TreeClassifier_SelectedAsBestModelCode.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rxa04G9W38SF",
        "outputId": "c721d8cb-1129-4458-cee9-eaef9e1cdfcd"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6aSHsG3L3tcI",
        "outputId": "fdbbbebb-4df9-446a-8904-f3451be83ecb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 1.2212 - accuracy: 0.7500\n",
            "Epoch 1: val_loss improved from inf to 3.04472, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 4s 41ms/step - loss: 1.1398 - accuracy: 0.7917 - val_loss: 3.0447 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 2/50\n",
            "16/24 [===================>..........] - ETA: 0s - loss: 1.2074 - accuracy: 0.8125"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r23/24 [===========================>..] - ETA: 0s - loss: 1.2600 - accuracy: 0.8174\n",
            "Epoch 2: val_loss improved from 3.04472 to 2.58725, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 1.2779 - accuracy: 0.8083 - val_loss: 2.5872 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 3/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 1.0475 - accuracy: 0.9176\n",
            "Epoch 3: val_loss improved from 2.58725 to 2.02873, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 1.1770 - accuracy: 0.8750 - val_loss: 2.0287 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 4/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 1.1792 - accuracy: 0.8588\n",
            "Epoch 4: val_loss improved from 2.02873 to 1.66783, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.1856 - accuracy: 0.8583 - val_loss: 1.6678 - val_accuracy: 0.6667 - lr: 0.0100\n",
            "Epoch 5/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 1.4169 - accuracy: 0.8316\n",
            "Epoch 5: val_loss did not improve from 1.66783\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 1.3291 - accuracy: 0.8417 - val_loss: 1.7253 - val_accuracy: 0.6667 - lr: 0.0100\n",
            "Epoch 6/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 1.4197 - accuracy: 0.7273\n",
            "Epoch 6: val_loss did not improve from 1.66783\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 1.5001 - accuracy: 0.7083 - val_loss: 1.6920 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 1.2069 - accuracy: 0.8333\n",
            "Epoch 7: val_loss improved from 1.66783 to 1.43713, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.2069 - accuracy: 0.8333 - val_loss: 1.4371 - val_accuracy: 0.5333 - lr: 0.0100\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 1.1434 - accuracy: 0.8667\n",
            "Epoch 8: val_loss improved from 1.43713 to 1.40865, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 1.1434 - accuracy: 0.8667 - val_loss: 1.4087 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 9/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.9512 - accuracy: 0.9091\n",
            "Epoch 9: val_loss did not improve from 1.40865\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 0.9301 - accuracy: 0.9167 - val_loss: 1.4608 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 10/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.9678 - accuracy: 0.8727\n",
            "Epoch 10: val_loss improved from 1.40865 to 1.37876, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.9508 - accuracy: 0.8750 - val_loss: 1.3788 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 11/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 0.9688 - accuracy: 0.8471\n",
            "Epoch 11: val_loss improved from 1.37876 to 1.24925, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.0041 - accuracy: 0.8583 - val_loss: 1.2493 - val_accuracy: 0.6000 - lr: 0.0100\n",
            "Epoch 12/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 1.0956 - accuracy: 0.8556\n",
            "Epoch 12: val_loss improved from 1.24925 to 1.15333, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.0806 - accuracy: 0.8750 - val_loss: 1.1533 - val_accuracy: 0.8667 - lr: 0.0100\n",
            "Epoch 13/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 1.0099 - accuracy: 0.8105\n",
            "Epoch 13: val_loss did not improve from 1.15333\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 1.0232 - accuracy: 0.8250 - val_loss: 1.3427 - val_accuracy: 0.5333 - lr: 0.0100\n",
            "Epoch 14/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 1.0928 - accuracy: 0.8273\n",
            "Epoch 14: val_loss did not improve from 1.15333\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 1.1059 - accuracy: 0.8250 - val_loss: 1.2387 - val_accuracy: 0.5333 - lr: 0.0100\n",
            "Epoch 15/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 1.1368 - accuracy: 0.8348\n",
            "Epoch 15: val_loss did not improve from 1.15333\n",
            "\n",
            "Epoch 15: ReduceLROnPlateau reducing learning rate to 0.004999999888241291.\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 1.1208 - accuracy: 0.8417 - val_loss: 1.2019 - val_accuracy: 0.8667 - lr: 0.0100\n",
            "Epoch 16/50\n",
            "12/24 [==============>...............] - ETA: 0s - loss: 1.0115 - accuracy: 0.8333\n",
            "Epoch 16: val_loss did not improve from 1.15333\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 1.0313 - accuracy: 0.8333 - val_loss: 1.1722 - val_accuracy: 0.6667 - lr: 0.0050\n",
            "Epoch 17/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 1.0135 - accuracy: 0.8727\n",
            "Epoch 17: val_loss improved from 1.15333 to 1.08183, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 0.9903 - accuracy: 0.8833 - val_loss: 1.0818 - val_accuracy: 0.8667 - lr: 0.0050\n",
            "Epoch 18/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 1.0694 - accuracy: 0.8364\n",
            "Epoch 18: val_loss improved from 1.08183 to 0.99238, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 1.0679 - accuracy: 0.8333 - val_loss: 0.9924 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 19/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 1.0239 - accuracy: 0.8364\n",
            "Epoch 19: val_loss improved from 0.99238 to 0.91364, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.0079 - accuracy: 0.8333 - val_loss: 0.9136 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 20/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 0.8663 - accuracy: 0.8667\n",
            "Epoch 20: val_loss improved from 0.91364 to 0.84440, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.8923 - accuracy: 0.8500 - val_loss: 0.8444 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 21/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.8118 - accuracy: 0.9250\n",
            "Epoch 21: val_loss improved from 0.84440 to 0.77325, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 0.8118 - accuracy: 0.9250 - val_loss: 0.7732 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 22/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.9929 - accuracy: 0.8522\n",
            "Epoch 22: val_loss did not improve from 0.77325\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 0.9750 - accuracy: 0.8583 - val_loss: 0.7749 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 23/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.8361 - accuracy: 0.8364\n",
            "Epoch 23: val_loss improved from 0.77325 to 0.76214, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 0.8188 - accuracy: 0.8500 - val_loss: 0.7621 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 24/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.7094 - accuracy: 0.9158\n",
            "Epoch 24: val_loss improved from 0.76214 to 0.60812, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.7054 - accuracy: 0.9250 - val_loss: 0.6081 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 25/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.7969 - accuracy: 0.9053\n",
            "Epoch 25: val_loss did not improve from 0.60812\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 0.7546 - accuracy: 0.9167 - val_loss: 0.6120 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 26/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.8561 - accuracy: 0.8571\n",
            "Epoch 26: val_loss did not improve from 0.60812\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.8693 - accuracy: 0.8500 - val_loss: 0.6139 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 27/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.8311 - accuracy: 0.8700\n",
            "Epoch 27: val_loss did not improve from 0.60812\n",
            "\n",
            "Epoch 27: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455.\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.8456 - accuracy: 0.8500 - val_loss: 0.6446 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 28/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 0.8670 - accuracy: 0.7556\n",
            "Epoch 28: val_loss improved from 0.60812 to 0.60066, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.8197 - accuracy: 0.7917 - val_loss: 0.6007 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 29/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.7409 - accuracy: 0.8636\n",
            "Epoch 29: val_loss improved from 0.60066 to 0.56026, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 0.7212 - accuracy: 0.8750 - val_loss: 0.5603 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 30/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.7853 - accuracy: 0.8818\n",
            "Epoch 30: val_loss improved from 0.56026 to 0.52643, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.8227 - accuracy: 0.8667 - val_loss: 0.5264 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 31/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.8689 - accuracy: 0.8583\n",
            "Epoch 31: val_loss did not improve from 0.52643\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 0.8689 - accuracy: 0.8583 - val_loss: 0.5505 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 32/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.8740 - accuracy: 0.8364\n",
            "Epoch 32: val_loss did not improve from 0.52643\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 0.8764 - accuracy: 0.8250 - val_loss: 0.5657 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 33/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.8109 - accuracy: 0.8381\n",
            "Epoch 33: val_loss did not improve from 0.52643\n",
            "\n",
            "Epoch 33: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228.\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.7961 - accuracy: 0.8417 - val_loss: 0.5313 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 34/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.6547 - accuracy: 0.9158\n",
            "Epoch 34: val_loss improved from 0.52643 to 0.50661, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.6428 - accuracy: 0.9167 - val_loss: 0.5066 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 35/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.6896 - accuracy: 0.8737\n",
            "Epoch 35: val_loss improved from 0.50661 to 0.50185, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.6680 - accuracy: 0.8833 - val_loss: 0.5018 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 36/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 0.5750 - accuracy: 0.9333\n",
            "Epoch 36: val_loss improved from 0.50185 to 0.49504, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.6302 - accuracy: 0.9000 - val_loss: 0.4950 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 37/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 0.5889 - accuracy: 0.9222\n",
            "Epoch 37: val_loss improved from 0.49504 to 0.47977, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.5638 - accuracy: 0.9333 - val_loss: 0.4798 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 38/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.6012 - accuracy: 0.9333\n",
            "Epoch 38: val_loss improved from 0.47977 to 0.45957, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.6012 - accuracy: 0.9333 - val_loss: 0.4596 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 39/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 0.7677 - accuracy: 0.8556\n",
            "Epoch 39: val_loss improved from 0.45957 to 0.45184, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.7724 - accuracy: 0.8417 - val_loss: 0.4518 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 40/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.5719 - accuracy: 0.8947\n",
            "Epoch 40: val_loss improved from 0.45184 to 0.44195, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.5968 - accuracy: 0.8917 - val_loss: 0.4419 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 41/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 0.6303 - accuracy: 0.9111\n",
            "Epoch 41: val_loss improved from 0.44195 to 0.43992, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.6389 - accuracy: 0.8833 - val_loss: 0.4399 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 42/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.7192 - accuracy: 0.8750\n",
            "Epoch 42: val_loss improved from 0.43992 to 0.41852, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.7192 - accuracy: 0.8750 - val_loss: 0.4185 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 43/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.5700 - accuracy: 0.9304\n",
            "Epoch 43: val_loss improved from 0.41852 to 0.40759, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.5629 - accuracy: 0.9333 - val_loss: 0.4076 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 44/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.5195 - accuracy: 0.9304\n",
            "Epoch 44: val_loss improved from 0.40759 to 0.40709, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.5168 - accuracy: 0.9333 - val_loss: 0.4071 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 45/50\n",
            "16/24 [===================>..........] - ETA: 0s - loss: 0.6579 - accuracy: 0.8375\n",
            "Epoch 45: val_loss improved from 0.40709 to 0.40421, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.6683 - accuracy: 0.8500 - val_loss: 0.4042 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 46/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.5420 - accuracy: 0.9200\n",
            "Epoch 46: val_loss improved from 0.40421 to 0.40120, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 14ms/step - loss: 0.5396 - accuracy: 0.9167 - val_loss: 0.4012 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 47/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.6767 - accuracy: 0.8900\n",
            "Epoch 47: val_loss did not improve from 0.40120\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.7583 - accuracy: 0.8500 - val_loss: 0.4072 - val_accuracy: 1.0000 - lr: 0.0012\n",
            "Epoch 48/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.6143 - accuracy: 0.9200\n",
            "Epoch 48: val_loss did not improve from 0.40120\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.6012 - accuracy: 0.9250 - val_loss: 0.4333 - val_accuracy: 0.9333 - lr: 0.0012\n",
            "Epoch 49/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.5640 - accuracy: 0.9474\n",
            "Epoch 49: val_loss did not improve from 0.40120\n",
            "\n",
            "Epoch 49: ReduceLROnPlateau reducing learning rate to 0.0006249999860301614.\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.6259 - accuracy: 0.9167 - val_loss: 0.4085 - val_accuracy: 0.9333 - lr: 0.0012\n",
            "Epoch 50/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.5582 - accuracy: 0.9333\n",
            "Epoch 50: val_loss improved from 0.40120 to 0.39781, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 0.5510 - accuracy: 0.9333 - val_loss: 0.3978 - val_accuracy: 0.9333 - lr: 6.2500e-04\n",
            "Fold Training Accuracy: 1.0\n",
            "Fold Validation Accuracy: 0.9333333373069763\n",
            "Fold Test Accuracy: 1.0\n",
            "Epoch 1/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 1.3570 - accuracy: 0.7000\n",
            "Epoch 1: val_loss improved from inf to 3.30504, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 3s 19ms/step - loss: 1.2940 - accuracy: 0.7167 - val_loss: 3.3050 - val_accuracy: 0.3333 - lr: 0.0100\n",
            "Epoch 2/50\n",
            "16/24 [===================>..........] - ETA: 0s - loss: 1.5442 - accuracy: 0.7875\n",
            "Epoch 2: val_loss did not improve from 3.30504\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.5117 - accuracy: 0.7917 - val_loss: 3.8200 - val_accuracy: 0.3333 - lr: 0.0100\n",
            "Epoch 3/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 1.6093 - accuracy: 0.7294\n",
            "Epoch 3: val_loss improved from 3.30504 to 2.35851, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 1.4363 - accuracy: 0.7750 - val_loss: 2.3585 - val_accuracy: 0.3333 - lr: 0.0100\n",
            "Epoch 4/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 1.3560 - accuracy: 0.7556\n",
            "Epoch 4: val_loss improved from 2.35851 to 2.10684, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 1.3323 - accuracy: 0.7833 - val_loss: 2.1068 - val_accuracy: 0.4000 - lr: 0.0100\n",
            "Epoch 5/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 1.3165 - accuracy: 0.8174\n",
            "Epoch 5: val_loss improved from 2.10684 to 1.86059, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 1.3064 - accuracy: 0.8167 - val_loss: 1.8606 - val_accuracy: 0.3333 - lr: 0.0100\n",
            "Epoch 6/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.6736 - accuracy: 0.7048\n",
            "Epoch 6: val_loss did not improve from 1.86059\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 1.6184 - accuracy: 0.7000 - val_loss: 1.8869 - val_accuracy: 0.3333 - lr: 0.0100\n",
            "Epoch 7/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 1.3298 - accuracy: 0.7652\n",
            "Epoch 7: val_loss improved from 1.86059 to 1.72057, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.3420 - accuracy: 0.7583 - val_loss: 1.7206 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 8/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.1416 - accuracy: 0.8286\n",
            "Epoch 8: val_loss improved from 1.72057 to 1.46245, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.1602 - accuracy: 0.8250 - val_loss: 1.4625 - val_accuracy: 0.4000 - lr: 0.0100\n",
            "Epoch 9/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.1931 - accuracy: 0.8095\n",
            "Epoch 9: val_loss improved from 1.46245 to 1.21356, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.2082 - accuracy: 0.8083 - val_loss: 1.2136 - val_accuracy: 1.0000 - lr: 0.0100\n",
            "Epoch 10/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.1632 - accuracy: 0.8476\n",
            "Epoch 10: val_loss did not improve from 1.21356\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 1.1169 - accuracy: 0.8667 - val_loss: 1.2533 - val_accuracy: 0.8667 - lr: 0.0100\n",
            "Epoch 11/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 1.2883 - accuracy: 0.7826\n",
            "Epoch 11: val_loss did not improve from 1.21356\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.3373 - accuracy: 0.7667 - val_loss: 1.2796 - val_accuracy: 0.7333 - lr: 0.0100\n",
            "Epoch 12/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 1.0410 - accuracy: 0.8609\n",
            "Epoch 12: val_loss did not improve from 1.21356\n",
            "\n",
            "Epoch 12: ReduceLROnPlateau reducing learning rate to 0.004999999888241291.\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.0280 - accuracy: 0.8667 - val_loss: 1.4053 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 13/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 1.1347 - accuracy: 0.8167\n",
            "Epoch 13: val_loss did not improve from 1.21356\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.1347 - accuracy: 0.8167 - val_loss: 1.3242 - val_accuracy: 0.4667 - lr: 0.0050\n",
            "Epoch 14/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 1.0060 - accuracy: 0.8588\n",
            "Epoch 14: val_loss improved from 1.21356 to 1.15675, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.9730 - accuracy: 0.8833 - val_loss: 1.1568 - val_accuracy: 0.8000 - lr: 0.0050\n",
            "Epoch 15/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.8777 - accuracy: 0.9158\n",
            "Epoch 15: val_loss improved from 1.15675 to 0.97458, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.8854 - accuracy: 0.9083 - val_loss: 0.9746 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 16/50\n",
            "16/24 [===================>..........] - ETA: 0s - loss: 0.9626 - accuracy: 0.8750\n",
            "Epoch 16: val_loss improved from 0.97458 to 0.90726, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.9037 - accuracy: 0.8833 - val_loss: 0.9073 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 17/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 0.8691 - accuracy: 0.8706\n",
            "Epoch 17: val_loss improved from 0.90726 to 0.88468, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.8640 - accuracy: 0.8750 - val_loss: 0.8847 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 18/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 0.8450 - accuracy: 0.8706\n",
            "Epoch 18: val_loss improved from 0.88468 to 0.77017, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.8286 - accuracy: 0.8750 - val_loss: 0.7702 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 19/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.8397 - accuracy: 0.8545\n",
            "Epoch 19: val_loss improved from 0.77017 to 0.65844, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.8342 - accuracy: 0.8667 - val_loss: 0.6584 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 20/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.9425 - accuracy: 0.8600\n",
            "Epoch 20: val_loss did not improve from 0.65844\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.8914 - accuracy: 0.8750 - val_loss: 0.7666 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 21/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.9622 - accuracy: 0.8190\n",
            "Epoch 21: val_loss did not improve from 0.65844\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 0.9542 - accuracy: 0.8167 - val_loss: 0.7269 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 22/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.7870 - accuracy: 0.8700\n",
            "Epoch 22: val_loss improved from 0.65844 to 0.60500, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.8128 - accuracy: 0.8583 - val_loss: 0.6050 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 23/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.7538 - accuracy: 0.9091\n",
            "Epoch 23: val_loss improved from 0.60500 to 0.59886, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.7778 - accuracy: 0.8833 - val_loss: 0.5989 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 24/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 0.7787 - accuracy: 0.8941\n",
            "Epoch 24: val_loss improved from 0.59886 to 0.56106, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.7819 - accuracy: 0.8833 - val_loss: 0.5611 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 25/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.7131 - accuracy: 0.8783\n",
            "Epoch 25: val_loss improved from 0.56106 to 0.53263, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.7055 - accuracy: 0.8833 - val_loss: 0.5326 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 26/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.7567 - accuracy: 0.8545\n",
            "Epoch 26: val_loss did not improve from 0.53263\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 0.7493 - accuracy: 0.8583 - val_loss: 0.6191 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 27/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 0.7759 - accuracy: 0.9059\n",
            "Epoch 27: val_loss did not improve from 0.53263\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.8153 - accuracy: 0.8750 - val_loss: 0.5480 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 28/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 0.7574 - accuracy: 0.8667\n",
            "Epoch 28: val_loss improved from 0.53263 to 0.52549, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.7226 - accuracy: 0.8917 - val_loss: 0.5255 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 29/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.7568 - accuracy: 0.8476\n",
            "Epoch 29: val_loss did not improve from 0.52549\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.7690 - accuracy: 0.8250 - val_loss: 0.5518 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 30/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.7650 - accuracy: 0.8526\n",
            "Epoch 30: val_loss did not improve from 0.52549\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 0.7191 - accuracy: 0.8750 - val_loss: 0.5600 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 31/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.9730 - accuracy: 0.8211\n",
            "Epoch 31: val_loss did not improve from 0.52549\n",
            "\n",
            "Epoch 31: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455.\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.9529 - accuracy: 0.8250 - val_loss: 0.5615 - val_accuracy: 0.8667 - lr: 0.0050\n",
            "Epoch 32/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.6375 - accuracy: 0.8957\n",
            "Epoch 32: val_loss improved from 0.52549 to 0.46498, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.6805 - accuracy: 0.8833 - val_loss: 0.4650 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 33/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.7217 - accuracy: 0.8842\n",
            "Epoch 33: val_loss did not improve from 0.46498\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.7291 - accuracy: 0.8750 - val_loss: 0.4776 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 34/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.8659 - accuracy: 0.8105\n",
            "Epoch 34: val_loss improved from 0.46498 to 0.45330, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.8363 - accuracy: 0.8167 - val_loss: 0.4533 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 35/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.5950 - accuracy: 0.8917\n",
            "Epoch 35: val_loss improved from 0.45330 to 0.42592, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 0.5950 - accuracy: 0.8917 - val_loss: 0.4259 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 36/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.7126 - accuracy: 0.8762\n",
            "Epoch 36: val_loss improved from 0.42592 to 0.41529, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 0.6741 - accuracy: 0.8917 - val_loss: 0.4153 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 37/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.7093 - accuracy: 0.8762\n",
            "Epoch 37: val_loss improved from 0.41529 to 0.40355, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.6828 - accuracy: 0.8917 - val_loss: 0.4035 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 38/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.6771 - accuracy: 0.9368\n",
            "Epoch 38: val_loss improved from 0.40355 to 0.38928, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.7185 - accuracy: 0.9250 - val_loss: 0.3893 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 39/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.7775 - accuracy: 0.8545\n",
            "Epoch 39: val_loss did not improve from 0.38928\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.7921 - accuracy: 0.8333 - val_loss: 0.3947 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 40/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.8046 - accuracy: 0.8000\n",
            "Epoch 40: val_loss improved from 0.38928 to 0.38754, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 0.7911 - accuracy: 0.8167 - val_loss: 0.3875 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 41/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.6555 - accuracy: 0.8750\n",
            "Epoch 41: val_loss improved from 0.38754 to 0.38457, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.6555 - accuracy: 0.8750 - val_loss: 0.3846 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 42/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.7453 - accuracy: 0.8500\n",
            "Epoch 42: val_loss improved from 0.38457 to 0.38120, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.7453 - accuracy: 0.8500 - val_loss: 0.3812 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 43/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.7795 - accuracy: 0.8348\n",
            "Epoch 43: val_loss improved from 0.38120 to 0.37791, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 0.7633 - accuracy: 0.8417 - val_loss: 0.3779 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 44/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.6667 - accuracy: 0.9048\n",
            "Epoch 44: val_loss improved from 0.37791 to 0.35862, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 0.6612 - accuracy: 0.9083 - val_loss: 0.3586 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 45/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.5555 - accuracy: 0.9200\n",
            "Epoch 45: val_loss improved from 0.35862 to 0.34416, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 14ms/step - loss: 0.5364 - accuracy: 0.9250 - val_loss: 0.3442 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 46/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.6397 - accuracy: 0.9429\n",
            "Epoch 46: val_loss improved from 0.34416 to 0.34099, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 0.6118 - accuracy: 0.9500 - val_loss: 0.3410 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 47/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.6877 - accuracy: 0.8870\n",
            "Epoch 47: val_loss improved from 0.34099 to 0.33774, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 0.6799 - accuracy: 0.8917 - val_loss: 0.3377 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 48/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.6535 - accuracy: 0.8857\n",
            "Epoch 48: val_loss did not improve from 0.33774\n",
            "24/24 [==============================] - 0s 14ms/step - loss: 0.6222 - accuracy: 0.9000 - val_loss: 0.3476 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 49/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.6163 - accuracy: 0.8909\n",
            "Epoch 49: val_loss did not improve from 0.33774\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 0.5974 - accuracy: 0.9000 - val_loss: 0.3501 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 50/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.5838 - accuracy: 0.9091\n",
            "Epoch 50: val_loss improved from 0.33774 to 0.33765, saving model to best_model_fold.h5\n",
            "\n",
            "Epoch 50: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228.\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.5645 - accuracy: 0.9167 - val_loss: 0.3376 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Fold Training Accuracy: 0.9916666746139526\n",
            "Fold Validation Accuracy: 1.0\n",
            "Fold Test Accuracy: 1.0\n",
            "Epoch 1/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 1.1630 - accuracy: 0.7333\n",
            "Epoch 1: val_loss improved from inf to 5.63359, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 2s 21ms/step - loss: 1.1055 - accuracy: 0.7667 - val_loss: 5.6336 - val_accuracy: 0.2667 - lr: 0.0100\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 1.2978 - accuracy: 0.7750\n",
            "Epoch 2: val_loss improved from 5.63359 to 3.77222, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 1.2978 - accuracy: 0.7750 - val_loss: 3.7722 - val_accuracy: 0.2667 - lr: 0.0100\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 1.3604 - accuracy: 0.8500\n",
            "Epoch 3: val_loss improved from 3.77222 to 2.76602, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 1.3604 - accuracy: 0.8500 - val_loss: 2.7660 - val_accuracy: 0.2667 - lr: 0.0100\n",
            "Epoch 4/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 1.3180 - accuracy: 0.7913\n",
            "Epoch 4: val_loss did not improve from 2.76602\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.2961 - accuracy: 0.8000 - val_loss: 3.3666 - val_accuracy: 0.2667 - lr: 0.0100\n",
            "Epoch 5/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 1.2283 - accuracy: 0.8696\n",
            "Epoch 5: val_loss did not improve from 2.76602\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.2179 - accuracy: 0.8667 - val_loss: 3.0752 - val_accuracy: 0.2667 - lr: 0.0100\n",
            "Epoch 6/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 1.2964 - accuracy: 0.8300\n",
            "Epoch 6: val_loss improved from 2.76602 to 2.19872, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.2951 - accuracy: 0.8250 - val_loss: 2.1987 - val_accuracy: 0.2667 - lr: 0.0100\n",
            "Epoch 7/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 1.2802 - accuracy: 0.7789\n",
            "Epoch 7: val_loss improved from 2.19872 to 1.73395, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.3063 - accuracy: 0.8000 - val_loss: 1.7340 - val_accuracy: 0.2667 - lr: 0.0100\n",
            "Epoch 8/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 1.2065 - accuracy: 0.8455\n",
            "Epoch 8: val_loss did not improve from 1.73395\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 1.2316 - accuracy: 0.8417 - val_loss: 1.8467 - val_accuracy: 0.2667 - lr: 0.0100\n",
            "Epoch 9/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 1.2205 - accuracy: 0.8600\n",
            "Epoch 9: val_loss improved from 1.73395 to 1.31156, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.1464 - accuracy: 0.8833 - val_loss: 1.3116 - val_accuracy: 0.8667 - lr: 0.0100\n",
            "Epoch 10/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.2314 - accuracy: 0.8000\n",
            "Epoch 10: val_loss improved from 1.31156 to 1.28959, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.2117 - accuracy: 0.7917 - val_loss: 1.2896 - val_accuracy: 0.8667 - lr: 0.0100\n",
            "Epoch 11/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 1.0368 - accuracy: 0.8316\n",
            "Epoch 11: val_loss improved from 1.28959 to 1.06334, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.0202 - accuracy: 0.8333 - val_loss: 1.0633 - val_accuracy: 0.9333 - lr: 0.0100\n",
            "Epoch 12/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.9922 - accuracy: 0.8667\n",
            "Epoch 12: val_loss did not improve from 1.06334\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 1.0245 - accuracy: 0.8667 - val_loss: 1.1585 - val_accuracy: 0.9333 - lr: 0.0100\n",
            "Epoch 13/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 1.2043 - accuracy: 0.8818\n",
            "Epoch 13: val_loss did not improve from 1.06334\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 1.1716 - accuracy: 0.8917 - val_loss: 1.2735 - val_accuracy: 0.8000 - lr: 0.0100\n",
            "Epoch 14/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 1.1443 - accuracy: 0.8316\n",
            "Epoch 14: val_loss improved from 1.06334 to 0.99133, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.1518 - accuracy: 0.8250 - val_loss: 0.9913 - val_accuracy: 0.9333 - lr: 0.0100\n",
            "Epoch 15/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 1.0050 - accuracy: 0.8778\n",
            "Epoch 15: val_loss did not improve from 0.99133\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.0411 - accuracy: 0.8750 - val_loss: 1.3062 - val_accuracy: 0.6667 - lr: 0.0100\n",
            "Epoch 16/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 1.3183 - accuracy: 0.8087\n",
            "Epoch 16: val_loss did not improve from 0.99133\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.2945 - accuracy: 0.8167 - val_loss: 1.5161 - val_accuracy: 0.4000 - lr: 0.0100\n",
            "Epoch 17/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 1.2639 - accuracy: 0.7412\n",
            "Epoch 17: val_loss did not improve from 0.99133\n",
            "\n",
            "Epoch 17: ReduceLROnPlateau reducing learning rate to 0.004999999888241291.\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.1461 - accuracy: 0.8000 - val_loss: 1.2263 - val_accuracy: 0.7333 - lr: 0.0100\n",
            "Epoch 18/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.9508 - accuracy: 0.8957\n",
            "Epoch 18: val_loss did not improve from 0.99133\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 0.9542 - accuracy: 0.8833 - val_loss: 1.0788 - val_accuracy: 0.8667 - lr: 0.0050\n",
            "Epoch 19/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 0.9889 - accuracy: 0.8706\n",
            "Epoch 19: val_loss improved from 0.99133 to 0.88387, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.9866 - accuracy: 0.8500 - val_loss: 0.8839 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 20/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 1.0239 - accuracy: 0.8316\n",
            "Epoch 20: val_loss improved from 0.88387 to 0.76959, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.0082 - accuracy: 0.8333 - val_loss: 0.7696 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 21/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 1.0692 - accuracy: 0.7789\n",
            "Epoch 21: val_loss improved from 0.76959 to 0.75209, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.0496 - accuracy: 0.8083 - val_loss: 0.7521 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 22/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.9180 - accuracy: 0.8500\n",
            "Epoch 22: val_loss improved from 0.75209 to 0.71255, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.9213 - accuracy: 0.8500 - val_loss: 0.7125 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 23/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 0.9113 - accuracy: 0.9111\n",
            "Epoch 23: val_loss improved from 0.71255 to 0.67447, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.8698 - accuracy: 0.9167 - val_loss: 0.6745 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 24/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.8956 - accuracy: 0.8500\n",
            "Epoch 24: val_loss improved from 0.67447 to 0.65876, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 0.9575 - accuracy: 0.8167 - val_loss: 0.6588 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 25/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.9947 - accuracy: 0.8435\n",
            "Epoch 25: val_loss improved from 0.65876 to 0.64821, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.9915 - accuracy: 0.8417 - val_loss: 0.6482 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 26/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.9005 - accuracy: 0.8571\n",
            "Epoch 26: val_loss improved from 0.64821 to 0.59846, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 0.8525 - accuracy: 0.8750 - val_loss: 0.5985 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 27/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.7953 - accuracy: 0.9474\n",
            "Epoch 27: val_loss improved from 0.59846 to 0.56026, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 14ms/step - loss: 0.7984 - accuracy: 0.9417 - val_loss: 0.5603 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 28/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.6820 - accuracy: 0.9217\n",
            "Epoch 28: val_loss improved from 0.56026 to 0.51791, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.6730 - accuracy: 0.9250 - val_loss: 0.5179 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 29/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 0.6074 - accuracy: 0.9222\n",
            "Epoch 29: val_loss did not improve from 0.51791\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.6188 - accuracy: 0.9333 - val_loss: 0.5216 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 30/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.6916 - accuracy: 0.9000\n",
            "Epoch 30: val_loss did not improve from 0.51791\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.6916 - accuracy: 0.9000 - val_loss: 0.5341 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 31/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.6444 - accuracy: 0.8783\n",
            "Epoch 31: val_loss improved from 0.51791 to 0.47853, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.6508 - accuracy: 0.8750 - val_loss: 0.4785 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 32/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.7339 - accuracy: 0.8583\n",
            "Epoch 32: val_loss did not improve from 0.47853\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.7339 - accuracy: 0.8583 - val_loss: 0.5197 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 33/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.7390 - accuracy: 0.9000\n",
            "Epoch 33: val_loss did not improve from 0.47853\n",
            "24/24 [==============================] - 0s 14ms/step - loss: 0.7347 - accuracy: 0.8917 - val_loss: 0.5143 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 34/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.6498 - accuracy: 0.9273\n",
            "Epoch 34: val_loss improved from 0.47853 to 0.46896, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.6553 - accuracy: 0.9167 - val_loss: 0.4690 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 35/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.7033 - accuracy: 0.9391\n",
            "Epoch 35: val_loss did not improve from 0.46896\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.6941 - accuracy: 0.9417 - val_loss: 0.4749 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 36/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.8248 - accuracy: 0.8421\n",
            "Epoch 36: val_loss did not improve from 0.46896\n",
            "24/24 [==============================] - 0s 14ms/step - loss: 0.7812 - accuracy: 0.8417 - val_loss: 0.5085 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 37/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.7821 - accuracy: 0.8522\n",
            "Epoch 37: val_loss did not improve from 0.46896\n",
            "\n",
            "Epoch 37: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455.\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 0.7673 - accuracy: 0.8583 - val_loss: 0.5833 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 38/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.7555 - accuracy: 0.8800\n",
            "Epoch 38: val_loss did not improve from 0.46896\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.8101 - accuracy: 0.8583 - val_loss: 0.4879 - val_accuracy: 0.9333 - lr: 0.0025\n",
            "Epoch 39/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.6718 - accuracy: 0.8800Restoring model weights from the end of the best epoch: 34.\n",
            "\n",
            "Epoch 39: val_loss did not improve from 0.46896\n",
            "24/24 [==============================] - 0s 15ms/step - loss: 0.6550 - accuracy: 0.8833 - val_loss: 0.4702 - val_accuracy: 0.9333 - lr: 0.0025\n",
            "Epoch 39: early stopping\n",
            "Fold Training Accuracy: 1.0\n",
            "Fold Validation Accuracy: 0.9333333373069763\n",
            "Fold Test Accuracy: 1.0\n",
            "Epoch 1/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 1.7985 - accuracy: 0.5300\n",
            "Epoch 1: val_loss improved from inf to 2.02242, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 2s 21ms/step - loss: 1.7887 - accuracy: 0.5583 - val_loss: 2.0224 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 2/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 1.4870 - accuracy: 0.7818\n",
            "Epoch 2: val_loss did not improve from 2.02242\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 1.5107 - accuracy: 0.7833 - val_loss: 2.2705 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 3/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 1.2487 - accuracy: 0.8700\n",
            "Epoch 3: val_loss improved from 2.02242 to 1.91066, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.2576 - accuracy: 0.8667 - val_loss: 1.9107 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 4/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.4769 - accuracy: 0.7619\n",
            "Epoch 4: val_loss improved from 1.91066 to 1.60317, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.5327 - accuracy: 0.7500 - val_loss: 1.6032 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 5/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.4827 - accuracy: 0.7333\n",
            "Epoch 5: val_loss did not improve from 1.60317\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.4914 - accuracy: 0.7417 - val_loss: 1.6630 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 6/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 1.1911 - accuracy: 0.8957\n",
            "Epoch 6: val_loss did not improve from 1.60317\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.1895 - accuracy: 0.8917 - val_loss: 1.6474 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 7/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.1915 - accuracy: 0.8762\n",
            "Epoch 7: val_loss improved from 1.60317 to 1.55853, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 1.2765 - accuracy: 0.8667 - val_loss: 1.5585 - val_accuracy: 0.6667 - lr: 0.0100\n",
            "Epoch 8/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 1.0301 - accuracy: 0.9043\n",
            "Epoch 8: val_loss improved from 1.55853 to 1.40903, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 1.0193 - accuracy: 0.9083 - val_loss: 1.4090 - val_accuracy: 0.7333 - lr: 0.0100\n",
            "Epoch 9/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 1.1601 - accuracy: 0.8500\n",
            "Epoch 9: val_loss did not improve from 1.40903\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 1.1783 - accuracy: 0.8583 - val_loss: 1.5195 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 10/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 1.0636 - accuracy: 0.8800\n",
            "Epoch 10: val_loss improved from 1.40903 to 1.29823, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.1021 - accuracy: 0.8750 - val_loss: 1.2982 - val_accuracy: 0.5333 - lr: 0.0100\n",
            "Epoch 11/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.1365 - accuracy: 0.8476\n",
            "Epoch 11: val_loss improved from 1.29823 to 1.24189, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.1280 - accuracy: 0.8500 - val_loss: 1.2419 - val_accuracy: 0.6000 - lr: 0.0100\n",
            "Epoch 12/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 1.0862 - accuracy: 0.8105\n",
            "Epoch 12: val_loss improved from 1.24189 to 1.22483, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.1526 - accuracy: 0.8083 - val_loss: 1.2248 - val_accuracy: 0.7333 - lr: 0.0100\n",
            "Epoch 13/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.1037 - accuracy: 0.8095\n",
            "Epoch 13: val_loss did not improve from 1.22483\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.1499 - accuracy: 0.8083 - val_loss: 1.3182 - val_accuracy: 0.6667 - lr: 0.0100\n",
            "Epoch 14/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 1.2753 - accuracy: 0.7750\n",
            "Epoch 14: val_loss did not improve from 1.22483\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.2753 - accuracy: 0.7750 - val_loss: 1.4708 - val_accuracy: 0.7333 - lr: 0.0100\n",
            "Epoch 15/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 1.0542 - accuracy: 0.9083\n",
            "Epoch 15: val_loss improved from 1.22483 to 1.20850, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 1.0542 - accuracy: 0.9083 - val_loss: 1.2085 - val_accuracy: 0.8667 - lr: 0.0100\n",
            "Epoch 16/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.2716 - accuracy: 0.8190\n",
            "Epoch 16: val_loss improved from 1.20850 to 1.13572, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 1.2407 - accuracy: 0.8250 - val_loss: 1.1357 - val_accuracy: 0.7333 - lr: 0.0100\n",
            "Epoch 17/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 0.9170 - accuracy: 0.8957\n",
            "Epoch 17: val_loss improved from 1.13572 to 0.84655, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.9417 - accuracy: 0.8833 - val_loss: 0.8466 - val_accuracy: 1.0000 - lr: 0.0100\n",
            "Epoch 18/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 1.1009 - accuracy: 0.7765\n",
            "Epoch 18: val_loss improved from 0.84655 to 0.77195, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 1.1154 - accuracy: 0.7833 - val_loss: 0.7720 - val_accuracy: 1.0000 - lr: 0.0100\n",
            "Epoch 19/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.0216 - accuracy: 0.8476\n",
            "Epoch 19: val_loss did not improve from 0.77195\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.9886 - accuracy: 0.8667 - val_loss: 0.8897 - val_accuracy: 0.9333 - lr: 0.0100\n",
            "Epoch 20/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 1.0118 - accuracy: 0.8471\n",
            "Epoch 20: val_loss did not improve from 0.77195\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.9726 - accuracy: 0.8583 - val_loss: 1.1138 - val_accuracy: 0.7333 - lr: 0.0100\n",
            "Epoch 21/50\n",
            "16/24 [===================>..........] - ETA: 0s - loss: 0.9324 - accuracy: 0.9125\n",
            "Epoch 21: val_loss improved from 0.77195 to 0.69659, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.9003 - accuracy: 0.9083 - val_loss: 0.6966 - val_accuracy: 0.9333 - lr: 0.0100\n",
            "Epoch 22/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 1.0211 - accuracy: 0.8455\n",
            "Epoch 22: val_loss improved from 0.69659 to 0.67330, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 18ms/step - loss: 0.9901 - accuracy: 0.8583 - val_loss: 0.6733 - val_accuracy: 1.0000 - lr: 0.0100\n",
            "Epoch 23/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.9822 - accuracy: 0.8952\n",
            "Epoch 23: val_loss improved from 0.67330 to 0.63351, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.0189 - accuracy: 0.8917 - val_loss: 0.6335 - val_accuracy: 1.0000 - lr: 0.0100\n",
            "Epoch 24/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 0.8109 - accuracy: 0.8941\n",
            "Epoch 24: val_loss did not improve from 0.63351\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.7731 - accuracy: 0.9000 - val_loss: 0.8997 - val_accuracy: 0.8000 - lr: 0.0100\n",
            "Epoch 25/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.0469 - accuracy: 0.7619\n",
            "Epoch 25: val_loss did not improve from 0.63351\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 1.0072 - accuracy: 0.7833 - val_loss: 0.6746 - val_accuracy: 0.9333 - lr: 0.0100\n",
            "Epoch 26/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.9453 - accuracy: 0.8182\n",
            "Epoch 26: val_loss did not improve from 0.63351\n",
            "\n",
            "Epoch 26: ReduceLROnPlateau reducing learning rate to 0.004999999888241291.\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 0.9425 - accuracy: 0.8250 - val_loss: 0.7238 - val_accuracy: 1.0000 - lr: 0.0100\n",
            "Epoch 27/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.9516 - accuracy: 0.8300\n",
            "Epoch 27: val_loss did not improve from 0.63351\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 1.0398 - accuracy: 0.8083 - val_loss: 0.7966 - val_accuracy: 0.8667 - lr: 0.0050\n",
            "Epoch 28/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 1.0626 - accuracy: 0.8333Restoring model weights from the end of the best epoch: 23.\n",
            "\n",
            "Epoch 28: val_loss did not improve from 0.63351\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 0.9868 - accuracy: 0.8667 - val_loss: 0.8852 - val_accuracy: 0.8000 - lr: 0.0050\n",
            "Epoch 28: early stopping\n",
            "Fold Training Accuracy: 0.9833333492279053\n",
            "Fold Validation Accuracy: 1.0\n",
            "Fold Test Accuracy: 1.0\n",
            "Epoch 1/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 1.3823 - accuracy: 0.7263\n",
            "Epoch 1: val_loss improved from inf to 2.53267, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 2s 21ms/step - loss: 1.3645 - accuracy: 0.7250 - val_loss: 2.5327 - val_accuracy: 0.2667 - lr: 0.0100\n",
            "Epoch 2/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 1.2614 - accuracy: 0.7647\n",
            "Epoch 2: val_loss did not improve from 2.53267\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 1.1870 - accuracy: 0.8083 - val_loss: 3.4289 - val_accuracy: 0.2667 - lr: 0.0100\n",
            "Epoch 3/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 1.3868 - accuracy: 0.8353\n",
            "Epoch 3: val_loss did not improve from 2.53267\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 1.3079 - accuracy: 0.8417 - val_loss: 3.1876 - val_accuracy: 0.2667 - lr: 0.0100\n",
            "Epoch 4/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 1.1987 - accuracy: 0.8421\n",
            "Epoch 4: val_loss improved from 2.53267 to 2.00121, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 17ms/step - loss: 1.2336 - accuracy: 0.8167 - val_loss: 2.0012 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 5/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 1.1843 - accuracy: 0.8455\n",
            "Epoch 5: val_loss did not improve from 2.00121\n",
            "24/24 [==============================] - 1s 22ms/step - loss: 1.3023 - accuracy: 0.8083 - val_loss: 2.1050 - val_accuracy: 0.2667 - lr: 0.0100\n",
            "Epoch 6/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 1.2735 - accuracy: 0.7889\n",
            "Epoch 6: val_loss improved from 2.00121 to 1.79542, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.2350 - accuracy: 0.8167 - val_loss: 1.7954 - val_accuracy: 0.4667 - lr: 0.0100\n",
            "Epoch 7/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 1.3237 - accuracy: 0.8211\n",
            "Epoch 7: val_loss did not improve from 1.79542\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 1.2887 - accuracy: 0.8333 - val_loss: 1.8328 - val_accuracy: 0.6000 - lr: 0.0100\n",
            "Epoch 8/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 1.1450 - accuracy: 0.8700\n",
            "Epoch 8: val_loss improved from 1.79542 to 1.58468, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 1s 23ms/step - loss: 1.1250 - accuracy: 0.8750 - val_loss: 1.5847 - val_accuracy: 0.6000 - lr: 0.0100\n",
            "Epoch 9/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 1.1811 - accuracy: 0.8600\n",
            "Epoch 9: val_loss did not improve from 1.58468\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 1.2376 - accuracy: 0.8417 - val_loss: 1.9145 - val_accuracy: 0.5333 - lr: 0.0100\n",
            "Epoch 10/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 1.4378 - accuracy: 0.8100\n",
            "Epoch 10: val_loss did not improve from 1.58468\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.4152 - accuracy: 0.8083 - val_loss: 1.7897 - val_accuracy: 0.6667 - lr: 0.0100\n",
            "Epoch 11/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 1.2759 - accuracy: 0.8000\n",
            "Epoch 11: val_loss did not improve from 1.58468\n",
            "\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 0.004999999888241291.\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 1.2724 - accuracy: 0.8000 - val_loss: 1.7687 - val_accuracy: 0.6000 - lr: 0.0100\n",
            "Epoch 12/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 1.0147 - accuracy: 0.8545\n",
            "Epoch 12: val_loss improved from 1.58468 to 1.54240, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 1.0135 - accuracy: 0.8667 - val_loss: 1.5424 - val_accuracy: 0.5333 - lr: 0.0050\n",
            "Epoch 13/50\n",
            "23/24 [===========================>..] - ETA: 0s - loss: 1.0666 - accuracy: 0.8696\n",
            "Epoch 13: val_loss improved from 1.54240 to 1.37321, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 1.0734 - accuracy: 0.8667 - val_loss: 1.3732 - val_accuracy: 0.6667 - lr: 0.0050\n",
            "Epoch 14/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 1.1431 - accuracy: 0.7429\n",
            "Epoch 14: val_loss improved from 1.37321 to 1.04190, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 1.1586 - accuracy: 0.7500 - val_loss: 1.0419 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 15/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.9970 - accuracy: 0.8700\n",
            "Epoch 15: val_loss improved from 1.04190 to 0.99814, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 1.0118 - accuracy: 0.8667 - val_loss: 0.9981 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 16/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.9692 - accuracy: 0.8842\n",
            "Epoch 16: val_loss did not improve from 0.99814\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.9140 - accuracy: 0.9000 - val_loss: 1.0720 - val_accuracy: 0.6667 - lr: 0.0050\n",
            "Epoch 17/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.8550 - accuracy: 0.9000\n",
            "Epoch 17: val_loss did not improve from 0.99814\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.8399 - accuracy: 0.9000 - val_loss: 1.0018 - val_accuracy: 0.7333 - lr: 0.0050\n",
            "Epoch 18/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.9739 - accuracy: 0.8400\n",
            "Epoch 18: val_loss improved from 0.99814 to 0.76793, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.9333 - accuracy: 0.8583 - val_loss: 0.7679 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 19/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.9156 - accuracy: 0.9000\n",
            "Epoch 19: val_loss did not improve from 0.76793\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 0.8779 - accuracy: 0.9083 - val_loss: 0.8431 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 20/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 1.2210 - accuracy: 0.8000\n",
            "Epoch 20: val_loss did not improve from 0.76793\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 1.1519 - accuracy: 0.8000 - val_loss: 0.8439 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 21/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.8616 - accuracy: 0.8632\n",
            "Epoch 21: val_loss improved from 0.76793 to 0.67012, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 9ms/step - loss: 0.8411 - accuracy: 0.8667 - val_loss: 0.6701 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 22/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.8689 - accuracy: 0.8952\n",
            "Epoch 22: val_loss did not improve from 0.67012\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.9388 - accuracy: 0.8583 - val_loss: 0.6702 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 23/50\n",
            "18/24 [=====================>........] - ETA: 0s - loss: 0.7473 - accuracy: 0.9000\n",
            "Epoch 23: val_loss improved from 0.67012 to 0.62038, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.7324 - accuracy: 0.9167 - val_loss: 0.6204 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 24/50\n",
            "17/24 [====================>.........] - ETA: 0s - loss: 0.8943 - accuracy: 0.8588\n",
            "Epoch 24: val_loss improved from 0.62038 to 0.58872, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.8566 - accuracy: 0.8667 - val_loss: 0.5887 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 25/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.8840 - accuracy: 0.8842\n",
            "Epoch 25: val_loss did not improve from 0.58872\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.9135 - accuracy: 0.8500 - val_loss: 0.6474 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 26/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.7595 - accuracy: 0.8857\n",
            "Epoch 26: val_loss improved from 0.58872 to 0.54672, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.7981 - accuracy: 0.8750 - val_loss: 0.5467 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 27/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.7219 - accuracy: 0.9083\n",
            "Epoch 27: val_loss improved from 0.54672 to 0.47809, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.7219 - accuracy: 0.9083 - val_loss: 0.4781 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 28/50\n",
            "19/24 [======================>.......] - ETA: 0s - loss: 0.7375 - accuracy: 0.8737\n",
            "Epoch 28: val_loss improved from 0.47809 to 0.45402, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.7323 - accuracy: 0.8667 - val_loss: 0.4540 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 29/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.7143 - accuracy: 0.8800\n",
            "Epoch 29: val_loss did not improve from 0.45402\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.7254 - accuracy: 0.8667 - val_loss: 0.4553 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 30/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.7840 - accuracy: 0.8857\n",
            "Epoch 30: val_loss did not improve from 0.45402\n",
            "24/24 [==============================] - 0s 14ms/step - loss: 0.7666 - accuracy: 0.8833 - val_loss: 0.7498 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 31/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.7054 - accuracy: 0.9000\n",
            "Epoch 31: val_loss improved from 0.45402 to 0.44046, saving model to best_model_fold.h5\n",
            "24/24 [==============================] - 0s 20ms/step - loss: 0.7608 - accuracy: 0.8750 - val_loss: 0.4405 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 32/50\n",
            "22/24 [==========================>...] - ETA: 0s - loss: 0.7838 - accuracy: 0.8455\n",
            "Epoch 32: val_loss did not improve from 0.44046\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 0.7755 - accuracy: 0.8500 - val_loss: 0.5464 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 33/50\n",
            "21/24 [=========================>....] - ETA: 0s - loss: 0.8377 - accuracy: 0.8667\n",
            "Epoch 33: val_loss did not improve from 0.44046\n",
            "24/24 [==============================] - 0s 16ms/step - loss: 0.8748 - accuracy: 0.8333 - val_loss: 0.6012 - val_accuracy: 0.9333 - lr: 0.0050\n",
            "Epoch 34/50\n",
            "20/24 [========================>.....] - ETA: 0s - loss: 0.8609 - accuracy: 0.8600\n",
            "Epoch 34: val_loss did not improve from 0.44046\n",
            "\n",
            "Epoch 34: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455.\n",
            "24/24 [==============================] - 0s 14ms/step - loss: 0.8065 - accuracy: 0.8833 - val_loss: 0.4790 - val_accuracy: 1.0000 - lr: 0.0050\n",
            "Epoch 35/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.7364 - accuracy: 0.8583\n",
            "Epoch 35: val_loss did not improve from 0.44046\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 0.7364 - accuracy: 0.8583 - val_loss: 0.4459 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 36/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.8256 - accuracy: 0.8250Restoring model weights from the end of the best epoch: 31.\n",
            "\n",
            "Epoch 36: val_loss did not improve from 0.44046\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.8256 - accuracy: 0.8250 - val_loss: 0.4576 - val_accuracy: 1.0000 - lr: 0.0025\n",
            "Epoch 36: early stopping\n",
            "Fold Training Accuracy: 0.9833333492279053\n",
            "Fold Validation Accuracy: 1.0\n",
            "Fold Test Accuracy: 1.0\n",
            "Average Training Accuracy: 0.9916666746139526\n",
            "Average Validation Accuracy: 0.9733333349227905\n",
            "Average Test Accuracy: 1.0\n"
          ]
        }
      ],
      "source": [
        "# Increase network size, adding regularization, dropout, call back to improve the model\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import KFold\n",
        "from keras.utils import to_categorical\n",
        "from keras import models, layers, optimizers, regularizers\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
        "from keras.models import load_model\n",
        "\n",
        "# Load data from CSV using Pandas\n",
        "dataset = pd.read_csv('/content/drive/MyDrive/Tree Classifier Project/TreeClassiferDataNew.csv')\n",
        "\n",
        "# Split into input (X) and output (y) variables\n",
        "X = dataset.iloc[:, 0:3]    # X is the data\n",
        "y = dataset.iloc[:, 3]      # y is the label\n",
        "# Using .iloc ensures that we are correctly using integer-based indexing for the DataFrame.\n",
        "\n",
        "# One-hot encode the target labels\n",
        "y = to_categorical(y)\n",
        "\n",
        "# Convert data to numpy arrays\n",
        "X = np.array(X)\n",
        "y = np.array(y)\n",
        "\n",
        "# Define a function to build the model\n",
        "def build_model():\n",
        "    model = models.Sequential()\n",
        "    model.add(layers.Dense(512, activation='relu', input_shape=(X.shape[1],), kernel_regularizer=regularizers.l2(0.001)))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.Dropout(0.5))\n",
        "    model.add(layers.Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.Dropout(0.5))\n",
        "    model.add(layers.Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.Dropout(0.5))\n",
        "    model.add(layers.Dense(3, activation='softmax'))\n",
        "\n",
        "    optimizer = optimizers.Adam(learning_rate=0.01)\n",
        "    model.compile(optimizer=optimizer,\n",
        "                  loss='categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "    return model\n",
        "\n",
        "# Initialize k-fold cross-validation\n",
        "k = 5\n",
        "kf = KFold(n_splits=k, shuffle=True, random_state=1)\n",
        "\n",
        "val_acc_per_fold = []\n",
        "test_acc_per_fold = []\n",
        "train_acc_per_fold = []\n",
        "\n",
        "for train_index, val_index in kf.split(X):\n",
        "    # Split the data into training and validation sets for the current fold\n",
        "    X_train, X_val = X[train_index], X[val_index]\n",
        "    y_train, y_val = y[train_index], y[val_index]\n",
        "\n",
        "    # Further split the validation set into validation and test sets\n",
        "    X_val, X_test = X_val[:len(X_val)//2], X_val[len(X_val)//2:]\n",
        "    y_val, y_test = y_val[:len(y_val)//2], y_val[len(y_val)//2:]\n",
        "\n",
        "    # Build and compile the model\n",
        "    model = build_model()\n",
        "\n",
        "    # Define the early stopping and model checkpoint callbacks\n",
        "    early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1, restore_best_weights=True)\n",
        "    model_checkpoint = ModelCheckpoint(filepath='best_model_fold.h5', monitor='val_loss', save_best_only=True, verbose=1)\n",
        "    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1, min_lr=0.00001)\n",
        "\n",
        "    # Train the model\n",
        "    history = model.fit(X_train, y_train,\n",
        "                        epochs=50,\n",
        "                        batch_size=5,\n",
        "                        validation_data=(X_val, y_val),\n",
        "                        callbacks=[early_stopping, model_checkpoint, reduce_lr])\n",
        "\n",
        "    # Load the best model for the current fold\n",
        "    best_model = load_model('best_model_fold.h5')\n",
        "\n",
        "    # Evaluate the best model on the training, validation, and test sets\n",
        "    train_loss, train_accuracy = best_model.evaluate(X_train, y_train, verbose=0)\n",
        "    val_loss, val_accuracy = best_model.evaluate(X_val, y_val, verbose=0)\n",
        "    test_loss, test_accuracy = best_model.evaluate(X_test, y_test, verbose=0)\n",
        "\n",
        "    train_acc_per_fold.append(train_accuracy)\n",
        "    val_acc_per_fold.append(val_accuracy)\n",
        "    test_acc_per_fold.append(test_accuracy)\n",
        "    print(f\"Fold Training Accuracy: {train_accuracy}\")\n",
        "    print(f\"Fold Validation Accuracy: {val_accuracy}\")\n",
        "    print(f\"Fold Test Accuracy: {test_accuracy}\")\n",
        "\n",
        "# Calculate the average performance across all folds\n",
        "print(f\"Average Training Accuracy: {np.mean(train_acc_per_fold)}\")\n",
        "print(f\"Average Validation Accuracy: {np.mean(val_acc_per_fold)}\")\n",
        "print(f\"Average Test Accuracy: {np.mean(test_acc_per_fold)}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Confusion matrix\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Assuming best_model, X_test, and y_test are already defined and available\n",
        "# Also assuming y_test is one-hot encoded and needs to be converted back to labels\n",
        "\n",
        "# Predict the labels for the test set\n",
        "y_pred = np.argmax(best_model.predict(X_test), axis=1)\n",
        "# Convert the one-hot encoded y_test back to labels\n",
        "y_true = np.argmax(y_test, axis=1)\n",
        "\n",
        "# Generate the confusion matrix\n",
        "cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "2fz6Fj904enA",
        "outputId": "06549c6b-b930-4815-f279-c63d54fb336a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 445ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}