics.f1_score(y_test1, pred)\n",
        "print('F1 score: %f' % f1)\n",
        "# kappa\n",
        "kappa = metrics.cohen_kappa_score(y_test1, pred)\n",
        "print('Cohens kappa: %f' % kappa)\n",
        "\n",
        "labels=[\"B\", \"A\"]\n",
        "cm=metrics.confusion_matrix(y_test1,pred)\n",
        "disp = metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "outputId": "4f99109b-dcd7-4ae5-804e-a448af39f499",
        "id": "2JoTpQ4bER1K"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient: Insomnia\n",
            "63/63 - 1s - 575ms/epoch - 9ms/step\n",
            "Accuracy: 0.687249\n",
            "Specificity: 0.652107\n",
            "Precision: 0.754003\n",
            "Sensitivity: 0.532922\n",
            "F1 score: 0.624473\n",
            "Cohens kappa: 0.369787\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fe08ada0f50>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels=[\"B\", \"A\"]\n",
        "index=4\n",
        "#pred = new_model.predict(x_test1)\n",
        "real=y_test1\n",
        "prediction=0\n",
        "if (pred[index]>=0.5): prediction=1\n",
        "act=labels[int(real[index])]\n",
        "pr=labels[prediction]\n",
        "print(\"Patient:\", disease)\n",
        "print (\"Actual = \" + act + \", Predection = \" + pr)\n",
        "plt.plot(np.arange(1024)/512 ,x_test1[index,:])\n",
        "plt.xlabel(\"time duration in sec\")\n",
        "plt.ylabel(\"Amplitude of signal\")\n",
        "plt.ylim([-100, 100])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "b649e640-78ee-49a8-8f30-e5bd8b2a98d9",
        "id": "UjD_xiZlER1O"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient: Insomnia\n",
            "Actual = A, Predection = A\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_keras = conv_model.predict(x_test1).ravel()\n",
        "fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test1, y_pred_keras)\n",
        "auc_keras = auc(fpr_keras, tpr_keras)\n",
        "plt.title(disease)\n",
        "plt.plot(fpr_keras, tpr_keras, label='ROC curve')\n",
        "plt.fill_between(fpr_keras, tpr_keras, 0, color='blue', alpha=.1, label='AUC = {:.3f}'.format(auc_keras))\n",
        "plt.axis([0, 1, 0, 1])\n",
        "plt.grid()\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.xlabel('False positive rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "21f24b04-b5bf-446a-b3b5-4c4224a46ed9",
        "id": "Z1ajabCgER1Q"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'False positive rate')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Narcolepsy"
      ],
      "metadata": {
        "id": "gyjCxFZBF0oI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load model, history and dataset\n",
        "conv_model = tf.keras.models.load_model('/content/drive/MyDrive/GUI/GUI/narco_cap.h5')\n",
        "history=np.load('/content/drive/MyDrive/GUI/Data/narco_cap_hist.npy',allow_pickle='TRUE').item()\n",
        "x_test1 = np.loadtxt('/content/drive/MyDrive/GUI/Data/narco_cap_xtest.csv', delimiter=',')\n",
        "y_test1 = np.loadtxt('/content/drive/MyDrive/GUI/Data/narco_cap_ytest.csv', delimiter=',')\n",
        "disease='Narcolepsy'   # disorder of loded dataset"
      ],
      "metadata": {
        "id": "yQwAiEckF2zk"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "    ## GRAPHS FROM RESTORE MODEL\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "print('Patient:',disease)\n",
        "prob = conv_model.predict(x_test1,verbose = 2)\n",
        "pred = (prob > 0.5).astype(\"int32\")\n",
        "\n",
        "# reduce to 1d array\n",
        "yhat_probs = prob[:, 0]\n",
        "yhat_classes = pred[:, 0]\n",
        "\n",
        "cm=metrics.confusion_matrix(y_test1,pred)\n",
        "# accuracy: (tp + tn) / (p + n)\n",
        "accuracy = metrics.accuracy_score(y_test1,pred)\n",
        "print('Accuracy: %f' % accuracy)\n",
        "# specificity = tn(tn+fp)\n",
        "specificity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "print('Specificity: %f' % specificity)\n",
        "# precision tp / (tp + fp)\n",
        "precision = metrics.precision_score(y_test1,pred)\n",
        "print('Precision: %f' % precision)\n",
        "# recall: tp / (tp + fn)\n",
        "recall = metrics.recall_score(y_test1, pred)\n",
        "print('Sensitivity: %f' % recall)\n",
        "# f1: 2 tp / (2 tp + fp + fn)\n",
        "f1 = metrics.f1_score(y_test1, pred)\n",
        "print('F1 score: %f' % f1)\n",
        "# kappa\n",
        "kappa = metrics.cohen_kappa_score(y_test1, pred)\n",
        "print('Cohens kappa: %f' % kappa)\n",
        "\n",
        "labels=[\"B\", \"A\"]\n",
        "cm=metrics.confusion_matrix(y_test1,pred)\n",
        "disp = metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "outputId": "4d0d617e-328d-4d8c-f7fb-045d2711159f",
        "id": "71CFTcj8F2zn"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient: Narcolepsy\n",
            "60/60 - 1s - 553ms/epoch - 9ms/step\n",
            "Accuracy: 0.832636\n",
            "Specificity: 0.836887\n",
            "Precision: 0.828542\n",
            "Sensitivity: 0.840625\n",
            "F1 score: 0.834540\n",
            "Cohens kappa: 0.665246\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fe08c21f690>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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P2aoE/ri0mWWXh5acmVlrctFdNTNrVRUFuWJWBpakkyV9Lx2/V9IBpa+amVWsnK0M/BtgDHBSOl4F/LpkNTKziqYofqsExXRXD4yI/SQ9DhARyyRtU+J6mVkly9no6gZJNaTGp6QBFD311szyqFJaacUoprt6GYWVAXaU9D8Ulln6cUlrZWaVrYqeyRUzd/UaSTMozCMTcFxEPFPymplZZaqg523FKGbRzPdSmO1/W/O0iHitlBUzswqWpyAH/I1NH7TpAQwHngM+UMJ6mVkFUxU9lS+mu7pX8+O0OsmZrWQ3M6somWc8RMRjktr8BJiZ5VyeuquSzml22AXYD3i9ZDUys8qWt4EHoGez/XoKz+huLk11zKwq5CXIpZeAe0bEtzqpPmZWDfIQ5CR1jYh6SV6q18w2EvkZXX2EwvO3mZKmAjcCbzadjIhbSlw3M6tEOXwm1wOoo/BNh6b35QJwkDPbWuUkyO2YRlZnsSm4NamiX9HMOlwVRYC2glwNsANvD25NquhXNLOOlpfu6oKI+GGn1cTMqkcHB7n0Jsd0YH5EHCtpOHA90A+YAXwxItZL6g5cBXyQwmO0EyLilbbu3dZSS9WzKp6ZdZ4ojK4Ws2XwNaD56kYXAZdExK7AMuDUlH4qsCylX5LytamtIHdkpiqa2dajA9eTkzQE+ARwZToWhYHOm1KWycBxaX9cOiadPzLlb1WrQS4ilhZXRTPb2nTwNx5+AZzLphXH+wHLI6I+Hc8DBqf9wcBcgHR+RcrfqmJWBjYze7viW3L9JU1vtk1ofhtJxwKLI2JGqarq766aWTbZljZfEhGj2zh/CPApScdQeCe3F3Ap0Kdp1hUwBJif8s8HhgLzJHUFelMYgGiVW3JmlonouO5qRJwfEUMiYhhwInBPRHwBuBc4PmUbD0xJ+1PTMen8PRHRZkkOcmaWWSd8d/U7wDmS5lB45jYppU8C+qX0c4Dz2ruRu6tmll0JXgaOiPuA+9L+S8ABLeRZB3w2y30d5Mwsu5zMeDAze6ccrkJiZvZ2DnJmlmd5WTTTzKxF7q6aWX5lexm47BzkzCw7Bzkzy6umGQ/VwkHOzDJTY/VEOQc5M8vGz+TMLO/cXTWzfHOQM7M8c0vOzPLNQc7Mcis8rcvMcszvyZlZ/rW94nhFcZAzs8zcktvKnHPxaxz40VUsX9KV044YCcDJ31zI2M/XsWJp4Y/4Dz8ZxKP39GLkqDV87X/nAoVm/9U/34n/TOtdrqpvtW6ZOIC/X1uLBMN3W8c3L3mNpYu78eMzdmblsq6M2GsN5/7yNbptE1xxwXt44t89AXhrnVi+pBu3PPtUmX+DMvLLwJtIOg64Fdg9Ip4tZVnldOefa5n6h/58+9K5b0u/9XcDuOmKHd+W9spzPTjr6PfT2CBqd9zA5f94nofu6kVjQ5sfAbcOtGRBN/4yqT+/u+9Zum8b/Oi0nblvSl8euacnn/l/b3DYccu59DtDmHZdLZ8cX8fpP3h947VTJvVnzqxty1j7ylBNAw+l/lrXScAD6WduzXp4B1YtK+6/F2+t7bIxoHXr3lhNjzZypaFevLWuCw31hb+T2oEbeOKBnnz42OUAfOyzS3mwhRb2vX/py2HHLevs6lYcNRa3VYKSteQk7QB8CDgcuA24oFRlVapPnrKEI49fxgtPbsvEH7yH1SsKf9wj932Tb148lx2HbOCnZ7/XrbhO1n/QBo4/YzFf3H8PuvcI9vvISkbstYbtezdQ03VTniULu73tukXzurFo7jaM+tDqMtS6ggRVNfBQypbcOGBaRDwP1En6YEuZJE2QNF3S9A28VcLqdK6/Tu7HKWN258yPvZ+li7ox4YJNXZ7nHt+eCYfvxtljR3Di2Yvo1r1C/pO3lVi1vIYH7+jN5Idnc+3js1i3pobp9/Vq97r7/tKXD31iOTU1nVDJCtcJ313tMKUMcicB16f962mlyxoREyNidESM7kb3Elancy1f0o3GRhEh/n5NP0aOWvuOPHPn9GDtmzUMG7muDDXcej1+/w7sNHQ9ffo10LUbHHLMcp5+dHveXFFDQ30hz5IF3ei/04a3XffPKX3cVW0SRW4VoCRBTlItcARwpaRXgG8Dn5O01fTLanfc9A/k4LEreOW5HgAMHPoWXWoKf/s7Dl7P0F3XsWjeNmWp49Zqx8EbeOax7Vi3RkTAzAd6svOIdexzyGru/2sfAO66sZYxR63YeM1rL3Rn9Yqu7DF6TbmqXTGaXgbuiJacpB6SHpH0hKSnJf0gpQ+X9LCkOZL+LGmblN49Hc9J54e1V0apnskdD1wdEac1+2X+CXwY+FeJyiyb837zKnuPWU3v2nr+NH02V/98IHuPeZNdPrCWCFg0bxsuO3cIAHse8CYnnPUy9fWisVH88rtDWLnUb/J0pt32W8OHP7GCrxw1kpquwa57rmXsyXUc8NGV/PiMnfnjTwex655rOeqkpRuv+eeUvnxk3DK2nv9MtyGiIxfNfAs4IiJWS+oGPCDp78A5wCURcb2kK4BTgcvTz2URsaukE4GLgBPaKkBRggeIku4FLoqIac3SvkrhVZIzWruul2rjQB3Z4fWx0rnj9ZnlroJlVDNozoyIGL2l1/fsMyT2PfRrReW9/7Zziy5L0nYU3sY4A/gbsFNE1EsaA3w/Io6SdEfaf1BSV2AhMCDaCGQlaUJExOEtpF1WirLMrPN15KCCpBpgBrAr8GvgRWB5RKQnpMwDBqf9wcBcgBQAVwD9gCWt3d/9JDPLJoDiu6v9JU1vdjwxIia+7XYRDcAoSX0oTB7YrUPqmTjImVl2xbfklhTbXY2I5elR1xigj6SuqTU3BJifss0HhgLzUne1N1DX1n1LPePBzHKoA0dXB6QWHJK2BT4GPAPcS2EAE2A8MCXtT03HpPP3tPU8DtySM7Mt0IGjq4OAyem5XBfghoj4q6TZwPWSfgQ8DkxK+ScBV0uaAywFTmyvAAc5M8umA1/0jYgngX1bSH8JOKCF9HXAZ7OU4SBnZpkUXgaukOkMRXCQM7Psqmi6tYOcmWXmlpyZ5VcFTb4vhoOcmWXUoXNXS85Bzsyyc3fVzHLLH5c2s9xzS87Mcq16YpyDnJllp8bq6a86yJlZNoFfBjaz/BLhl4HNLOcc5Mws1xzkzCy3/EzOzPLOo6tmlmPh7qqZ5VjgIGdmOVc9vVUHOTPLzu/JmVm+OciZWW5FQEP19Fcd5MwsO7fkzCzXHOTMLLcCqKJvPHQpdwXMrNoERGNxWzskDZV0r6TZkp6W9LWUXivpLkkvpJ99U7okXSZpjqQnJe3XXhkOcmaWTVAYeChma1898M2I2AM4CPiKpD2A84C7I2IEcHc6BhgLjEjbBODy9gpwkDOz7CKK29q9TSyIiMfS/irgGWAwMA6YnLJNBo5L++OAq6LgIaCPpEFtleFncmaWXfEDD/0lTW92PDEiJraUUdIwYF/gYWBgRCxIpxYCA9P+YGBus8vmpbQFtMJBzswyyjRBf0lEjG4vk6QdgJuBr0fESkmbSosISVs80uEgZ2bZBNCBSy1J6kYhwF0TEbek5EWSBkXEgtQdXZzS5wNDm10+JKW1ys/kzCy7Dnomp0KTbRLwTERc3OzUVGB82h8PTGmW/qU0ynoQsKJZt7ZFbsmZWUYdOq3rEOCLwFOSZqa07wIXAjdIOhV4FfhcOnc7cAwwB1gDnNJeAQ5yZpZNQBTxDlxRt4p4AFArp49sIX8AX8lShoOcmWVXRTMeHOTMLDvPXTWz3Iro0NHVUnOQM7Ps3JIzs/wKoqGh3JUomoOcmWVTZUstOciZWXYd9ApJZ3CQM7NMAgi35MwstyLckjOzfKumgQdFBQ0FS3qDwjy1vOkPLCl3JSyTPP+d7RwRA7b0YknTKPz5FGNJRBy9pWV1hIoKcnklaXoxa2pZ5fDfWX54qSUzyzUHOTPLNQe5ztHimvZW0fx3lhN+JmdmueaWnJnlmoNcCUlqkDRT0hOSHpN0cLnrZO2TdJykkLRbueti756DXGmtjYhREbEPcD7wk3JXyIpyEvBA+mlVzkGu8/QClpW7Eta29P3PDwGnAieWuTrWATytq7S2TV8g6gEMAo4oc32sfeOAaRHxvKQ6SR+MiBnlrpRtObfkSqupu7obcDRwlZp/Gtwq0UnA9Wn/etxlrXp+haSEJK2OiB2aHS8C9oqIxW1cZmUiqRaYB7xBYUWhmvRz5/A/lKrlllwnSSN1NUBduetirToeuDoido6IYRExFHgZ+HCZ62Xvgp/Jlda2zb4KLmB8RFTPGjVbn5OAizZLuzml/6vzq2Mdwd1VM8s1d1fNLNcc5Mws1xzkzCzXHOTMLNcc5Mws1xzkqkyzlU1mSbpR0nbv4l5/lHR82r9S0h5t5D1sS1ZRkfSKpHd89KS19M3yrM5Y1vclfStrHS3fHOSqT9NUsT2B9cDpzU9K2qJ3HyPiyxExu40shwFeKsqqjoNcdbsf2DW1su6XNBWYLalG0v9KelTSk5JOA1DBryQ9J+kfwI5NN5J0n6TRaf/otP7dE5LuljSMQjD9RmpFfljSAEk3pzIelXRIurafpDslPS3pSgovQbdJ0l8kzUjXTNjs3CUp/W5JA1LaLpKmpWvu97pv1hbPeKhSqcU2FpiWkvYD9oyIl1OgWBER+0vqDvxb0p3AvsBIYA9gIDAb+P1m9x0A/A44NN2rNiKWSroCWB0RP0v5rgUuiYgHJL0XuAPYHbgAeCAifijpExSWLGrP/01lbAs8KunmiKgDtgemR8Q3JH0v3fssCt9fOD0iXpB0IPAbvMKLtcJBrvo0nyp2PzCJQjfykYh4OaV/HNi76Xkb0BsYARwKXJemlr0u6Z4W7n8Q8K+me0XE0lbq8VFgj2aLqvRKa7EdCnwmXfs3ScWsofdVSZ9O+0NTXeuARuDPKf1PwC2pjIOBG5uV3b2IMmwr5SBXfdZGxKjmCekf+5vNk4CzI+KOzfId04H16AIcFBHrWqhL0SQdRiFgjomINZLuo7D+Xksilbt88z8Ds9b4mVw+3QGcIakbgKT3S9qewiTzE9Izu0HA4S1c+xBwqKTh6dralL4K6Nks353A2U0HkpqCzr+Az6e0sUDfduraG1iWAtxuFFqSTbpQWBmEdM8HImIl8LKkz6YyJGmfdsqwrZiDXD5dSeF522OSZgG/pdBqvxV4IZ27Cnhw8wsj4g1gAoWu4RNs6i7eBny6aeAB+CowOg1szGbTKO8PKATJpyl0W19rp67TgK6SngEupBBkm7wJHJB+hyOAH6b0LwCnpvo9TWE1X7MWeRUSM8s1t+TMLNcc5Mws1xzkzCzXHOTMLNcc5Mws1xzkzCzXHOTMLNcc5Mws1/4/OB/AS7KaWj4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels=[\"B\", \"A\"]\n",
        "index=4\n",
        "#pred = new_model.predict(x_test1)\n",
        "real=y_test1\n",
        "prediction=0\n",
        "if (pred[index]>=0.5): prediction=1\n",
        "act=labels[int(real[index])]\n",
        "pr=labels[prediction]\n",
        "print(\"Patient:\", disease)\n",
        "print (\"Actual = \" + act + \", Predection = \" + pr)\n",
        "plt.plot(np.arange(1024)/512 ,x_test1[index,:])\n",
        "plt.xlabel(\"time duration in sec\")\n",
        "plt.ylabel(\"Amplitude of signal\")\n",
        "plt.ylim([-100, 100])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "e78e2481-36fd-4d4c-b583-82ac03bd9326",
        "id": "dMVyccThF2zs"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient: Narcolepsy\n",
            "Actual = A, Predection = A\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_keras = conv_model.predict(x_test1).ravel()\n",
        "fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test1, y_pred_keras)\n",
        "auc_keras = auc(fpr_keras, tpr_keras)\n",
        "plt.title(disease)\n",
        "plt.plot(fpr_keras, tpr_keras, label='ROC curve')\n",
        "plt.fill_between(fpr_keras, tpr_keras, 0, color='blue', alpha=.1, label='AUC = {:.3f}'.format(auc_keras))\n",
        "plt.axis([0, 1, 0, 1])\n",
        "plt.grid()\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.xlabel('False positive rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "66107c0d-2e1a-46a5-92c9-d2d29cf6e2cf",
        "id": "qxjQZnAGF2zw"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'False positive rate')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "NFLE"
      ],
      "metadata": {
        "id": "0nCmAv11GUU2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load model, history and dataset\n",
        "conv_model = tf.keras.models.load_model('/content/drive/MyDrive/GUI/GUI/nfle_cap.h5')\n",
        "history=np.load('/content/drive/MyDrive/GUI/Data/nfle_cap_hist.npy',allow_pickle='TRUE').item()\n",
        "x_test1 = np.loadtxt('/content/drive/MyDrive/GUI/Data/nfle_cap_xtest.csv', delimiter=',')\n",
        "y_test1 = np.loadtxt('/content/drive/MyDrive/GUI/Data/nfle_cap_ytest.csv', delimiter=',')\n",
        "disease='NFLE'   # disorder of loded dataset"
      ],
      "metadata": {
        "id": "UgxSm53GGWTi"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "    ## GRAPHS FROM RESTORE MODEL\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "print('Patient:',disease)\n",
        "prob = conv_model.predict(x_test1,verbose = 2)\n",
        "pred = (prob > 0.5).astype(\"int32\")\n",
        "\n",
        "# reduce to 1d array\n",
        "yhat_probs = prob[:, 0]\n",
        "yhat_classes = pred[:, 0]\n",
        "\n",
        "cm=metrics.confusion_matrix(y_test1,pred)\n",
        "# accuracy: (tp + tn) / (p + n)\n",
        "accuracy = metrics.accuracy_score(y_test1,pred)\n",
        "print('Accuracy: %f' % accuracy)\n",
        "# specificity = tn(tn+fp)\n",
        "specificity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "print('Specificity: %f' % specificity)\n",
        "# precision tp / (tp + fp)\n",
        "precision = metrics.precision_score(y_test1,pred)\n",
        "print('Precision: %f' % precision)\n",
        "# recall: tp / (tp + fn)\n",
        "recall = metrics.recall_score(y_test1, pred)\n",
        "print('Sensitivity: %f' % recall)\n",
        "# f1: 2 tp / (2 tp + fp + fn)\n",
        "f1 = metrics.f1_score(y_test1, pred)\n",
        "print('F1 score: %f' % f1)\n",
        "# kappa\n",
        "kappa = metrics.cohen_kappa_score(y_test1, pred)\n",
        "print('Cohens kappa: %f' % kappa)\n",
        "\n",
        "labels=[\"B\", \"A\"]\n",
        "cm=metrics.confusion_matrix(y_test1,pred)\n",
        "disp = metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "outputId": "449ec16c-68fa-407d-d537-f4cc369bf077",
        "id": "9Lij97H2GWTm"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient: NFLE\n",
            "458/458 - 3s - 3s/epoch - 8ms/step\n",
            "Accuracy: 0.766862\n",
            "Specificity: 0.797615\n",
            "Precision: 0.740867\n",
            "Sensitivity: 0.812405\n",
            "F1 score: 0.774988\n",
            "Cohens kappa: 0.534178\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fe092cfa950>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels=[\"B\", \"A\"]\n",
        "index=4\n",
        "#pred = new_model.predict(x_test1)\n",
        "real=y_test1\n",
        "prediction=0\n",
        "if (pred[index]>=0.5): prediction=1\n",
        "act=labels[int(real[index])]\n",
        "pr=labels[prediction]\n",
        "print(\"Patient:\", disease)\n",
        "print (\"Actual = \" + act + \", Predection = \" + pr)\n",
        "plt.plot(np.arange(1024)/512 ,x_test1[index,:])\n",
        "plt.xlabel(\"time duration in sec\")\n",
        "plt.ylabel(\"Amplitude of signal\")\n",
        "plt.ylim([-100, 100])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "9f5f59d3-cf1c-4289-af7e-ef11ee9f9356",
        "id": "w8c1SnkvGWTq"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient: NFLE\n",
            "Actual = B, Predection = B\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_keras = conv_model.predict(x_test1).ravel()\n",
        "fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test1, y_pred_keras)\n",
        "auc_keras = auc(fpr_keras, tpr_keras)\n",
        "plt.title(disease)\n",
        "plt.plot(fpr_keras, tpr_keras, label='ROC curve')\n",
        "plt.fill_between(fpr_keras, tpr_keras, 0, color='blue', alpha=.1, label='AUC = {:.3f}'.format(auc_keras))\n",
        "plt.axis([0, 1, 0, 1])\n",
        "plt.grid()\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.xlabel('False positive rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "37095672-03f9-49cb-e348-a2eb3f60dbc9",
        "id": "B-UEm6ngGWTt"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'False positive rate')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "RBD"
      ],
      "metadata": {
        "id": "HfOfgYOtGqnK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load model, history and dataset\n",
        "conv_model = tf.keras.models.load_model('/content/drive/MyDrive/GUI/GUI/rbd_cap.h5')\n",
        "history=np.load('/content/drive/MyDrive/GUI/Data/rbd_cap_hist.npy',allow_pickle='TRUE').item()\n",
        "x_test1 = np.loadtxt('/content/drive/MyDrive/GUI/Data/rbd_cap_xtest.csv', delimiter=',')\n",
        "y_test1 = np.loadtxt('/content/drive/MyDrive/GUI/Data/rbd_cap_ytest.csv', delimiter=',')\n",
        "disease='RBD'   # disorder of loded dataset"
      ],
      "metadata": {
        "id": "staAd3vSGr8U"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "    ## GRAPHS FROM RESTORE MODEL\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "print('Patient:',disease)\n",
        "prob = conv_model.predict(x_test1,verbose = 2)\n",
        "pred = (prob > 0.5).astype(\"int32\")\n",
        "\n",
        "# reduce to 1d array\n",
        "yhat_probs = prob[:, 0]\n",
        "yhat_classes = pred[:, 0]\n",
        "\n",
        "cm=metrics.confusion_matrix(y_test1,pred)\n",
        "# accuracy: (tp + tn) / (p + n)\n",
        "accuracy = metrics.accuracy_score(y_test1,pred)\n",
        "print('Accuracy: %f' % accuracy)\n",
        "# specificity = tn(tn+fp)\n",
        "specificity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "print('Specificity: %f' % specificity)\n",
        "# precision tp / (tp + fp)\n",
        "precision = metrics.precision_score(y_test1,pred)\n",
        "print('Precision: %f' % precision)\n",
        "# recall: tp / (tp + fn)\n",
        "recall = metrics.recall_score(y_test1, pred)\n",
        "print('Sensitivity: %f' % recall)\n",
        "# f1: 2 tp / (2 tp + fp + fn)\n",
        "f1 = metrics.f1_score(y_test1, pred)\n",
        "print('F1 score: %f' % f1)\n",
        "# kappa\n",
        "kappa = metrics.cohen_kappa_score(y_test1, pred)\n",
        "print('Cohens kappa: %f' % kappa)\n",
        "\n",
        "labels=[\"B\", \"A\"]\n",
        "cm=metrics.confusion_matrix(y_test1,pred)\n",
        "disp = metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "outputId": "9a57db09-0df5-4db6-9741-59657d352f91",
        "id": "c86skMZTGr8Y"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient: RBD\n",
            "276/276 - 2s - 2s/epoch - 8ms/step\n",
            "Accuracy: 0.790172\n",
            "Specificity: 0.751910\n",
            "Precision: 0.839760\n",
            "Sensitivity: 0.723132\n",
            "F1 score: 0.777095\n",
            "Cohens kappa: 0.580970\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fe080e60090>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels=[\"B\", \"A\"]\n",
        "index=4\n",
        "#pred = new_model.predict(x_test1)\n",
        "real=y_test1\n",
        "prediction=0\n",
        "if (pred[index]>=0.5): prediction=1\n",
        "act=labels[int(real[index])]\n",
        "pr=labels[prediction]\n",
        "print(\"Patient:\", disease)\n",
        "print (\"Actual = \" + act + \", Predection = \" + pr)\n",
        "plt.plot(np.arange(1024)/512 ,x_test1[index,:])\n",
        "plt.xlabel(\"time duration in sec\")\n",
        "plt.ylabel(\"Amplitude of signal\")\n",
        "plt.ylim([-100, 100])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "5a05ebd2-95ab-4024-e25a-e70eeb38b68b",
        "id": "BwKTNzj1Gr8b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient: RBD\n",
            "Actual = B, Predection = B\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_keras = conv_model.predict(x_test1).ravel()\n",
        "fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test1, y_pred_keras)\n",
        "auc_keras = auc(fpr_keras, tpr_keras)\n",
        "plt.title(disease)\n",
        "plt.plot(fpr_keras, tpr_keras, label='ROC curve')\n",
        "plt.fill_between(fpr_keras, tpr_keras, 0, color='blue', alpha=.1, label='AUC = {:.3f}'.format(auc_keras))\n",
        "plt.axis([0, 1, 0, 1])\n",
        "plt.grid()\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.xlabel('False positive rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "480c5d1c-1271-4614-b22f-9964772ba772",
        "id": "endFqtB2Gr8c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'False positive rate')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PLM"
      ],
      "metadata": {
        "id": "2v78mClsG2dr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load model, history and dataset\n",
        "conv_model = tf.keras.models.load_model('/content/drive/MyDrive/GUI/GUI/plm_cap.h5')\n",
        "history=np.load('/content/drive/MyDrive/GUI/Data/plm_cap_hist.npy',allow_pickle='TRUE').item()\n",
        "x_test1 = np.loadtxt('/content/drive/MyDrive/GUI/Data/plm_cap_xtest.csv', delimiter=',')\n",
        "y_test1 = np.loadtxt('/content/drive/MyDrive/GUI/Data/plm_cap_ytest.csv', delimiter=',')\n",
        "disease='PLM'   # disorder of loded dataset"
      ],
      "metadata": {
        "id": "5p47VkuWG7Wn"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "    ## GRAPHS FROM RESTORE MODEL\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "print('Patient:',disease)\n",
        "prob = conv_model.predict(x_test1,verbose = 2)\n",
        "pred = (prob > 0.5).astype(\"int32\")\n",
        "\n",
        "# reduce to 1d array\n",
        "yhat_probs = prob[:, 0]\n",
        "yhat_classes = pred[:, 0]\n",
        "\n",
        "cm=metrics.confusion_matrix(y_test1,pred)\n",
        "# accuracy: (tp + tn) / (p + n)\n",
        "accuracy = metrics.accuracy_score(y_test1,pred)\n",
        "print('Accuracy: %f' % accuracy)\n",
        "# specificity = tn(tn+fp)\n",
        "specificity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "print('Specificity: %f' % specificity)\n",
        "# precision tp / (tp + fp)\n",
        "precision = metrics.precision_score(y_test1,pred)\n",
        "print('Precision: %f' % precision)\n",
        "# recall: tp / (tp + fn)\n",
        "recall = metrics.recall_score(y_test1, pred)\n",
        "print('Sensitivity: %f' % recall)\n",
        "# f1: 2 tp / (2 tp + fp + fn)\n",
        "f1 = metrics.f1_score(y_test1, pred)\n",
        "print('F1 score: %f' % f1)\n",
        "# kappa\n",
        "kappa = metrics.cohen_kappa_score(y_test1, pred)\n",
        "print('Cohens kappa: %f' % kappa)\n",
        "\n",
        "labels=[\"B\", \"A\"]\n",
        "cm=metrics.confusion_matrix(y_test1,pred)\n",
        "disp = metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "outputId": "663837ec-7f0b-46d6-aa24-f648597de299",
        "id": "jFYRH1suG7Wu"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient: PLM\n",
            "113/113 - 1s - 985ms/epoch - 9ms/step\n",
            "Accuracy: 0.785057\n",
            "Specificity: 0.746305\n",
            "Precision: 0.835581\n",
            "Sensitivity: 0.716410\n",
            "F1 score: 0.771420\n",
            "Cohens kappa: 0.570824\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fe072a5df50>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels=[\"B\", \"A\"]\n",
        "index=4\n",
        "#pred = new_model.predict(x_test1)\n",
        "real=y_test1\n",
        "prediction=0\n",
        "if (pred[index]>=0.5): prediction=1\n",
        "act=labels[int(real[index])]\n",
        "pr=labels[prediction]\n",
        "print(\"Patient:\", disease)\n",
        "print (\"Actual = \" + act + \", Predection = \" + pr)\n",
        "plt.plot(np.arange(1024)/512 ,x_test1[index,:])\n",
        "plt.xlabel(\"time duration in sec\")\n",
        "plt.ylabel(\"Amplitude of signal\")\n",
        "plt.ylim([-100, 100])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "ad9b9777-d479-4571-a85d-b6aa612a78df",
        "id": "90VS2rp_G7W2"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient: PLM\n",
            "Actual = B, Predection = B\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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iCcATwH3GmAbg10AusBwoBX48QLwPGWNWG2NWZ2Rk+Cu8kPC1p/bT2d3D43efx9TkvgsSLZo6kalJsbxyqHyAZ4eXY2UNxEdHjHrhpeGYkRZPWUObo+JwxXWtvH2ikltXW+uWv3NibH9hUYFxotxda833E1pHasCkISKX2T9vAq7B+oOdC1xtb/MbEYnCShh/McY8CWCMKTfGdBtjerA64Nf6M4ZQl1fRxHt51dx9SS6zJ539LUREuHzRFN45UUnzGFhg6Fh5I3N9tPCSU9Pt0SqFNS1DHvvigVKMgXsum82sjHjey6vyd3hqHDhW1sSkxBiS46KDHUqvwa40LrZ/Xufldq2/AhLrr8LvgSPGmJ94bM/0OOxG4KC/YggHD76ZR1SEcIv9zdabq5dk0t7VwyuHy+juMfz8tRN885mDYZdEjDEcK2tkfgCbpoDeTsXT1UMnjW351cxIi2Naahzn56az/VQNnd1aMVeNzomKwI0YdGrA0VPGmG/aP+8IXDgAnA98AjggIu4exX8DbhOR5VhzRgqAzwQ4rpDxf9tO8+TuYj5/aS7pCTEDHrd6egrTUifw41eO89y+Ul63+zdK69v4n0+sCui39tGobGyntqUz4L8809OsSYSnh7jSMMaw/VQNVy+xvtesy03jf7edZn9RHaump/o9TjU29fRYI6c2rh34i2EwOJncdy/wR6y5Gb8FVgJfNca84o+AjDHvAt7+mm3yx/uFm7bObn708jEumJ3Olz40+Kq7Lpfw8XOm8/0Xj1LT3MG/X7OArh7D9188yu4zdaya7nUsQcjZX2TVy1yclRTQ902JiyI+OmLI5qnq5g4a2rp6k9q5s9IQgffyqjVpqBErqm2ltbM74FfYQ3Eyeuof7Y7oy4E0rKuA7/s1KjWgzYfLqW/t5DMXz3I02/uuC2fx8n0XsfvrH+LOC2dx29ocIl3CK4fDZ92I/cX1uMTq3A8kEWFaahxFtYMnDXfz1Yx0qzkrJT6ahZkT2XJS+zXUyLlHTg013DbQnCQN91+mq4E/G2MO4f1KQPlIXkXTgCN2Ht9VxNSkWNblpjt6LZdLmDclkdioCACSJkRxXm4aLx8sC5s1sXcW1DB3ciJx0b5beMmp7JQJFNa0DnrM6epmAHJSP6iJdcHsdHadruWMg/4Qpbxxz02aMyl0Rk6Bs6SxS0RewUoaL9uztLWHz0+25FWx4SdvcefDO8/aV9/aybsnKrlxZdaoakpduXgKBdUtvetth7LCmha25VezYcHkoLx/dkochbUtgybY09UtiMC01A+GA9++bgaxkRH821MHwiY5q9ByvLyRrOQJJIZY7TMnSeNTwFeBNcaYFqwS6YHuHB83/rL9DADv5lWxv6jvbOQdp2roMXDhnNHNP7l84RRE4MWDodtE9caxCu59dA+f/vNOIlzCx88NTPmQ/qalxtHS0U1N88DFD05XNzM1aQIxkRG926YmT+BfrpzHu3lVvHNCm6nU8Fm11kLrKgOcTe7rMcbsNsbU2Y+rjTH7/R/a+NPW2c3rRyq4YflU4qIj+L9tfZceff9UNdGRLpZPSx7V+2QkxrB2RiovHQzNgoY7Cmr4xz/t4Jm9JRwta+SeS+eQmRS4SX2eptmTCYtqB26iOl3T0junw9Ota6aRnhDN/27TJWTV8HR195Bf2czcEOsEB2dXGipAtpysorWzm5tXZvPhFVk8s7eEisYPKs9vy69hxbTk3v6J0bhq8RSOlzeRV9E05LHHyxvZdbpm1O/p1P+8dZJJiTFsv389z3/hAr64fnbA3rs/90ppZwYZQXW62nvSiImM4MYVWbx5rIL6Vl0FUDlXUN1MR3cPc71M3A22wWaEzwxkIApeP1pBXHQE58xK5c4LZmKArz99EGMMjW2dHCqp55xZ3hdiGa6rl2TiEnhmb/GgxxXXtXLdL97l5l9v5c1j/q9j1drRzTsnqrhqcSaTEmNZnJUU1Pkk7qRROMAIqoa2TmqaO3rndPR39ZJMOrsNT+4u8luMauzZc8Zqml6aHdhh5k4MdqXxdwAReS1AsYxrxhjeOFrJBbPTiYmMYFZGAv98+VxePlTOM3tL2Hy4nB5jjcrxhUkTYzl/djpP7SketKN20/5S2ruscQ9/3ur/Zpb38qpo7+ph/YLQKEeeEBNJanz0gHM13KOjpg9Qknr5tGTW5abxk1eOU96g65UpZ3afqSMxNpLcjPDq03CJyL8Bc0XkS/1vgQpwvCiqbaW4rpUL5nyQFD51wSxW5iTz1Sf38+9PH2Te5ERW+3BC3g3LsyiqbR20/PfeojqyUybw/87NYfupGnp6/DsS6Km9xSRNiOKcmb65ovKFaalxAw67dc/RGOhKQ0T4rxuX0NHdw78/fZAuLS2ihmCMYevJKlbmpOAKwZU3B0saG4FurFnjiV5uY97hkgZ+9uoJGtr83x690+4zWO0xgzjCJTz48VUsyJxIbkYCv/2H1T79EF2+aDLRES6e2zdwh/iBonqWZiexaGoSTe1dg3YIj1ZlYzsvHyzjo6uyiY4Mne62nNS4Afs0CtxzNAZYihNgRno8X1w/h82Hy1n67Vf4/F920xiAz5QKT3sK6yiobuGaJZlDHxwEg9WeOgb8QET2G2NeDGBMIaGqqZ3b/7idysZ2aprb+fYNi/36fttP1ZIYG3nWehFTkmJ56nPn++U9J8ZGcfG8DF44UMK/X7PgrIRU39LJmZoWblubw8JMazb24dL6Qf9AjsZjOwvp6jHcFqDV+ZyaljKBTQdK6eruOWuRmzPVLaQnxJAQM/jEw89enMuMtHg2HSjlhQOlrJ6Rwh3na7ehOttTu4uJiXRx1ZIpwQ7FKydf57aIyE/cCxuJyI9FJPR6Z3youK6VW/5nK/WtneRmxPPCAf/Pnt5RUMPq6SmjmrQ3Etctm0p5Qzs7Cs4eHXWg2Kr5tCQriXlTEolwSe82X+vpMfxtRyHnzkoNuXbcnNQ4unsMpfVn90kUVDd7HTnVn8slXLM0k199fCW5GfG8eUzX21Bn6+jq4bn9JXxo4eSQm9Tn5iRp/AGrWOEt9q0Bq4DhmJUSF0VW8gT+/I9rueuiWVQ1tXPCwdDUkapp7iCvook1MwNf3G79/EnERrl4fv/ZTVT7i62+jiVZScRGRTB3ciIHihv8EsfW/Oreq5pQ4y6R7q0z/MwAczQGsy43nR0FWjpdne3NYxXUtXRy0xBrggeTk6SRa4z5pjEm3759G5jl78CCKS46kv/91DmcOyutt8bTFj8uqrPT/pa/dkbgk0Z8TCTrF0zubX7xtK+wjulpcSTFWd94lmRN5EBRnV+uuh7ZfobkuCiuWBR6l+QDzdVo6+ymtL6N6aneO8EHcl5uGi0d3b3Ve5Vye2J3EekJ0aOu+uBPTpJGq4hc4H4gIucD/usNDTHTUuPISY3jvZPVfnuP/UX1RLgk4KW/3a5bOpXq5g625n/wbzTGsOt0bZ/y6Uuyk6lt6aS4zrf//XUtHbxyqJwbV2T5ZOKir2UmxRLpkrOShvvKw13d1qlz7bk22/L995lS4ae6qZ3XjlRw44osoiJCZyBIf04iuxv4lYgUiEgB8EvG2QJI63LT2JZfTbefhpseLm1gzqSEoP3BvGReBgkxkTy3r6R3W0F1C1VNHX2SxlI7qR3w8Tfk9/Kq6eju4dqlU336ur4SGeEiO2XCWSv4FdiPcwaYozGQ1Pho5k9JZKsfv4io8PP03hK6egwfHWQ1zlAwZK1pY8w+YJmITLQf+6dRO4Stm53OozsKOVhcz7JR1n3y5lBJPec7LHXuD7FREVy3LJPHdxZRWt/G6eqW3iGhF3lcJs+bkkik3Rl+lQ+HA245WUVCTCTLQnD2q9uM9HhOVTX32Xaqyurnmpk+vOYpsK42Ht1xhvau7j6FDtX4ZIzhsR2FLMtOCrnlXftzfA1kjGkYjwkD4Dy7OeE9PyyqU9XUTnlDOwsDvMBQf/9yxXymJk9g1+laFk2dSHpCDPeun9Pbng9Wcpk3JdHnI6i25lezZkbKWcNZQ8mMtHgKqpv79OecqmomNT6a5LjoYb/eeblptHX2sK9Q+zWUNQP8WHkjG0NwIEh/gV/VZpRE5ErgZ0AE8DtjjN9XEcxIjGFZdhLP7ClBELbmV/OVK+b5pA/iSKmVhxdkBjdppMZH8/qXL6arxwzaTLY0O4lN9hBkX9SEKqlrJb+ymdvWhPYvy8z0eFo6uilraOutuHuysplZI7jKAFiRY12xHiqpZ20QRs2p0PLo9jPERUdw3bLQbKL1FLpf7bwQkQjgV8BVwELgNhFZGIj33rg2h2PljfzgpaO8e6KSOx/e6ZOZ4odLrKSxMMhJA6y2+6H6VZZlJ1Pf2tnbnj9abx+35itcPC90R4sAvc2Su07X9m47VdXMrIyRJY2MhBgmxkZystJ/Q7lVeGho6+S5/SXcsHzqkJNEQ8GQSUNE4kTk6yLyW/vxHBG51v+hebUWyLOH/nYAjwI3BOKNb16ZzcY107hvwxye/Nz5lDW08dBb+aN+3SOlDWQmxZISP/wmjmBYbn9D3nOmdogjnXnzWCWZSbEht6Rlf4umTiQuOoLtp6zh0Y1tnVQ2tjMzfWRxiwizJyVwsqJ56IPVmPbM3hLaOnvYGOJX225OrjT+CLQD59mPi4Hv+i2iwWUBhR6Pi+xtvUTkLvfs9cpK3826jY508f2bl3Lfhrksn5bMNUsy+dOWglGvk3C4tCHoTVPDMWdSIvHREYMWOXSqp8fw3skqLp6bEdTy505ERbhYMyOVd+1V+E5WWn/sR3qlAZCbkUCeXmmEpeqm9t6m5dEwxvDI+2dYmDkxJMuge+N0ct8PgU4Ae8nXkP0NN8Y8ZIxZbYxZnZHhvyaPz16SS1N711mr6w1HW2c3JyubQ6JpyqkIl7A0O7m33v9o5FU20djWxZogTGociUvnZZBf1cypqubepXgXjWIAw8yMeCob22lq7/JViCpA/t/vt3PVz945a0nm4dqaX83h0gZuOycn5L84uTlJGh0iMgEwACKSi3XlEQzFgOcg5mx7W8Atzkri4rkZPPhGHt/bdGRE3zryKpro7jFhdaUBsHpGCodK6kddqdXdxOVu8gp1l8yz1vh4N6+KvWfqyEiMISt55MvQzrTLqRdUaRNVOCmsaen9fX9k+5kRv44xhh++dIypSbF8dFW2r8LzOydJ45vAS8A0EfkL8BrwFb9GNbAdwBwRmSki0Vjl258NUix8+/pFLM1O5g/vneLmX2+hZJgzpY+VNQIwb0pot+f3d87MNHoM7Dw9un6N3afrSJoQ1fvHM9RNT4tjysRYtp6sYlt+NStzkkf17XCm3bTVf/6HCm1b7KH3cycn8OqRihGvMbP1ZDV7C+v4wvo5IVkJYSBDJg1jzGbgJuCTwCPAamPMm/4Na8BYuoB7gJeBI8BjxphDwYgFrAlfj9x1Li/ddxEtHd1sOjDwuhTeHK9oJCpCBlzAJ1StnJ5MpEt4P39064bvOmOVKQnFhWa8ERHW5aax6UAZJfVtXL9sdEXl3DWrNGmElyOljSTERPLZS3KpbGxnu5cK0U68dKiMCVHWOvLhZLA1wle6b8B0oBQoAXLsbUFhjNlkjJlrjMk1xvxnsOLwlJuRwIy0OLYN84/oifImZqUnhHSdGW/ioiNZnJU0qhFUdS1WZd9VPlyJMBA+f9ls1s5M5XOX5HL1KNc7mBAdQWZSrDZPhZn8qmZmpMdx2fzJpCdE8+mHd/LM3sGXTe7PGMPrRys4f3ZaWF1lwOCT+35s/4wFVgP7sDrAlwI7+WA0lcIqC/HCgVK6e4zjNTGOlTX2TvIKN/OnJLL5cPmIn7/bTjgrc8IraeRmJPDYZ3z30Z+RFs+pak0a4aSgqpll05JJmhDF058/n8//dQ/3PrqX145U8LONyx01WeZVNFFU28pnL8kNQMS+NeBXXGPMpcaYS7GuMFbaI5JWASsIUudzKDsvN43Gti4OlTgrC9Hc3kVxXSvzQrzOzEBmT0qgurmD6qaRjYl49UgFE6IiWO6HWl7hxFtNKxW62ru6Kapt6a03lp0Sx5OfXcdnLprFs/tK2ONwKPrrRysAuGz+JL/F6i9O2kXmGWMOuB8YYw4CCy/kkVwAACAASURBVPwXUnhy16dyWrnUvajTnDBNGu64R7I4VXeP4eWDZWxYOJkJ0eF1ae5rs9LjqWvppLa5I9ihKAcKa1roMfQpHxPhEu65bDaxUS6e2FXk6HVeO1rBgsyJvSVpwomTpLFfRH4nIpfYt98C+/0dWLiZNDGW2ZMS2OIwaRwvt0ZOzZ0cXiOn3NxxjyRp7Cioobq5gytDcMGlQJs9aeTnUQVevj2pc0a/mmOJsVFcNn8Smw+XDzmaqr6lk12na7lsfmiXzhmIk6RxB3AIuNe+Hba3qX7Om5XmeBnPE+WNREe6wm7klNuUibEkxESSZye/4XhhfymxUS4uCfF6U4Ewb4p1xXa0bFwWkA477qZEb8PENyyYTEVjOweHaKJ++0Ql3T0mLJumwNmQ2zZjzAPGmBvt2wPGmLZABBdu1vUu4zl0u+bx8iZyMxIcd5qHGnftpOPlw/uG3NXdw6YDpaxfMJn4MCjO5m+ZSbFMjI3kaNnwk68KvILqZtLio3uXQPZ06bxJuARe7TdAZFt+NSc8vly9frSClLgolk8Lr0Egbk4KFp4Skfz+t0AEF27O6V3Gc+ihtyfKG5kXpk1TbnMmJXCiYug/dqeqmjltjxB6ak8x1c0dXB8GJaADQUSYnzmRoz6oY6T8L7+yecBFt1Lio1k1PYVXj1T0btt6spqND23jQw+8TVVTO83tXbx8qIwrFk0J2y+MTr7qrfa4Hwt8FAiPYkEBlhofzexJCewcYrJPfWsnJfVtYdsJ7jZ3ciKP7yqitrljwCq9R8sauO4X79LZbUiJi6K2pZNl2Ul8aMHkAEcbuhZMSeSJ3cX09Jiwmeg4Xp2qauaiuQM3q25YMJnvvXiUkrpWWjq6+Mc/7ejd998vH2Pl9BRaOrr56OrwKRvSn5PlXvv37P5URHYB3/BPSOFtzYwUnt9fOugfAPe3ynAqVOjNbPtKKa+yiTXx3r9HPLajiM5uwy2rs+kxVgf6TSuz9Y+jh3lTJtLUfpriutY+KyWq0FLb3EFFY/ugZfzX20njqT3FvHyojAnREbz5L5fw27fz+f17p3h0RyGzMuLDbn6SpyGTRr/Z3y6sKw9tjB7AmhmpPLK9kOMVjcyf4j0phMpqfaPl/uU5Ud7ktVKtMYaXD5Wxfv4kfviRZYEOL2zMz7SuOA+XNmjSCGGH7AXTFk0duIT57EkJXDQ3gx+9fAyAX35sBZMnxvLly+fxzokqjpU3cvt5M8Kmoq03Tv74/9jjfhdwCrjFP+GEP/cfzx0FtQMmjaNljaTERTF5YkwgQ/O5qUkTiIuOGLBf41BJA8V1rdy7fk6AIwsvC6ZMxCVwqLieK3QYcshyT9wdqhz+D25ewtefPsjli6Zw7VKr725CdASP3nUu75+q5vKF4f1/7CRpfMoY06fjW0Rm+imesJedMoEpE2PZklfFJ86d7vWYI/bCS+H8bQPA5bJGUJ0YYATVy4fKcAlsWKj9F4OZEB3B3MmJHCh2Vk1ABcfh0gamOlhlMzNpAr+7fc1Z21Pio7lycaa/wgsYJ/M0/u5wm8IaDXPp/AzeOVFFR9cH8zW6ewzGGBraOjlU0tC75nS4W5g5kYMl9WcVazPGsOlAKWtnppIaJkvZBtOSrCT2FNY5muOjBldS18rv3snn/qcOsM8HK0y6HQmzVTb9ZcArDRGZDywCkkTkJo9dE7FGUakBXL5wCo9sL+SVw2UUVDXzbl4VB4sbyE6ZwFWLM+nqMVwyyAiMcLIiJ5lHdxSSX9VMbsYHHYTbT9VwsrKZz1wcfgXZguHyRVN4fFcR75yo5LL5emU2UpWN7dz86y2U1ltTyZ7fX8qL917I1FEslgVWzamTlc1h37TkC4M1T80DrgWSges8tjcCn/ZnUOHuorkZzEyP556/7gFgYmwk86ckcqamhQdePc6kxBhWhllJ8IG4R4HsPl3bJ2k8s6+EuOgIrluq8zGcuHhuBhmJMfzo5eOkxseM+0KOI/W9TUeoburgqc+tIzkumiseeJtfvpHHf924ZFSve6LcWmXTPWhhPBusyu0zxpg7gGuNMXd43L5ojNkSwBjDToRLuP/qBUS6hKsWT2HvNy7n759dx5OfW8cd58/gwY+vDLs1NAaSm5HAxNhIdnusGd7TY9h8uJxL500a9wUJnYqOdPHN6xZypLSBGx98j12nR7fA1XjU1tnNiwfL+OjqbFbkpDAzPZ6bV2XxxK4iGka5NPFYGfHoC4M1T33FGPND4GMiclv//caYL/o1sjC3YeFkDn77CmIiXb0d3tkpcXzzukVBjsy3XC5heU5KnwWZdhTUUNnYzhWL9VJ+OK5dOpUlWUlc8/N3+f27p1g1XefQDse2/GpaO7vZ4DFx9NY1OTyyvZBN+0vZuDZnxK99tKyR2CgXM8K0VpwvDfZ194j9cyewy8tNDSE2KiLsR0g5sTInmWPljTTa3+ae3VfChKgINiwIz4JswTQ9LZ7b101n04Gy3rWolTNvHqskNsrFeblpvduWZSeRmxHPE7udlSwfyOGSBuZNTgzb0h++NFjz1HP2z4e93fwRjIj8SESOish+EXlKRJLt7TNEpFVE9tq33/jj/dXIrMxJwRjYV1hPZ3cPL9prZcRF6xzQkfjCZXPISp7AA5uP9257P7+aPAd1vsaKocqL9+dePnVdbnqf5VNFhJtWZrOjoLa3/tlwdfcYDhTXsyR74El948lga4Q/JyLPDnTzUzybgcXGmKXAceBrHvtOGmOW27e7/fT+agSW5yQjYi3h+tqRCmqaO7huafiPRw+W2KgIPn3hTHYU1LLrdA3vnKjk1oe2ceOvtox4pcRw8vCWAuZ/4yV+9uoJx885WdnMmZoWLvVSbvymlVmIwNN7SkYUT35lE03tXWFbldbXBvsq+N8Bi8JmjHnF4+E24COBjkEN38TYKOZMSuC5fdYvZU5qXNiuFRAqblkzjZ+9doL/fOEI5Q1Womhs72LTwbIBJ436WneP4Y2jFURECPsL67llTfaAK821dHTx+3dO4XIJd1+cO+JmnIqGNr734hE6unr49Vt53HnhTEcl9N+wl0+91MsaLZlJE1gzI5Xn9pfwxfWzh91kvDXfKr+3MkdHtMHgzVNvuW/AVqAWqAG22tv87R+BFz0ezxSRPSLylohcONCTROQuEdkpIjsrKyv9H6UC4JJ5kzhR0cTpmha+++HFRI6R0WHBEhcdyacvmsXuM3VUNrbz97vPY8rEWHacCtyoqj9vLeDOP+/kjj/u4IFXj3P3/+2me4Bmox++dIwfbz7Oj14+xl/ePz3i93zhQCltnT38xw2LaOvs4b08Z/06rx0tZ+7kBLJTvNfuun7ZVPIqmnrrRw3H03uKmT8lkVkZ4b2Uga84WU/jGuAk8HPgl0CeiFw10jcUkVdF5KCX2w0ex9yPVefqL/amUiDHGLMC+BLwVxHxOvbNGPOQMWa1MWZ1RsbYmEAXDr5yxTye+Ox5vPOVSwctHa2c++zFufztrnN5/Z8vZvWMVJZkJ3EwQKVGyurb+MXreUxPi+PBj6/k/qsXsK+wjreOV5x1bFd3D8/uK+HapZmcOyuVn756grqWka15/soh64//rWtySIyJ5MWDZUM+p761kx0FtawfpNz+dUunkhgTyb8+sZ8dBTU0t3fR1N414Nrs3T2GY2WN7D5Ty+4zddywPGtE/56xyGnBwkuNMXkAIpILvEDfqwDHjDEbBtsvIp/EmlS43ti1KYwx7UC7fX+XiJwE5mKN7FIhIDLCpUNEfUxEehf2AqvUyObD5TS2dZIYe/bKcUN5YlcRnd093LQym+jIgb8vHiiq58MPvkd3j+Hb1y/i6iWZdHb38Nt38vnr+4VnzVjfll9DTXMH1yzJZEZ6PNf+4l0efPMk/3b1gmHFV9vcwfaCGj57cS7RkS4+sjqbP289zcfPyWG1lyrKbm8dt5ZPHWy0XlJcFF+5ch5ff+YQH/3NViJdQlePwSVWOfN7188hNT6a5LgoKhra+fLj+9h12hpGnhgTyc2rNGm4OUkaje6EYcvHmhXucyJyJfAV4GJjTIvH9gygxhjTLSKzgDl2HEqNG+6JZXkVTawY5noMB4rq+fLj+wD4y/tn+OnG5X1m8Lt1dffw5cf3khYfzQ8+srS33E1UhIvrl03l4a0F1Ld09lnu9NEdZ0iaEMWl8ycRGxXBlYum8Oj2M3zpQ3P7jGQaymtHK+juMb2Vfu+5dDZvHK1g40Pb+OS6GXzlyvlek92rh8tJjY8esqP6E+fN4JqlU/nTe6do7ugmNT6aqqZ2/r6riM32Eq05qXFUNrYTGSHct2EOCTGRbFgwmUmJWjnJzUnS2Ckim4DHAIO1ct8Odz0qY8yTPoznl0AMsNnurNpmj5S6CPiOiHQCPcDdxhidMqvGlVkZ1sSy/MrmYSeN5w+UEOkS/vPGxXz/xaPc/OstvPevl53VybzzdC3Hy5v42cblXDqv7zf365ZN5XfvnuLlQ2XcsmYaADXNHbxyqJyPn5vTmyA+sjqbFw6Usv1UzZBNlR1dPXz58X28dayChrYuMpNiWZxlJce0hBie+tz5fPu5Q/zu3VNMT4vjE+fN6PP8muYOXjpUxkdXZTvqfE+Nj+ZLl8/rs+2+9XP545ZTFNW28vKhMtbOTOX7Ny8ZsNN/vHOSNGKBcuBi+3ElMAGrHpUBfJY0jDGzB9j+BPCEr95HqXA0LSWOCJdwqmp48w2MMbx0sIx1s9O5dU0OM9LiufWhbbx0sIybV/VddnTz4XKiI1x9ZlW7Lc1OYnpaHM/tL+lNGi8dLKOju4ePeLzOuTPTiI508fbxSq9J4w/vnuLxXUWszEnmreOVFNW2kpU8gYa2Lj62NqfP6KaU+GgeuHU5R0obefFg2VlJ4/GdhXR09XD7ur7bhyMpLor7NswF4Hs3LRkzJX78xclyr3cEIhCl1OCiI13kpMaRX+V9/ZKBFNa0crq6hTsvsJbBWTszlWmpE3h2X0mfpGGM4dUj5aybneZ1mKuIcMWiKfzxvVO0dnQzITqC5/eXMCs9vs/SxROiI1g7I5W3T5w9erGxrZPvv3gUlwtOlDeyJDuJ79ywiAvnZLC/qJ4VXgo1iggXzEnn/7adpq2zu0+T12M7C1kzI4W5k31TSFATxtCcjJ6aKSI/EZEnAzC5Tyk1iGmpcRTWtA7rOYdLrRFXS7OtP8giwtWLM3kvr4r6lg8K+Z2oaOJ0dQsfGmTRrHW5aXR2G3adrqWkrpVt+dVcszTzrLkP62ancby86azRSVtPVtPR3cPDd6zl+Hev4qnPnc9l8ycTFeFi1fSUAdeOP29WGu1dPez1WB+jsrFdy5UHgZO0+jRQAPwCaySV+6aUCrCs5FhK64eZNEoaiHAJ86Z88G38qiXWui4vH/pgSKu7M9hb05TbmhmpRLiE909V86ctBYgIt9pNVZ5W2J3Se/stgrSvqI5Il7BsWvKACcKb1TOs13OPaAI4UGy99lIt7xFQTvo02owxP/d7JEqpIWUmTaCqqeOsZprBHC1rZEZaXJ/jl2UnMW9yIr956yQ3rcwiMsLFK4fLWTYtmckTBx4pFB8TyYLMRN46Xkl+ZTNXL8n0OqFuaXYSLoE9hXV9SnvsK6xn3pTEYY2qAkiOi2b2pAR2Fnww/mVfYT0ugcVZmjQCycmVxs9E5Jsicp6IrHTf/B6ZUuosmUnWH/Qye2U6J4rrWslJ7fuHXUT40uVzya9q5vFdRZyubmZfYR1XOShnvzInhf1F9TS1d/HpC2d6PSY+JpI5kxL7TEbs6THsK6ob8VLHq6ensOt0bW8xwwPF9cyelOCozIjyHSdJYwnWSn3f54OmqYDXpVJKQZa9bGnJMJqoSupavS53evnCyayensKPXznGb946iYhVbmMol9j1nRZmTuztJ/Fm0dSJfZJGQXUzjW1dLB/kOYNZNT2FhrYu8iqbMMawv6iOJVlaDyrQnKTojwKzjDEjqwuglPKZTHfSqHN2pdHS0UVtS6fXpCEi3H/NAm58cAuPbC/kI6uyHa2lfem8SfzqYytZM3PwuSKLspJ4ck8xFY1tTEqMZV+R3QcxbWTNSe5Z4TsLaomLjqCqqYNlI3wtNXJOrjQOYq0TrpQKMnfzVGmdsyuNEvu4rAGSwYqcFC6ck07ShCj+6UNzHb2miHDN0swhZ0kvnmoNw3UXCdx9uo746Ahmj7Dw34y0ONLio9l5uqa3Q3zlMCc5qtFzcqWRDBwVkR3Y9Z8AjDHX+y0qpZRXsVERpMZHO26eKravSLJSBr6C+P3ta+js7vF538BCd9IorufSeZPYebqWFTkpI66ALCKssvs14qMjSYiJZP4U38zPUM45+ZR80+9RKKUcy0qe4Lh5yn2lMVizU3Ska9AChiOVGBvFjLQ4DpU00NjWybGyBr5w2ZxRvebqGSm8cricR+vOcOGcDC3BHwROZoT3WTtDRC4AbgMCsaaGUqqfqcmx5Fc6KyVSUteKS2ByYoyfo/JuUVYS+4vq2HOmjh5jdWaPxjkzraq/nd2GT5wXmMWoVF+OrkdFZAXwMaxO8VNoHSilgmZq8gTePVGFMWbIVeiK61qZMjE2aN/Il2Yl8cL+Uv743imi7Vnfo3q97CTuXT+HSJecVVBRBcaASUNE5mJdUdwGVAF/A8QYc2mAYlNKeZGVPIHmjm4aWrv6lCj3ZqDhtoHykVXZ/PL1PN44VsmFc9JH3W8iIo477JV/DPb14yhwGXCtMeYCY8wvgO7AhKWUGog7CRQ7GEFVXNfaO0w3GNISYvj3axcQ4RJuW5sTtDiU7wyW9m8CNgJviMhLwKPAyFaLV0r5zNTeuRqtvSOUvOnuMZTWtXHNkuCuC3HrmhyuXTpVZ26PEQNeaRhjnjbGbATmA28A9wGTROTXInJ5oAJUSvU1NdmaHzHUsNuKxja6esygw20DRRPG2DFk75gxptkY81djzHVANrAH+Fe/R6aU8io9PobYKNeQI6iKa62kkh3E5ik19gxrSIUxptYY85AxZr2/AlJKDc7lEpZmJZ9Vdrw/d59HdghcaaixI+RmxojIt0SkWET22rerPfZ9TUTyROSYiFwRzDiVCqYV05M5VFJPW+fAY1OK7CuNUGieUmNHyCUN2wPGmOX2bROAiCzE6phfBFwJPCgiwyvKr9QYccHsdDq7reVZB1Jc10pKXBRx0dqfoHwnVJOGNzcAjxpj2o0xp4A8YG2QY1IqKNblppOVPIE/vleAMcbrMcW1rXqVoXwuVJPGPSKyX0T+ICLuKaRZQKHHMUX2tj5E5C4R2SkiOysrz17YXqmxIMIlfPaSXHadrmXTgTKvx5yubj5r8SWlRisoSUNEXhWRg15uNwC/BnKB5UApw1yP3O6oX22MWZ2RkeGH6JUKDRvXTGNJVhL/8fzh3tXs3No6uzlT0zLiMuRKDSQojZ3GmA1OjhOR3wLP2w+LAc8V7LPtbUqNS5ERLu68cCb3PrqXXWdqWWMvUgRwurqFHgO5kzRpKN8KueYpEcn0eHgj1iJQAM8CG0UkRkRmAnOA7YGOT6lQsn7BZKIjXLx6uG+H+ImKRgBy9UpD+VgoDqv4oYgsBwxQAHwGwBhzSEQeAw4DXcDnjTFaC0uNawkxkazISWbLyeo+23cW1BIb5WLuZF2kSPlWyCUNY8wnBtn3n8B/BjAcpULeutx0fvracepbOnur3r5/qoZV01P8sriSGt/0E6VUmFs3Ow1jYGt+FQAnK5s4UtrAhXN0IIjyPU0aSoW55dOSSYmL6h16+/t3TxEVIdy8MjvIkamxSJOGUmEuKsLF1Usy2Xy4nIPF9Ty6/QwfW5tDRpCWeFVjmyYNpcaA65dNpbWzmxsffI/oSBdfWD8n2CGpMUqThlJjwOoZqaQnRNPZbfjEudNJT9CrDOUfITd6Sik1fBEu4VcfW8nmw+V8Ua8ylB9p0lBqjDhnVhrnzEoLdhhqjNPmKaWUUo5p0lBKKeWYJg2llFKOadJQSinlmCYNpZRSjmnSUEop5ZgmDaWUUo5p0lBKKeWYJg2llFKOadJQSinlmCYNpZRSjoVU7SkR+Rswz36YDNQZY5aLyAzgCHDM3rfNGHN34CNUSqnxLaSShjHmVvd9EfkxUO+x+6QxZnngo1JKKeUWUknDTUQEuAW4LNixKKWU+kCo9mlcCJQbY054bJspIntE5C0RuTBYgSml1HgW8CsNEXkVmOJl1/3GmGfs+7cBj3jsKwVyjDHVIrIKeFpEFhljGry8/l3AXQA5OTm+DV4ppca5gCcNY8yGwfaLSCRwE7DK4zntQLt9f5eInATmAju9vP5DwEMAq1evNr6LXCmlVCg2T20AjhpjitwbRCRDRCLs+7OAOUB+kOJTSqlxKxQ7wjfSt2kK4CLgOyLSCfQAdxtjagIemVJKjXMhlzSMMZ/0su0J4InAR6OUUspTKDZPKaWUClGaNJRSSjmmSUMppZRjmjSUUko5pklDKaWUY5o0lFJKOaZJQymllGOaNJRSSjmmSUMppZRjmjSUUko5pklDKaWUY5o0lFJKOaZJQymllGOaNJRSSjmmSUMppZRjmjSUUko5pklDKaWUY5o0lFJKOaZJQymllGNBSRoi8lEROSQiPSKyut++r4lInogcE5ErPLZfaW/LE5GvBj5qpZRSwbrSOAjcBLztuVFEFgIbgUXAlcCDIhIhIhHAr4CrgIXAbfaxSimlAigyGG9qjDkCICL9d90APGqMaQdOiUgesNbel2eMybef96h97OHARKyUUgqClDQGkQVs83hcZG8DKOy3/RxvLyAidwF32Q+bROTYKOJJB6pG8Xx/0biGR+MaHo1reMZiXNMH2uG3pCEirwJTvOy63xjzjL/e1xjzEPCQL15LRHYaY1YPfWRgaVzDo3ENj8Y1POMtLr8lDWPMhhE8rRiY5vE4297GINuVUkoFSKgNuX0W2CgiMSIyE5gDbAd2AHNEZKaIRGN1lj8bxDiVUmpcCkqfhojcCPwCyABeEJG9xpgrjDGHROQxrA7uLuDzxphu+zn3AC8DEcAfjDGHAhCqT5q5/EDjGh6Na3g0ruEZV3GJMcYfr6uUUmoMCrXmKaWUUiFMk4ZSSinHxmXSGKokid0R/zd7//siMsNjn9cyJwGK60siclhE9ovIayIy3WNft4jstW8+HSTgIK5Pikilx/vf6bHvdhE5Yd9uD3BcD3jEdFxE6jz2+fN8/UFEKkTk4AD7RUR+bse9X0RWeuzz5/kaKq6P2/EcEJEtIrLMY1+BvX2viOwMcFyXiEi9x//XNzz2+a28kIO4/sUjpoP2ZyrV3ufP8zVNRN6w/xYcEpF7vRzjv8+YMWZc3bA60k8Cs4BoYB+wsN8xnwN+Y9/fCPzNvr/QPj4GmGm/TkQA47oUiLPvf9Ydl/24KYjn65PAL708NxXIt3+m2PdTAhVXv+O/gDWAwq/ny37ti4CVwMEB9l8NvAgIcC7wvr/Pl8O41rnfD6tkz/se+wqA9CCdr0uA50f7GfB1XP2OvQ54PUDnKxNYad9PBI57+Z3022dsPF5prMUuSWKM6QDcJUk83QA8bN//O7BeRASPMifGmFOAZ5kTv8dljHnDGNNiP9yGNV/F35ycr4FcAWw2xtQYY2qBzVg1xYIR123AIz5670EZY94GagY55Abgz8ayDUgWkUz8e76GjMsYs8V+Xwjc58vJ+RrIaD6bvo4rkJ+vUmPMbvt+I3CEDypnuPntMzYek0YWZ5ck6X/Ce48xxnQB9UCaw+f6My5Pn8L6JuEWKyI7RWSbiHzYRzENJ66b7cvgv4uIeyJmSJwvuxlvJvC6x2Z/nS8nBordn+druPp/vgzwiojsEqtUT6CdJyL7RORFEVlkbwuJ8yUicVh/eJ/w2ByQ8yVW0/kK4P1+u/z2GQu12lPKARH5f8Bq4GKPzdONMcUiMgt4XUQOGGNOBiik54BHjDHtIvIZrKu0ywL03k5sBP5u7Dk/tmCer5AmIpdiJY0LPDZfYJ+vScBmETlqfxMPhN1Y/19NInI18DTWxN9QcR3wnjHG86rE7+dLRBKwEtV9xpgGX772YMbjlcZgpUrOOkZEIoEkoNrhc/0ZFyKyAbgfuN5Y1YABMMYU2z/zgTexvn0EJC5jTLVHLL8DVjl9rj/j8rCRfk0HfjxfTgwUuz/PlyMishTr//AGY0y1e7vH+aoAnsJ3zbJDMsY0GGOa7PubgCgRSScEzpdtsM+XX86XiERhJYy/GGOe9HKI/z5j/uioCeUb1tVVPlZzhbvzbFG/Yz5P347wx+z7i+jbEZ6P7zrCncS1Aqvjb06/7SlAjH0/HTiBjzoEHcaV6XH/RmCb+aDT7ZQdX4p9PzVQcdnHzcfqlJRAnC+P95jBwB2719C3k3K7v8+Xw7hysPrp1vXbHg8ketzfAlwZwLimuP//sP74nrHPnaPPgL/isvcnYfV7xAfqfNn/9j8DPx3kGL99xnx2csPphjWy4DjWH+D77W3fwfr2DhALPG7/Am0HZnk89377eceAqwIc16tAObDXvj1rb18HHLB/aQ4AnwpwXN8DDtnv/wYw3+O5/2ifxzzgjkDGZT/+FvD9fs/z9/l6BCgFOrHajD8F3A3cbe8XrEXFTtrvvzpA52uouH4H1Hp8vnba22fZ52qf/f98f4Djusfj87UNj6Tm7TMQqLjsYz6JNTjG83n+Pl8XYPWZ7Pf4v7o6UJ8xLSOilFLKsfHYp6GUUmqENGkopZRyTJOGUkopxzRpKKWUckyThlJKKcc0aaiwIyLJIvI5j8dTReTvAXjfAntSmS9e6xIRWefx+G4R+QcfvG5AzoUav3TIrQo7dr2d540xiwP8vgVY492rHB4faazaZd72fQur0u5/+y5CpfxPrzRUOPo+kGuvVfAjEZnhXvNArLU9nhaRzfaVwT1irUOyxy5O6F7vIFdEXrILyr0jnU7cagAAAzFJREFUIvP7v4mIpInIK/aaBb/DmjCF5/vZj//ZTgKIyJsi8lN7DYV7ReQ6sdZk2SMir4rIZDvp3Q38k/1vuFBEviUi/2y/xnI71v0i8pSIpHi89g9EZLtY64Nc6CXm/ufiSfvfeUJEfujtZIrI9+WDdVr+296WISJPiMgO+3a+vT1BRP4o1loR+0Xk5pH8B6rwpQULVTj6KrDYGLMceq88PC3GKrkSizXr9V+NMStE5AHgH4CfAg9hzZ49ISLnAA9ydpHFbwLvGmO+IyLXYM0IdiLaGLPaji0FONcYY8RanOorxpgvi8hv8LjSEJH1Hs//M/AFY8xbIvIdO4777H2Rxpi1duG+bwIbhohluX0u2oFjIvILY0xvlVMRScMq/TLfjjHZ3vUz4AFjzLsikgO8DCwAvg7UG2OWePz71DiiSUONRW8Ya52BRhGpx6rCC1Y5haV2ddB1wOMi4n5OjJfXuQi4CcAY84KI1Ho5xpu/edzPBv4m1loG0Vi1fgYkIklAsjHmLXvTw1glbdzcxel2YdVFGsprxph6+7UPA9PpWxq7HmgDfi8izwPP29s3AAs9zs9E+7xtwKrHBoD5YP0NNU5o0lBjUbvH/R6Pxz1Yn3kXUOe+UhmBLvo27cb229/scf8XwE+MMc+KyCVYtbBGw/1v6cbZ76/nuTjrOcaYLhFZC6wHPoJV5+kyrH/fucaYNs/jPZKIGqe0T0OFo0asZS5HxFhrD5wSkY9C73rKy7wc+jbwMfuYq7CqgoJVNHKS3ecRA1w7yNsl8UHpac/1mL3+G+yrglqP/opPAG/1P85X7KuHJGOVHP8nwH0eXsFaItd9nDvBbsaqAu3ers1T44wmDRV2jLXOw3siclBEfjTCl/k48CkRcVci9bZM6LeBi0TkEFYz1Rn7/Tuxqulux/ojenSQ9/kWVjPYLsBz1NVzwI3ujvB+z7kd+JGI7Mfqk/jOMP9tw5EIPG+/17vAl+ztXwRW253dh7E67gG+C6TY534f1rr1ahzRIbdKKaUc0ysNpZRSjmnSUEop5ZgmDaWUUo5p0lBKKeWYJg2llFKOadJQSinlmCYNpZRSjv1/EyFavxlXKAUAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_keras = conv_model.predict(x_test1).ravel()\n",
        "fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test1, y_pred_keras)\n",
        "auc_keras = auc(fpr_keras, tpr_keras)\n",
        "plt.title(disease)\n",
        "plt.plot(fpr_keras, tpr_keras, label='ROC curve')\n",
        "plt.fill_between(fpr_keras, tpr_keras, 0, color='blue', alpha=.1, label='AUC = {:.3f}'.format(auc_keras))\n",
        "plt.axis([0, 1, 0, 1])\n",
        "plt.grid()\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.xlabel('False positive rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "1e26f63d-a8bb-45ef-fc46-8d632d63ad57",
        "id": "2VFoeCcrG7W5"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'False positive rate')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}