us8k_cnn_baseline_crop.ipynb

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  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "colab": {
      "name": "us8k-cnn-baseline-crop.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i7x6wKsIGKLK",
        "colab_type": "text"
      },
      "source": [
        "**Reproduction of:**\n",
        "\n",
        "**Deep Convolutional Neural Networks and Data Augmentation for Environmental  Sound Classification** \n",
        "\n",
        "Justin Salamon and Juan Pablo Bello\n",
        "\n",
        "Some code taken from https://github.com/jaron/deep-listening/blob/master/4-us8k-cnn-salamon.ipynb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ES4Z8SEGUuJ",
        "colab_type": "text"
      },
      "source": [
        "# Load dataset & set enviroment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OKjtxFXwwtKN",
        "colab_type": "code",
        "outputId": "6e0042ca-663e-4244-894c-667779a14551",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!wget -O UrbanSound8K.tar.gz https://goo.gl/8hY5ER\n",
        "!tar -xvf UrbanSound8K.tar.gz\n",
        "!rm UrbanSound8K.tar.gz"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mSe han truncado las últimas 5000 líneas del flujo de salida.\u001b[0m\n",
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            "UrbanSound8K/audio/fold9/81722-3-0-22.wav\n",
            "UrbanSound8K/audio/fold9/81722-3-0-26.wav\n",
            "UrbanSound8K/audio/fold9/81722-3-0-28.wav\n",
            "UrbanSound8K/audio/fold9/85661-3-0-0.wav\n",
            "UrbanSound8K/audio/fold9/85664-3-0-0.wav\n",
            "UrbanSound8K/audio/fold9/87562-6-0-0.wav\n",
            "UrbanSound8K/audio/fold9/94182-9-0-0.wav\n",
            "UrbanSound8K/audio/fold9/94182-9-0-10.wav\n",
            "UrbanSound8K/audio/fold9/94182-9-0-16.wav\n",
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            "UrbanSound8K/audio/fold9/94182-9-0-23.wav\n",
            "UrbanSound8K/audio/fold9/94182-9-0-25.wav\n",
            "UrbanSound8K/audio/fold9/94182-9-0-9.wav\n",
            "UrbanSound8K/audio/fold9/96921-9-0-1.wav\n",
            "UrbanSound8K/audio/fold9/96921-9-0-11.wav\n",
            "UrbanSound8K/audio/fold9/96921-9-0-17.wav\n",
            "UrbanSound8K/audio/fold9/96921-9-0-18.wav\n",
            "UrbanSound8K/audio/fold9/96921-9-0-4.wav\n",
            "UrbanSound8K/audio/fold9/96921-9-0-9.wav\n",
            "UrbanSound8K/audio/fold9/98859-7-0-0.wav\n",
            "UrbanSound8K/audio/fold9/98859-7-1-0.wav\n",
            "UrbanSound8K/audio/fold9/98859-7-2-0.wav\n",
            "UrbanSound8K/audio/fold9/98859-7-2-1.wav\n",
            "UrbanSound8K/audio/fold9/98859-7-2-2.wav\n",
            "UrbanSound8K/audio/fold9/98859-7-2-3.wav\n",
            "UrbanSound8K/audio/fold9/98859-7-2-4.wav\n",
            "UrbanSound8K/audio/fold9/98859-7-2-5.wav\n",
            "UrbanSound8K/audio/fold9/99500-2-0-1.wav\n",
            "UrbanSound8K/audio/fold9/99500-2-0-11.wav\n",
            "UrbanSound8K/audio/fold9/99500-2-0-2.wav\n",
            "UrbanSound8K/audio/fold9/99500-2-0-23.wav\n",
            "UrbanSound8K/audio/fold9/99500-2-0-29.wav\n",
            "UrbanSound8K/audio/fold9/99500-2-0-39.wav\n",
            "UrbanSound8K/audio/fold9/99500-2-0-41.wav\n",
            "UrbanSound8K/audio/fold9/99500-2-0-50.wav\n",
            "UrbanSound8K/FREESOUNDCREDITS.txt\n",
            "UrbanSound8K/metadata/\n",
            "UrbanSound8K/metadata/.DS_Store\n",
            "UrbanSound8K/metadata/UrbanSound8K.csv\n",
            "UrbanSound8K/UrbanSound8K_README.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVY1_XEBAAL3",
        "colab_type": "code",
        "outputId": "cb8ea79c-7a49-4321-c03a-dcd60d9d37cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        }
      },
      "source": [
        "# get latest librosa version for audio preprocessing\n",
        "!pip install --upgrade librosa"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting librosa\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/77/b5/1817862d64a7c231afd15419d8418ae1f000742cac275e85c74b219cbccb/librosa-0.7.2.tar.gz (1.6MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6MB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: audioread>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (2.1.8)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.18.5)\n",
            "Requirement already satisfied, skipping upgrade: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.22.2.post1)\n",
            "Requirement already satisfied, skipping upgrade: joblib>=0.12 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.15.1)\n",
            "Requirement already satisfied, skipping upgrade: decorator>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (4.4.2)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.3 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: resampy>=0.2.2 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.2.2)\n",
            "Requirement already satisfied, skipping upgrade: numba>=0.43.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.48.0)\n",
            "Collecting soundfile>=0.9.0\n",
            "  Downloading https://files.pythonhosted.org/packages/eb/f2/3cbbbf3b96fb9fa91582c438b574cff3f45b29c772f94c400e2c99ef5db9/SoundFile-0.10.3.post1-py2.py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: llvmlite<0.32.0,>=0.31.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.43.0->librosa) (0.31.0)\n",
            "Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from numba>=0.43.0->librosa) (47.1.1)\n",
            "Requirement already satisfied, skipping upgrade: cffi>=1.0 in /usr/local/lib/python3.6/dist-packages (from soundfile>=0.9.0->librosa) (1.14.0)\n",
            "Requirement already satisfied, skipping upgrade: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi>=1.0->soundfile>=0.9.0->librosa) (2.20)\n",
            "Building wheels for collected packages: librosa\n",
            "  Building wheel for librosa (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for librosa: filename=librosa-0.7.2-cp36-none-any.whl size=1612885 sha256=4e403e9015e4df5711ad789c3acab7cb67265978dacd44447b5ee84f7570bc6b\n",
            "  Stored in directory: /root/.cache/pip/wheels/4c/6e/d7/bb93911540d2d1e44d690a1561871e5b6af82b69e80938abef\n",
            "Successfully built librosa\n",
            "Installing collected packages: soundfile, librosa\n",
            "  Found existing installation: librosa 0.6.3\n",
            "    Uninstalling librosa-0.6.3:\n",
            "      Successfully uninstalled librosa-0.6.3\n",
            "Successfully installed librosa-0.7.2 soundfile-0.10.3.post1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_knbqkBzycx",
        "colab_type": "code",
        "outputId": "8b2044b7-cc10-40be-b228-c7e6b02ff13f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "!git clone https://github.com/grudloff/Salomon2017Replication.git\n",
        "!mv Salomon2017Replication/*.py .\n",
        "!rm Salomon2017Replication -r"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Salomon2017Replication'...\n",
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            "remote: Total 95 (delta 54), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (95/95), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WIC_SAsEX3Cg",
        "colab_type": "code",
        "outputId": "9c4c1dfa-9e8a-4e60-f9bd-de6c75110ba3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "from preprocessing import save_folds, load_folds\n",
        "from model import build_model\n",
        "from evaluation import evaluate\n",
        "import numpy as np\n",
        "\n",
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "data_dir = \"UrbanSound8K/audio\" # Where you have saved the UrbanSound8K data set\"       \n",
        "save_dir = \"data/us8k\"\n",
        "load_dir = save_dir"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YX-vEiM474Db",
        "colab_type": "text"
      },
      "source": [
        "# Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bRrH-0tNz5Ub",
        "colab_type": "code",
        "outputId": "7441b060-ee98-47b8-8d35-fe00198e34fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "save_folds(data_dir, save_dir)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Saving fold1\n",
            "Features of fold1  =  (873, 128, 128, 1)\n",
            "Labels of fold1  =  (873,)\n",
            "Saved data/us8k/fold1_x.npy\n",
            "Saved data/us8k/fold1_y.npy\n",
            "\n",
            "Saving fold2\n",
            "Features of fold2  =  (888, 128, 128, 1)\n",
            "Labels of fold2  =  (888,)\n",
            "Saved data/us8k/fold2_x.npy\n",
            "Saved data/us8k/fold2_y.npy\n",
            "\n",
            "Saving fold3\n",
            "Features of fold3  =  (925, 128, 128, 1)\n",
            "Labels of fold3  =  (925,)\n",
            "Saved data/us8k/fold3_x.npy\n",
            "Saved data/us8k/fold3_y.npy\n",
            "\n",
            "Saving fold4\n",
            "Features of fold4  =  (990, 128, 128, 1)\n",
            "Labels of fold4  =  (990,)\n",
            "Saved data/us8k/fold4_x.npy\n",
            "Saved data/us8k/fold4_y.npy\n",
            "\n",
            "Saving fold5\n",
            "Features of fold5  =  (936, 128, 128, 1)\n",
            "Labels of fold5  =  (936,)\n",
            "Saved data/us8k/fold5_x.npy\n",
            "Saved data/us8k/fold5_y.npy\n",
            "\n",
            "Saving fold6\n",
            "Features of fold6  =  (823, 128, 128, 1)\n",
            "Labels of fold6  =  (823,)\n",
            "Saved data/us8k/fold6_x.npy\n",
            "Saved data/us8k/fold6_y.npy\n",
            "\n",
            "Saving fold7\n",
            "Features of fold7  =  (838, 128, 128, 1)\n",
            "Labels of fold7  =  (838,)\n",
            "Saved data/us8k/fold7_x.npy\n",
            "Saved data/us8k/fold7_y.npy\n",
            "\n",
            "Saving fold8\n",
            "Features of fold8  =  (806, 128, 128, 1)\n",
            "Labels of fold8  =  (806,)\n",
            "Saved data/us8k/fold8_x.npy\n",
            "Saved data/us8k/fold8_y.npy\n",
            "\n",
            "Saving fold9\n",
            "Features of fold9  =  (816, 128, 128, 1)\n",
            "Labels of fold9  =  (816,)\n",
            "Saved data/us8k/fold9_x.npy\n",
            "Saved data/us8k/fold9_y.npy\n",
            "\n",
            "Saving fold10\n",
            "Features of fold10  =  (837, 128, 128, 1)\n",
            "Labels of fold10  =  (837,)\n",
            "Saved data/us8k/fold10_x.npy\n",
            "Saved data/us8k/fold10_y.npy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z0UBfioDwWBs",
        "colab_type": "text"
      },
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QLYCNfTzwWBt",
        "colab_type": "text"
      },
      "source": [
        "10-Fold Crossvalidation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQyEXvd9wWBt",
        "colab_type": "code",
        "outputId": "5b626bcb-1ee0-4ae6-8691-ff02acc7be63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "acc = np.zeros(10)\n",
        "roc = np.zeros(10)\n",
        "CM = 0\n",
        "\n",
        "for f in range(1,10+1):\n",
        "\n",
        "    # load data\n",
        "    train_x, test_x, val_x, train_y, test_y, val_y = load_folds(load_dir, f)\n",
        "    \n",
        "    print(\"Building model...\")\n",
        "    model = build_model(channels = 1)\n",
        "\n",
        "    # now fit the model to the training data, evaluating loss against the validation data\n",
        "    print(\"Training model...\")\n",
        "    model.fit(train_x, train_y, validation_data=(test_x, test_y), \n",
        "              callbacks=[EarlyStopping(restore_best_weights=True, patience=15)], \n",
        "              batch_size=100, epochs = 200)\n",
        "\n",
        "    # now evaluate the trained model against the unseen test data\n",
        "    print(\"Evaluating model...\")\n",
        "    roc[f-1], acc[f-1], cm = evaluate(model, val_x, val_y)\n",
        "    CM += cm\n",
        "    \n",
        "print ('\\nAverage R.O.C:', np.mean(roc))\n",
        "print ('Average Accuracy:', np.mean(acc))\n",
        "\n",
        "# using all folds: best ROC = 0.91, f-score = 0.592 (50 epochs)\n",
        "# using 2 folds: average ROC = 0.792, average f-score = 0.335\n",
        "\n",
        "# if you want to save the model, uncomment this...\n",
        "#filepath = \"models/salamon-cnn-model.h5\"\n",
        "#model.save(filepath)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "*** Train on {3, 4, 5, 6, 7, 8, 9, 10} Validate on 1 Test on 2 ***\n",
            "val_x shape:  (873, 128, 128, 1)\n",
            "test_x shape:  (888, 128, 128, 1)\n",
            "train_x shape:  (6971, 128, 128, 1)\n",
            "val_y shape:  (873,)\n",
            "test_y shape:  (888,)\n",
            "train_y shape:  (6971,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 2s 24ms/step - loss: 3.3891 - accuracy: 0.1192 - val_loss: 2.4458 - val_accuracy: 0.1509\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 2.4416 - accuracy: 0.1354 - val_loss: 2.4365 - val_accuracy: 0.1340\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 2.4283 - accuracy: 0.1360 - val_loss: 2.4022 - val_accuracy: 0.1734\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 2.3984 - accuracy: 0.1667 - val_loss: 2.3435 - val_accuracy: 0.2173\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3521 - accuracy: 0.1965 - val_loss: 2.2868 - val_accuracy: 0.2579\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3099 - accuracy: 0.2162 - val_loss: 2.2043 - val_accuracy: 0.3052\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.2765 - accuracy: 0.2284 - val_loss: 2.1364 - val_accuracy: 0.3333\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 2.2317 - accuracy: 0.2421 - val_loss: 2.1398 - val_accuracy: 0.2804\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 2.2091 - accuracy: 0.2444 - val_loss: 2.1515 - val_accuracy: 0.2691\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 1s 20ms/step - loss: 2.1295 - accuracy: 0.2734 - val_loss: 1.9373 - val_accuracy: 0.3671\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 1s 20ms/step - loss: 2.0794 - accuracy: 0.2916 - val_loss: 1.8527 - val_accuracy: 0.3930\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 1.9945 - accuracy: 0.3258 - val_loss: 1.8342 - val_accuracy: 0.4065\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 1.9566 - accuracy: 0.3393 - val_loss: 1.7977 - val_accuracy: 0.4223\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.9149 - accuracy: 0.3634 - val_loss: 1.6586 - val_accuracy: 0.4707\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 1.8396 - accuracy: 0.3905 - val_loss: 1.6426 - val_accuracy: 0.4955\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8033 - accuracy: 0.4124 - val_loss: 1.6264 - val_accuracy: 0.5225\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7536 - accuracy: 0.4263 - val_loss: 1.5962 - val_accuracy: 0.5090\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7262 - accuracy: 0.4400 - val_loss: 1.6121 - val_accuracy: 0.4921\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6814 - accuracy: 0.4652 - val_loss: 1.7896 - val_accuracy: 0.4212\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6631 - accuracy: 0.4750 - val_loss: 1.5522 - val_accuracy: 0.5383\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6093 - accuracy: 0.4933 - val_loss: 1.4533 - val_accuracy: 0.5473\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5821 - accuracy: 0.5085 - val_loss: 1.4941 - val_accuracy: 0.5563\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5312 - accuracy: 0.5206 - val_loss: 1.4343 - val_accuracy: 0.5732\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5287 - accuracy: 0.5235 - val_loss: 1.4525 - val_accuracy: 0.5631\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4882 - accuracy: 0.5408 - val_loss: 1.4410 - val_accuracy: 0.5484\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4865 - accuracy: 0.5394 - val_loss: 1.4469 - val_accuracy: 0.5867\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4456 - accuracy: 0.5547 - val_loss: 1.4317 - val_accuracy: 0.5608\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4343 - accuracy: 0.5573 - val_loss: 1.4301 - val_accuracy: 0.5923\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 1.4045 - accuracy: 0.5706 - val_loss: 1.4449 - val_accuracy: 0.5619\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 1s 20ms/step - loss: 1.3679 - accuracy: 0.5795 - val_loss: 1.3651 - val_accuracy: 0.6396\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3397 - accuracy: 0.5940 - val_loss: 1.4544 - val_accuracy: 0.5935\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3300 - accuracy: 0.5936 - val_loss: 1.3241 - val_accuracy: 0.6250\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2988 - accuracy: 0.6098 - val_loss: 1.3009 - val_accuracy: 0.6171\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2933 - accuracy: 0.6058 - val_loss: 1.3367 - val_accuracy: 0.6160\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2775 - accuracy: 0.6094 - val_loss: 1.3099 - val_accuracy: 0.6070\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2628 - accuracy: 0.6188 - val_loss: 1.2921 - val_accuracy: 0.6273\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2477 - accuracy: 0.6216 - val_loss: 1.4493 - val_accuracy: 0.4932\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2685 - accuracy: 0.6256 - val_loss: 1.3971 - val_accuracy: 0.5417\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 1.2858 - accuracy: 0.6120 - val_loss: 1.3187 - val_accuracy: 0.5991\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 1.1989 - accuracy: 0.6392 - val_loss: 1.4081 - val_accuracy: 0.6002\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2272 - accuracy: 0.6336 - val_loss: 1.2507 - val_accuracy: 0.6532\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1751 - accuracy: 0.6477 - val_loss: 1.2853 - val_accuracy: 0.6329\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1921 - accuracy: 0.6473 - val_loss: 1.2388 - val_accuracy: 0.6408\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1574 - accuracy: 0.6609 - val_loss: 1.2456 - val_accuracy: 0.6340\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1409 - accuracy: 0.6534 - val_loss: 1.4159 - val_accuracy: 0.5709\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1598 - accuracy: 0.6533 - val_loss: 1.2542 - val_accuracy: 0.5822\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1155 - accuracy: 0.6712 - val_loss: 1.2763 - val_accuracy: 0.6182\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0986 - accuracy: 0.6678 - val_loss: 1.2685 - val_accuracy: 0.5946\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0908 - accuracy: 0.6761 - val_loss: 1.1862 - val_accuracy: 0.6475\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0945 - accuracy: 0.6785 - val_loss: 1.5295 - val_accuracy: 0.5462\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1034 - accuracy: 0.6719 - val_loss: 1.2740 - val_accuracy: 0.6396\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0409 - accuracy: 0.6947 - val_loss: 1.2634 - val_accuracy: 0.5732\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0453 - accuracy: 0.6965 - val_loss: 1.2225 - val_accuracy: 0.6475\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0324 - accuracy: 0.6966 - val_loss: 1.3656 - val_accuracy: 0.5968\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0130 - accuracy: 0.7117 - val_loss: 1.4736 - val_accuracy: 0.5597\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0277 - accuracy: 0.6967 - val_loss: 1.2673 - val_accuracy: 0.6329\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9913 - accuracy: 0.7079 - val_loss: 1.3031 - val_accuracy: 0.5743\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 0.9902 - accuracy: 0.7074 - val_loss: 1.3854 - val_accuracy: 0.6182\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9971 - accuracy: 0.7109 - val_loss: 1.3278 - val_accuracy: 0.6318\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9570 - accuracy: 0.7262 - val_loss: 1.2237 - val_accuracy: 0.6340\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9686 - accuracy: 0.7180 - val_loss: 1.2956 - val_accuracy: 0.6329\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9680 - accuracy: 0.7119 - val_loss: 1.3086 - val_accuracy: 0.6227\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9410 - accuracy: 0.7284 - val_loss: 1.2780 - val_accuracy: 0.6453\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9328 - accuracy: 0.7290 - val_loss: 2.1190 - val_accuracy: 0.3941\n",
            "Evaluating model...\n",
            "ROC: 0.916\n",
            "\n",
            "Accuracy = 0.59\n",
            "F-Score: 0.59\n",
            "\n",
            "*** Train on {1, 3, 4, 5, 6, 7, 8, 10} Validate on 2 Test on 9 ***\n",
            "val_x shape:  (888, 128, 128, 1)\n",
            "test_x shape:  (816, 128, 128, 1)\n",
            "train_x shape:  (7028, 128, 128, 1)\n",
            "val_y shape:  (888,)\n",
            "test_y shape:  (816,)\n",
            "train_y shape:  (7028,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "71/71 [==============================] - 2s 21ms/step - loss: 3.0864 - accuracy: 0.1165 - val_loss: 2.4159 - val_accuracy: 0.2034\n",
            "Epoch 2/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.4285 - accuracy: 0.1268 - val_loss: 2.3653 - val_accuracy: 0.2157\n",
            "Epoch 3/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.4073 - accuracy: 0.1318 - val_loss: 2.3604 - val_accuracy: 0.2475\n",
            "Epoch 4/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.3658 - accuracy: 0.1626 - val_loss: 2.3819 - val_accuracy: 0.2439\n",
            "Epoch 5/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.3143 - accuracy: 0.1928 - val_loss: 2.2255 - val_accuracy: 0.2978\n",
            "Epoch 6/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.2714 - accuracy: 0.2228 - val_loss: 2.2099 - val_accuracy: 0.3064\n",
            "Epoch 7/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.2182 - accuracy: 0.2487 - val_loss: 2.3076 - val_accuracy: 0.2181\n",
            "Epoch 8/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.1863 - accuracy: 0.2674 - val_loss: 2.1123 - val_accuracy: 0.3272\n",
            "Epoch 9/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.1212 - accuracy: 0.2925 - val_loss: 2.0678 - val_accuracy: 0.3395\n",
            "Epoch 10/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.0713 - accuracy: 0.3123 - val_loss: 2.1065 - val_accuracy: 0.2953\n",
            "Epoch 11/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.0010 - accuracy: 0.3409 - val_loss: 1.9818 - val_accuracy: 0.3824\n",
            "Epoch 12/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.9711 - accuracy: 0.3483 - val_loss: 1.9311 - val_accuracy: 0.4461\n",
            "Epoch 13/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.9252 - accuracy: 0.3623 - val_loss: 1.8983 - val_accuracy: 0.4522\n",
            "Epoch 14/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.8959 - accuracy: 0.3808 - val_loss: 1.9691 - val_accuracy: 0.3922\n",
            "Epoch 15/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.8240 - accuracy: 0.4047 - val_loss: 1.8327 - val_accuracy: 0.4387\n",
            "Epoch 16/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.8124 - accuracy: 0.4075 - val_loss: 2.0501 - val_accuracy: 0.3395\n",
            "Epoch 17/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.7394 - accuracy: 0.4294 - val_loss: 2.1985 - val_accuracy: 0.2819\n",
            "Epoch 18/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.7421 - accuracy: 0.4307 - val_loss: 1.8726 - val_accuracy: 0.4105\n",
            "Epoch 19/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.7065 - accuracy: 0.4422 - val_loss: 1.7503 - val_accuracy: 0.4951\n",
            "Epoch 20/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.6835 - accuracy: 0.4612 - val_loss: 1.6820 - val_accuracy: 0.4767\n",
            "Epoch 21/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.6685 - accuracy: 0.4593 - val_loss: 1.8124 - val_accuracy: 0.4142\n",
            "Epoch 22/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.6462 - accuracy: 0.4674 - val_loss: 1.5930 - val_accuracy: 0.5074\n",
            "Epoch 23/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5959 - accuracy: 0.4876 - val_loss: 1.7027 - val_accuracy: 0.4142\n",
            "Epoch 24/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5804 - accuracy: 0.4973 - val_loss: 1.8224 - val_accuracy: 0.4277\n",
            "Epoch 25/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5825 - accuracy: 0.4912 - val_loss: 1.5560 - val_accuracy: 0.5110\n",
            "Epoch 26/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5448 - accuracy: 0.5054 - val_loss: 1.6735 - val_accuracy: 0.5208\n",
            "Epoch 27/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5301 - accuracy: 0.5108 - val_loss: 1.5455 - val_accuracy: 0.5233\n",
            "Epoch 28/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4813 - accuracy: 0.5235 - val_loss: 4.0659 - val_accuracy: 0.2181\n",
            "Epoch 29/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.5258 - accuracy: 0.1288 - val_loss: 2.3891 - val_accuracy: 0.1042\n",
            "Epoch 30/200\n",
            "71/71 [==============================] - 1s 19ms/step - loss: 2.1834 - accuracy: 0.2544 - val_loss: 1.8438 - val_accuracy: 0.4449\n",
            "Epoch 31/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.8416 - accuracy: 0.4088 - val_loss: 1.6977 - val_accuracy: 0.4632\n",
            "Epoch 32/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.7338 - accuracy: 0.4368 - val_loss: 1.6129 - val_accuracy: 0.5000\n",
            "Epoch 33/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.6092 - accuracy: 0.4745 - val_loss: 1.5691 - val_accuracy: 0.5380\n",
            "Epoch 34/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.6331 - accuracy: 0.4804 - val_loss: 1.5949 - val_accuracy: 0.4400\n",
            "Epoch 35/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5432 - accuracy: 0.5108 - val_loss: 1.5614 - val_accuracy: 0.5221\n",
            "Epoch 36/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5034 - accuracy: 0.5249 - val_loss: 1.7060 - val_accuracy: 0.4596\n",
            "Epoch 37/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5389 - accuracy: 0.5097 - val_loss: 1.6356 - val_accuracy: 0.4571\n",
            "Epoch 38/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4424 - accuracy: 0.5462 - val_loss: 1.5107 - val_accuracy: 0.5086\n",
            "Epoch 39/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4330 - accuracy: 0.5539 - val_loss: 1.4884 - val_accuracy: 0.5221\n",
            "Epoch 40/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4015 - accuracy: 0.5618 - val_loss: 1.7968 - val_accuracy: 0.4608\n",
            "Epoch 41/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3742 - accuracy: 0.5756 - val_loss: 1.5627 - val_accuracy: 0.4755\n",
            "Epoch 42/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3396 - accuracy: 0.5821 - val_loss: 2.2834 - val_accuracy: 0.3444\n",
            "Epoch 43/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3986 - accuracy: 0.5599 - val_loss: 1.5839 - val_accuracy: 0.5025\n",
            "Epoch 44/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3294 - accuracy: 0.5985 - val_loss: 1.6900 - val_accuracy: 0.4534\n",
            "Epoch 45/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3351 - accuracy: 0.5867 - val_loss: 1.6146 - val_accuracy: 0.5233\n",
            "Epoch 46/200\n",
            "71/71 [==============================] - 1s 19ms/step - loss: 1.2670 - accuracy: 0.6188 - val_loss: 1.4690 - val_accuracy: 0.5772\n",
            "Epoch 47/200\n",
            "71/71 [==============================] - 1s 19ms/step - loss: 1.2998 - accuracy: 0.6049 - val_loss: 1.5820 - val_accuracy: 0.4755\n",
            "Epoch 48/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.2491 - accuracy: 0.6190 - val_loss: 1.7388 - val_accuracy: 0.4755\n",
            "Epoch 49/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.2405 - accuracy: 0.6192 - val_loss: 1.4224 - val_accuracy: 0.5539\n",
            "Epoch 50/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.2372 - accuracy: 0.6180 - val_loss: 1.5187 - val_accuracy: 0.5404\n",
            "Epoch 51/200\n",
            "71/71 [==============================] - 1s 17ms/step - loss: 1.2242 - accuracy: 0.6255 - val_loss: 1.5441 - val_accuracy: 0.4902\n",
            "Epoch 52/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.2000 - accuracy: 0.6365 - val_loss: 1.5500 - val_accuracy: 0.4988\n",
            "Epoch 53/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1700 - accuracy: 0.6471 - val_loss: 1.4510 - val_accuracy: 0.5748\n",
            "Epoch 54/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.2134 - accuracy: 0.6312 - val_loss: 1.4748 - val_accuracy: 0.5012\n",
            "Epoch 55/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1412 - accuracy: 0.6514 - val_loss: 1.4995 - val_accuracy: 0.5748\n",
            "Epoch 56/200\n",
            "71/71 [==============================] - 1s 17ms/step - loss: 1.1717 - accuracy: 0.6520 - val_loss: 1.4239 - val_accuracy: 0.5674\n",
            "Epoch 57/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1406 - accuracy: 0.6564 - val_loss: 1.3347 - val_accuracy: 0.6275\n",
            "Epoch 58/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1156 - accuracy: 0.6679 - val_loss: 1.4184 - val_accuracy: 0.6140\n",
            "Epoch 59/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1772 - accuracy: 0.6498 - val_loss: 1.3720 - val_accuracy: 0.6054\n",
            "Epoch 60/200\n",
            "71/71 [==============================] - 1s 19ms/step - loss: 1.0977 - accuracy: 0.6715 - val_loss: 1.3938 - val_accuracy: 0.6127\n",
            "Epoch 61/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0879 - accuracy: 0.6661 - val_loss: 1.4085 - val_accuracy: 0.6066\n",
            "Epoch 62/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0517 - accuracy: 0.6826 - val_loss: 1.4302 - val_accuracy: 0.6176\n",
            "Epoch 63/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0821 - accuracy: 0.6746 - val_loss: 1.4552 - val_accuracy: 0.5404\n",
            "Epoch 64/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0728 - accuracy: 0.6776 - val_loss: 1.6175 - val_accuracy: 0.5699\n",
            "Epoch 65/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0677 - accuracy: 0.6793 - val_loss: 1.5775 - val_accuracy: 0.5735\n",
            "Epoch 66/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0530 - accuracy: 0.6827 - val_loss: 1.4947 - val_accuracy: 0.5441\n",
            "Epoch 67/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0216 - accuracy: 0.6907 - val_loss: 1.4709 - val_accuracy: 0.5772\n",
            "Epoch 68/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0276 - accuracy: 0.6935 - val_loss: 1.5862 - val_accuracy: 0.5748\n",
            "Epoch 69/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0101 - accuracy: 0.6989 - val_loss: 1.4442 - val_accuracy: 0.6250\n",
            "Epoch 70/200\n",
            "71/71 [==============================] - 2s 22ms/step - loss: 1.0361 - accuracy: 0.6891 - val_loss: 1.3799 - val_accuracy: 0.6287\n",
            "Epoch 71/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9783 - accuracy: 0.7073 - val_loss: 1.6090 - val_accuracy: 0.5650\n",
            "Epoch 72/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9705 - accuracy: 0.7102 - val_loss: 1.6534 - val_accuracy: 0.5478\n",
            "Evaluating model...\n",
            "ROC: 0.905\n",
            "\n",
            "Accuracy = 0.54\n",
            "F-Score: 0.54\n",
            "\n",
            "*** Train on {2, 4, 5, 6, 7, 8, 9, 10} Validate on 3 Test on 1 ***\n",
            "val_x shape:  (925, 128, 128, 1)\n",
            "test_x shape:  (873, 128, 128, 1)\n",
            "train_x shape:  (6934, 128, 128, 1)\n",
            "val_y shape:  (925,)\n",
            "test_y shape:  (873,)\n",
            "train_y shape:  (6934,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 2s 21ms/step - loss: 3.3297 - accuracy: 0.1245 - val_loss: 2.4387 - val_accuracy: 0.1157\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4300 - accuracy: 0.1510 - val_loss: 2.4217 - val_accuracy: 0.1569\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4089 - accuracy: 0.1592 - val_loss: 2.4029 - val_accuracy: 0.1329\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3879 - accuracy: 0.1774 - val_loss: 2.3968 - val_accuracy: 0.1913\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3742 - accuracy: 0.1868 - val_loss: 2.3418 - val_accuracy: 0.2016\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3362 - accuracy: 0.2005 - val_loss: 2.3093 - val_accuracy: 0.2279\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.2961 - accuracy: 0.2173 - val_loss: 2.3520 - val_accuracy: 0.1753\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.2387 - accuracy: 0.2437 - val_loss: 2.1789 - val_accuracy: 0.3047\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1770 - accuracy: 0.2664 - val_loss: 2.1075 - val_accuracy: 0.3047\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1496 - accuracy: 0.2681 - val_loss: 2.0890 - val_accuracy: 0.3356\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1240 - accuracy: 0.2743 - val_loss: 2.0662 - val_accuracy: 0.3299\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.0803 - accuracy: 0.2913 - val_loss: 1.9716 - val_accuracy: 0.3677\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.0241 - accuracy: 0.3147 - val_loss: 1.9623 - val_accuracy: 0.3517\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.9957 - accuracy: 0.3291 - val_loss: 1.8949 - val_accuracy: 0.4307\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.9653 - accuracy: 0.3385 - val_loss: 1.9454 - val_accuracy: 0.3654\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.9141 - accuracy: 0.3610 - val_loss: 1.7038 - val_accuracy: 0.4651\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8558 - accuracy: 0.3820 - val_loss: 1.7114 - val_accuracy: 0.5143\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8123 - accuracy: 0.4018 - val_loss: 1.7785 - val_accuracy: 0.4605\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7568 - accuracy: 0.4312 - val_loss: 1.5026 - val_accuracy: 0.5418\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7327 - accuracy: 0.4399 - val_loss: 1.6504 - val_accuracy: 0.5040\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7179 - accuracy: 0.4523 - val_loss: 1.5497 - val_accuracy: 0.5166\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6814 - accuracy: 0.4632 - val_loss: 1.5244 - val_accuracy: 0.5155\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6370 - accuracy: 0.4821 - val_loss: 1.4710 - val_accuracy: 0.5556\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5840 - accuracy: 0.5019 - val_loss: 1.4821 - val_accuracy: 0.5556\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5733 - accuracy: 0.5108 - val_loss: 1.4482 - val_accuracy: 0.5361\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5607 - accuracy: 0.5160 - val_loss: 1.6269 - val_accuracy: 0.5040\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5102 - accuracy: 0.5326 - val_loss: 1.4589 - val_accuracy: 0.5601\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4632 - accuracy: 0.5473 - val_loss: 1.5949 - val_accuracy: 0.5292\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4098 - accuracy: 0.5629 - val_loss: 1.4172 - val_accuracy: 0.5945\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4042 - accuracy: 0.5738 - val_loss: 1.3889 - val_accuracy: 0.6037\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 1s 17ms/step - loss: 1.3843 - accuracy: 0.5851 - val_loss: 1.9044 - val_accuracy: 0.4353\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4697 - accuracy: 0.5562 - val_loss: 1.4089 - val_accuracy: 0.5876\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2846 - accuracy: 0.6135 - val_loss: 1.3708 - val_accuracy: 0.6082\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3270 - accuracy: 0.6072 - val_loss: 1.3443 - val_accuracy: 0.6174\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2695 - accuracy: 0.6170 - val_loss: 1.5659 - val_accuracy: 0.5578\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2649 - accuracy: 0.6259 - val_loss: 1.3680 - val_accuracy: 0.6197\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2556 - accuracy: 0.6234 - val_loss: 1.4240 - val_accuracy: 0.5670\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2249 - accuracy: 0.6382 - val_loss: 1.4711 - val_accuracy: 0.4983\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1937 - accuracy: 0.6439 - val_loss: 1.3471 - val_accuracy: 0.6346\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1516 - accuracy: 0.6644 - val_loss: 1.6467 - val_accuracy: 0.5178\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 1s 17ms/step - loss: 1.1743 - accuracy: 0.6618 - val_loss: 1.5193 - val_accuracy: 0.5842\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1207 - accuracy: 0.6728 - val_loss: 1.4597 - val_accuracy: 0.5601\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1724 - accuracy: 0.6536 - val_loss: 1.3996 - val_accuracy: 0.5853\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2043 - accuracy: 0.6426 - val_loss: 1.2197 - val_accuracy: 0.6529\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1614 - accuracy: 0.6651 - val_loss: 1.2909 - val_accuracy: 0.6277\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1412 - accuracy: 0.6658 - val_loss: 1.3835 - val_accuracy: 0.6323\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0728 - accuracy: 0.6856 - val_loss: 1.3686 - val_accuracy: 0.5899\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0440 - accuracy: 0.6973 - val_loss: 1.2948 - val_accuracy: 0.6254\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0451 - accuracy: 0.7010 - val_loss: 1.4453 - val_accuracy: 0.5659\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0137 - accuracy: 0.7038 - val_loss: 1.2511 - val_accuracy: 0.6644\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0098 - accuracy: 0.7110 - val_loss: 1.4027 - val_accuracy: 0.5911\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0078 - accuracy: 0.7192 - val_loss: 1.5287 - val_accuracy: 0.5567\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0001 - accuracy: 0.7142 - val_loss: 1.3094 - val_accuracy: 0.6289\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9670 - accuracy: 0.7179 - val_loss: 1.2918 - val_accuracy: 0.6621\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9502 - accuracy: 0.7286 - val_loss: 1.4263 - val_accuracy: 0.6300\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9615 - accuracy: 0.7322 - val_loss: 1.3208 - val_accuracy: 0.6346\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9353 - accuracy: 0.7323 - val_loss: 1.3640 - val_accuracy: 0.6575\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9283 - accuracy: 0.7351 - val_loss: 1.3017 - val_accuracy: 0.6380\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9044 - accuracy: 0.7465 - val_loss: 1.4439 - val_accuracy: 0.6254\n",
            "Evaluating model...\n",
            "ROC: 0.91\n",
            "\n",
            "Accuracy = 0.59\n",
            "F-Score: 0.59\n",
            "\n",
            "*** Train on {1, 2, 3, 5, 7, 8, 9, 10} Validate on 4 Test on 6 ***\n",
            "val_x shape:  (990, 128, 128, 1)\n",
            "test_x shape:  (823, 128, 128, 1)\n",
            "train_x shape:  (6919, 128, 128, 1)\n",
            "val_y shape:  (990,)\n",
            "test_y shape:  (823,)\n",
            "train_y shape:  (6919,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 2s 21ms/step - loss: 2.7200 - accuracy: 0.1233 - val_loss: 2.4235 - val_accuracy: 0.1519\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4176 - accuracy: 0.1337 - val_loss: 2.4181 - val_accuracy: 0.1701\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4100 - accuracy: 0.1308 - val_loss: 2.4088 - val_accuracy: 0.1640\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3916 - accuracy: 0.1572 - val_loss: 2.4049 - val_accuracy: 0.1349\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3858 - accuracy: 0.1596 - val_loss: 2.3524 - val_accuracy: 0.1810\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3208 - accuracy: 0.1929 - val_loss: 2.3401 - val_accuracy: 0.1896\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 1s 17ms/step - loss: 2.2627 - accuracy: 0.2245 - val_loss: 2.2874 - val_accuracy: 0.2430\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1954 - accuracy: 0.2523 - val_loss: 2.1838 - val_accuracy: 0.2527\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1376 - accuracy: 0.2733 - val_loss: 2.1569 - val_accuracy: 0.2454\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.0942 - accuracy: 0.2985 - val_loss: 2.1339 - val_accuracy: 0.3123\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.0464 - accuracy: 0.3097 - val_loss: 2.1097 - val_accuracy: 0.2965\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.9903 - accuracy: 0.3433 - val_loss: 2.0701 - val_accuracy: 0.3001\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.9578 - accuracy: 0.3457 - val_loss: 2.0083 - val_accuracy: 0.3329\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.9141 - accuracy: 0.3730 - val_loss: 1.9420 - val_accuracy: 0.3475\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8760 - accuracy: 0.3752 - val_loss: 1.9059 - val_accuracy: 0.3706\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8546 - accuracy: 0.3888 - val_loss: 1.8920 - val_accuracy: 0.3621\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8067 - accuracy: 0.4053 - val_loss: 1.9584 - val_accuracy: 0.3171\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7703 - accuracy: 0.4236 - val_loss: 1.9666 - val_accuracy: 0.3244\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7565 - accuracy: 0.4264 - val_loss: 1.8998 - val_accuracy: 0.3973\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7242 - accuracy: 0.4369 - val_loss: 2.4738 - val_accuracy: 0.2904\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8660 - accuracy: 0.3901 - val_loss: 1.7955 - val_accuracy: 0.4022\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6457 - accuracy: 0.4571 - val_loss: 1.7117 - val_accuracy: 0.4727\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 1s 17ms/step - loss: 1.6194 - accuracy: 0.4768 - val_loss: 1.7510 - val_accuracy: 0.3876\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6075 - accuracy: 0.4755 - val_loss: 3.1041 - val_accuracy: 0.2369\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6552 - accuracy: 0.4628 - val_loss: 1.7021 - val_accuracy: 0.4557\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5215 - accuracy: 0.5098 - val_loss: 1.6854 - val_accuracy: 0.4143\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5295 - accuracy: 0.5122 - val_loss: 1.7949 - val_accuracy: 0.4058\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4861 - accuracy: 0.5235 - val_loss: 1.7725 - val_accuracy: 0.3730\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 1s 17ms/step - loss: 1.4740 - accuracy: 0.5303 - val_loss: 1.7789 - val_accuracy: 0.4216\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4577 - accuracy: 0.5270 - val_loss: 2.1469 - val_accuracy: 0.3390\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4506 - accuracy: 0.5414 - val_loss: 1.8138 - val_accuracy: 0.4216\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4091 - accuracy: 0.5476 - val_loss: 1.6381 - val_accuracy: 0.4714\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4022 - accuracy: 0.5598 - val_loss: 2.1876 - val_accuracy: 0.3779\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3727 - accuracy: 0.5713 - val_loss: 1.9034 - val_accuracy: 0.3706\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 1s 17ms/step - loss: 1.3281 - accuracy: 0.5877 - val_loss: 1.9132 - val_accuracy: 0.3925\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3370 - accuracy: 0.5842 - val_loss: 1.6686 - val_accuracy: 0.5006\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3186 - accuracy: 0.5858 - val_loss: 1.8805 - val_accuracy: 0.3888\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3196 - accuracy: 0.5933 - val_loss: 1.6459 - val_accuracy: 0.4557\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 1s 17ms/step - loss: 1.2794 - accuracy: 0.6011 - val_loss: 1.6442 - val_accuracy: 0.4836\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2616 - accuracy: 0.6132 - val_loss: 1.7774 - val_accuracy: 0.4569\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2374 - accuracy: 0.6127 - val_loss: 1.7070 - val_accuracy: 0.5067\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2657 - accuracy: 0.6114 - val_loss: 1.6353 - val_accuracy: 0.4860\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 1.1897 - accuracy: 0.6329 - val_loss: 1.8940 - val_accuracy: 0.4605\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2218 - accuracy: 0.6195 - val_loss: 1.8020 - val_accuracy: 0.4763\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 1s 17ms/step - loss: 1.2081 - accuracy: 0.6325 - val_loss: 1.8464 - val_accuracy: 0.4374\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1655 - accuracy: 0.6401 - val_loss: 1.7644 - val_accuracy: 0.4581\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1579 - accuracy: 0.6517 - val_loss: 2.4152 - val_accuracy: 0.3171\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1800 - accuracy: 0.6382 - val_loss: 1.9220 - val_accuracy: 0.4326\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1218 - accuracy: 0.6596 - val_loss: 1.5275 - val_accuracy: 0.4957\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1306 - accuracy: 0.6591 - val_loss: 1.9649 - val_accuracy: 0.3998\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 1s 17ms/step - loss: 1.1244 - accuracy: 0.6680 - val_loss: 1.6159 - val_accuracy: 0.5043\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0761 - accuracy: 0.6767 - val_loss: 2.1292 - val_accuracy: 0.4046\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1372 - accuracy: 0.6513 - val_loss: 1.5860 - val_accuracy: 0.5188\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 1s 17ms/step - loss: 1.0745 - accuracy: 0.6731 - val_loss: 1.7728 - val_accuracy: 0.5358\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0714 - accuracy: 0.6750 - val_loss: 1.7993 - val_accuracy: 0.4508\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0355 - accuracy: 0.6842 - val_loss: 1.6503 - val_accuracy: 0.5176\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0524 - accuracy: 0.6815 - val_loss: 1.6110 - val_accuracy: 0.5383\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0286 - accuracy: 0.6843 - val_loss: 1.5257 - val_accuracy: 0.5614\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0182 - accuracy: 0.6942 - val_loss: 1.8861 - val_accuracy: 0.4872\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0399 - accuracy: 0.6917 - val_loss: 2.0742 - val_accuracy: 0.4156\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9974 - accuracy: 0.7049 - val_loss: 2.6951 - val_accuracy: 0.3827\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0528 - accuracy: 0.6809 - val_loss: 1.6208 - val_accuracy: 0.5674\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9755 - accuracy: 0.7096 - val_loss: 2.0408 - val_accuracy: 0.4836\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 1s 17ms/step - loss: 0.9915 - accuracy: 0.7030 - val_loss: 1.6488 - val_accuracy: 0.5565\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9860 - accuracy: 0.7073 - val_loss: 1.6139 - val_accuracy: 0.5298\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9374 - accuracy: 0.7276 - val_loss: 1.6617 - val_accuracy: 0.5395\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9485 - accuracy: 0.7174 - val_loss: 1.7665 - val_accuracy: 0.5286\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0219 - accuracy: 0.6981 - val_loss: 1.7189 - val_accuracy: 0.5407\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9126 - accuracy: 0.7305 - val_loss: 1.6508 - val_accuracy: 0.5577\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9481 - accuracy: 0.7177 - val_loss: 1.7451 - val_accuracy: 0.5152\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9035 - accuracy: 0.7323 - val_loss: 2.3709 - val_accuracy: 0.4241\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9049 - accuracy: 0.7352 - val_loss: 1.7117 - val_accuracy: 0.5504\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9178 - accuracy: 0.7281 - val_loss: 1.6751 - val_accuracy: 0.5310\n",
            "Evaluating model...\n",
            "ROC: 0.904\n",
            "\n",
            "Accuracy = 0.57\n",
            "F-Score: 0.57\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 6, 7, 8, 9} Validate on 5 Test on 10 ***\n",
            "val_x shape:  (936, 128, 128, 1)\n",
            "test_x shape:  (837, 128, 128, 1)\n",
            "train_x shape:  (6959, 128, 128, 1)\n",
            "val_y shape:  (936,)\n",
            "test_y shape:  (837,)\n",
            "train_y shape:  (6959,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 2s 22ms/step - loss: 3.9072 - accuracy: 0.1167 - val_loss: 2.4553 - val_accuracy: 0.2091\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4547 - accuracy: 0.1275 - val_loss: 2.4493 - val_accuracy: 0.1732\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4369 - accuracy: 0.1454 - val_loss: 2.4199 - val_accuracy: 0.2162\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4380 - accuracy: 0.1443 - val_loss: 2.4384 - val_accuracy: 0.1219\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4348 - accuracy: 0.1289 - val_loss: 2.4316 - val_accuracy: 0.1326\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4294 - accuracy: 0.1295 - val_loss: 2.4254 - val_accuracy: 0.1493\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4296 - accuracy: 0.1332 - val_loss: 2.4010 - val_accuracy: 0.2067\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4256 - accuracy: 0.1237 - val_loss: 2.4232 - val_accuracy: 0.1231\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4136 - accuracy: 0.1365 - val_loss: 2.4207 - val_accuracy: 0.1207\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4168 - accuracy: 0.1252 - val_loss: 2.4999 - val_accuracy: 0.1601\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4337 - accuracy: 0.1262 - val_loss: 2.4158 - val_accuracy: 0.1219\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4175 - accuracy: 0.1207 - val_loss: 2.4223 - val_accuracy: 0.1207\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3582 - accuracy: 0.1849 - val_loss: 2.2918 - val_accuracy: 0.2342\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3026 - accuracy: 0.2140 - val_loss: 2.2533 - val_accuracy: 0.2676\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.2762 - accuracy: 0.2302 - val_loss: 2.2979 - val_accuracy: 0.2987\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.2581 - accuracy: 0.2322 - val_loss: 2.2406 - val_accuracy: 0.3082\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.2166 - accuracy: 0.2463 - val_loss: 2.1653 - val_accuracy: 0.3023\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 1s 20ms/step - loss: 2.1846 - accuracy: 0.2585 - val_loss: 2.1044 - val_accuracy: 0.2700\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1617 - accuracy: 0.2597 - val_loss: 2.0947 - val_accuracy: 0.3154\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1299 - accuracy: 0.2677 - val_loss: 2.0719 - val_accuracy: 0.3417\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1054 - accuracy: 0.2755 - val_loss: 2.0510 - val_accuracy: 0.3011\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.0617 - accuracy: 0.2959 - val_loss: 2.0324 - val_accuracy: 0.3608\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.0338 - accuracy: 0.3090 - val_loss: 1.9314 - val_accuracy: 0.3596\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.0031 - accuracy: 0.3210 - val_loss: 1.8909 - val_accuracy: 0.3775\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.9677 - accuracy: 0.3419 - val_loss: 1.9483 - val_accuracy: 0.3226\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.9351 - accuracy: 0.3603 - val_loss: 1.8790 - val_accuracy: 0.4086\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8924 - accuracy: 0.3651 - val_loss: 1.9163 - val_accuracy: 0.3130\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8847 - accuracy: 0.3778 - val_loss: 1.7670 - val_accuracy: 0.4289\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8438 - accuracy: 0.4014 - val_loss: 1.7793 - val_accuracy: 0.3704\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8372 - accuracy: 0.3993 - val_loss: 1.9134 - val_accuracy: 0.3309\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7976 - accuracy: 0.4088 - val_loss: 1.7460 - val_accuracy: 0.4050\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7705 - accuracy: 0.4239 - val_loss: 1.6832 - val_accuracy: 0.4755\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7287 - accuracy: 0.4338 - val_loss: 1.8801 - val_accuracy: 0.3716\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6924 - accuracy: 0.4542 - val_loss: 1.6535 - val_accuracy: 0.4421\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7171 - accuracy: 0.4481 - val_loss: 1.7364 - val_accuracy: 0.4492\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6572 - accuracy: 0.4676 - val_loss: 1.6194 - val_accuracy: 0.4122\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6617 - accuracy: 0.4685 - val_loss: 1.6047 - val_accuracy: 0.5042\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5821 - accuracy: 0.4929 - val_loss: 1.5137 - val_accuracy: 0.4600\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5744 - accuracy: 0.4999 - val_loss: 1.4838 - val_accuracy: 0.5293\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5779 - accuracy: 0.5047 - val_loss: 1.5268 - val_accuracy: 0.4540\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5149 - accuracy: 0.5241 - val_loss: 1.4265 - val_accuracy: 0.5341\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4942 - accuracy: 0.5300 - val_loss: 1.4607 - val_accuracy: 0.5209\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4928 - accuracy: 0.5300 - val_loss: 1.5328 - val_accuracy: 0.4612\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4808 - accuracy: 0.5356 - val_loss: 1.3973 - val_accuracy: 0.5472\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4414 - accuracy: 0.5568 - val_loss: 1.3867 - val_accuracy: 0.5352\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4159 - accuracy: 0.5630 - val_loss: 1.3651 - val_accuracy: 0.5532\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3950 - accuracy: 0.5666 - val_loss: 1.3801 - val_accuracy: 0.5508\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3539 - accuracy: 0.5788 - val_loss: 1.3942 - val_accuracy: 0.5281\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3603 - accuracy: 0.5821 - val_loss: 1.3042 - val_accuracy: 0.5926\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3349 - accuracy: 0.5805 - val_loss: 1.3457 - val_accuracy: 0.5926\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3279 - accuracy: 0.5949 - val_loss: 1.3489 - val_accuracy: 0.5830\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3045 - accuracy: 0.6032 - val_loss: 1.7098 - val_accuracy: 0.4600\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2910 - accuracy: 0.5992 - val_loss: 1.3568 - val_accuracy: 0.5603\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2654 - accuracy: 0.6089 - val_loss: 1.2905 - val_accuracy: 0.6057\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3789 - accuracy: 0.5795 - val_loss: 1.3349 - val_accuracy: 0.5735\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2561 - accuracy: 0.6173 - val_loss: 1.2553 - val_accuracy: 0.6189\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 1s 17ms/step - loss: 1.2334 - accuracy: 0.6205 - val_loss: 1.2773 - val_accuracy: 0.5950\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2069 - accuracy: 0.6393 - val_loss: 1.2832 - val_accuracy: 0.5938\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2268 - accuracy: 0.6156 - val_loss: 1.3328 - val_accuracy: 0.5878\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1637 - accuracy: 0.6459 - val_loss: 1.2509 - val_accuracy: 0.6272\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1446 - accuracy: 0.6477 - val_loss: 1.3421 - val_accuracy: 0.5926\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1355 - accuracy: 0.6541 - val_loss: 1.5517 - val_accuracy: 0.4779\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1370 - accuracy: 0.6512 - val_loss: 1.4220 - val_accuracy: 0.5102\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1367 - accuracy: 0.6564 - val_loss: 1.2163 - val_accuracy: 0.6249\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1190 - accuracy: 0.6603 - val_loss: 1.2189 - val_accuracy: 0.6487\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1216 - accuracy: 0.6681 - val_loss: 1.3070 - val_accuracy: 0.5579\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0843 - accuracy: 0.6719 - val_loss: 1.2269 - val_accuracy: 0.6069\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0894 - accuracy: 0.6774 - val_loss: 1.3190 - val_accuracy: 0.5532\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0709 - accuracy: 0.6854 - val_loss: 1.3041 - val_accuracy: 0.5603\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0531 - accuracy: 0.6847 - val_loss: 1.2939 - val_accuracy: 0.5675\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0886 - accuracy: 0.6811 - val_loss: 1.2309 - val_accuracy: 0.5986\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0530 - accuracy: 0.6866 - val_loss: 1.2248 - val_accuracy: 0.5962\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 1.0350 - accuracy: 0.6944 - val_loss: 1.3566 - val_accuracy: 0.5938\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0087 - accuracy: 0.6988 - val_loss: 1.3054 - val_accuracy: 0.5484\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 0.9959 - accuracy: 0.7031 - val_loss: 1.2061 - val_accuracy: 0.6237\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9966 - accuracy: 0.7010 - val_loss: 1.1756 - val_accuracy: 0.6344\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9940 - accuracy: 0.7060 - val_loss: 1.2201 - val_accuracy: 0.6416\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9690 - accuracy: 0.7173 - val_loss: 1.2205 - val_accuracy: 0.6476\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9626 - accuracy: 0.7100 - val_loss: 1.3211 - val_accuracy: 0.5878\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9599 - accuracy: 0.7158 - val_loss: 1.1924 - val_accuracy: 0.6177\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9222 - accuracy: 0.7311 - val_loss: 1.2422 - val_accuracy: 0.6022\n",
            "Epoch 82/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9669 - accuracy: 0.7139 - val_loss: 1.2403 - val_accuracy: 0.6249\n",
            "Epoch 83/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9384 - accuracy: 0.7212 - val_loss: 1.1677 - val_accuracy: 0.6452\n",
            "Epoch 84/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9295 - accuracy: 0.7240 - val_loss: 1.2460 - val_accuracy: 0.6404\n",
            "Epoch 85/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9520 - accuracy: 0.7209 - val_loss: 1.2339 - val_accuracy: 0.5998\n",
            "Epoch 86/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8844 - accuracy: 0.7403 - val_loss: 1.1685 - val_accuracy: 0.6177\n",
            "Epoch 87/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9245 - accuracy: 0.7247 - val_loss: 1.2155 - val_accuracy: 0.5878\n",
            "Epoch 88/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8825 - accuracy: 0.7392 - val_loss: 1.2390 - val_accuracy: 0.6249\n",
            "Epoch 89/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9046 - accuracy: 0.7388 - val_loss: 1.1529 - val_accuracy: 0.6559\n",
            "Epoch 90/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8720 - accuracy: 0.7396 - val_loss: 1.2946 - val_accuracy: 0.6225\n",
            "Epoch 91/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8787 - accuracy: 0.7477 - val_loss: 1.2463 - val_accuracy: 0.6368\n",
            "Epoch 92/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8676 - accuracy: 0.7464 - val_loss: 1.2899 - val_accuracy: 0.6045\n",
            "Epoch 93/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8829 - accuracy: 0.7355 - val_loss: 1.2221 - val_accuracy: 0.6380\n",
            "Epoch 94/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8264 - accuracy: 0.7620 - val_loss: 1.4315 - val_accuracy: 0.5341\n",
            "Epoch 95/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8503 - accuracy: 0.7526 - val_loss: 1.1518 - val_accuracy: 0.6189\n",
            "Epoch 96/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8229 - accuracy: 0.7648 - val_loss: 1.3813 - val_accuracy: 0.5579\n",
            "Epoch 97/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8355 - accuracy: 0.7541 - val_loss: 1.3487 - val_accuracy: 0.5496\n",
            "Epoch 98/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8149 - accuracy: 0.7665 - val_loss: 1.1681 - val_accuracy: 0.6416\n",
            "Epoch 99/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8252 - accuracy: 0.7639 - val_loss: 1.1871 - val_accuracy: 0.6153\n",
            "Epoch 100/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8142 - accuracy: 0.7612 - val_loss: 1.3935 - val_accuracy: 0.5556\n",
            "Epoch 101/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8079 - accuracy: 0.7711 - val_loss: 1.2403 - val_accuracy: 0.6308\n",
            "Epoch 102/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7930 - accuracy: 0.7737 - val_loss: 1.2979 - val_accuracy: 0.6129\n",
            "Epoch 103/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7892 - accuracy: 0.7718 - val_loss: 1.2486 - val_accuracy: 0.6177\n",
            "Epoch 104/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7948 - accuracy: 0.7781 - val_loss: 1.1875 - val_accuracy: 0.6404\n",
            "Epoch 105/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7861 - accuracy: 0.7734 - val_loss: 1.2733 - val_accuracy: 0.6404\n",
            "Epoch 106/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7659 - accuracy: 0.7824 - val_loss: 1.3463 - val_accuracy: 0.5974\n",
            "Epoch 107/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7978 - accuracy: 0.7692 - val_loss: 1.1900 - val_accuracy: 0.6643\n",
            "Epoch 108/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7571 - accuracy: 0.7850 - val_loss: 1.1662 - val_accuracy: 0.6487\n",
            "Epoch 109/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7652 - accuracy: 0.7793 - val_loss: 1.2288 - val_accuracy: 0.6165\n",
            "Epoch 110/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7501 - accuracy: 0.7853 - val_loss: 1.2458 - val_accuracy: 0.6499\n",
            "Evaluating model...\n",
            "ROC: 0.958\n",
            "\n",
            "Accuracy = 0.72\n",
            "F-Score: 0.72\n",
            "\n",
            "*** Train on {1, 2, 4, 5, 7, 8, 9, 10} Validate on 6 Test on 3 ***\n",
            "val_x shape:  (823, 128, 128, 1)\n",
            "test_x shape:  (925, 128, 128, 1)\n",
            "train_x shape:  (6984, 128, 128, 1)\n",
            "val_y shape:  (823,)\n",
            "test_y shape:  (925,)\n",
            "train_y shape:  (6984,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 2s 22ms/step - loss: 2.6982 - accuracy: 0.1432 - val_loss: 2.3976 - val_accuracy: 0.1989\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4035 - accuracy: 0.1671 - val_loss: 2.3690 - val_accuracy: 0.2335\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4047 - accuracy: 0.1725 - val_loss: 2.3768 - val_accuracy: 0.2130\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4075 - accuracy: 0.1534 - val_loss: 2.3774 - val_accuracy: 0.2086\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3772 - accuracy: 0.1711 - val_loss: 2.3639 - val_accuracy: 0.1968\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3814 - accuracy: 0.1624 - val_loss: 2.2968 - val_accuracy: 0.2497\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3063 - accuracy: 0.2131 - val_loss: 2.2078 - val_accuracy: 0.3222\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.2497 - accuracy: 0.2370 - val_loss: 2.1449 - val_accuracy: 0.3449\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1833 - accuracy: 0.2673 - val_loss: 2.0832 - val_accuracy: 0.3719\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1360 - accuracy: 0.2836 - val_loss: 2.0299 - val_accuracy: 0.3762\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1094 - accuracy: 0.2952 - val_loss: 1.9850 - val_accuracy: 0.3665\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.0695 - accuracy: 0.3106 - val_loss: 1.9609 - val_accuracy: 0.3859\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.0274 - accuracy: 0.3265 - val_loss: 1.9290 - val_accuracy: 0.3773\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.0149 - accuracy: 0.3340 - val_loss: 1.8929 - val_accuracy: 0.4162\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.9488 - accuracy: 0.3570 - val_loss: 1.8653 - val_accuracy: 0.3946\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.9007 - accuracy: 0.3753 - val_loss: 1.8063 - val_accuracy: 0.4151\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8790 - accuracy: 0.3840 - val_loss: 1.8063 - val_accuracy: 0.4314\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8314 - accuracy: 0.3925 - val_loss: 1.7643 - val_accuracy: 0.3935\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7686 - accuracy: 0.4257 - val_loss: 1.6778 - val_accuracy: 0.4497\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7599 - accuracy: 0.4327 - val_loss: 1.6249 - val_accuracy: 0.4778\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7015 - accuracy: 0.4433 - val_loss: 1.7668 - val_accuracy: 0.3784\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6660 - accuracy: 0.4674 - val_loss: 1.5615 - val_accuracy: 0.4995\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5962 - accuracy: 0.4908 - val_loss: 1.6067 - val_accuracy: 0.4541\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5558 - accuracy: 0.5049 - val_loss: 1.5913 - val_accuracy: 0.4541\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5526 - accuracy: 0.5054 - val_loss: 1.5705 - val_accuracy: 0.4995\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5761 - accuracy: 0.4930 - val_loss: 1.4946 - val_accuracy: 0.5114\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4821 - accuracy: 0.5388 - val_loss: 1.4479 - val_accuracy: 0.5200\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4073 - accuracy: 0.5515 - val_loss: 1.4635 - val_accuracy: 0.5168\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4029 - accuracy: 0.5637 - val_loss: 1.4336 - val_accuracy: 0.5384\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3833 - accuracy: 0.5737 - val_loss: 1.4616 - val_accuracy: 0.5276\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3407 - accuracy: 0.5944 - val_loss: 1.3908 - val_accuracy: 0.5492\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2981 - accuracy: 0.5991 - val_loss: 1.4581 - val_accuracy: 0.5168\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2759 - accuracy: 0.6092 - val_loss: 1.3296 - val_accuracy: 0.5686\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2244 - accuracy: 0.6287 - val_loss: 1.3843 - val_accuracy: 0.5459\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2131 - accuracy: 0.6345 - val_loss: 1.3853 - val_accuracy: 0.5416\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2181 - accuracy: 0.6323 - val_loss: 1.3424 - val_accuracy: 0.5654\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1682 - accuracy: 0.6436 - val_loss: 1.3429 - val_accuracy: 0.5892\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1666 - accuracy: 0.6436 - val_loss: 1.3173 - val_accuracy: 0.5546\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1100 - accuracy: 0.6690 - val_loss: 1.5188 - val_accuracy: 0.5146\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1595 - accuracy: 0.6468 - val_loss: 1.3314 - val_accuracy: 0.5643\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1030 - accuracy: 0.6687 - val_loss: 1.3700 - val_accuracy: 0.5524\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0507 - accuracy: 0.6863 - val_loss: 1.3361 - val_accuracy: 0.5881\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0396 - accuracy: 0.6969 - val_loss: 1.5025 - val_accuracy: 0.5514\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0704 - accuracy: 0.6794 - val_loss: 1.2736 - val_accuracy: 0.5773\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0009 - accuracy: 0.7082 - val_loss: 1.3217 - val_accuracy: 0.5611\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0107 - accuracy: 0.7059 - val_loss: 1.3669 - val_accuracy: 0.5449\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9589 - accuracy: 0.7294 - val_loss: 1.3672 - val_accuracy: 0.5600\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9402 - accuracy: 0.7258 - val_loss: 1.3495 - val_accuracy: 0.5946\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9362 - accuracy: 0.7237 - val_loss: 1.2859 - val_accuracy: 0.5957\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9196 - accuracy: 0.7314 - val_loss: 1.3264 - val_accuracy: 0.5892\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9476 - accuracy: 0.7290 - val_loss: 1.2838 - val_accuracy: 0.6011\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8902 - accuracy: 0.7431 - val_loss: 1.3399 - val_accuracy: 0.6227\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8988 - accuracy: 0.7424 - val_loss: 1.2920 - val_accuracy: 0.6011\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8755 - accuracy: 0.7520 - val_loss: 1.2823 - val_accuracy: 0.6216\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8615 - accuracy: 0.7521 - val_loss: 1.2485 - val_accuracy: 0.6151\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8352 - accuracy: 0.7527 - val_loss: 1.2739 - val_accuracy: 0.6270\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8163 - accuracy: 0.7647 - val_loss: 1.2688 - val_accuracy: 0.6076\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7905 - accuracy: 0.7773 - val_loss: 1.3328 - val_accuracy: 0.6141\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8067 - accuracy: 0.7756 - val_loss: 1.2574 - val_accuracy: 0.6249\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7642 - accuracy: 0.7887 - val_loss: 1.4002 - val_accuracy: 0.6043\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7864 - accuracy: 0.7791 - val_loss: 1.3733 - val_accuracy: 0.5946\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7813 - accuracy: 0.7844 - val_loss: 1.2725 - val_accuracy: 0.6389\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7796 - accuracy: 0.7819 - val_loss: 1.4531 - val_accuracy: 0.6173\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7374 - accuracy: 0.7932 - val_loss: 1.2849 - val_accuracy: 0.5859\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7627 - accuracy: 0.7888 - val_loss: 1.3157 - val_accuracy: 0.6216\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7036 - accuracy: 0.8063 - val_loss: 1.3137 - val_accuracy: 0.6314\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7091 - accuracy: 0.8031 - val_loss: 1.4814 - val_accuracy: 0.5914\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.7077 - accuracy: 0.8048 - val_loss: 1.3910 - val_accuracy: 0.6162\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.6964 - accuracy: 0.8111 - val_loss: 1.3958 - val_accuracy: 0.6108\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.6836 - accuracy: 0.8100 - val_loss: 1.3350 - val_accuracy: 0.6346\n",
            "Evaluating model...\n",
            "ROC: 0.917\n",
            "\n",
            "Accuracy = 0.63\n",
            "F-Score: 0.63\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 5, 6, 9, 10} Validate on 7 Test on 8 ***\n",
            "val_x shape:  (838, 128, 128, 1)\n",
            "test_x shape:  (806, 128, 128, 1)\n",
            "train_x shape:  (7088, 128, 128, 1)\n",
            "val_y shape:  (838,)\n",
            "test_y shape:  (806,)\n",
            "train_y shape:  (7088,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "71/71 [==============================] - 2s 21ms/step - loss: 3.2553 - accuracy: 0.1222 - val_loss: 2.4365 - val_accuracy: 0.1625\n",
            "Epoch 2/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.4327 - accuracy: 0.1440 - val_loss: 2.4115 - val_accuracy: 0.2531\n",
            "Epoch 3/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.3988 - accuracy: 0.1637 - val_loss: 2.3807 - val_accuracy: 0.2432\n",
            "Epoch 4/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.3872 - accuracy: 0.1737 - val_loss: 2.3505 - val_accuracy: 0.2916\n",
            "Epoch 5/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.3644 - accuracy: 0.1816 - val_loss: 2.2935 - val_accuracy: 0.2866\n",
            "Epoch 6/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.3084 - accuracy: 0.2139 - val_loss: 2.2337 - val_accuracy: 0.3288\n",
            "Epoch 7/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.2602 - accuracy: 0.2273 - val_loss: 2.2429 - val_accuracy: 0.3251\n",
            "Epoch 8/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.2873 - accuracy: 0.2190 - val_loss: 2.1707 - val_accuracy: 0.3710\n",
            "Epoch 9/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.1698 - accuracy: 0.2740 - val_loss: 2.0587 - val_accuracy: 0.3970\n",
            "Epoch 10/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.1035 - accuracy: 0.2950 - val_loss: 1.8939 - val_accuracy: 0.4330\n",
            "Epoch 11/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.0202 - accuracy: 0.3273 - val_loss: 2.1099 - val_accuracy: 0.3226\n",
            "Epoch 12/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.9705 - accuracy: 0.3564 - val_loss: 1.8997 - val_accuracy: 0.4417\n",
            "Epoch 13/200\n",
            "71/71 [==============================] - 1s 19ms/step - loss: 1.8976 - accuracy: 0.3764 - val_loss: 1.7475 - val_accuracy: 0.4454\n",
            "Epoch 14/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.8352 - accuracy: 0.4083 - val_loss: 1.7641 - val_accuracy: 0.4888\n",
            "Epoch 15/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.9352 - accuracy: 0.3712 - val_loss: 2.1737 - val_accuracy: 0.2605\n",
            "Epoch 16/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.1064 - accuracy: 0.2960 - val_loss: 1.7963 - val_accuracy: 0.4553\n",
            "Epoch 17/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.7416 - accuracy: 0.4352 - val_loss: 1.8849 - val_accuracy: 0.4119\n",
            "Epoch 18/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.7070 - accuracy: 0.4478 - val_loss: 1.6468 - val_accuracy: 0.5335\n",
            "Epoch 19/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.6059 - accuracy: 0.4918 - val_loss: 1.6251 - val_accuracy: 0.5397\n",
            "Epoch 20/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5948 - accuracy: 0.4977 - val_loss: 1.6548 - val_accuracy: 0.4801\n",
            "Epoch 21/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5169 - accuracy: 0.5196 - val_loss: 1.6373 - val_accuracy: 0.4938\n",
            "Epoch 22/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4844 - accuracy: 0.5416 - val_loss: 1.6456 - val_accuracy: 0.5199\n",
            "Epoch 23/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4315 - accuracy: 0.5525 - val_loss: 1.6468 - val_accuracy: 0.5025\n",
            "Epoch 24/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4014 - accuracy: 0.5662 - val_loss: 1.6221 - val_accuracy: 0.5360\n",
            "Epoch 25/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4370 - accuracy: 0.5506 - val_loss: 1.5851 - val_accuracy: 0.5211\n",
            "Epoch 26/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3848 - accuracy: 0.5751 - val_loss: 1.5501 - val_accuracy: 0.5136\n",
            "Epoch 27/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3556 - accuracy: 0.5883 - val_loss: 1.6134 - val_accuracy: 0.5223\n",
            "Epoch 28/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3021 - accuracy: 0.6031 - val_loss: 1.5849 - val_accuracy: 0.5310\n",
            "Epoch 29/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3082 - accuracy: 0.6030 - val_loss: 1.6860 - val_accuracy: 0.5112\n",
            "Epoch 30/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.2636 - accuracy: 0.6198 - val_loss: 1.7033 - val_accuracy: 0.4839\n",
            "Epoch 31/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.2504 - accuracy: 0.6181 - val_loss: 1.5998 - val_accuracy: 0.5347\n",
            "Epoch 32/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1882 - accuracy: 0.6442 - val_loss: 1.5553 - val_accuracy: 0.5558\n",
            "Epoch 33/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1894 - accuracy: 0.6419 - val_loss: 1.5525 - val_accuracy: 0.5335\n",
            "Epoch 34/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1897 - accuracy: 0.6392 - val_loss: 1.5364 - val_accuracy: 0.5459\n",
            "Epoch 35/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1481 - accuracy: 0.6579 - val_loss: 1.6473 - val_accuracy: 0.5409\n",
            "Epoch 36/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1305 - accuracy: 0.6649 - val_loss: 1.7365 - val_accuracy: 0.4789\n",
            "Epoch 37/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1218 - accuracy: 0.6656 - val_loss: 1.7039 - val_accuracy: 0.5273\n",
            "Epoch 38/200\n",
            "71/71 [==============================] - 1s 19ms/step - loss: 1.1364 - accuracy: 0.6620 - val_loss: 1.7716 - val_accuracy: 0.5273\n",
            "Epoch 39/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1045 - accuracy: 0.6744 - val_loss: 1.7778 - val_accuracy: 0.5149\n",
            "Epoch 40/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0831 - accuracy: 0.6817 - val_loss: 1.6083 - val_accuracy: 0.5434\n",
            "Epoch 41/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0678 - accuracy: 0.6886 - val_loss: 1.7695 - val_accuracy: 0.5223\n",
            "Epoch 42/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0744 - accuracy: 0.6799 - val_loss: 1.8256 - val_accuracy: 0.4479\n",
            "Epoch 43/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0321 - accuracy: 0.7037 - val_loss: 1.6572 - val_accuracy: 0.5310\n",
            "Epoch 44/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0490 - accuracy: 0.7039 - val_loss: 1.6682 - val_accuracy: 0.5385\n",
            "Epoch 45/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0296 - accuracy: 0.6927 - val_loss: 1.6761 - val_accuracy: 0.5199\n",
            "Epoch 46/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0308 - accuracy: 0.6931 - val_loss: 1.7472 - val_accuracy: 0.5161\n",
            "Epoch 47/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9925 - accuracy: 0.7120 - val_loss: 1.6167 - val_accuracy: 0.5496\n",
            "Epoch 48/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9650 - accuracy: 0.7222 - val_loss: 1.7210 - val_accuracy: 0.5261\n",
            "Epoch 49/200\n",
            "71/71 [==============================] - 1s 17ms/step - loss: 0.9556 - accuracy: 0.7246 - val_loss: 1.6905 - val_accuracy: 0.5484\n",
            "Evaluating model...\n",
            "ROC: 0.921\n",
            "\n",
            "Accuracy = 0.59\n",
            "F-Score: 0.59\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 5, 6, 7, 9} Validate on 8 Test on 10 ***\n",
            "val_x shape:  (806, 128, 128, 1)\n",
            "test_x shape:  (837, 128, 128, 1)\n",
            "train_x shape:  (7089, 128, 128, 1)\n",
            "val_y shape:  (806,)\n",
            "test_y shape:  (837,)\n",
            "train_y shape:  (7089,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "71/71 [==============================] - 2s 22ms/step - loss: 2.6439 - accuracy: 0.1220 - val_loss: 2.4199 - val_accuracy: 0.1649\n",
            "Epoch 2/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.4112 - accuracy: 0.1419 - val_loss: 2.3727 - val_accuracy: 0.2115\n",
            "Epoch 3/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.3730 - accuracy: 0.1656 - val_loss: 2.3122 - val_accuracy: 0.1959\n",
            "Epoch 4/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.3128 - accuracy: 0.1897 - val_loss: 2.2383 - val_accuracy: 0.3094\n",
            "Epoch 5/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.2541 - accuracy: 0.2201 - val_loss: 2.1331 - val_accuracy: 0.3465\n",
            "Epoch 6/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.2273 - accuracy: 0.2322 - val_loss: 2.1419 - val_accuracy: 0.3441\n",
            "Epoch 7/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.1670 - accuracy: 0.2604 - val_loss: 2.0812 - val_accuracy: 0.3070\n",
            "Epoch 8/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.0871 - accuracy: 0.2842 - val_loss: 1.9362 - val_accuracy: 0.3967\n",
            "Epoch 9/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.0250 - accuracy: 0.3116 - val_loss: 2.0797 - val_accuracy: 0.2891\n",
            "Epoch 10/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.9643 - accuracy: 0.3331 - val_loss: 1.7830 - val_accuracy: 0.4516\n",
            "Epoch 11/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.8946 - accuracy: 0.3583 - val_loss: 1.6718 - val_accuracy: 0.5317\n",
            "Epoch 12/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.8510 - accuracy: 0.3769 - val_loss: 1.5911 - val_accuracy: 0.5579\n",
            "Epoch 13/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.8190 - accuracy: 0.3814 - val_loss: 1.5777 - val_accuracy: 0.5568\n",
            "Epoch 14/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.7590 - accuracy: 0.4122 - val_loss: 1.5124 - val_accuracy: 0.5591\n",
            "Epoch 15/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.7473 - accuracy: 0.4159 - val_loss: 1.5259 - val_accuracy: 0.5496\n",
            "Epoch 16/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.6895 - accuracy: 0.4350 - val_loss: 1.5075 - val_accuracy: 0.5352\n",
            "Epoch 17/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.6338 - accuracy: 0.4586 - val_loss: 1.4397 - val_accuracy: 0.5651\n",
            "Epoch 18/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.6175 - accuracy: 0.4738 - val_loss: 1.4074 - val_accuracy: 0.5806\n",
            "Epoch 19/200\n",
            "71/71 [==============================] - 1s 19ms/step - loss: 1.5799 - accuracy: 0.4896 - val_loss: 1.4109 - val_accuracy: 0.6033\n",
            "Epoch 20/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5011 - accuracy: 0.5194 - val_loss: 1.2904 - val_accuracy: 0.6607\n",
            "Epoch 21/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5237 - accuracy: 0.5152 - val_loss: 1.3366 - val_accuracy: 0.6308\n",
            "Epoch 22/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4592 - accuracy: 0.5377 - val_loss: 1.3570 - val_accuracy: 0.6308\n",
            "Epoch 23/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4595 - accuracy: 0.5427 - val_loss: 1.2370 - val_accuracy: 0.6894\n",
            "Epoch 24/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3984 - accuracy: 0.5628 - val_loss: 1.2601 - val_accuracy: 0.6296\n",
            "Epoch 25/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3423 - accuracy: 0.5819 - val_loss: 1.1731 - val_accuracy: 0.6989\n",
            "Epoch 26/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3450 - accuracy: 0.5815 - val_loss: 1.1621 - val_accuracy: 0.6487\n",
            "Epoch 27/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.2861 - accuracy: 0.6004 - val_loss: 1.2437 - val_accuracy: 0.6679\n",
            "Epoch 28/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.2530 - accuracy: 0.6210 - val_loss: 1.1710 - val_accuracy: 0.6380\n",
            "Epoch 29/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.2375 - accuracy: 0.6262 - val_loss: 1.0665 - val_accuracy: 0.6930\n",
            "Epoch 30/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1834 - accuracy: 0.6399 - val_loss: 1.1223 - val_accuracy: 0.7037\n",
            "Epoch 31/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1875 - accuracy: 0.6406 - val_loss: 1.2259 - val_accuracy: 0.6380\n",
            "Epoch 32/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1463 - accuracy: 0.6630 - val_loss: 1.1145 - val_accuracy: 0.6918\n",
            "Epoch 33/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1086 - accuracy: 0.6713 - val_loss: 1.0993 - val_accuracy: 0.6858\n",
            "Epoch 34/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1413 - accuracy: 0.6627 - val_loss: 1.1306 - val_accuracy: 0.6464\n",
            "Epoch 35/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0902 - accuracy: 0.6770 - val_loss: 1.0206 - val_accuracy: 0.7216\n",
            "Epoch 36/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0470 - accuracy: 0.6964 - val_loss: 1.0901 - val_accuracy: 0.6918\n",
            "Epoch 37/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0498 - accuracy: 0.6912 - val_loss: 0.9903 - val_accuracy: 0.7276\n",
            "Epoch 38/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0069 - accuracy: 0.7087 - val_loss: 1.0227 - val_accuracy: 0.7049\n",
            "Epoch 39/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0105 - accuracy: 0.7053 - val_loss: 1.0561 - val_accuracy: 0.6977\n",
            "Epoch 40/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9749 - accuracy: 0.7158 - val_loss: 1.0203 - val_accuracy: 0.6906\n",
            "Epoch 41/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9653 - accuracy: 0.7203 - val_loss: 0.9755 - val_accuracy: 0.7216\n",
            "Epoch 42/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9535 - accuracy: 0.7225 - val_loss: 1.0697 - val_accuracy: 0.7073\n",
            "Epoch 43/200\n",
            "71/71 [==============================] - 1s 19ms/step - loss: 0.9588 - accuracy: 0.7194 - val_loss: 1.1191 - val_accuracy: 0.6667\n",
            "Epoch 44/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9171 - accuracy: 0.7402 - val_loss: 1.0195 - val_accuracy: 0.7061\n",
            "Epoch 45/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9063 - accuracy: 0.7397 - val_loss: 0.9575 - val_accuracy: 0.7336\n",
            "Epoch 46/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8889 - accuracy: 0.7445 - val_loss: 0.9986 - val_accuracy: 0.7049\n",
            "Epoch 47/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8544 - accuracy: 0.7589 - val_loss: 0.9425 - val_accuracy: 0.7288\n",
            "Epoch 48/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8512 - accuracy: 0.7553 - val_loss: 1.0839 - val_accuracy: 0.6977\n",
            "Epoch 49/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8802 - accuracy: 0.7455 - val_loss: 1.0549 - val_accuracy: 0.6810\n",
            "Epoch 50/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9488 - accuracy: 0.7273 - val_loss: 1.0144 - val_accuracy: 0.7180\n",
            "Epoch 51/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8416 - accuracy: 0.7624 - val_loss: 0.9327 - val_accuracy: 0.7395\n",
            "Epoch 52/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8159 - accuracy: 0.7678 - val_loss: 1.2383 - val_accuracy: 0.6093\n",
            "Epoch 53/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8117 - accuracy: 0.7770 - val_loss: 1.0896 - val_accuracy: 0.6738\n",
            "Epoch 54/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8546 - accuracy: 0.7656 - val_loss: 0.9756 - val_accuracy: 0.7300\n",
            "Epoch 55/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.7529 - accuracy: 0.7933 - val_loss: 0.9809 - val_accuracy: 0.7288\n",
            "Epoch 56/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.7738 - accuracy: 0.7873 - val_loss: 1.0065 - val_accuracy: 0.7216\n",
            "Epoch 57/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.7395 - accuracy: 0.7935 - val_loss: 0.9762 - val_accuracy: 0.7467\n",
            "Epoch 58/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.7299 - accuracy: 0.7928 - val_loss: 1.0075 - val_accuracy: 0.7384\n",
            "Epoch 59/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.7339 - accuracy: 0.7956 - val_loss: 1.0193 - val_accuracy: 0.7025\n",
            "Epoch 60/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.7788 - accuracy: 0.7846 - val_loss: 0.9939 - val_accuracy: 0.7240\n",
            "Epoch 61/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.7311 - accuracy: 0.7929 - val_loss: 1.0512 - val_accuracy: 0.7013\n",
            "Epoch 62/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.6928 - accuracy: 0.8108 - val_loss: 0.9866 - val_accuracy: 0.7384\n",
            "Epoch 63/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.7187 - accuracy: 0.7973 - val_loss: 1.0524 - val_accuracy: 0.6894\n",
            "Epoch 64/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.6740 - accuracy: 0.8155 - val_loss: 1.1295 - val_accuracy: 0.6511\n",
            "Epoch 65/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.6897 - accuracy: 0.8103 - val_loss: 0.9449 - val_accuracy: 0.7527\n",
            "Epoch 66/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.6689 - accuracy: 0.8196 - val_loss: 0.9405 - val_accuracy: 0.7395\n",
            "Evaluating model...\n",
            "ROC: 0.932\n",
            "\n",
            "Accuracy = 0.67\n",
            "F-Score: 0.67\n",
            "\n",
            "*** Train on {1, 2, 3, 5, 6, 7, 8, 10} Validate on 9 Test on 4 ***\n",
            "val_x shape:  (816, 128, 128, 1)\n",
            "test_x shape:  (990, 128, 128, 1)\n",
            "train_x shape:  (6926, 128, 128, 1)\n",
            "val_y shape:  (816,)\n",
            "test_y shape:  (990,)\n",
            "train_y shape:  (6926,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 2s 22ms/step - loss: 3.1961 - accuracy: 0.1112 - val_loss: 2.4401 - val_accuracy: 0.1636\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 2.4313 - accuracy: 0.1203 - val_loss: 2.4126 - val_accuracy: 0.1071\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.4122 - accuracy: 0.1412 - val_loss: 2.4154 - val_accuracy: 0.1788\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 2.3802 - accuracy: 0.1656 - val_loss: 2.3951 - val_accuracy: 0.1061\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 2.3444 - accuracy: 0.1738 - val_loss: 2.3220 - val_accuracy: 0.1949\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.3153 - accuracy: 0.1850 - val_loss: 2.3012 - val_accuracy: 0.2253\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.2858 - accuracy: 0.2085 - val_loss: 2.2971 - val_accuracy: 0.2495\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.2539 - accuracy: 0.2216 - val_loss: 2.2193 - val_accuracy: 0.2980\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.2163 - accuracy: 0.2405 - val_loss: 2.2270 - val_accuracy: 0.2808\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1565 - accuracy: 0.2556 - val_loss: 2.0909 - val_accuracy: 0.3414\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.1094 - accuracy: 0.2801 - val_loss: 2.1916 - val_accuracy: 0.2131\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.0650 - accuracy: 0.2886 - val_loss: 2.0211 - val_accuracy: 0.3152\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 1.9956 - accuracy: 0.3233 - val_loss: 1.9266 - val_accuracy: 0.3384\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 2.0166 - accuracy: 0.3088 - val_loss: 1.8208 - val_accuracy: 0.3798\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 1.9148 - accuracy: 0.3459 - val_loss: 1.8127 - val_accuracy: 0.3758\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8641 - accuracy: 0.3727 - val_loss: 1.9806 - val_accuracy: 0.3616\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.8013 - accuracy: 0.3919 - val_loss: 1.7330 - val_accuracy: 0.3859\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7764 - accuracy: 0.3949 - val_loss: 1.6656 - val_accuracy: 0.4212\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.7249 - accuracy: 0.4235 - val_loss: 1.6398 - val_accuracy: 0.4192\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6754 - accuracy: 0.4495 - val_loss: 1.7324 - val_accuracy: 0.4293\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6961 - accuracy: 0.4412 - val_loss: 1.7276 - val_accuracy: 0.4273\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.6328 - accuracy: 0.4726 - val_loss: 1.6478 - val_accuracy: 0.4889\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5585 - accuracy: 0.4961 - val_loss: 1.6152 - val_accuracy: 0.4545\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5299 - accuracy: 0.5081 - val_loss: 1.5750 - val_accuracy: 0.4727\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5204 - accuracy: 0.5175 - val_loss: 1.6405 - val_accuracy: 0.4636\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4684 - accuracy: 0.5269 - val_loss: 1.7272 - val_accuracy: 0.4354\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4708 - accuracy: 0.5302 - val_loss: 1.5360 - val_accuracy: 0.4919\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3928 - accuracy: 0.5654 - val_loss: 1.4399 - val_accuracy: 0.5566\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.4202 - accuracy: 0.5501 - val_loss: 1.6790 - val_accuracy: 0.4616\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3602 - accuracy: 0.5781 - val_loss: 1.8771 - val_accuracy: 0.3616\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.5122 - accuracy: 0.5215 - val_loss: 1.3848 - val_accuracy: 0.6040\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.3590 - accuracy: 0.5780 - val_loss: 1.3720 - val_accuracy: 0.5818\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2998 - accuracy: 0.5930 - val_loss: 1.3091 - val_accuracy: 0.5949\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2630 - accuracy: 0.6097 - val_loss: 1.3205 - val_accuracy: 0.6232\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2500 - accuracy: 0.6149 - val_loss: 1.3787 - val_accuracy: 0.5859\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2191 - accuracy: 0.6282 - val_loss: 1.2154 - val_accuracy: 0.6404\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1834 - accuracy: 0.6379 - val_loss: 1.1956 - val_accuracy: 0.6475\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1643 - accuracy: 0.6412 - val_loss: 1.3785 - val_accuracy: 0.6131\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2049 - accuracy: 0.6193 - val_loss: 1.3244 - val_accuracy: 0.6182\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.2034 - accuracy: 0.6340 - val_loss: 1.2627 - val_accuracy: 0.6444\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1062 - accuracy: 0.6652 - val_loss: 1.4503 - val_accuracy: 0.5434\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1216 - accuracy: 0.6620 - val_loss: 1.1408 - val_accuracy: 0.6667\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.1010 - accuracy: 0.6647 - val_loss: 1.2127 - val_accuracy: 0.6323\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0842 - accuracy: 0.6764 - val_loss: 1.5355 - val_accuracy: 0.5394\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0663 - accuracy: 0.6884 - val_loss: 1.2503 - val_accuracy: 0.6273\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0547 - accuracy: 0.6819 - val_loss: 1.2674 - val_accuracy: 0.6091\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0059 - accuracy: 0.7034 - val_loss: 1.1833 - val_accuracy: 0.6566\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0086 - accuracy: 0.6926 - val_loss: 1.5151 - val_accuracy: 0.5505\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9959 - accuracy: 0.6987 - val_loss: 1.2154 - val_accuracy: 0.6283\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 1s 19ms/step - loss: 0.9914 - accuracy: 0.6997 - val_loss: 1.4522 - val_accuracy: 0.5566\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 1.0588 - accuracy: 0.6792 - val_loss: 1.2503 - val_accuracy: 0.6152\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9577 - accuracy: 0.7186 - val_loss: 1.6702 - val_accuracy: 0.5081\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9532 - accuracy: 0.7172 - val_loss: 1.3450 - val_accuracy: 0.5980\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9090 - accuracy: 0.7332 - val_loss: 1.5856 - val_accuracy: 0.5343\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.9224 - accuracy: 0.7288 - val_loss: 1.2827 - val_accuracy: 0.6535\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8996 - accuracy: 0.7301 - val_loss: 1.5348 - val_accuracy: 0.5444\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 1s 18ms/step - loss: 0.8800 - accuracy: 0.7431 - val_loss: 1.3509 - val_accuracy: 0.6354\n",
            "Evaluating model...\n",
            "ROC: 0.933\n",
            "\n",
            "Accuracy = 0.67\n",
            "F-Score: 0.67\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 5, 6, 7, 8} Validate on 10 Test on 9 ***\n",
            "val_x shape:  (837, 128, 128, 1)\n",
            "test_x shape:  (816, 128, 128, 1)\n",
            "train_x shape:  (7079, 128, 128, 1)\n",
            "val_y shape:  (837,)\n",
            "test_y shape:  (816,)\n",
            "train_y shape:  (7079,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "71/71 [==============================] - 2s 22ms/step - loss: 2.6723 - accuracy: 0.1115 - val_loss: 2.4355 - val_accuracy: 0.1593\n",
            "Epoch 2/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.4310 - accuracy: 0.1407 - val_loss: 2.4284 - val_accuracy: 0.1299\n",
            "Epoch 3/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.4184 - accuracy: 0.1564 - val_loss: 2.4123 - val_accuracy: 0.1814\n",
            "Epoch 4/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.4021 - accuracy: 0.1632 - val_loss: 2.3793 - val_accuracy: 0.1495\n",
            "Epoch 5/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.3919 - accuracy: 0.1756 - val_loss: 2.3331 - val_accuracy: 0.1728\n",
            "Epoch 6/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.3725 - accuracy: 0.1831 - val_loss: 2.2930 - val_accuracy: 0.2108\n",
            "Epoch 7/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.3233 - accuracy: 0.2161 - val_loss: 2.2258 - val_accuracy: 0.2904\n",
            "Epoch 8/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.2718 - accuracy: 0.2365 - val_loss: 2.1918 - val_accuracy: 0.3015\n",
            "Epoch 9/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.2467 - accuracy: 0.2510 - val_loss: 2.1947 - val_accuracy: 0.3064\n",
            "Epoch 10/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.1500 - accuracy: 0.2862 - val_loss: 2.0374 - val_accuracy: 0.3162\n",
            "Epoch 11/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.1051 - accuracy: 0.3039 - val_loss: 1.9849 - val_accuracy: 0.3468\n",
            "Epoch 12/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 2.0258 - accuracy: 0.3269 - val_loss: 1.9993 - val_accuracy: 0.2721\n",
            "Epoch 13/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.9638 - accuracy: 0.3471 - val_loss: 1.8143 - val_accuracy: 0.3971\n",
            "Epoch 14/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.8784 - accuracy: 0.3823 - val_loss: 1.8152 - val_accuracy: 0.4044\n",
            "Epoch 15/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.8479 - accuracy: 0.3807 - val_loss: 1.6888 - val_accuracy: 0.4669\n",
            "Epoch 16/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.8103 - accuracy: 0.3967 - val_loss: 1.7559 - val_accuracy: 0.4510\n",
            "Epoch 17/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.7001 - accuracy: 0.4437 - val_loss: 1.7098 - val_accuracy: 0.4718\n",
            "Epoch 18/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.6611 - accuracy: 0.4592 - val_loss: 1.5822 - val_accuracy: 0.5429\n",
            "Epoch 19/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.6019 - accuracy: 0.4804 - val_loss: 1.6509 - val_accuracy: 0.5098\n",
            "Epoch 20/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5628 - accuracy: 0.4964 - val_loss: 1.5006 - val_accuracy: 0.5662\n",
            "Epoch 21/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.5246 - accuracy: 0.5135 - val_loss: 1.4861 - val_accuracy: 0.5772\n",
            "Epoch 22/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4996 - accuracy: 0.5285 - val_loss: 1.4798 - val_accuracy: 0.6127\n",
            "Epoch 23/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4011 - accuracy: 0.5564 - val_loss: 1.4316 - val_accuracy: 0.6152\n",
            "Epoch 24/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.4190 - accuracy: 0.5538 - val_loss: 1.4003 - val_accuracy: 0.6728\n",
            "Epoch 25/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3425 - accuracy: 0.5841 - val_loss: 1.5933 - val_accuracy: 0.5650\n",
            "Epoch 26/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3685 - accuracy: 0.5824 - val_loss: 1.5812 - val_accuracy: 0.6054\n",
            "Epoch 27/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3238 - accuracy: 0.6011 - val_loss: 1.4415 - val_accuracy: 0.6642\n",
            "Epoch 28/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.3614 - accuracy: 0.5771 - val_loss: 1.5014 - val_accuracy: 0.6556\n",
            "Epoch 29/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.2575 - accuracy: 0.6197 - val_loss: 1.4487 - val_accuracy: 0.6201\n",
            "Epoch 30/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.2723 - accuracy: 0.6103 - val_loss: 1.3513 - val_accuracy: 0.6900\n",
            "Epoch 31/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1618 - accuracy: 0.6485 - val_loss: 1.3282 - val_accuracy: 0.6998\n",
            "Epoch 32/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1702 - accuracy: 0.6528 - val_loss: 1.3995 - val_accuracy: 0.6544\n",
            "Epoch 33/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1585 - accuracy: 0.6539 - val_loss: 1.4967 - val_accuracy: 0.5944\n",
            "Epoch 34/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1543 - accuracy: 0.6591 - val_loss: 1.2840 - val_accuracy: 0.6936\n",
            "Epoch 35/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.1263 - accuracy: 0.6673 - val_loss: 1.2877 - val_accuracy: 0.6752\n",
            "Epoch 36/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0560 - accuracy: 0.6901 - val_loss: 1.3549 - val_accuracy: 0.6936\n",
            "Epoch 37/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0610 - accuracy: 0.6899 - val_loss: 1.3232 - val_accuracy: 0.6887\n",
            "Epoch 38/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0424 - accuracy: 0.6963 - val_loss: 1.4015 - val_accuracy: 0.6850\n",
            "Epoch 39/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 1.0294 - accuracy: 0.7019 - val_loss: 1.2449 - val_accuracy: 0.6740\n",
            "Epoch 40/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9909 - accuracy: 0.7069 - val_loss: 1.2461 - val_accuracy: 0.6838\n",
            "Epoch 41/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9628 - accuracy: 0.7260 - val_loss: 1.2475 - val_accuracy: 0.7181\n",
            "Epoch 42/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9469 - accuracy: 0.7264 - val_loss: 1.3541 - val_accuracy: 0.7292\n",
            "Epoch 43/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9432 - accuracy: 0.7269 - val_loss: 1.1810 - val_accuracy: 0.7181\n",
            "Epoch 44/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9374 - accuracy: 0.7247 - val_loss: 1.2962 - val_accuracy: 0.6936\n",
            "Epoch 45/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9261 - accuracy: 0.7351 - val_loss: 1.2279 - val_accuracy: 0.6912\n",
            "Epoch 46/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9127 - accuracy: 0.7382 - val_loss: 1.3579 - val_accuracy: 0.7304\n",
            "Epoch 47/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.9264 - accuracy: 0.7334 - val_loss: 1.3409 - val_accuracy: 0.7145\n",
            "Epoch 48/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8521 - accuracy: 0.7560 - val_loss: 1.3858 - val_accuracy: 0.7108\n",
            "Epoch 49/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8444 - accuracy: 0.7579 - val_loss: 1.3520 - val_accuracy: 0.7206\n",
            "Epoch 50/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8486 - accuracy: 0.7583 - val_loss: 1.2054 - val_accuracy: 0.7206\n",
            "Epoch 51/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8285 - accuracy: 0.7655 - val_loss: 1.3327 - val_accuracy: 0.7157\n",
            "Epoch 52/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8180 - accuracy: 0.7693 - val_loss: 1.3275 - val_accuracy: 0.7292\n",
            "Epoch 53/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8026 - accuracy: 0.7796 - val_loss: 1.2730 - val_accuracy: 0.7230\n",
            "Epoch 54/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.7912 - accuracy: 0.7727 - val_loss: 1.1872 - val_accuracy: 0.7169\n",
            "Epoch 55/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.8041 - accuracy: 0.7737 - val_loss: 1.2736 - val_accuracy: 0.7047\n",
            "Epoch 56/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.7729 - accuracy: 0.7833 - val_loss: 1.4204 - val_accuracy: 0.6605\n",
            "Epoch 57/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.7475 - accuracy: 0.7981 - val_loss: 1.4011 - val_accuracy: 0.7218\n",
            "Epoch 58/200\n",
            "71/71 [==============================] - 1s 18ms/step - loss: 0.7572 - accuracy: 0.7908 - val_loss: 1.2873 - val_accuracy: 0.7181\n",
            "Evaluating model...\n",
            "ROC: 0.95\n",
            "\n",
            "Accuracy = 0.67\n",
            "F-Score: 0.67\n",
            "\n",
            "Average R.O.C: 0.9245136140344219\n",
            "Average Accuracy: 0.6231499254703522\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Z8IjgNVu5is",
        "colab_type": "code",
        "outputId": "81b2cfd1-dd44-4e83-881a-0a3d739fa31e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        }
      },
      "source": [
        "plt.boxplot(acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boxes': [<matplotlib.lines.Line2D at 0x7fe2587dccf8>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7fe2587dc588>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe2700d2c18>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7fe2700d2780>],\n",
              " 'means': [],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7fe2700d2e80>],\n",
              " 'whiskers': [<matplotlib.lines.Line2D at 0x7fe2587dc128>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe2587dc9b0>]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOEcgP5hwbP2",
        "colab_type": "code",
        "outputId": "83e3b78c-9819-42df-d29f-4a294b053cda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        }
      },
      "source": [
        "classes = ['air_conditioner',\n",
        "'car_horn',\n",
        "'children_playing',\n",
        "'dog_bark',\n",
        "'drilling',\n",
        "'engine_idling',\n",
        "'gun_shot',\n",
        "'jackhammer',\n",
        "'siren',\n",
        "'street_music']\n",
        "df_cm = pd.DataFrame(CM, index = classes,\n",
        "                  columns = classes)\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(df_cm, annot=True,  fmt='d')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2700d8978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tr-6p6O50zHi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save(\"acc_baseline_crop.npy\", acc)\n",
        "np.save(\"cm_baseline_crop.npy\", CM)\n",
        "acc_baseline = acc\n",
        "CM_baseline = CM"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYi1MCupTK4v",
        "colab_type": "code",
        "outputId": "39ecefb7-9068-4a7c-cfa0-3f922ed3cca3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "save_folds(data_dir, save_dir, frames= 68, bands= 68, channels = 4, normalize = True, wavelet = 'bior1.5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Saving fold1\n",
            "Features of fold1  =  (873, 68, 68, 4)\n",
            "Labels of fold1  =  (873,)\n",
            "Saved data/us8k/fold1_x.npy\n",
            "Saved data/us8k/fold1_y.npy\n",
            "\n",
            "Saving fold2\n",
            "Features of fold2  =  (888, 68, 68, 4)\n",
            "Labels of fold2  =  (888,)\n",
            "Saved data/us8k/fold2_x.npy\n",
            "Saved data/us8k/fold2_y.npy\n",
            "\n",
            "Saving fold3\n",
            "Features of fold3  =  (925, 68, 68, 4)\n",
            "Labels of fold3  =  (925,)\n",
            "Saved data/us8k/fold3_x.npy\n",
            "Saved data/us8k/fold3_y.npy\n",
            "\n",
            "Saving fold4\n",
            "Features of fold4  =  (990, 68, 68, 4)\n",
            "Labels of fold4  =  (990,)\n",
            "Saved data/us8k/fold4_x.npy\n",
            "Saved data/us8k/fold4_y.npy\n",
            "\n",
            "Saving fold5\n",
            "Features of fold5  =  (936, 68, 68, 4)\n",
            "Labels of fold5  =  (936,)\n",
            "Saved data/us8k/fold5_x.npy\n",
            "Saved data/us8k/fold5_y.npy\n",
            "\n",
            "Saving fold6\n",
            "Features of fold6  =  (823, 68, 68, 4)\n",
            "Labels of fold6  =  (823,)\n",
            "Saved data/us8k/fold6_x.npy\n",
            "Saved data/us8k/fold6_y.npy\n",
            "\n",
            "Saving fold7\n",
            "Features of fold7  =  (838, 68, 68, 4)\n",
            "Labels of fold7  =  (838,)\n",
            "Saved data/us8k/fold7_x.npy\n",
            "Saved data/us8k/fold7_y.npy\n",
            "\n",
            "Saving fold8\n",
            "Features of fold8  =  (806, 68, 68, 4)\n",
            "Labels of fold8  =  (806,)\n",
            "Saved data/us8k/fold8_x.npy\n",
            "Saved data/us8k/fold8_y.npy\n",
            "\n",
            "Saving fold9\n",
            "Features of fold9  =  (816, 68, 68, 4)\n",
            "Labels of fold9  =  (816,)\n",
            "Saved data/us8k/fold9_x.npy\n",
            "Saved data/us8k/fold9_y.npy\n",
            "\n",
            "Saving fold10\n",
            "Features of fold10  =  (837, 68, 68, 4)\n",
            "Labels of fold10  =  (837,)\n",
            "Saved data/us8k/fold10_x.npy\n",
            "Saved data/us8k/fold10_y.npy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2K7guJNX8Mx",
        "colab_type": "code",
        "outputId": "784e78b6-6554-4093-d05b-50ea2ad1ee32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "acc = np.zeros(10)\n",
        "roc = np.zeros(10)\n",
        "CM = 0\n",
        "\n",
        "for f in range(1,10+1):\n",
        "\n",
        "    # load data\n",
        "    train_x, test_x, val_x, train_y, test_y, val_y = load_folds(load_dir, f, frames=68, bands=68, channels=4)\n",
        "    \n",
        "    print(\"Building model...\")\n",
        "    model = build_model(f_size=3, frames=68, bands=68, channels = 4)\n",
        "\n",
        "    # now fit the model to the training data, evaluating loss against the validation data\n",
        "    print(\"Training model...\")\n",
        "    model.fit(train_x, train_y, validation_data=(test_x, test_y), \n",
        "              callbacks=[EarlyStopping(restore_best_weights=True, patience=15)], \n",
        "              batch_size=100, epochs = 200)\n",
        "\n",
        "    # now evaluate the trained model against the unseen test data\n",
        "    print(\"Evaluating model...\")\n",
        "    roc[f-1], acc[f-1], cm = evaluate(model, val_x, val_y)\n",
        "    CM += cm\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "*** Train on {3, 4, 5, 6, 7, 8, 9, 10} Validate on 1 Test on 2 ***\n",
            "val_x shape:  (873, 68, 68, 4)\n",
            "test_x shape:  (888, 68, 68, 4)\n",
            "train_x shape:  (6971, 68, 68, 4)\n",
            "val_y shape:  (873,)\n",
            "test_y shape:  (888,)\n",
            "train_y shape:  (6971,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 1s 21ms/step - loss: 2.4348 - accuracy: 0.1310 - val_loss: 2.3760 - val_accuracy: 0.2162\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3732 - accuracy: 0.1598 - val_loss: 2.3200 - val_accuracy: 0.3468\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3234 - accuracy: 0.1941 - val_loss: 2.2546 - val_accuracy: 0.3333\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.2710 - accuracy: 0.2234 - val_loss: 2.1808 - val_accuracy: 0.3986\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.2001 - accuracy: 0.2576 - val_loss: 2.0886 - val_accuracy: 0.3727\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.1364 - accuracy: 0.2751 - val_loss: 2.0390 - val_accuracy: 0.3784\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.0660 - accuracy: 0.3051 - val_loss: 1.9784 - val_accuracy: 0.3896\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.0115 - accuracy: 0.3279 - val_loss: 1.9150 - val_accuracy: 0.4245\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.9640 - accuracy: 0.3417 - val_loss: 1.8677 - val_accuracy: 0.3998\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.9044 - accuracy: 0.3806 - val_loss: 1.7984 - val_accuracy: 0.4381\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8768 - accuracy: 0.3817 - val_loss: 1.7705 - val_accuracy: 0.4550\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8432 - accuracy: 0.3958 - val_loss: 1.7451 - val_accuracy: 0.4437\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8092 - accuracy: 0.4090 - val_loss: 1.8200 - val_accuracy: 0.4628\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 1s 12ms/step - loss: 1.7797 - accuracy: 0.4173 - val_loss: 1.7269 - val_accuracy: 0.5180\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7490 - accuracy: 0.4308 - val_loss: 1.6317 - val_accuracy: 0.4910\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7111 - accuracy: 0.4434 - val_loss: 1.5947 - val_accuracy: 0.4989\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6879 - accuracy: 0.4539 - val_loss: 1.5701 - val_accuracy: 0.4854\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6453 - accuracy: 0.4677 - val_loss: 1.6225 - val_accuracy: 0.5417\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6331 - accuracy: 0.4743 - val_loss: 1.5452 - val_accuracy: 0.5248\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6194 - accuracy: 0.4748 - val_loss: 1.5898 - val_accuracy: 0.5507\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5694 - accuracy: 0.4969 - val_loss: 1.5145 - val_accuracy: 0.5619\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.5509 - accuracy: 0.5133 - val_loss: 1.5648 - val_accuracy: 0.5755\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5305 - accuracy: 0.5166 - val_loss: 1.4456 - val_accuracy: 0.5405\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5196 - accuracy: 0.5154 - val_loss: 1.5639 - val_accuracy: 0.5743\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4973 - accuracy: 0.5309 - val_loss: 1.4204 - val_accuracy: 0.5721\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4879 - accuracy: 0.5316 - val_loss: 1.4168 - val_accuracy: 0.5642\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4403 - accuracy: 0.5533 - val_loss: 1.4008 - val_accuracy: 0.5574\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4475 - accuracy: 0.5450 - val_loss: 1.3682 - val_accuracy: 0.5935\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4291 - accuracy: 0.5553 - val_loss: 1.3868 - val_accuracy: 0.5721\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4054 - accuracy: 0.5619 - val_loss: 1.3286 - val_accuracy: 0.5935\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3813 - accuracy: 0.5629 - val_loss: 1.3639 - val_accuracy: 0.5912\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3712 - accuracy: 0.5768 - val_loss: 1.2791 - val_accuracy: 0.6036\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3523 - accuracy: 0.5805 - val_loss: 1.2861 - val_accuracy: 0.5890\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3340 - accuracy: 0.5867 - val_loss: 1.2908 - val_accuracy: 0.5923\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3414 - accuracy: 0.5897 - val_loss: 1.2669 - val_accuracy: 0.6092\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3088 - accuracy: 0.5937 - val_loss: 1.3158 - val_accuracy: 0.5935\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3004 - accuracy: 0.5983 - val_loss: 1.2553 - val_accuracy: 0.6205\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.2803 - accuracy: 0.6092 - val_loss: 1.3443 - val_accuracy: 0.6070\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2715 - accuracy: 0.6127 - val_loss: 1.2862 - val_accuracy: 0.5743\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2563 - accuracy: 0.6203 - val_loss: 1.3983 - val_accuracy: 0.5766\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2594 - accuracy: 0.6144 - val_loss: 1.2462 - val_accuracy: 0.5619\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2417 - accuracy: 0.6214 - val_loss: 1.2241 - val_accuracy: 0.6205\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2228 - accuracy: 0.6283 - val_loss: 1.1880 - val_accuracy: 0.6092\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2024 - accuracy: 0.6358 - val_loss: 1.2422 - val_accuracy: 0.6194\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2000 - accuracy: 0.6353 - val_loss: 1.2078 - val_accuracy: 0.6047\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2040 - accuracy: 0.6356 - val_loss: 1.1639 - val_accuracy: 0.6340\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1971 - accuracy: 0.6429 - val_loss: 1.2649 - val_accuracy: 0.6104\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1702 - accuracy: 0.6450 - val_loss: 1.1739 - val_accuracy: 0.6205\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1576 - accuracy: 0.6566 - val_loss: 1.1948 - val_accuracy: 0.6182\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1696 - accuracy: 0.6500 - val_loss: 1.2551 - val_accuracy: 0.5968\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1609 - accuracy: 0.6484 - val_loss: 1.2116 - val_accuracy: 0.5957\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1362 - accuracy: 0.6622 - val_loss: 1.2293 - val_accuracy: 0.5946\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1242 - accuracy: 0.6606 - val_loss: 1.1317 - val_accuracy: 0.6329\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1169 - accuracy: 0.6593 - val_loss: 1.2169 - val_accuracy: 0.5991\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1191 - accuracy: 0.6643 - val_loss: 1.2029 - val_accuracy: 0.6115\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0946 - accuracy: 0.6662 - val_loss: 1.1931 - val_accuracy: 0.6261\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0988 - accuracy: 0.6811 - val_loss: 1.1531 - val_accuracy: 0.6205\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0847 - accuracy: 0.6824 - val_loss: 1.1587 - val_accuracy: 0.6126\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0893 - accuracy: 0.6781 - val_loss: 1.1815 - val_accuracy: 0.6250\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0742 - accuracy: 0.6825 - val_loss: 1.2142 - val_accuracy: 0.6104\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0715 - accuracy: 0.6800 - val_loss: 1.2934 - val_accuracy: 0.5968\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0469 - accuracy: 0.6854 - val_loss: 1.1391 - val_accuracy: 0.6194\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0294 - accuracy: 0.6979 - val_loss: 1.1543 - val_accuracy: 0.6227\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0371 - accuracy: 0.6910 - val_loss: 1.1916 - val_accuracy: 0.6194\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0367 - accuracy: 0.6977 - val_loss: 1.0914 - val_accuracy: 0.6340\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0413 - accuracy: 0.6926 - val_loss: 1.1820 - val_accuracy: 0.6216\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0255 - accuracy: 0.6947 - val_loss: 1.1334 - val_accuracy: 0.6385\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0090 - accuracy: 0.7092 - val_loss: 1.2614 - val_accuracy: 0.6014\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 1s 12ms/step - loss: 1.0067 - accuracy: 0.7058 - val_loss: 1.1106 - val_accuracy: 0.6306\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 1s 12ms/step - loss: 0.9874 - accuracy: 0.7118 - val_loss: 1.0720 - val_accuracy: 0.6261\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9990 - accuracy: 0.6993 - val_loss: 1.1265 - val_accuracy: 0.6306\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9866 - accuracy: 0.7081 - val_loss: 1.0789 - val_accuracy: 0.6363\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9903 - accuracy: 0.7135 - val_loss: 1.1528 - val_accuracy: 0.5721\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9683 - accuracy: 0.7178 - val_loss: 1.0522 - val_accuracy: 0.6577\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9666 - accuracy: 0.7218 - val_loss: 1.2526 - val_accuracy: 0.5912\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9458 - accuracy: 0.7203 - val_loss: 1.1747 - val_accuracy: 0.6318\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.9646 - accuracy: 0.7218 - val_loss: 1.0709 - val_accuracy: 0.6408\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9536 - accuracy: 0.7213 - val_loss: 1.0676 - val_accuracy: 0.6712\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9278 - accuracy: 0.7307 - val_loss: 1.1036 - val_accuracy: 0.6329\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9507 - accuracy: 0.7241 - val_loss: 1.1468 - val_accuracy: 0.6295\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9216 - accuracy: 0.7276 - val_loss: 1.1332 - val_accuracy: 0.6182\n",
            "Epoch 82/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9226 - accuracy: 0.7292 - val_loss: 1.2096 - val_accuracy: 0.6205\n",
            "Epoch 83/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9232 - accuracy: 0.7325 - val_loss: 1.0586 - val_accuracy: 0.6464\n",
            "Epoch 84/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9335 - accuracy: 0.7312 - val_loss: 1.1263 - val_accuracy: 0.6408\n",
            "Epoch 85/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9121 - accuracy: 0.7358 - val_loss: 1.0414 - val_accuracy: 0.6779\n",
            "Epoch 86/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9066 - accuracy: 0.7402 - val_loss: 1.0397 - val_accuracy: 0.6385\n",
            "Epoch 87/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9093 - accuracy: 0.7429 - val_loss: 1.0043 - val_accuracy: 0.6824\n",
            "Epoch 88/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9008 - accuracy: 0.7373 - val_loss: 1.1383 - val_accuracy: 0.6441\n",
            "Epoch 89/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8893 - accuracy: 0.7441 - val_loss: 1.0581 - val_accuracy: 0.6712\n",
            "Epoch 90/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8800 - accuracy: 0.7471 - val_loss: 1.1244 - val_accuracy: 0.6396\n",
            "Epoch 91/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8776 - accuracy: 0.7480 - val_loss: 1.1388 - val_accuracy: 0.6408\n",
            "Epoch 92/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9006 - accuracy: 0.7429 - val_loss: 1.0914 - val_accuracy: 0.6363\n",
            "Epoch 93/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.8663 - accuracy: 0.7566 - val_loss: 1.1305 - val_accuracy: 0.6351\n",
            "Epoch 94/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8590 - accuracy: 0.7487 - val_loss: 1.0332 - val_accuracy: 0.6520\n",
            "Epoch 95/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8760 - accuracy: 0.7537 - val_loss: 1.0314 - val_accuracy: 0.6486\n",
            "Epoch 96/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8522 - accuracy: 0.7619 - val_loss: 1.1016 - val_accuracy: 0.6430\n",
            "Epoch 97/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8503 - accuracy: 0.7530 - val_loss: 1.0897 - val_accuracy: 0.6295\n",
            "Epoch 98/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8446 - accuracy: 0.7660 - val_loss: 1.2781 - val_accuracy: 0.6261\n",
            "Epoch 99/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8416 - accuracy: 0.7564 - val_loss: 1.0495 - val_accuracy: 0.6520\n",
            "Epoch 100/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8366 - accuracy: 0.7650 - val_loss: 1.0426 - val_accuracy: 0.6351\n",
            "Epoch 101/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8277 - accuracy: 0.7637 - val_loss: 1.0451 - val_accuracy: 0.6475\n",
            "Epoch 102/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8298 - accuracy: 0.7633 - val_loss: 1.0526 - val_accuracy: 0.6441\n",
            "Evaluating model...\n",
            "ROC: 0.937\n",
            "\n",
            "Accuracy = 0.67\n",
            "F-Score: 0.67\n",
            "\n",
            "*** Train on {1, 3, 5, 6, 7, 8, 9, 10} Validate on 2 Test on 4 ***\n",
            "val_x shape:  (888, 68, 68, 4)\n",
            "test_x shape:  (990, 68, 68, 4)\n",
            "train_x shape:  (6854, 68, 68, 4)\n",
            "val_y shape:  (888,)\n",
            "test_y shape:  (990,)\n",
            "train_y shape:  (6854,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "69/69 [==============================] - 1s 15ms/step - loss: 2.4419 - accuracy: 0.1218 - val_loss: 2.4077 - val_accuracy: 0.1061\n",
            "Epoch 2/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 2.3908 - accuracy: 0.1494 - val_loss: 2.3766 - val_accuracy: 0.1525\n",
            "Epoch 3/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 2.3603 - accuracy: 0.1611 - val_loss: 2.3527 - val_accuracy: 0.1848\n",
            "Epoch 4/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 2.3218 - accuracy: 0.1900 - val_loss: 2.2910 - val_accuracy: 0.2303\n",
            "Epoch 5/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 2.2558 - accuracy: 0.2242 - val_loss: 2.2262 - val_accuracy: 0.2475\n",
            "Epoch 6/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 2.1910 - accuracy: 0.2505 - val_loss: 2.1594 - val_accuracy: 0.2556\n",
            "Epoch 7/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 2.1136 - accuracy: 0.2845 - val_loss: 2.0740 - val_accuracy: 0.3162\n",
            "Epoch 8/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 2.0484 - accuracy: 0.3048 - val_loss: 2.0018 - val_accuracy: 0.4182\n",
            "Epoch 9/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.9751 - accuracy: 0.3337 - val_loss: 1.9215 - val_accuracy: 0.3919\n",
            "Epoch 10/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.9173 - accuracy: 0.3545 - val_loss: 1.8541 - val_accuracy: 0.3929\n",
            "Epoch 11/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.8810 - accuracy: 0.3671 - val_loss: 1.8018 - val_accuracy: 0.4788\n",
            "Epoch 12/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.8433 - accuracy: 0.3837 - val_loss: 1.8368 - val_accuracy: 0.4303\n",
            "Epoch 13/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.8010 - accuracy: 0.3998 - val_loss: 1.7419 - val_accuracy: 0.4434\n",
            "Epoch 14/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.7428 - accuracy: 0.4306 - val_loss: 1.7186 - val_accuracy: 0.4848\n",
            "Epoch 15/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.7148 - accuracy: 0.4370 - val_loss: 1.6763 - val_accuracy: 0.5242\n",
            "Epoch 16/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.6889 - accuracy: 0.4459 - val_loss: 1.6398 - val_accuracy: 0.5263\n",
            "Epoch 17/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.6387 - accuracy: 0.4654 - val_loss: 1.5983 - val_accuracy: 0.5384\n",
            "Epoch 18/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.6231 - accuracy: 0.4721 - val_loss: 1.6934 - val_accuracy: 0.4232\n",
            "Epoch 19/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.6030 - accuracy: 0.4800 - val_loss: 1.5494 - val_accuracy: 0.5374\n",
            "Epoch 20/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.5656 - accuracy: 0.4937 - val_loss: 1.5271 - val_accuracy: 0.5596\n",
            "Epoch 21/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.5509 - accuracy: 0.5026 - val_loss: 1.5945 - val_accuracy: 0.5091\n",
            "Epoch 22/200\n",
            "69/69 [==============================] - 1s 10ms/step - loss: 1.5174 - accuracy: 0.5109 - val_loss: 1.5871 - val_accuracy: 0.5576\n",
            "Epoch 23/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.4937 - accuracy: 0.5244 - val_loss: 1.4748 - val_accuracy: 0.5444\n",
            "Epoch 24/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.4538 - accuracy: 0.5324 - val_loss: 1.4391 - val_accuracy: 0.5768\n",
            "Epoch 25/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.4390 - accuracy: 0.5374 - val_loss: 1.4527 - val_accuracy: 0.5475\n",
            "Epoch 26/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.4451 - accuracy: 0.5479 - val_loss: 1.4392 - val_accuracy: 0.6020\n",
            "Epoch 27/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.4034 - accuracy: 0.5515 - val_loss: 1.4123 - val_accuracy: 0.5909\n",
            "Epoch 28/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.3916 - accuracy: 0.5622 - val_loss: 1.3851 - val_accuracy: 0.5990\n",
            "Epoch 29/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.3890 - accuracy: 0.5611 - val_loss: 1.4024 - val_accuracy: 0.5949\n",
            "Epoch 30/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.3503 - accuracy: 0.5779 - val_loss: 1.3636 - val_accuracy: 0.5717\n",
            "Epoch 31/200\n",
            "69/69 [==============================] - 1s 10ms/step - loss: 1.3593 - accuracy: 0.5702 - val_loss: 1.4036 - val_accuracy: 0.5889\n",
            "Epoch 32/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.3210 - accuracy: 0.5830 - val_loss: 1.3964 - val_accuracy: 0.5859\n",
            "Epoch 33/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.3104 - accuracy: 0.5937 - val_loss: 1.3823 - val_accuracy: 0.5899\n",
            "Epoch 34/200\n",
            "69/69 [==============================] - 1s 10ms/step - loss: 1.2909 - accuracy: 0.6030 - val_loss: 1.3587 - val_accuracy: 0.6101\n",
            "Epoch 35/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.2966 - accuracy: 0.5865 - val_loss: 1.4441 - val_accuracy: 0.5919\n",
            "Epoch 36/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.2759 - accuracy: 0.6091 - val_loss: 1.3169 - val_accuracy: 0.5808\n",
            "Epoch 37/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.2653 - accuracy: 0.6091 - val_loss: 1.3026 - val_accuracy: 0.6071\n",
            "Epoch 38/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.2474 - accuracy: 0.6141 - val_loss: 1.2752 - val_accuracy: 0.6091\n",
            "Epoch 39/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.2322 - accuracy: 0.6135 - val_loss: 1.2861 - val_accuracy: 0.6242\n",
            "Epoch 40/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.2116 - accuracy: 0.6271 - val_loss: 1.2853 - val_accuracy: 0.6394\n",
            "Epoch 41/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.2022 - accuracy: 0.6316 - val_loss: 1.4289 - val_accuracy: 0.5869\n",
            "Epoch 42/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.1889 - accuracy: 0.6296 - val_loss: 1.3140 - val_accuracy: 0.6242\n",
            "Epoch 43/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.1989 - accuracy: 0.6315 - val_loss: 1.2570 - val_accuracy: 0.6333\n",
            "Epoch 44/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.1650 - accuracy: 0.6450 - val_loss: 1.2523 - val_accuracy: 0.6152\n",
            "Epoch 45/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.1633 - accuracy: 0.6460 - val_loss: 1.1826 - val_accuracy: 0.6465\n",
            "Epoch 46/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.1485 - accuracy: 0.6488 - val_loss: 1.2063 - val_accuracy: 0.6374\n",
            "Epoch 47/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.1632 - accuracy: 0.6436 - val_loss: 1.2479 - val_accuracy: 0.6424\n",
            "Epoch 48/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.1251 - accuracy: 0.6598 - val_loss: 1.2772 - val_accuracy: 0.6222\n",
            "Epoch 49/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.1166 - accuracy: 0.6673 - val_loss: 1.2003 - val_accuracy: 0.6414\n",
            "Epoch 50/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.1093 - accuracy: 0.6745 - val_loss: 1.1887 - val_accuracy: 0.6364\n",
            "Epoch 51/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.1213 - accuracy: 0.6609 - val_loss: 1.1891 - val_accuracy: 0.6455\n",
            "Epoch 52/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.0899 - accuracy: 0.6711 - val_loss: 1.4720 - val_accuracy: 0.5859\n",
            "Epoch 53/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.0873 - accuracy: 0.6673 - val_loss: 1.2451 - val_accuracy: 0.6444\n",
            "Epoch 54/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.0770 - accuracy: 0.6780 - val_loss: 1.1969 - val_accuracy: 0.6556\n",
            "Epoch 55/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.0689 - accuracy: 0.6841 - val_loss: 1.2169 - val_accuracy: 0.6182\n",
            "Epoch 56/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.0585 - accuracy: 0.6837 - val_loss: 1.1499 - val_accuracy: 0.6556\n",
            "Epoch 57/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.0379 - accuracy: 0.6868 - val_loss: 1.2031 - val_accuracy: 0.6444\n",
            "Epoch 58/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.0500 - accuracy: 0.6879 - val_loss: 1.2159 - val_accuracy: 0.6152\n",
            "Epoch 59/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.0391 - accuracy: 0.6885 - val_loss: 1.1133 - val_accuracy: 0.6586\n",
            "Epoch 60/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.0085 - accuracy: 0.6989 - val_loss: 1.1603 - val_accuracy: 0.6485\n",
            "Epoch 61/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.0089 - accuracy: 0.7011 - val_loss: 1.1437 - val_accuracy: 0.6485\n",
            "Epoch 62/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.0110 - accuracy: 0.6989 - val_loss: 1.1108 - val_accuracy: 0.6707\n",
            "Epoch 63/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 1.0085 - accuracy: 0.7053 - val_loss: 1.1791 - val_accuracy: 0.6808\n",
            "Epoch 64/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9800 - accuracy: 0.7159 - val_loss: 1.1348 - val_accuracy: 0.6808\n",
            "Epoch 65/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9903 - accuracy: 0.7107 - val_loss: 1.1367 - val_accuracy: 0.6768\n",
            "Epoch 66/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9842 - accuracy: 0.7095 - val_loss: 1.1499 - val_accuracy: 0.6818\n",
            "Epoch 67/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9668 - accuracy: 0.7107 - val_loss: 1.1499 - val_accuracy: 0.6848\n",
            "Epoch 68/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9720 - accuracy: 0.7133 - val_loss: 1.0952 - val_accuracy: 0.6798\n",
            "Epoch 69/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9501 - accuracy: 0.7221 - val_loss: 1.1426 - val_accuracy: 0.6758\n",
            "Epoch 70/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9493 - accuracy: 0.7288 - val_loss: 1.1270 - val_accuracy: 0.6657\n",
            "Epoch 71/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9549 - accuracy: 0.7248 - val_loss: 1.1510 - val_accuracy: 0.6727\n",
            "Epoch 72/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9356 - accuracy: 0.7234 - val_loss: 1.0994 - val_accuracy: 0.6687\n",
            "Epoch 73/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9273 - accuracy: 0.7326 - val_loss: 1.3758 - val_accuracy: 0.5980\n",
            "Epoch 74/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9400 - accuracy: 0.7238 - val_loss: 1.0971 - val_accuracy: 0.6778\n",
            "Epoch 75/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9225 - accuracy: 0.7318 - val_loss: 1.2577 - val_accuracy: 0.6525\n",
            "Epoch 76/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9239 - accuracy: 0.7286 - val_loss: 1.0844 - val_accuracy: 0.6869\n",
            "Epoch 77/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8974 - accuracy: 0.7381 - val_loss: 1.0693 - val_accuracy: 0.6960\n",
            "Epoch 78/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9041 - accuracy: 0.7393 - val_loss: 1.0384 - val_accuracy: 0.6869\n",
            "Epoch 79/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.9066 - accuracy: 0.7402 - val_loss: 1.0717 - val_accuracy: 0.6778\n",
            "Epoch 80/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8862 - accuracy: 0.7469 - val_loss: 1.1136 - val_accuracy: 0.6919\n",
            "Epoch 81/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8992 - accuracy: 0.7450 - val_loss: 1.0832 - val_accuracy: 0.6970\n",
            "Epoch 82/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8776 - accuracy: 0.7464 - val_loss: 1.0995 - val_accuracy: 0.6596\n",
            "Epoch 83/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8627 - accuracy: 0.7559 - val_loss: 1.0580 - val_accuracy: 0.6939\n",
            "Epoch 84/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8820 - accuracy: 0.7492 - val_loss: 1.0460 - val_accuracy: 0.6949\n",
            "Epoch 85/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8514 - accuracy: 0.7584 - val_loss: 1.0474 - val_accuracy: 0.6909\n",
            "Epoch 86/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8657 - accuracy: 0.7508 - val_loss: 1.0485 - val_accuracy: 0.6960\n",
            "Epoch 87/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8440 - accuracy: 0.7603 - val_loss: 1.0754 - val_accuracy: 0.6939\n",
            "Epoch 88/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8553 - accuracy: 0.7509 - val_loss: 1.0696 - val_accuracy: 0.6919\n",
            "Epoch 89/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8409 - accuracy: 0.7580 - val_loss: 1.2377 - val_accuracy: 0.6848\n",
            "Epoch 90/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8469 - accuracy: 0.7600 - val_loss: 1.0463 - val_accuracy: 0.6970\n",
            "Epoch 91/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8307 - accuracy: 0.7638 - val_loss: 1.0460 - val_accuracy: 0.7030\n",
            "Epoch 92/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8246 - accuracy: 0.7667 - val_loss: 1.1142 - val_accuracy: 0.6828\n",
            "Epoch 93/200\n",
            "69/69 [==============================] - 1s 11ms/step - loss: 0.8421 - accuracy: 0.7628 - val_loss: 1.0715 - val_accuracy: 0.6828\n",
            "Evaluating model...\n",
            "ROC: 0.941\n",
            "\n",
            "Accuracy = 0.63\n",
            "F-Score: 0.63\n",
            "\n",
            "*** Train on {2, 4, 5, 6, 7, 8, 9, 10} Validate on 3 Test on 1 ***\n",
            "val_x shape:  (925, 68, 68, 4)\n",
            "test_x shape:  (873, 68, 68, 4)\n",
            "train_x shape:  (6934, 68, 68, 4)\n",
            "val_y shape:  (925,)\n",
            "test_y shape:  (873,)\n",
            "train_y shape:  (6934,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 1s 14ms/step - loss: 2.4243 - accuracy: 0.1203 - val_loss: 2.3610 - val_accuracy: 0.2371\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3754 - accuracy: 0.1555 - val_loss: 2.3226 - val_accuracy: 0.2875\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3408 - accuracy: 0.1772 - val_loss: 2.2554 - val_accuracy: 0.2967\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.2935 - accuracy: 0.2085 - val_loss: 2.1879 - val_accuracy: 0.3482\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.2347 - accuracy: 0.2338 - val_loss: 2.1077 - val_accuracy: 0.3093\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.1692 - accuracy: 0.2530 - val_loss: 2.0107 - val_accuracy: 0.3746\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.1072 - accuracy: 0.2876 - val_loss: 1.9197 - val_accuracy: 0.4800\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.0512 - accuracy: 0.3131 - val_loss: 1.8599 - val_accuracy: 0.4800\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.9808 - accuracy: 0.3367 - val_loss: 1.8117 - val_accuracy: 0.5029\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.9448 - accuracy: 0.3520 - val_loss: 1.7310 - val_accuracy: 0.5475\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.8921 - accuracy: 0.3656 - val_loss: 1.6545 - val_accuracy: 0.5578\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8482 - accuracy: 0.3848 - val_loss: 1.5861 - val_accuracy: 0.5945\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8148 - accuracy: 0.4003 - val_loss: 1.6364 - val_accuracy: 0.5510\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7876 - accuracy: 0.4083 - val_loss: 1.6346 - val_accuracy: 0.5166\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7417 - accuracy: 0.4358 - val_loss: 1.5449 - val_accuracy: 0.5922\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7055 - accuracy: 0.4407 - val_loss: 1.5186 - val_accuracy: 0.5613\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6920 - accuracy: 0.4501 - val_loss: 1.4588 - val_accuracy: 0.5773\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6475 - accuracy: 0.4688 - val_loss: 1.4420 - val_accuracy: 0.5865\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6267 - accuracy: 0.4788 - val_loss: 1.4369 - val_accuracy: 0.5567\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6127 - accuracy: 0.4762 - val_loss: 1.4593 - val_accuracy: 0.6334\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.5508 - accuracy: 0.5030 - val_loss: 1.3850 - val_accuracy: 0.6231\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5461 - accuracy: 0.5040 - val_loss: 1.3855 - val_accuracy: 0.6197\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5077 - accuracy: 0.5221 - val_loss: 1.2903 - val_accuracy: 0.6541\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4918 - accuracy: 0.5225 - val_loss: 1.3070 - val_accuracy: 0.6426\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4728 - accuracy: 0.5330 - val_loss: 1.3023 - val_accuracy: 0.6770\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4338 - accuracy: 0.5460 - val_loss: 1.2495 - val_accuracy: 0.6460\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4285 - accuracy: 0.5499 - val_loss: 1.2369 - val_accuracy: 0.6747\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4021 - accuracy: 0.5613 - val_loss: 1.3275 - val_accuracy: 0.6632\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3911 - accuracy: 0.5723 - val_loss: 1.2445 - val_accuracy: 0.6472\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3693 - accuracy: 0.5699 - val_loss: 1.2215 - val_accuracy: 0.6919\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3539 - accuracy: 0.5773 - val_loss: 1.2307 - val_accuracy: 0.7022\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3506 - accuracy: 0.5799 - val_loss: 1.1754 - val_accuracy: 0.6896\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.3148 - accuracy: 0.5946 - val_loss: 1.2925 - val_accuracy: 0.6231\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3235 - accuracy: 0.5903 - val_loss: 1.2154 - val_accuracy: 0.7056\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2828 - accuracy: 0.6060 - val_loss: 1.1170 - val_accuracy: 0.6541\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2886 - accuracy: 0.6083 - val_loss: 1.1412 - val_accuracy: 0.6930\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.2658 - accuracy: 0.6115 - val_loss: 1.3729 - val_accuracy: 0.6071\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2585 - accuracy: 0.6096 - val_loss: 1.1959 - val_accuracy: 0.7205\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.2321 - accuracy: 0.6206 - val_loss: 1.1222 - val_accuracy: 0.6976\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.2169 - accuracy: 0.6297 - val_loss: 1.2278 - val_accuracy: 0.6529\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.2266 - accuracy: 0.6258 - val_loss: 1.1238 - val_accuracy: 0.6243\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1904 - accuracy: 0.6410 - val_loss: 1.2195 - val_accuracy: 0.6838\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.1897 - accuracy: 0.6432 - val_loss: 1.4026 - val_accuracy: 0.5899\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1886 - accuracy: 0.6418 - val_loss: 1.2164 - val_accuracy: 0.6598\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.1477 - accuracy: 0.6474 - val_loss: 1.2167 - val_accuracy: 0.6987\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1608 - accuracy: 0.6539 - val_loss: 1.1224 - val_accuracy: 0.7194\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1295 - accuracy: 0.6532 - val_loss: 1.1084 - val_accuracy: 0.7194\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1406 - accuracy: 0.6609 - val_loss: 1.1185 - val_accuracy: 0.7262\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.1011 - accuracy: 0.6658 - val_loss: 1.1007 - val_accuracy: 0.7079\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1128 - accuracy: 0.6648 - val_loss: 1.1062 - val_accuracy: 0.7228\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0791 - accuracy: 0.6804 - val_loss: 1.2296 - val_accuracy: 0.6449\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0954 - accuracy: 0.6696 - val_loss: 1.0728 - val_accuracy: 0.7148\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0830 - accuracy: 0.6774 - val_loss: 1.0952 - val_accuracy: 0.7182\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.0641 - accuracy: 0.6780 - val_loss: 1.1862 - val_accuracy: 0.6575\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0566 - accuracy: 0.6803 - val_loss: 1.0740 - val_accuracy: 0.7125\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.0618 - accuracy: 0.6855 - val_loss: 1.1356 - val_accuracy: 0.6712\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0464 - accuracy: 0.6870 - val_loss: 1.1433 - val_accuracy: 0.6964\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0254 - accuracy: 0.6905 - val_loss: 1.1124 - val_accuracy: 0.7010\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0254 - accuracy: 0.6956 - val_loss: 1.1632 - val_accuracy: 0.6163\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.0203 - accuracy: 0.7031 - val_loss: 1.1307 - val_accuracy: 0.7079\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0151 - accuracy: 0.7035 - val_loss: 1.1250 - val_accuracy: 0.6838\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0007 - accuracy: 0.7051 - val_loss: 1.1227 - val_accuracy: 0.6804\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0098 - accuracy: 0.7039 - val_loss: 1.0386 - val_accuracy: 0.7331\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.9800 - accuracy: 0.7140 - val_loss: 1.1005 - val_accuracy: 0.7262\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9745 - accuracy: 0.7160 - val_loss: 1.0368 - val_accuracy: 0.7285\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9789 - accuracy: 0.7072 - val_loss: 1.0370 - val_accuracy: 0.7274\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9766 - accuracy: 0.7120 - val_loss: 1.0829 - val_accuracy: 0.7262\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9520 - accuracy: 0.7219 - val_loss: 1.0403 - val_accuracy: 0.7205\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9515 - accuracy: 0.7243 - val_loss: 1.0310 - val_accuracy: 0.7400\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9353 - accuracy: 0.7294 - val_loss: 1.0787 - val_accuracy: 0.6987\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9368 - accuracy: 0.7316 - val_loss: 1.0800 - val_accuracy: 0.6987\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9314 - accuracy: 0.7277 - val_loss: 1.1404 - val_accuracy: 0.6850\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9275 - accuracy: 0.7338 - val_loss: 0.9956 - val_accuracy: 0.7285\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9324 - accuracy: 0.7286 - val_loss: 1.0273 - val_accuracy: 0.7434\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9117 - accuracy: 0.7351 - val_loss: 1.0436 - val_accuracy: 0.7365\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9163 - accuracy: 0.7322 - val_loss: 0.9992 - val_accuracy: 0.7423\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9024 - accuracy: 0.7299 - val_loss: 1.0516 - val_accuracy: 0.7285\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8904 - accuracy: 0.7444 - val_loss: 1.0478 - val_accuracy: 0.7113\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8961 - accuracy: 0.7446 - val_loss: 1.0429 - val_accuracy: 0.7251\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8762 - accuracy: 0.7455 - val_loss: 0.9655 - val_accuracy: 0.7331\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8671 - accuracy: 0.7511 - val_loss: 0.9929 - val_accuracy: 0.7400\n",
            "Epoch 82/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8798 - accuracy: 0.7453 - val_loss: 1.0138 - val_accuracy: 0.7297\n",
            "Epoch 83/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8589 - accuracy: 0.7576 - val_loss: 1.0776 - val_accuracy: 0.7182\n",
            "Epoch 84/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8729 - accuracy: 0.7456 - val_loss: 0.9720 - val_accuracy: 0.7297\n",
            "Epoch 85/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8540 - accuracy: 0.7550 - val_loss: 1.0477 - val_accuracy: 0.7331\n",
            "Epoch 86/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8649 - accuracy: 0.7525 - val_loss: 0.9849 - val_accuracy: 0.7549\n",
            "Epoch 87/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8305 - accuracy: 0.7675 - val_loss: 1.2620 - val_accuracy: 0.6575\n",
            "Epoch 88/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8484 - accuracy: 0.7589 - val_loss: 1.1124 - val_accuracy: 0.7182\n",
            "Epoch 89/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8303 - accuracy: 0.7596 - val_loss: 1.0087 - val_accuracy: 0.7491\n",
            "Epoch 90/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.8256 - accuracy: 0.7612 - val_loss: 1.1470 - val_accuracy: 0.6907\n",
            "Epoch 91/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8423 - accuracy: 0.7612 - val_loss: 0.9984 - val_accuracy: 0.7480\n",
            "Epoch 92/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.8004 - accuracy: 0.7685 - val_loss: 1.0439 - val_accuracy: 0.7033\n",
            "Epoch 93/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8239 - accuracy: 0.7600 - val_loss: 0.9357 - val_accuracy: 0.7434\n",
            "Epoch 94/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7960 - accuracy: 0.7697 - val_loss: 1.1581 - val_accuracy: 0.6919\n",
            "Epoch 95/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8177 - accuracy: 0.7661 - val_loss: 0.9261 - val_accuracy: 0.7537\n",
            "Epoch 96/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7985 - accuracy: 0.7700 - val_loss: 1.0665 - val_accuracy: 0.7320\n",
            "Epoch 97/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7983 - accuracy: 0.7769 - val_loss: 1.0434 - val_accuracy: 0.7171\n",
            "Epoch 98/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.8051 - accuracy: 0.7682 - val_loss: 1.0125 - val_accuracy: 0.7400\n",
            "Epoch 99/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7795 - accuracy: 0.7779 - val_loss: 0.9430 - val_accuracy: 0.7652\n",
            "Epoch 100/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7727 - accuracy: 0.7809 - val_loss: 0.9468 - val_accuracy: 0.7629\n",
            "Epoch 101/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7878 - accuracy: 0.7788 - val_loss: 0.9792 - val_accuracy: 0.7480\n",
            "Epoch 102/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7811 - accuracy: 0.7817 - val_loss: 1.0838 - val_accuracy: 0.7228\n",
            "Epoch 103/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7602 - accuracy: 0.7880 - val_loss: 1.0288 - val_accuracy: 0.7090\n",
            "Epoch 104/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7580 - accuracy: 0.7848 - val_loss: 1.0705 - val_accuracy: 0.7239\n",
            "Epoch 105/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.7558 - accuracy: 0.7847 - val_loss: 0.9762 - val_accuracy: 0.7572\n",
            "Epoch 106/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.7781 - accuracy: 0.7789 - val_loss: 1.2704 - val_accuracy: 0.6564\n",
            "Epoch 107/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7697 - accuracy: 0.7867 - val_loss: 1.0383 - val_accuracy: 0.7320\n",
            "Epoch 108/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.7564 - accuracy: 0.7866 - val_loss: 0.9840 - val_accuracy: 0.7388\n",
            "Epoch 109/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7501 - accuracy: 0.7874 - val_loss: 0.9913 - val_accuracy: 0.7411\n",
            "Epoch 110/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7453 - accuracy: 0.7876 - val_loss: 0.9759 - val_accuracy: 0.7514\n",
            "Evaluating model...\n",
            "ROC: 0.937\n",
            "\n",
            "Accuracy = 0.61\n",
            "F-Score: 0.61\n",
            "\n",
            "*** Train on {1, 2, 3, 5, 6, 7, 9, 10} Validate on 4 Test on 8 ***\n",
            "val_x shape:  (990, 68, 68, 4)\n",
            "test_x shape:  (806, 68, 68, 4)\n",
            "train_x shape:  (6936, 68, 68, 4)\n",
            "val_y shape:  (990,)\n",
            "test_y shape:  (806,)\n",
            "train_y shape:  (6936,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 1s 14ms/step - loss: 2.4370 - accuracy: 0.1178 - val_loss: 2.3727 - val_accuracy: 0.1811\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3944 - accuracy: 0.1450 - val_loss: 2.3225 - val_accuracy: 0.2953\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3575 - accuracy: 0.1737 - val_loss: 2.2712 - val_accuracy: 0.3573\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3114 - accuracy: 0.1975 - val_loss: 2.1945 - val_accuracy: 0.4069\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.2581 - accuracy: 0.2210 - val_loss: 2.1255 - val_accuracy: 0.4404\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.1969 - accuracy: 0.2591 - val_loss: 2.0281 - val_accuracy: 0.4739\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.1514 - accuracy: 0.2791 - val_loss: 1.9522 - val_accuracy: 0.4690\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.0933 - accuracy: 0.2976 - val_loss: 1.8905 - val_accuracy: 0.4541\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.0272 - accuracy: 0.3253 - val_loss: 1.7842 - val_accuracy: 0.4826\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.9696 - accuracy: 0.3492 - val_loss: 1.6796 - val_accuracy: 0.5012\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.9362 - accuracy: 0.3676 - val_loss: 1.6785 - val_accuracy: 0.4913\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8707 - accuracy: 0.3847 - val_loss: 1.6396 - val_accuracy: 0.4640\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8145 - accuracy: 0.4041 - val_loss: 1.5929 - val_accuracy: 0.5310\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7901 - accuracy: 0.4158 - val_loss: 1.6102 - val_accuracy: 0.5050\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7518 - accuracy: 0.4262 - val_loss: 1.5864 - val_accuracy: 0.5025\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7011 - accuracy: 0.4443 - val_loss: 1.5153 - val_accuracy: 0.4901\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6781 - accuracy: 0.4547 - val_loss: 1.4090 - val_accuracy: 0.5633\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.6207 - accuracy: 0.4769 - val_loss: 1.3780 - val_accuracy: 0.5794\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5879 - accuracy: 0.4866 - val_loss: 1.3955 - val_accuracy: 0.5112\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5809 - accuracy: 0.4889 - val_loss: 1.3528 - val_accuracy: 0.5757\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5532 - accuracy: 0.4971 - val_loss: 1.3792 - val_accuracy: 0.5062\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5140 - accuracy: 0.5215 - val_loss: 1.2962 - val_accuracy: 0.5993\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4849 - accuracy: 0.5271 - val_loss: 1.4639 - val_accuracy: 0.5087\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4614 - accuracy: 0.5352 - val_loss: 1.5195 - val_accuracy: 0.4454\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4542 - accuracy: 0.5415 - val_loss: 1.3056 - val_accuracy: 0.5385\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.4046 - accuracy: 0.5567 - val_loss: 1.4428 - val_accuracy: 0.5620\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.4110 - accuracy: 0.5557 - val_loss: 1.2730 - val_accuracy: 0.5918\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3667 - accuracy: 0.5649 - val_loss: 1.2099 - val_accuracy: 0.5819\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3562 - accuracy: 0.5744 - val_loss: 1.2446 - val_accuracy: 0.5558\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.3406 - accuracy: 0.5797 - val_loss: 1.2314 - val_accuracy: 0.6129\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3338 - accuracy: 0.5790 - val_loss: 1.4608 - val_accuracy: 0.5136\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2907 - accuracy: 0.5995 - val_loss: 1.3063 - val_accuracy: 0.5199\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2861 - accuracy: 0.6041 - val_loss: 1.1455 - val_accuracy: 0.6427\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2611 - accuracy: 0.6104 - val_loss: 1.2059 - val_accuracy: 0.6141\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2495 - accuracy: 0.6142 - val_loss: 1.2531 - val_accuracy: 0.6303\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2520 - accuracy: 0.6158 - val_loss: 1.1618 - val_accuracy: 0.6315\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2342 - accuracy: 0.6210 - val_loss: 1.1730 - val_accuracy: 0.5943\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.2116 - accuracy: 0.6293 - val_loss: 1.1765 - val_accuracy: 0.6129\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1948 - accuracy: 0.6373 - val_loss: 1.1174 - val_accuracy: 0.6154\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1822 - accuracy: 0.6459 - val_loss: 1.1647 - val_accuracy: 0.6253\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1746 - accuracy: 0.6354 - val_loss: 1.3358 - val_accuracy: 0.5112\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.1519 - accuracy: 0.6521 - val_loss: 1.1490 - val_accuracy: 0.6352\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1397 - accuracy: 0.6528 - val_loss: 1.1455 - val_accuracy: 0.6402\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1132 - accuracy: 0.6694 - val_loss: 1.1483 - val_accuracy: 0.6067\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.1335 - accuracy: 0.6584 - val_loss: 1.0780 - val_accuracy: 0.6836\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.1070 - accuracy: 0.6658 - val_loss: 1.0863 - val_accuracy: 0.6377\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0947 - accuracy: 0.6703 - val_loss: 1.1455 - val_accuracy: 0.5955\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.0785 - accuracy: 0.6799 - val_loss: 1.1135 - val_accuracy: 0.6390\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.0574 - accuracy: 0.6844 - val_loss: 1.0914 - val_accuracy: 0.6613\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0649 - accuracy: 0.6792 - val_loss: 1.1045 - val_accuracy: 0.6439\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0582 - accuracy: 0.6844 - val_loss: 1.1111 - val_accuracy: 0.6725\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0580 - accuracy: 0.6933 - val_loss: 1.0645 - val_accuracy: 0.6787\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0339 - accuracy: 0.7020 - val_loss: 1.1470 - val_accuracy: 0.6278\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0265 - accuracy: 0.6964 - val_loss: 1.1533 - val_accuracy: 0.6538\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0069 - accuracy: 0.7029 - val_loss: 1.3039 - val_accuracy: 0.5893\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0125 - accuracy: 0.6990 - val_loss: 1.0786 - val_accuracy: 0.6824\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.0013 - accuracy: 0.7026 - val_loss: 1.2164 - val_accuracy: 0.6092\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9836 - accuracy: 0.7127 - val_loss: 1.1175 - val_accuracy: 0.6551\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9945 - accuracy: 0.7021 - val_loss: 1.1158 - val_accuracy: 0.6402\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9775 - accuracy: 0.7099 - val_loss: 1.0612 - val_accuracy: 0.6625\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.9608 - accuracy: 0.7259 - val_loss: 1.0851 - val_accuracy: 0.6253\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9500 - accuracy: 0.7219 - val_loss: 1.2270 - val_accuracy: 0.5893\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9521 - accuracy: 0.7291 - val_loss: 1.1450 - val_accuracy: 0.6452\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9552 - accuracy: 0.7180 - val_loss: 1.0679 - val_accuracy: 0.6849\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.9281 - accuracy: 0.7308 - val_loss: 1.0635 - val_accuracy: 0.6836\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9061 - accuracy: 0.7363 - val_loss: 1.1309 - val_accuracy: 0.6675\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9279 - accuracy: 0.7298 - val_loss: 1.0651 - val_accuracy: 0.7010\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9122 - accuracy: 0.7300 - val_loss: 1.1059 - val_accuracy: 0.6799\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8903 - accuracy: 0.7415 - val_loss: 1.0887 - val_accuracy: 0.6811\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8875 - accuracy: 0.7481 - val_loss: 1.1919 - val_accuracy: 0.6402\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9007 - accuracy: 0.7364 - val_loss: 1.0382 - val_accuracy: 0.6725\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.8841 - accuracy: 0.7484 - val_loss: 1.0409 - val_accuracy: 0.7122\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.8722 - accuracy: 0.7475 - val_loss: 1.0404 - val_accuracy: 0.6998\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8650 - accuracy: 0.7546 - val_loss: 1.2436 - val_accuracy: 0.6824\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8504 - accuracy: 0.7566 - val_loss: 1.1102 - val_accuracy: 0.6514\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8734 - accuracy: 0.7546 - val_loss: 1.1122 - val_accuracy: 0.6625\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.8850 - accuracy: 0.7487 - val_loss: 1.0837 - val_accuracy: 0.7146\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8374 - accuracy: 0.7651 - val_loss: 1.1126 - val_accuracy: 0.6576\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8342 - accuracy: 0.7584 - val_loss: 1.0374 - val_accuracy: 0.7022\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8420 - accuracy: 0.7594 - val_loss: 1.0190 - val_accuracy: 0.6923\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8393 - accuracy: 0.7620 - val_loss: 1.0537 - val_accuracy: 0.6774\n",
            "Epoch 82/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.8216 - accuracy: 0.7685 - val_loss: 1.2625 - val_accuracy: 0.6638\n",
            "Epoch 83/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8095 - accuracy: 0.7670 - val_loss: 1.1672 - val_accuracy: 0.6414\n",
            "Epoch 84/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.8218 - accuracy: 0.7674 - val_loss: 1.0240 - val_accuracy: 0.6836\n",
            "Epoch 85/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8072 - accuracy: 0.7667 - val_loss: 1.1305 - val_accuracy: 0.6117\n",
            "Epoch 86/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8106 - accuracy: 0.7690 - val_loss: 1.0432 - val_accuracy: 0.6687\n",
            "Epoch 87/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.7783 - accuracy: 0.7845 - val_loss: 1.1668 - val_accuracy: 0.6663\n",
            "Epoch 88/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.7899 - accuracy: 0.7827 - val_loss: 1.1436 - val_accuracy: 0.6873\n",
            "Epoch 89/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.7977 - accuracy: 0.7748 - val_loss: 1.0638 - val_accuracy: 0.6600\n",
            "Epoch 90/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7862 - accuracy: 0.7761 - val_loss: 1.1006 - val_accuracy: 0.6600\n",
            "Epoch 91/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7793 - accuracy: 0.7787 - val_loss: 1.0298 - val_accuracy: 0.7208\n",
            "Epoch 92/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.7558 - accuracy: 0.7889 - val_loss: 1.0260 - val_accuracy: 0.6849\n",
            "Epoch 93/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7704 - accuracy: 0.7816 - val_loss: 1.0977 - val_accuracy: 0.6402\n",
            "Epoch 94/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7739 - accuracy: 0.7891 - val_loss: 1.0675 - val_accuracy: 0.6675\n",
            "Epoch 95/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7548 - accuracy: 0.7873 - val_loss: 1.1006 - val_accuracy: 0.6365\n",
            "Evaluating model...\n",
            "ROC: 0.96\n",
            "\n",
            "Accuracy = 0.73\n",
            "F-Score: 0.73\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 6, 7, 8, 9} Validate on 5 Test on 10 ***\n",
            "val_x shape:  (936, 68, 68, 4)\n",
            "test_x shape:  (837, 68, 68, 4)\n",
            "train_x shape:  (6959, 68, 68, 4)\n",
            "val_y shape:  (936,)\n",
            "test_y shape:  (837,)\n",
            "train_y shape:  (6959,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 2s 23ms/step - loss: 2.4336 - accuracy: 0.1234 - val_loss: 2.3799 - val_accuracy: 0.1661\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3813 - accuracy: 0.1532 - val_loss: 2.3400 - val_accuracy: 0.2043\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3493 - accuracy: 0.1745 - val_loss: 2.2906 - val_accuracy: 0.2234\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.2969 - accuracy: 0.2087 - val_loss: 2.2246 - val_accuracy: 0.2605\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.2504 - accuracy: 0.2243 - val_loss: 2.1375 - val_accuracy: 0.2939\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.1981 - accuracy: 0.2497 - val_loss: 2.0557 - val_accuracy: 0.3513\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.1391 - accuracy: 0.2726 - val_loss: 1.9814 - val_accuracy: 0.4241\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.0843 - accuracy: 0.2987 - val_loss: 1.9097 - val_accuracy: 0.4468\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.0197 - accuracy: 0.3292 - val_loss: 1.8583 - val_accuracy: 0.4779\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.9611 - accuracy: 0.3470 - val_loss: 1.8000 - val_accuracy: 0.4731\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8901 - accuracy: 0.3776 - val_loss: 1.7363 - val_accuracy: 0.4922\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8637 - accuracy: 0.3894 - val_loss: 1.7279 - val_accuracy: 0.4600\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8112 - accuracy: 0.3979 - val_loss: 1.6813 - val_accuracy: 0.4528\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7850 - accuracy: 0.4154 - val_loss: 1.6298 - val_accuracy: 0.5197\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7498 - accuracy: 0.4238 - val_loss: 1.5737 - val_accuracy: 0.5496\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7257 - accuracy: 0.4331 - val_loss: 1.5769 - val_accuracy: 0.5293\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6614 - accuracy: 0.4639 - val_loss: 1.5539 - val_accuracy: 0.5042\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6489 - accuracy: 0.4683 - val_loss: 1.4848 - val_accuracy: 0.5806\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6183 - accuracy: 0.4792 - val_loss: 1.4538 - val_accuracy: 0.5795\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5856 - accuracy: 0.4867 - val_loss: 1.4784 - val_accuracy: 0.5448\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5675 - accuracy: 0.4973 - val_loss: 1.4350 - val_accuracy: 0.5544\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5568 - accuracy: 0.5064 - val_loss: 1.5387 - val_accuracy: 0.5090\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5111 - accuracy: 0.5192 - val_loss: 1.4225 - val_accuracy: 0.5484\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4783 - accuracy: 0.5305 - val_loss: 1.3596 - val_accuracy: 0.5783\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4676 - accuracy: 0.5379 - val_loss: 1.3293 - val_accuracy: 0.5842\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 1.4380 - accuracy: 0.5377 - val_loss: 1.3507 - val_accuracy: 0.5830\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4167 - accuracy: 0.5509 - val_loss: 1.3370 - val_accuracy: 0.6081\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4067 - accuracy: 0.5535 - val_loss: 1.3065 - val_accuracy: 0.5914\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3871 - accuracy: 0.5617 - val_loss: 1.2974 - val_accuracy: 0.6045\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3639 - accuracy: 0.5778 - val_loss: 1.3235 - val_accuracy: 0.5866\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3473 - accuracy: 0.5800 - val_loss: 1.2285 - val_accuracy: 0.6404\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3349 - accuracy: 0.5863 - val_loss: 1.3661 - val_accuracy: 0.5532\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3198 - accuracy: 0.5955 - val_loss: 1.2340 - val_accuracy: 0.6320\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3074 - accuracy: 0.5913 - val_loss: 1.2373 - val_accuracy: 0.6177\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2982 - accuracy: 0.5938 - val_loss: 1.2643 - val_accuracy: 0.6129\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2705 - accuracy: 0.6124 - val_loss: 1.3813 - val_accuracy: 0.5926\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2688 - accuracy: 0.6005 - val_loss: 1.2480 - val_accuracy: 0.6320\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2485 - accuracy: 0.6178 - val_loss: 1.1955 - val_accuracy: 0.6583\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2258 - accuracy: 0.6199 - val_loss: 1.2054 - val_accuracy: 0.6428\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2169 - accuracy: 0.6215 - val_loss: 1.1875 - val_accuracy: 0.6679\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1955 - accuracy: 0.6313 - val_loss: 1.1528 - val_accuracy: 0.6846\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1966 - accuracy: 0.6288 - val_loss: 1.1689 - val_accuracy: 0.6655\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2037 - accuracy: 0.6346 - val_loss: 1.1536 - val_accuracy: 0.6953\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1798 - accuracy: 0.6370 - val_loss: 1.1725 - val_accuracy: 0.6810\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1707 - accuracy: 0.6379 - val_loss: 1.1515 - val_accuracy: 0.6392\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1501 - accuracy: 0.6475 - val_loss: 1.2129 - val_accuracy: 0.6464\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1551 - accuracy: 0.6406 - val_loss: 1.1557 - val_accuracy: 0.6308\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1357 - accuracy: 0.6609 - val_loss: 1.1232 - val_accuracy: 0.6810\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1294 - accuracy: 0.6607 - val_loss: 1.1020 - val_accuracy: 0.6930\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1146 - accuracy: 0.6643 - val_loss: 1.0963 - val_accuracy: 0.6822\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1127 - accuracy: 0.6632 - val_loss: 1.1356 - val_accuracy: 0.6595\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0852 - accuracy: 0.6698 - val_loss: 1.1231 - val_accuracy: 0.6714\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0929 - accuracy: 0.6666 - val_loss: 1.0805 - val_accuracy: 0.6918\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0981 - accuracy: 0.6652 - val_loss: 1.0860 - val_accuracy: 0.6750\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0565 - accuracy: 0.6843 - val_loss: 1.0616 - val_accuracy: 0.7133\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0674 - accuracy: 0.6773 - val_loss: 1.0796 - val_accuracy: 0.6667\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0481 - accuracy: 0.6840 - val_loss: 1.0539 - val_accuracy: 0.6918\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0550 - accuracy: 0.6819 - val_loss: 1.0657 - val_accuracy: 0.6846\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0427 - accuracy: 0.6905 - val_loss: 1.0416 - val_accuracy: 0.7073\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0172 - accuracy: 0.6978 - val_loss: 1.1165 - val_accuracy: 0.6882\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0268 - accuracy: 0.6961 - val_loss: 1.1380 - val_accuracy: 0.6977\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0158 - accuracy: 0.6939 - val_loss: 1.0835 - val_accuracy: 0.6798\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0004 - accuracy: 0.7038 - val_loss: 1.0744 - val_accuracy: 0.7025\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0151 - accuracy: 0.6992 - val_loss: 1.0811 - val_accuracy: 0.6679\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9834 - accuracy: 0.7109 - val_loss: 1.0826 - val_accuracy: 0.6941\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9894 - accuracy: 0.7061 - val_loss: 1.0358 - val_accuracy: 0.7157\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9817 - accuracy: 0.7069 - val_loss: 1.0217 - val_accuracy: 0.6930\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.9598 - accuracy: 0.7176 - val_loss: 1.0761 - val_accuracy: 0.6810\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9602 - accuracy: 0.7198 - val_loss: 1.0188 - val_accuracy: 0.7252\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9594 - accuracy: 0.7163 - val_loss: 1.0352 - val_accuracy: 0.7133\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9596 - accuracy: 0.7244 - val_loss: 0.9967 - val_accuracy: 0.7360\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9533 - accuracy: 0.7217 - val_loss: 1.0290 - val_accuracy: 0.7073\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9254 - accuracy: 0.7336 - val_loss: 1.0167 - val_accuracy: 0.7264\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9278 - accuracy: 0.7313 - val_loss: 1.0312 - val_accuracy: 0.7109\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9257 - accuracy: 0.7306 - val_loss: 1.1094 - val_accuracy: 0.7180\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9128 - accuracy: 0.7355 - val_loss: 1.0182 - val_accuracy: 0.7324\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9148 - accuracy: 0.7359 - val_loss: 1.0396 - val_accuracy: 0.7121\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9044 - accuracy: 0.7373 - val_loss: 0.9858 - val_accuracy: 0.7312\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9007 - accuracy: 0.7375 - val_loss: 1.0764 - val_accuracy: 0.7109\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9015 - accuracy: 0.7403 - val_loss: 1.0127 - val_accuracy: 0.7109\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9052 - accuracy: 0.7344 - val_loss: 0.9730 - val_accuracy: 0.7276\n",
            "Epoch 82/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8905 - accuracy: 0.7423 - val_loss: 1.0408 - val_accuracy: 0.7013\n",
            "Epoch 83/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.8653 - accuracy: 0.7465 - val_loss: 1.0612 - val_accuracy: 0.6750\n",
            "Epoch 84/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.8583 - accuracy: 0.7533 - val_loss: 0.9700 - val_accuracy: 0.7097\n",
            "Epoch 85/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8749 - accuracy: 0.7517 - val_loss: 1.0307 - val_accuracy: 0.6965\n",
            "Epoch 86/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8638 - accuracy: 0.7495 - val_loss: 0.9852 - val_accuracy: 0.7204\n",
            "Epoch 87/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8675 - accuracy: 0.7520 - val_loss: 0.9711 - val_accuracy: 0.7288\n",
            "Epoch 88/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8382 - accuracy: 0.7640 - val_loss: 1.0534 - val_accuracy: 0.6989\n",
            "Epoch 89/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8489 - accuracy: 0.7587 - val_loss: 1.0140 - val_accuracy: 0.6930\n",
            "Epoch 90/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8530 - accuracy: 0.7533 - val_loss: 1.0365 - val_accuracy: 0.6918\n",
            "Epoch 91/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8235 - accuracy: 0.7599 - val_loss: 1.0507 - val_accuracy: 0.7407\n",
            "Epoch 92/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8313 - accuracy: 0.7596 - val_loss: 0.9709 - val_accuracy: 0.7431\n",
            "Epoch 93/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8318 - accuracy: 0.7566 - val_loss: 0.9541 - val_accuracy: 0.7228\n",
            "Epoch 94/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8203 - accuracy: 0.7674 - val_loss: 0.9723 - val_accuracy: 0.7312\n",
            "Epoch 95/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8111 - accuracy: 0.7665 - val_loss: 0.9523 - val_accuracy: 0.7455\n",
            "Epoch 96/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8220 - accuracy: 0.7691 - val_loss: 0.9908 - val_accuracy: 0.7157\n",
            "Epoch 97/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8107 - accuracy: 0.7685 - val_loss: 0.9800 - val_accuracy: 0.7133\n",
            "Epoch 98/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8015 - accuracy: 0.7732 - val_loss: 1.0129 - val_accuracy: 0.7252\n",
            "Epoch 99/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7902 - accuracy: 0.7740 - val_loss: 1.1235 - val_accuracy: 0.6906\n",
            "Epoch 100/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7950 - accuracy: 0.7770 - val_loss: 1.0593 - val_accuracy: 0.7109\n",
            "Epoch 101/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7729 - accuracy: 0.7788 - val_loss: 1.0313 - val_accuracy: 0.7001\n",
            "Epoch 102/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7750 - accuracy: 0.7797 - val_loss: 1.0508 - val_accuracy: 0.7073\n",
            "Epoch 103/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7816 - accuracy: 0.7811 - val_loss: 1.0859 - val_accuracy: 0.6870\n",
            "Epoch 104/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7841 - accuracy: 0.7767 - val_loss: 0.9499 - val_accuracy: 0.7264\n",
            "Epoch 105/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7812 - accuracy: 0.7776 - val_loss: 0.9401 - val_accuracy: 0.7192\n",
            "Epoch 106/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7540 - accuracy: 0.7879 - val_loss: 1.0099 - val_accuracy: 0.7025\n",
            "Epoch 107/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7627 - accuracy: 0.7819 - val_loss: 1.2624 - val_accuracy: 0.6738\n",
            "Epoch 108/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7449 - accuracy: 0.7926 - val_loss: 0.9983 - val_accuracy: 0.7157\n",
            "Epoch 109/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7590 - accuracy: 0.7935 - val_loss: 0.9828 - val_accuracy: 0.7240\n",
            "Epoch 110/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7351 - accuracy: 0.7882 - val_loss: 1.0549 - val_accuracy: 0.6798\n",
            "Epoch 111/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7592 - accuracy: 0.7880 - val_loss: 0.9657 - val_accuracy: 0.7240\n",
            "Epoch 112/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7449 - accuracy: 0.7888 - val_loss: 0.9772 - val_accuracy: 0.7121\n",
            "Epoch 113/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7311 - accuracy: 0.7955 - val_loss: 0.9655 - val_accuracy: 0.7407\n",
            "Epoch 114/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7378 - accuracy: 0.7880 - val_loss: 1.0009 - val_accuracy: 0.7157\n",
            "Epoch 115/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7322 - accuracy: 0.7916 - val_loss: 0.9223 - val_accuracy: 0.7384\n",
            "Epoch 116/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7157 - accuracy: 0.8021 - val_loss: 0.9661 - val_accuracy: 0.7348\n",
            "Epoch 117/200\n",
            "70/70 [==============================] - 1s 10ms/step - loss: 0.7268 - accuracy: 0.7968 - val_loss: 0.9534 - val_accuracy: 0.7073\n",
            "Epoch 118/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7107 - accuracy: 0.8024 - val_loss: 0.9570 - val_accuracy: 0.7336\n",
            "Epoch 119/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7284 - accuracy: 0.7993 - val_loss: 0.9675 - val_accuracy: 0.7073\n",
            "Epoch 120/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7004 - accuracy: 0.8021 - val_loss: 0.9318 - val_accuracy: 0.7395\n",
            "Epoch 121/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7036 - accuracy: 0.8059 - val_loss: 1.0536 - val_accuracy: 0.7085\n",
            "Epoch 122/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6947 - accuracy: 0.8043 - val_loss: 0.9447 - val_accuracy: 0.7276\n",
            "Epoch 123/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7014 - accuracy: 0.8036 - val_loss: 1.0242 - val_accuracy: 0.7360\n",
            "Epoch 124/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6887 - accuracy: 0.8082 - val_loss: 0.9657 - val_accuracy: 0.7013\n",
            "Epoch 125/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6994 - accuracy: 0.8049 - val_loss: 0.9281 - val_accuracy: 0.7264\n",
            "Epoch 126/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6901 - accuracy: 0.8129 - val_loss: 0.9554 - val_accuracy: 0.7407\n",
            "Epoch 127/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6892 - accuracy: 0.8099 - val_loss: 1.1351 - val_accuracy: 0.6965\n",
            "Epoch 128/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6950 - accuracy: 0.8107 - val_loss: 0.9375 - val_accuracy: 0.7216\n",
            "Epoch 129/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6830 - accuracy: 0.8145 - val_loss: 0.9645 - val_accuracy: 0.7455\n",
            "Epoch 130/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6892 - accuracy: 0.8049 - val_loss: 0.9376 - val_accuracy: 0.7240\n",
            "Evaluating model...\n",
            "ROC: 0.967\n",
            "\n",
            "Accuracy = 0.76\n",
            "F-Score: 0.76\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 7, 8, 9, 10} Validate on 6 Test on 5 ***\n",
            "val_x shape:  (823, 68, 68, 4)\n",
            "test_x shape:  (936, 68, 68, 4)\n",
            "train_x shape:  (6973, 68, 68, 4)\n",
            "val_y shape:  (823,)\n",
            "test_y shape:  (936,)\n",
            "train_y shape:  (6973,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 1s 14ms/step - loss: 2.4361 - accuracy: 0.1256 - val_loss: 2.4122 - val_accuracy: 0.1474\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3916 - accuracy: 0.1484 - val_loss: 2.3668 - val_accuracy: 0.2158\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3540 - accuracy: 0.1745 - val_loss: 2.3259 - val_accuracy: 0.2735\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3186 - accuracy: 0.1995 - val_loss: 2.2694 - val_accuracy: 0.2639\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.2667 - accuracy: 0.2247 - val_loss: 2.1970 - val_accuracy: 0.2991\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.2090 - accuracy: 0.2492 - val_loss: 2.1319 - val_accuracy: 0.3045\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.1494 - accuracy: 0.2776 - val_loss: 2.0528 - val_accuracy: 0.3558\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.0905 - accuracy: 0.2996 - val_loss: 1.9880 - val_accuracy: 0.3355\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.0320 - accuracy: 0.3297 - val_loss: 1.9084 - val_accuracy: 0.3878\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.9793 - accuracy: 0.3389 - val_loss: 1.8740 - val_accuracy: 0.3996\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.9486 - accuracy: 0.3496 - val_loss: 1.7984 - val_accuracy: 0.4274\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8976 - accuracy: 0.3726 - val_loss: 1.7285 - val_accuracy: 0.4434\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8472 - accuracy: 0.3947 - val_loss: 1.6591 - val_accuracy: 0.4594\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7955 - accuracy: 0.4051 - val_loss: 1.6203 - val_accuracy: 0.4669\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7768 - accuracy: 0.4176 - val_loss: 1.6054 - val_accuracy: 0.4605\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7339 - accuracy: 0.4312 - val_loss: 1.5372 - val_accuracy: 0.5214\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7060 - accuracy: 0.4473 - val_loss: 1.5687 - val_accuracy: 0.4722\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6895 - accuracy: 0.4469 - val_loss: 1.4960 - val_accuracy: 0.5150\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6495 - accuracy: 0.4657 - val_loss: 1.5637 - val_accuracy: 0.4733\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6283 - accuracy: 0.4745 - val_loss: 1.4675 - val_accuracy: 0.5299\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5753 - accuracy: 0.4903 - val_loss: 1.4004 - val_accuracy: 0.5491\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5796 - accuracy: 0.4872 - val_loss: 1.3745 - val_accuracy: 0.5908\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5516 - accuracy: 0.4975 - val_loss: 1.3552 - val_accuracy: 0.5929\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5117 - accuracy: 0.5143 - val_loss: 1.3902 - val_accuracy: 0.5427\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4830 - accuracy: 0.5332 - val_loss: 1.5543 - val_accuracy: 0.4947\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4702 - accuracy: 0.5342 - val_loss: 1.2710 - val_accuracy: 0.6303\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4587 - accuracy: 0.5331 - val_loss: 1.2740 - val_accuracy: 0.6250\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4333 - accuracy: 0.5450 - val_loss: 1.2705 - val_accuracy: 0.6218\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4233 - accuracy: 0.5484 - val_loss: 1.3077 - val_accuracy: 0.6303\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3907 - accuracy: 0.5599 - val_loss: 1.5182 - val_accuracy: 0.5107\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3969 - accuracy: 0.5659 - val_loss: 1.2035 - val_accuracy: 0.6560\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3761 - accuracy: 0.5655 - val_loss: 1.1953 - val_accuracy: 0.6314\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3550 - accuracy: 0.5722 - val_loss: 1.2153 - val_accuracy: 0.6293\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3305 - accuracy: 0.5873 - val_loss: 1.2147 - val_accuracy: 0.6432\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3203 - accuracy: 0.5855 - val_loss: 1.1857 - val_accuracy: 0.6538\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3118 - accuracy: 0.5886 - val_loss: 1.1735 - val_accuracy: 0.6485\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2967 - accuracy: 0.6022 - val_loss: 1.2649 - val_accuracy: 0.6079\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2930 - accuracy: 0.6019 - val_loss: 1.1756 - val_accuracy: 0.6464\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2773 - accuracy: 0.6042 - val_loss: 1.1636 - val_accuracy: 0.6474\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2421 - accuracy: 0.6139 - val_loss: 1.1797 - val_accuracy: 0.6378\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2408 - accuracy: 0.6215 - val_loss: 1.1159 - val_accuracy: 0.6688\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2401 - accuracy: 0.6161 - val_loss: 1.1154 - val_accuracy: 0.6741\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2226 - accuracy: 0.6178 - val_loss: 1.1845 - val_accuracy: 0.6421\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2262 - accuracy: 0.6256 - val_loss: 1.1032 - val_accuracy: 0.6848\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1860 - accuracy: 0.6363 - val_loss: 1.0991 - val_accuracy: 0.6613\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1864 - accuracy: 0.6406 - val_loss: 1.0675 - val_accuracy: 0.6795\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1730 - accuracy: 0.6410 - val_loss: 1.1045 - val_accuracy: 0.6656\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1828 - accuracy: 0.6402 - val_loss: 1.1630 - val_accuracy: 0.6421\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1530 - accuracy: 0.6538 - val_loss: 1.1109 - val_accuracy: 0.6571\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1322 - accuracy: 0.6583 - val_loss: 1.0911 - val_accuracy: 0.6806\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1312 - accuracy: 0.6604 - val_loss: 1.0837 - val_accuracy: 0.6848\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1281 - accuracy: 0.6591 - val_loss: 1.0669 - val_accuracy: 0.6987\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1262 - accuracy: 0.6623 - val_loss: 1.0535 - val_accuracy: 0.6827\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1075 - accuracy: 0.6646 - val_loss: 1.1367 - val_accuracy: 0.6517\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1030 - accuracy: 0.6671 - val_loss: 1.0497 - val_accuracy: 0.6934\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0887 - accuracy: 0.6780 - val_loss: 1.0299 - val_accuracy: 0.7009\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0800 - accuracy: 0.6790 - val_loss: 1.0598 - val_accuracy: 0.6902\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0726 - accuracy: 0.6729 - val_loss: 1.0295 - val_accuracy: 0.6912\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0649 - accuracy: 0.6831 - val_loss: 1.0278 - val_accuracy: 0.7137\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0515 - accuracy: 0.6866 - val_loss: 1.0622 - val_accuracy: 0.7019\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0506 - accuracy: 0.6854 - val_loss: 1.0244 - val_accuracy: 0.7083\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0494 - accuracy: 0.6902 - val_loss: 1.0298 - val_accuracy: 0.6902\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0243 - accuracy: 0.6885 - val_loss: 1.0017 - val_accuracy: 0.7030\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0233 - accuracy: 0.6950 - val_loss: 1.0417 - val_accuracy: 0.6806\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9987 - accuracy: 0.7018 - val_loss: 1.0118 - val_accuracy: 0.7030\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0134 - accuracy: 0.6983 - val_loss: 1.1093 - val_accuracy: 0.6763\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0139 - accuracy: 0.6984 - val_loss: 0.9890 - val_accuracy: 0.7094\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9911 - accuracy: 0.7107 - val_loss: 1.0042 - val_accuracy: 0.7126\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9753 - accuracy: 0.7082 - val_loss: 1.0321 - val_accuracy: 0.6998\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9930 - accuracy: 0.7051 - val_loss: 1.0474 - val_accuracy: 0.6987\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9806 - accuracy: 0.7115 - val_loss: 1.1157 - val_accuracy: 0.6816\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 1s 12ms/step - loss: 0.9686 - accuracy: 0.7149 - val_loss: 1.0444 - val_accuracy: 0.6955\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9674 - accuracy: 0.7136 - val_loss: 1.0032 - val_accuracy: 0.7115\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9624 - accuracy: 0.7165 - val_loss: 0.9830 - val_accuracy: 0.7190\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9329 - accuracy: 0.7251 - val_loss: 0.9923 - val_accuracy: 0.7147\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9499 - accuracy: 0.7201 - val_loss: 1.0109 - val_accuracy: 0.7062\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9385 - accuracy: 0.7224 - val_loss: 0.9537 - val_accuracy: 0.7276\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9287 - accuracy: 0.7264 - val_loss: 0.9784 - val_accuracy: 0.7254\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9252 - accuracy: 0.7277 - val_loss: 0.9910 - val_accuracy: 0.7212\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9182 - accuracy: 0.7426 - val_loss: 0.9943 - val_accuracy: 0.7286\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9131 - accuracy: 0.7350 - val_loss: 1.0539 - val_accuracy: 0.6976\n",
            "Epoch 82/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9040 - accuracy: 0.7401 - val_loss: 1.0369 - val_accuracy: 0.6923\n",
            "Epoch 83/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8982 - accuracy: 0.7404 - val_loss: 1.0870 - val_accuracy: 0.6976\n",
            "Epoch 84/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9183 - accuracy: 0.7292 - val_loss: 0.9987 - val_accuracy: 0.7244\n",
            "Epoch 85/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8776 - accuracy: 0.7463 - val_loss: 0.9550 - val_accuracy: 0.7329\n",
            "Epoch 86/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8838 - accuracy: 0.7463 - val_loss: 0.9631 - val_accuracy: 0.7254\n",
            "Epoch 87/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8842 - accuracy: 0.7407 - val_loss: 0.9845 - val_accuracy: 0.7318\n",
            "Epoch 88/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8589 - accuracy: 0.7509 - val_loss: 0.9529 - val_accuracy: 0.7265\n",
            "Epoch 89/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8498 - accuracy: 0.7513 - val_loss: 1.0357 - val_accuracy: 0.7222\n",
            "Epoch 90/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8564 - accuracy: 0.7523 - val_loss: 0.9686 - val_accuracy: 0.7564\n",
            "Epoch 91/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8506 - accuracy: 0.7538 - val_loss: 0.9959 - val_accuracy: 0.7265\n",
            "Epoch 92/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8457 - accuracy: 0.7565 - val_loss: 0.9659 - val_accuracy: 0.7244\n",
            "Epoch 93/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8360 - accuracy: 0.7589 - val_loss: 0.9363 - val_accuracy: 0.7318\n",
            "Epoch 94/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8451 - accuracy: 0.7563 - val_loss: 0.9876 - val_accuracy: 0.7404\n",
            "Epoch 95/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8402 - accuracy: 0.7532 - val_loss: 0.9318 - val_accuracy: 0.7372\n",
            "Epoch 96/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8277 - accuracy: 0.7642 - val_loss: 1.0266 - val_accuracy: 0.7318\n",
            "Epoch 97/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8273 - accuracy: 0.7601 - val_loss: 0.9519 - val_accuracy: 0.7415\n",
            "Epoch 98/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8011 - accuracy: 0.7704 - val_loss: 0.9793 - val_accuracy: 0.7532\n",
            "Epoch 99/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8129 - accuracy: 0.7627 - val_loss: 0.9317 - val_accuracy: 0.7297\n",
            "Epoch 100/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8178 - accuracy: 0.7698 - val_loss: 0.9434 - val_accuracy: 0.7447\n",
            "Epoch 101/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8118 - accuracy: 0.7635 - val_loss: 0.9321 - val_accuracy: 0.7404\n",
            "Epoch 102/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8049 - accuracy: 0.7672 - val_loss: 0.9610 - val_accuracy: 0.7521\n",
            "Epoch 103/200\n",
            "70/70 [==============================] - 1s 12ms/step - loss: 0.7941 - accuracy: 0.7738 - val_loss: 0.9609 - val_accuracy: 0.7457\n",
            "Epoch 104/200\n",
            "70/70 [==============================] - 1s 12ms/step - loss: 0.7903 - accuracy: 0.7740 - val_loss: 0.9670 - val_accuracy: 0.7297\n",
            "Epoch 105/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7898 - accuracy: 0.7734 - val_loss: 1.0162 - val_accuracy: 0.7372\n",
            "Epoch 106/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7655 - accuracy: 0.7796 - val_loss: 0.9367 - val_accuracy: 0.7415\n",
            "Epoch 107/200\n",
            "70/70 [==============================] - 1s 12ms/step - loss: 0.7792 - accuracy: 0.7822 - val_loss: 0.9836 - val_accuracy: 0.7190\n",
            "Epoch 108/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7783 - accuracy: 0.7820 - val_loss: 0.9247 - val_accuracy: 0.7489\n",
            "Epoch 109/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7752 - accuracy: 0.7847 - val_loss: 0.9832 - val_accuracy: 0.7436\n",
            "Epoch 110/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7741 - accuracy: 0.7836 - val_loss: 1.0188 - val_accuracy: 0.7297\n",
            "Epoch 111/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7780 - accuracy: 0.7789 - val_loss: 1.0070 - val_accuracy: 0.7361\n",
            "Epoch 112/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7551 - accuracy: 0.7830 - val_loss: 0.9724 - val_accuracy: 0.7340\n",
            "Epoch 113/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7559 - accuracy: 0.7855 - val_loss: 1.0213 - val_accuracy: 0.7105\n",
            "Epoch 114/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7403 - accuracy: 0.7946 - val_loss: 0.9650 - val_accuracy: 0.7447\n",
            "Epoch 115/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7500 - accuracy: 0.7896 - val_loss: 0.9464 - val_accuracy: 0.7436\n",
            "Epoch 116/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7435 - accuracy: 0.7925 - val_loss: 0.9662 - val_accuracy: 0.7179\n",
            "Epoch 117/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7420 - accuracy: 0.7955 - val_loss: 1.0222 - val_accuracy: 0.7308\n",
            "Epoch 118/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7340 - accuracy: 0.7956 - val_loss: 0.9378 - val_accuracy: 0.7543\n",
            "Epoch 119/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7211 - accuracy: 0.7976 - val_loss: 0.9334 - val_accuracy: 0.7479\n",
            "Epoch 120/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7334 - accuracy: 0.7936 - val_loss: 0.9482 - val_accuracy: 0.7350\n",
            "Epoch 121/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7303 - accuracy: 0.7890 - val_loss: 0.9462 - val_accuracy: 0.7436\n",
            "Epoch 122/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7303 - accuracy: 0.7976 - val_loss: 0.9069 - val_accuracy: 0.7660\n",
            "Epoch 123/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7185 - accuracy: 0.7998 - val_loss: 0.9171 - val_accuracy: 0.7575\n",
            "Epoch 124/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7072 - accuracy: 0.8037 - val_loss: 0.9053 - val_accuracy: 0.7479\n",
            "Epoch 125/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6867 - accuracy: 0.8014 - val_loss: 0.9368 - val_accuracy: 0.7447\n",
            "Epoch 126/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6950 - accuracy: 0.8035 - val_loss: 0.9329 - val_accuracy: 0.7361\n",
            "Epoch 127/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7034 - accuracy: 0.8040 - val_loss: 0.9077 - val_accuracy: 0.7671\n",
            "Epoch 128/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6858 - accuracy: 0.8080 - val_loss: 1.0183 - val_accuracy: 0.7297\n",
            "Epoch 129/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6920 - accuracy: 0.8087 - val_loss: 0.9150 - val_accuracy: 0.7521\n",
            "Epoch 130/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6934 - accuracy: 0.8054 - val_loss: 1.2859 - val_accuracy: 0.6549\n",
            "Epoch 131/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6927 - accuracy: 0.8038 - val_loss: 0.9667 - val_accuracy: 0.7350\n",
            "Epoch 132/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6967 - accuracy: 0.8120 - val_loss: 0.9440 - val_accuracy: 0.7543\n",
            "Epoch 133/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6881 - accuracy: 0.8084 - val_loss: 1.0209 - val_accuracy: 0.7436\n",
            "Epoch 134/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6909 - accuracy: 0.8088 - val_loss: 0.9448 - val_accuracy: 0.7415\n",
            "Epoch 135/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6704 - accuracy: 0.8128 - val_loss: 0.9167 - val_accuracy: 0.7479\n",
            "Epoch 136/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6862 - accuracy: 0.8163 - val_loss: 0.8891 - val_accuracy: 0.7575\n",
            "Epoch 137/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6615 - accuracy: 0.8153 - val_loss: 0.9632 - val_accuracy: 0.7340\n",
            "Epoch 138/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6725 - accuracy: 0.8177 - val_loss: 0.9238 - val_accuracy: 0.7276\n",
            "Epoch 139/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6640 - accuracy: 0.8174 - val_loss: 0.8772 - val_accuracy: 0.7618\n",
            "Epoch 140/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6557 - accuracy: 0.8161 - val_loss: 0.8953 - val_accuracy: 0.7714\n",
            "Epoch 141/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6504 - accuracy: 0.8139 - val_loss: 0.9112 - val_accuracy: 0.7692\n",
            "Epoch 142/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6520 - accuracy: 0.8167 - val_loss: 0.9321 - val_accuracy: 0.7511\n",
            "Epoch 143/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6534 - accuracy: 0.8196 - val_loss: 0.8863 - val_accuracy: 0.7756\n",
            "Epoch 144/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6422 - accuracy: 0.8215 - val_loss: 1.0645 - val_accuracy: 0.7286\n",
            "Epoch 145/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6283 - accuracy: 0.8291 - val_loss: 0.9392 - val_accuracy: 0.7564\n",
            "Epoch 146/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6325 - accuracy: 0.8281 - val_loss: 0.9464 - val_accuracy: 0.7564\n",
            "Epoch 147/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6302 - accuracy: 0.8260 - val_loss: 0.8834 - val_accuracy: 0.7618\n",
            "Epoch 148/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6300 - accuracy: 0.8230 - val_loss: 0.9156 - val_accuracy: 0.7639\n",
            "Epoch 149/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6490 - accuracy: 0.8196 - val_loss: 0.9422 - val_accuracy: 0.7650\n",
            "Epoch 150/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6165 - accuracy: 0.8367 - val_loss: 0.9368 - val_accuracy: 0.7415\n",
            "Epoch 151/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6307 - accuracy: 0.8227 - val_loss: 0.8845 - val_accuracy: 0.7585\n",
            "Epoch 152/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6342 - accuracy: 0.8227 - val_loss: 0.9157 - val_accuracy: 0.7361\n",
            "Epoch 153/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6235 - accuracy: 0.8268 - val_loss: 1.0296 - val_accuracy: 0.7041\n",
            "Epoch 154/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6168 - accuracy: 0.8328 - val_loss: 1.0245 - val_accuracy: 0.7340\n",
            "Evaluating model...\n",
            "ROC: 0.952\n",
            "\n",
            "Accuracy = 0.70\n",
            "F-Score: 0.7\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 5, 6, 9, 10} Validate on 7 Test on 8 ***\n",
            "val_x shape:  (838, 68, 68, 4)\n",
            "test_x shape:  (806, 68, 68, 4)\n",
            "train_x shape:  (7088, 68, 68, 4)\n",
            "val_y shape:  (838,)\n",
            "test_y shape:  (806,)\n",
            "train_y shape:  (7088,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "71/71 [==============================] - 1s 14ms/step - loss: 2.4395 - accuracy: 0.1215 - val_loss: 2.4037 - val_accuracy: 0.1576\n",
            "Epoch 2/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.3966 - accuracy: 0.1455 - val_loss: 2.3684 - val_accuracy: 0.1588\n",
            "Epoch 3/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.3696 - accuracy: 0.1541 - val_loss: 2.3360 - val_accuracy: 0.2370\n",
            "Epoch 4/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.3378 - accuracy: 0.1769 - val_loss: 2.2716 - val_accuracy: 0.3139\n",
            "Epoch 5/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.2894 - accuracy: 0.1982 - val_loss: 2.1721 - val_accuracy: 0.3623\n",
            "Epoch 6/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.2255 - accuracy: 0.2396 - val_loss: 2.0756 - val_accuracy: 0.4268\n",
            "Epoch 7/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.1583 - accuracy: 0.2617 - val_loss: 1.9707 - val_accuracy: 0.4640\n",
            "Epoch 8/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.0993 - accuracy: 0.2801 - val_loss: 1.8831 - val_accuracy: 0.5149\n",
            "Epoch 9/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.0350 - accuracy: 0.3104 - val_loss: 1.8218 - val_accuracy: 0.5025\n",
            "Epoch 10/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.9829 - accuracy: 0.3276 - val_loss: 1.7561 - val_accuracy: 0.4963\n",
            "Epoch 11/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.9412 - accuracy: 0.3500 - val_loss: 1.7018 - val_accuracy: 0.5397\n",
            "Epoch 12/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.8895 - accuracy: 0.3729 - val_loss: 1.6152 - val_accuracy: 0.5397\n",
            "Epoch 13/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.8611 - accuracy: 0.3794 - val_loss: 1.5757 - val_accuracy: 0.5658\n",
            "Epoch 14/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.7891 - accuracy: 0.4008 - val_loss: 1.5004 - val_accuracy: 0.5199\n",
            "Epoch 15/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.7698 - accuracy: 0.4170 - val_loss: 1.4999 - val_accuracy: 0.5682\n",
            "Epoch 16/200\n",
            "71/71 [==============================] - 1s 10ms/step - loss: 1.7333 - accuracy: 0.4230 - val_loss: 1.4825 - val_accuracy: 0.5546\n",
            "Epoch 17/200\n",
            "71/71 [==============================] - 1s 10ms/step - loss: 1.6951 - accuracy: 0.4468 - val_loss: 1.4293 - val_accuracy: 0.5533\n",
            "Epoch 18/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.6706 - accuracy: 0.4565 - val_loss: 1.4350 - val_accuracy: 0.5447\n",
            "Epoch 19/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.6366 - accuracy: 0.4705 - val_loss: 1.3788 - val_accuracy: 0.5670\n",
            "Epoch 20/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.6020 - accuracy: 0.4874 - val_loss: 1.3267 - val_accuracy: 0.5757\n",
            "Epoch 21/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.5847 - accuracy: 0.4858 - val_loss: 1.3574 - val_accuracy: 0.5583\n",
            "Epoch 22/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.5618 - accuracy: 0.4924 - val_loss: 1.3384 - val_accuracy: 0.5335\n",
            "Epoch 23/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.5417 - accuracy: 0.5023 - val_loss: 1.3956 - val_accuracy: 0.5571\n",
            "Epoch 24/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.5153 - accuracy: 0.5268 - val_loss: 1.3286 - val_accuracy: 0.5447\n",
            "Epoch 25/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.4856 - accuracy: 0.5244 - val_loss: 1.3366 - val_accuracy: 0.5558\n",
            "Epoch 26/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.4745 - accuracy: 0.5334 - val_loss: 1.2790 - val_accuracy: 0.5695\n",
            "Epoch 27/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.4492 - accuracy: 0.5357 - val_loss: 1.2759 - val_accuracy: 0.6005\n",
            "Epoch 28/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.4343 - accuracy: 0.5453 - val_loss: 1.3093 - val_accuracy: 0.5856\n",
            "Epoch 29/200\n",
            "71/71 [==============================] - 1s 10ms/step - loss: 1.4241 - accuracy: 0.5463 - val_loss: 1.2898 - val_accuracy: 0.5806\n",
            "Epoch 30/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.4092 - accuracy: 0.5506 - val_loss: 1.2793 - val_accuracy: 0.6042\n",
            "Epoch 31/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.3796 - accuracy: 0.5615 - val_loss: 1.2675 - val_accuracy: 0.6017\n",
            "Epoch 32/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.3582 - accuracy: 0.5652 - val_loss: 1.2357 - val_accuracy: 0.5893\n",
            "Epoch 33/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.3371 - accuracy: 0.5794 - val_loss: 1.2374 - val_accuracy: 0.6067\n",
            "Epoch 34/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.3373 - accuracy: 0.5738 - val_loss: 1.2126 - val_accuracy: 0.6030\n",
            "Epoch 35/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.3284 - accuracy: 0.5861 - val_loss: 1.2509 - val_accuracy: 0.6055\n",
            "Epoch 36/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.3035 - accuracy: 0.5928 - val_loss: 1.2646 - val_accuracy: 0.5732\n",
            "Epoch 37/200\n",
            "71/71 [==============================] - 1s 10ms/step - loss: 1.2999 - accuracy: 0.5988 - val_loss: 1.2180 - val_accuracy: 0.6203\n",
            "Epoch 38/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2571 - accuracy: 0.6092 - val_loss: 1.3484 - val_accuracy: 0.5608\n",
            "Epoch 39/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2573 - accuracy: 0.6119 - val_loss: 1.2495 - val_accuracy: 0.6141\n",
            "Epoch 40/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2474 - accuracy: 0.6158 - val_loss: 1.2064 - val_accuracy: 0.6079\n",
            "Epoch 41/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2384 - accuracy: 0.6161 - val_loss: 1.2427 - val_accuracy: 0.5943\n",
            "Epoch 42/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2502 - accuracy: 0.6216 - val_loss: 1.1887 - val_accuracy: 0.6079\n",
            "Epoch 43/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1921 - accuracy: 0.6304 - val_loss: 1.1952 - val_accuracy: 0.5782\n",
            "Epoch 44/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2028 - accuracy: 0.6304 - val_loss: 1.1446 - val_accuracy: 0.6588\n",
            "Epoch 45/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2021 - accuracy: 0.6237 - val_loss: 1.1732 - val_accuracy: 0.5906\n",
            "Epoch 46/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1672 - accuracy: 0.6438 - val_loss: 1.2566 - val_accuracy: 0.5583\n",
            "Epoch 47/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1707 - accuracy: 0.6483 - val_loss: 1.1885 - val_accuracy: 0.6104\n",
            "Epoch 48/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1611 - accuracy: 0.6460 - val_loss: 1.2432 - val_accuracy: 0.5856\n",
            "Epoch 49/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1314 - accuracy: 0.6517 - val_loss: 1.1613 - val_accuracy: 0.6501\n",
            "Epoch 50/200\n",
            "71/71 [==============================] - 1s 10ms/step - loss: 1.1388 - accuracy: 0.6483 - val_loss: 1.1608 - val_accuracy: 0.6055\n",
            "Epoch 51/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1390 - accuracy: 0.6497 - val_loss: 1.1480 - val_accuracy: 0.6203\n",
            "Epoch 52/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1064 - accuracy: 0.6669 - val_loss: 1.2043 - val_accuracy: 0.5695\n",
            "Epoch 53/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1067 - accuracy: 0.6693 - val_loss: 1.1998 - val_accuracy: 0.6340\n",
            "Epoch 54/200\n",
            "71/71 [==============================] - 1s 10ms/step - loss: 1.1093 - accuracy: 0.6566 - val_loss: 1.1956 - val_accuracy: 0.5757\n",
            "Epoch 55/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0820 - accuracy: 0.6755 - val_loss: 1.2435 - val_accuracy: 0.6005\n",
            "Epoch 56/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0894 - accuracy: 0.6724 - val_loss: 1.2122 - val_accuracy: 0.6191\n",
            "Epoch 57/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0786 - accuracy: 0.6728 - val_loss: 1.1724 - val_accuracy: 0.6538\n",
            "Epoch 58/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0715 - accuracy: 0.6778 - val_loss: 1.1633 - val_accuracy: 0.6588\n",
            "Epoch 59/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0682 - accuracy: 0.6752 - val_loss: 1.2300 - val_accuracy: 0.5980\n",
            "Evaluating model...\n",
            "ROC: 0.95\n",
            "\n",
            "Accuracy = 0.65\n",
            "F-Score: 0.65\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 5, 6, 9, 10} Validate on 8 Test on 7 ***\n",
            "val_x shape:  (806, 68, 68, 4)\n",
            "test_x shape:  (838, 68, 68, 4)\n",
            "train_x shape:  (7088, 68, 68, 4)\n",
            "val_y shape:  (806,)\n",
            "test_y shape:  (838,)\n",
            "train_y shape:  (7088,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "71/71 [==============================] - 1s 13ms/step - loss: 2.4397 - accuracy: 0.1191 - val_loss: 2.3916 - val_accuracy: 0.1993\n",
            "Epoch 2/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.3856 - accuracy: 0.1445 - val_loss: 2.3603 - val_accuracy: 0.2709\n",
            "Epoch 3/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.3578 - accuracy: 0.1655 - val_loss: 2.3246 - val_accuracy: 0.2399\n",
            "Epoch 4/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.3154 - accuracy: 0.1941 - val_loss: 2.2677 - val_accuracy: 0.2494\n",
            "Epoch 5/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.2746 - accuracy: 0.2146 - val_loss: 2.2103 - val_accuracy: 0.2768\n",
            "Epoch 6/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.2160 - accuracy: 0.2455 - val_loss: 2.1504 - val_accuracy: 0.3126\n",
            "Epoch 7/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.1584 - accuracy: 0.2565 - val_loss: 2.0405 - val_accuracy: 0.3484\n",
            "Epoch 8/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.0873 - accuracy: 0.2912 - val_loss: 1.9814 - val_accuracy: 0.3353\n",
            "Epoch 9/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 2.0363 - accuracy: 0.3117 - val_loss: 1.9218 - val_accuracy: 0.3640\n",
            "Epoch 10/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.9910 - accuracy: 0.3330 - val_loss: 1.8665 - val_accuracy: 0.4212\n",
            "Epoch 11/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.9283 - accuracy: 0.3513 - val_loss: 1.7817 - val_accuracy: 0.3902\n",
            "Epoch 12/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.8739 - accuracy: 0.3739 - val_loss: 1.7266 - val_accuracy: 0.4463\n",
            "Epoch 13/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.8351 - accuracy: 0.3871 - val_loss: 1.6968 - val_accuracy: 0.4356\n",
            "Epoch 14/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.8086 - accuracy: 0.3990 - val_loss: 1.6710 - val_accuracy: 0.4845\n",
            "Epoch 15/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.7670 - accuracy: 0.4173 - val_loss: 1.6086 - val_accuracy: 0.4773\n",
            "Epoch 16/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.7280 - accuracy: 0.4345 - val_loss: 1.5685 - val_accuracy: 0.5012\n",
            "Epoch 17/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.6947 - accuracy: 0.4465 - val_loss: 1.5387 - val_accuracy: 0.5000\n",
            "Epoch 18/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.6668 - accuracy: 0.4568 - val_loss: 1.5293 - val_accuracy: 0.5513\n",
            "Epoch 19/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.6304 - accuracy: 0.4722 - val_loss: 1.4853 - val_accuracy: 0.5692\n",
            "Epoch 20/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.6224 - accuracy: 0.4722 - val_loss: 1.4686 - val_accuracy: 0.5561\n",
            "Epoch 21/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.5807 - accuracy: 0.4893 - val_loss: 1.4222 - val_accuracy: 0.5919\n",
            "Epoch 22/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.5588 - accuracy: 0.5044 - val_loss: 1.4183 - val_accuracy: 0.5871\n",
            "Epoch 23/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.5329 - accuracy: 0.5138 - val_loss: 1.3735 - val_accuracy: 0.6014\n",
            "Epoch 24/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.5165 - accuracy: 0.5161 - val_loss: 1.4432 - val_accuracy: 0.5322\n",
            "Epoch 25/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.4816 - accuracy: 0.5243 - val_loss: 1.3760 - val_accuracy: 0.5632\n",
            "Epoch 26/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.4774 - accuracy: 0.5277 - val_loss: 1.3423 - val_accuracy: 0.6158\n",
            "Epoch 27/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.4472 - accuracy: 0.5440 - val_loss: 1.3075 - val_accuracy: 0.6122\n",
            "Epoch 28/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.4196 - accuracy: 0.5552 - val_loss: 1.2833 - val_accuracy: 0.6002\n",
            "Epoch 29/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.4045 - accuracy: 0.5662 - val_loss: 1.2991 - val_accuracy: 0.6396\n",
            "Epoch 30/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.4000 - accuracy: 0.5618 - val_loss: 1.3567 - val_accuracy: 0.6062\n",
            "Epoch 31/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.3837 - accuracy: 0.5736 - val_loss: 1.3110 - val_accuracy: 0.6169\n",
            "Epoch 32/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.3538 - accuracy: 0.5772 - val_loss: 1.2794 - val_accuracy: 0.5990\n",
            "Epoch 33/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.3474 - accuracy: 0.5759 - val_loss: 1.3011 - val_accuracy: 0.5967\n",
            "Epoch 34/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.3149 - accuracy: 0.5981 - val_loss: 1.2404 - val_accuracy: 0.6301\n",
            "Epoch 35/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2989 - accuracy: 0.5947 - val_loss: 1.2224 - val_accuracy: 0.6169\n",
            "Epoch 36/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2824 - accuracy: 0.6007 - val_loss: 1.2013 - val_accuracy: 0.6563\n",
            "Epoch 37/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2773 - accuracy: 0.6016 - val_loss: 1.1836 - val_accuracy: 0.6551\n",
            "Epoch 38/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2669 - accuracy: 0.6082 - val_loss: 1.1670 - val_accuracy: 0.6706\n",
            "Epoch 39/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2413 - accuracy: 0.6131 - val_loss: 1.1691 - val_accuracy: 0.6516\n",
            "Epoch 40/200\n",
            "71/71 [==============================] - 1s 12ms/step - loss: 1.2197 - accuracy: 0.6343 - val_loss: 1.1273 - val_accuracy: 0.6444\n",
            "Epoch 41/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2101 - accuracy: 0.6345 - val_loss: 1.1293 - val_accuracy: 0.6647\n",
            "Epoch 42/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.2089 - accuracy: 0.6280 - val_loss: 1.2165 - val_accuracy: 0.5990\n",
            "Epoch 43/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1836 - accuracy: 0.6402 - val_loss: 1.1555 - val_accuracy: 0.6611\n",
            "Epoch 44/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1786 - accuracy: 0.6409 - val_loss: 1.1681 - val_accuracy: 0.6408\n",
            "Epoch 45/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1754 - accuracy: 0.6411 - val_loss: 1.0960 - val_accuracy: 0.6683\n",
            "Epoch 46/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1577 - accuracy: 0.6483 - val_loss: 1.0748 - val_accuracy: 0.6647\n",
            "Epoch 47/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1416 - accuracy: 0.6614 - val_loss: 1.0995 - val_accuracy: 0.6838\n",
            "Epoch 48/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1281 - accuracy: 0.6621 - val_loss: 1.0910 - val_accuracy: 0.6659\n",
            "Epoch 49/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1263 - accuracy: 0.6576 - val_loss: 1.0828 - val_accuracy: 0.6516\n",
            "Epoch 50/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1155 - accuracy: 0.6730 - val_loss: 1.0850 - val_accuracy: 0.6527\n",
            "Epoch 51/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.1044 - accuracy: 0.6704 - val_loss: 1.1313 - val_accuracy: 0.6516\n",
            "Epoch 52/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0884 - accuracy: 0.6723 - val_loss: 1.0515 - val_accuracy: 0.6635\n",
            "Epoch 53/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0901 - accuracy: 0.6772 - val_loss: 1.1177 - val_accuracy: 0.6921\n",
            "Epoch 54/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0694 - accuracy: 0.6824 - val_loss: 1.1253 - val_accuracy: 0.6337\n",
            "Epoch 55/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0579 - accuracy: 0.6824 - val_loss: 1.1693 - val_accuracy: 0.6289\n",
            "Epoch 56/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0786 - accuracy: 0.6814 - val_loss: 1.0785 - val_accuracy: 0.6599\n",
            "Epoch 57/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0478 - accuracy: 0.6895 - val_loss: 1.0582 - val_accuracy: 0.6778\n",
            "Epoch 58/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0381 - accuracy: 0.6859 - val_loss: 1.0465 - val_accuracy: 0.6683\n",
            "Epoch 59/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0311 - accuracy: 0.6915 - val_loss: 1.0727 - val_accuracy: 0.6969\n",
            "Epoch 60/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0147 - accuracy: 0.6933 - val_loss: 1.0377 - val_accuracy: 0.7255\n",
            "Epoch 61/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0117 - accuracy: 0.6972 - val_loss: 1.1007 - val_accuracy: 0.6587\n",
            "Epoch 62/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 1.0035 - accuracy: 0.7067 - val_loss: 1.0406 - val_accuracy: 0.6587\n",
            "Epoch 63/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9989 - accuracy: 0.7074 - val_loss: 0.9948 - val_accuracy: 0.6826\n",
            "Epoch 64/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9743 - accuracy: 0.7130 - val_loss: 1.0458 - val_accuracy: 0.6671\n",
            "Epoch 65/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9719 - accuracy: 0.7168 - val_loss: 0.9674 - val_accuracy: 0.7172\n",
            "Epoch 66/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9650 - accuracy: 0.7215 - val_loss: 1.0227 - val_accuracy: 0.6659\n",
            "Epoch 67/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9757 - accuracy: 0.7197 - val_loss: 1.0028 - val_accuracy: 0.6742\n",
            "Epoch 68/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9556 - accuracy: 0.7294 - val_loss: 0.9914 - val_accuracy: 0.6874\n",
            "Epoch 69/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9692 - accuracy: 0.7177 - val_loss: 1.0369 - val_accuracy: 0.6742\n",
            "Epoch 70/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9599 - accuracy: 0.7207 - val_loss: 0.9984 - val_accuracy: 0.6790\n",
            "Epoch 71/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9378 - accuracy: 0.7281 - val_loss: 0.9533 - val_accuracy: 0.7542\n",
            "Epoch 72/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9460 - accuracy: 0.7383 - val_loss: 0.9941 - val_accuracy: 0.7088\n",
            "Epoch 73/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9181 - accuracy: 0.7325 - val_loss: 0.9736 - val_accuracy: 0.7196\n",
            "Epoch 74/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9274 - accuracy: 0.7386 - val_loss: 0.9454 - val_accuracy: 0.7112\n",
            "Epoch 75/200\n",
            "71/71 [==============================] - 1s 10ms/step - loss: 0.9154 - accuracy: 0.7381 - val_loss: 0.9405 - val_accuracy: 0.7196\n",
            "Epoch 76/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9040 - accuracy: 0.7358 - val_loss: 0.9739 - val_accuracy: 0.6933\n",
            "Epoch 77/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.9036 - accuracy: 0.7345 - val_loss: 0.9516 - val_accuracy: 0.7041\n",
            "Epoch 78/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8883 - accuracy: 0.7445 - val_loss: 0.9419 - val_accuracy: 0.7184\n",
            "Epoch 79/200\n",
            "71/71 [==============================] - 1s 10ms/step - loss: 0.8941 - accuracy: 0.7501 - val_loss: 0.9618 - val_accuracy: 0.7088\n",
            "Epoch 80/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8865 - accuracy: 0.7472 - val_loss: 0.9614 - val_accuracy: 0.7208\n",
            "Epoch 81/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8862 - accuracy: 0.7477 - val_loss: 0.9260 - val_accuracy: 0.7267\n",
            "Epoch 82/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8540 - accuracy: 0.7558 - val_loss: 0.9240 - val_accuracy: 0.7315\n",
            "Epoch 83/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8610 - accuracy: 0.7547 - val_loss: 0.9342 - val_accuracy: 0.7112\n",
            "Epoch 84/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8407 - accuracy: 0.7661 - val_loss: 1.0449 - val_accuracy: 0.6981\n",
            "Epoch 85/200\n",
            "71/71 [==============================] - 1s 10ms/step - loss: 0.8564 - accuracy: 0.7556 - val_loss: 0.9426 - val_accuracy: 0.7064\n",
            "Epoch 86/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8451 - accuracy: 0.7638 - val_loss: 0.9231 - val_accuracy: 0.7279\n",
            "Epoch 87/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8512 - accuracy: 0.7599 - val_loss: 0.9727 - val_accuracy: 0.6897\n",
            "Epoch 88/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8218 - accuracy: 0.7695 - val_loss: 0.9088 - val_accuracy: 0.7291\n",
            "Epoch 89/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8250 - accuracy: 0.7672 - val_loss: 0.8983 - val_accuracy: 0.7411\n",
            "Epoch 90/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8181 - accuracy: 0.7681 - val_loss: 0.9920 - val_accuracy: 0.7184\n",
            "Epoch 91/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8366 - accuracy: 0.7624 - val_loss: 0.9328 - val_accuracy: 0.7088\n",
            "Epoch 92/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8138 - accuracy: 0.7650 - val_loss: 0.9951 - val_accuracy: 0.6742\n",
            "Epoch 93/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.8051 - accuracy: 0.7754 - val_loss: 0.8818 - val_accuracy: 0.7494\n",
            "Epoch 94/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7951 - accuracy: 0.7815 - val_loss: 0.8811 - val_accuracy: 0.7327\n",
            "Epoch 95/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7877 - accuracy: 0.7817 - val_loss: 0.8902 - val_accuracy: 0.7506\n",
            "Epoch 96/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7832 - accuracy: 0.7789 - val_loss: 0.9387 - val_accuracy: 0.7148\n",
            "Epoch 97/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7899 - accuracy: 0.7810 - val_loss: 0.9231 - val_accuracy: 0.7232\n",
            "Epoch 98/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7818 - accuracy: 0.7765 - val_loss: 0.8823 - val_accuracy: 0.7339\n",
            "Epoch 99/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7765 - accuracy: 0.7813 - val_loss: 0.8914 - val_accuracy: 0.7327\n",
            "Epoch 100/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7866 - accuracy: 0.7747 - val_loss: 0.9888 - val_accuracy: 0.7327\n",
            "Epoch 101/200\n",
            "71/71 [==============================] - 1s 10ms/step - loss: 0.7678 - accuracy: 0.7872 - val_loss: 0.9469 - val_accuracy: 0.7029\n",
            "Epoch 102/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7780 - accuracy: 0.7851 - val_loss: 0.8682 - val_accuracy: 0.7506\n",
            "Epoch 103/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7698 - accuracy: 0.7865 - val_loss: 0.9338 - val_accuracy: 0.7005\n",
            "Epoch 104/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7631 - accuracy: 0.7891 - val_loss: 0.8719 - val_accuracy: 0.7601\n",
            "Epoch 105/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7741 - accuracy: 0.7836 - val_loss: 0.8606 - val_accuracy: 0.7589\n",
            "Epoch 106/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7478 - accuracy: 0.7978 - val_loss: 0.9184 - val_accuracy: 0.7053\n",
            "Epoch 107/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7366 - accuracy: 0.7973 - val_loss: 0.8456 - val_accuracy: 0.7637\n",
            "Epoch 108/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7449 - accuracy: 0.7915 - val_loss: 0.9803 - val_accuracy: 0.7053\n",
            "Epoch 109/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7441 - accuracy: 0.7912 - val_loss: 0.9424 - val_accuracy: 0.7029\n",
            "Epoch 110/200\n",
            "71/71 [==============================] - 1s 10ms/step - loss: 0.7274 - accuracy: 0.7984 - val_loss: 1.0100 - val_accuracy: 0.7017\n",
            "Epoch 111/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7311 - accuracy: 0.7994 - val_loss: 0.8929 - val_accuracy: 0.7351\n",
            "Epoch 112/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7372 - accuracy: 0.7936 - val_loss: 0.9001 - val_accuracy: 0.7208\n",
            "Epoch 113/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7224 - accuracy: 0.8033 - val_loss: 0.9321 - val_accuracy: 0.7196\n",
            "Epoch 114/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7156 - accuracy: 0.8042 - val_loss: 0.8911 - val_accuracy: 0.7363\n",
            "Epoch 115/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7291 - accuracy: 0.7973 - val_loss: 0.9106 - val_accuracy: 0.7315\n",
            "Epoch 116/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7017 - accuracy: 0.8050 - val_loss: 0.8970 - val_accuracy: 0.7434\n",
            "Epoch 117/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7018 - accuracy: 0.8064 - val_loss: 0.8866 - val_accuracy: 0.7613\n",
            "Epoch 118/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7182 - accuracy: 0.8062 - val_loss: 0.8765 - val_accuracy: 0.7554\n",
            "Epoch 119/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7091 - accuracy: 0.8012 - val_loss: 0.8748 - val_accuracy: 0.7458\n",
            "Epoch 120/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7009 - accuracy: 0.8081 - val_loss: 0.8558 - val_accuracy: 0.7792\n",
            "Epoch 121/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7040 - accuracy: 0.8039 - val_loss: 0.8818 - val_accuracy: 0.7542\n",
            "Epoch 122/200\n",
            "71/71 [==============================] - 1s 11ms/step - loss: 0.7035 - accuracy: 0.8053 - val_loss: 0.8811 - val_accuracy: 0.7327\n",
            "Evaluating model...\n",
            "ROC: 0.95\n",
            "\n",
            "Accuracy = 0.66\n",
            "F-Score: 0.66\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 5, 6, 7, 10} Validate on 9 Test on 8 ***\n",
            "val_x shape:  (816, 68, 68, 4)\n",
            "test_x shape:  (806, 68, 68, 4)\n",
            "train_x shape:  (7110, 68, 68, 4)\n",
            "val_y shape:  (816,)\n",
            "test_y shape:  (806,)\n",
            "train_y shape:  (7110,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "72/72 [==============================] - 1s 14ms/step - loss: 2.4333 - accuracy: 0.1218 - val_loss: 2.3549 - val_accuracy: 0.2903\n",
            "Epoch 2/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 2.3834 - accuracy: 0.1450 - val_loss: 2.3081 - val_accuracy: 0.3672\n",
            "Epoch 3/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 2.3432 - accuracy: 0.1688 - val_loss: 2.2525 - val_accuracy: 0.3201\n",
            "Epoch 4/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 2.3000 - accuracy: 0.1990 - val_loss: 2.1551 - val_accuracy: 0.4082\n",
            "Epoch 5/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 2.2269 - accuracy: 0.2308 - val_loss: 2.0102 - val_accuracy: 0.3958\n",
            "Epoch 6/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 2.1586 - accuracy: 0.2684 - val_loss: 1.9229 - val_accuracy: 0.4653\n",
            "Epoch 7/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 2.0856 - accuracy: 0.2938 - val_loss: 1.9074 - val_accuracy: 0.4107\n",
            "Epoch 8/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 2.0397 - accuracy: 0.3136 - val_loss: 1.8079 - val_accuracy: 0.4739\n",
            "Epoch 9/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.9768 - accuracy: 0.3363 - val_loss: 1.7069 - val_accuracy: 0.4677\n",
            "Epoch 10/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.9143 - accuracy: 0.3557 - val_loss: 1.8134 - val_accuracy: 0.4243\n",
            "Epoch 11/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.8854 - accuracy: 0.3691 - val_loss: 1.6845 - val_accuracy: 0.4305\n",
            "Epoch 12/200\n",
            "72/72 [==============================] - 1s 10ms/step - loss: 1.8389 - accuracy: 0.3951 - val_loss: 1.6082 - val_accuracy: 0.5347\n",
            "Epoch 13/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.8077 - accuracy: 0.4028 - val_loss: 1.4952 - val_accuracy: 0.5236\n",
            "Epoch 14/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.7632 - accuracy: 0.4228 - val_loss: 1.5388 - val_accuracy: 0.5323\n",
            "Epoch 15/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.7325 - accuracy: 0.4331 - val_loss: 1.6547 - val_accuracy: 0.5136\n",
            "Epoch 16/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.7103 - accuracy: 0.4406 - val_loss: 1.6479 - val_accuracy: 0.4913\n",
            "Epoch 17/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.6689 - accuracy: 0.4567 - val_loss: 1.4040 - val_accuracy: 0.5782\n",
            "Epoch 18/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.6426 - accuracy: 0.4624 - val_loss: 1.4523 - val_accuracy: 0.5558\n",
            "Epoch 19/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.6103 - accuracy: 0.4817 - val_loss: 1.3293 - val_accuracy: 0.5620\n",
            "Epoch 20/200\n",
            "72/72 [==============================] - 1s 10ms/step - loss: 1.5636 - accuracy: 0.4944 - val_loss: 1.3913 - val_accuracy: 0.5993\n",
            "Epoch 21/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.5573 - accuracy: 0.4994 - val_loss: 1.3843 - val_accuracy: 0.5831\n",
            "Epoch 22/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.5310 - accuracy: 0.5135 - val_loss: 1.9110 - val_accuracy: 0.3908\n",
            "Epoch 23/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.5087 - accuracy: 0.5218 - val_loss: 1.4462 - val_accuracy: 0.5782\n",
            "Epoch 24/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.4828 - accuracy: 0.5252 - val_loss: 1.3348 - val_accuracy: 0.6464\n",
            "Epoch 25/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.4576 - accuracy: 0.5415 - val_loss: 1.5226 - val_accuracy: 0.5509\n",
            "Epoch 26/200\n",
            "72/72 [==============================] - 1s 10ms/step - loss: 1.4592 - accuracy: 0.5307 - val_loss: 1.4489 - val_accuracy: 0.5782\n",
            "Epoch 27/200\n",
            "72/72 [==============================] - 1s 10ms/step - loss: 1.4210 - accuracy: 0.5402 - val_loss: 1.6050 - val_accuracy: 0.4603\n",
            "Epoch 28/200\n",
            "72/72 [==============================] - 1s 10ms/step - loss: 1.4125 - accuracy: 0.5540 - val_loss: 1.4602 - val_accuracy: 0.5310\n",
            "Epoch 29/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.3890 - accuracy: 0.5591 - val_loss: 1.5669 - val_accuracy: 0.5025\n",
            "Epoch 30/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.3590 - accuracy: 0.5689 - val_loss: 1.3015 - val_accuracy: 0.6290\n",
            "Epoch 31/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.3645 - accuracy: 0.5706 - val_loss: 1.2080 - val_accuracy: 0.6290\n",
            "Epoch 32/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.3357 - accuracy: 0.5807 - val_loss: 1.2902 - val_accuracy: 0.5794\n",
            "Epoch 33/200\n",
            "72/72 [==============================] - 1s 10ms/step - loss: 1.3202 - accuracy: 0.5886 - val_loss: 1.2565 - val_accuracy: 0.6402\n",
            "Epoch 34/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.2867 - accuracy: 0.5996 - val_loss: 1.2493 - val_accuracy: 0.6402\n",
            "Epoch 35/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.2921 - accuracy: 0.5938 - val_loss: 1.3300 - val_accuracy: 0.5695\n",
            "Epoch 36/200\n",
            "72/72 [==============================] - 1s 10ms/step - loss: 1.2491 - accuracy: 0.6114 - val_loss: 1.6563 - val_accuracy: 0.5434\n",
            "Epoch 37/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.2717 - accuracy: 0.6032 - val_loss: 1.3074 - val_accuracy: 0.5993\n",
            "Epoch 38/200\n",
            "72/72 [==============================] - 1s 10ms/step - loss: 1.2302 - accuracy: 0.6219 - val_loss: 1.3026 - val_accuracy: 0.5769\n",
            "Epoch 39/200\n",
            "72/72 [==============================] - 1s 10ms/step - loss: 1.2302 - accuracy: 0.6186 - val_loss: 1.5466 - val_accuracy: 0.5732\n",
            "Epoch 40/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.2294 - accuracy: 0.6219 - val_loss: 1.2126 - val_accuracy: 0.6340\n",
            "Epoch 41/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.1813 - accuracy: 0.6370 - val_loss: 1.3495 - val_accuracy: 0.5720\n",
            "Epoch 42/200\n",
            "72/72 [==============================] - 1s 10ms/step - loss: 1.1979 - accuracy: 0.6294 - val_loss: 1.2595 - val_accuracy: 0.5856\n",
            "Epoch 43/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.1688 - accuracy: 0.6428 - val_loss: 1.4285 - val_accuracy: 0.5794\n",
            "Epoch 44/200\n",
            "72/72 [==============================] - 1s 10ms/step - loss: 1.1669 - accuracy: 0.6474 - val_loss: 1.4002 - val_accuracy: 0.5372\n",
            "Epoch 45/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.1741 - accuracy: 0.6394 - val_loss: 1.4202 - val_accuracy: 0.6042\n",
            "Epoch 46/200\n",
            "72/72 [==============================] - 1s 11ms/step - loss: 1.1295 - accuracy: 0.6534 - val_loss: 1.2953 - val_accuracy: 0.6166\n",
            "Evaluating model...\n",
            "ROC: 0.933\n",
            "\n",
            "Accuracy = 0.62\n",
            "F-Score: 0.62\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 6, 7, 8, 9} Validate on 10 Test on 5 ***\n",
            "val_x shape:  (837, 68, 68, 4)\n",
            "test_x shape:  (936, 68, 68, 4)\n",
            "train_x shape:  (6959, 68, 68, 4)\n",
            "val_y shape:  (837,)\n",
            "test_y shape:  (936,)\n",
            "train_y shape:  (6959,)\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 1s 14ms/step - loss: 2.4349 - accuracy: 0.1219 - val_loss: 2.3703 - val_accuracy: 0.2147\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3631 - accuracy: 0.1609 - val_loss: 2.3012 - val_accuracy: 0.2650\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.3163 - accuracy: 0.1959 - val_loss: 2.2367 - val_accuracy: 0.2682\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.2574 - accuracy: 0.2237 - val_loss: 2.1527 - val_accuracy: 0.3152\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.1945 - accuracy: 0.2589 - val_loss: 2.0643 - val_accuracy: 0.3045\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.1165 - accuracy: 0.2816 - val_loss: 1.9520 - val_accuracy: 0.3451\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 2.0457 - accuracy: 0.3097 - val_loss: 1.9068 - val_accuracy: 0.3707\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.9860 - accuracy: 0.3364 - val_loss: 1.8262 - val_accuracy: 0.3900\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.9310 - accuracy: 0.3548 - val_loss: 1.7932 - val_accuracy: 0.4306\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8996 - accuracy: 0.3657 - val_loss: 1.7510 - val_accuracy: 0.4049\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8688 - accuracy: 0.3745 - val_loss: 1.6855 - val_accuracy: 0.4669\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.8303 - accuracy: 0.3976 - val_loss: 1.6880 - val_accuracy: 0.4412\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7924 - accuracy: 0.4110 - val_loss: 1.6530 - val_accuracy: 0.4712\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7669 - accuracy: 0.4174 - val_loss: 1.7391 - val_accuracy: 0.4252\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7453 - accuracy: 0.4274 - val_loss: 1.5489 - val_accuracy: 0.5491\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.7020 - accuracy: 0.4528 - val_loss: 1.5158 - val_accuracy: 0.5780\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6591 - accuracy: 0.4581 - val_loss: 1.4788 - val_accuracy: 0.5491\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6535 - accuracy: 0.4639 - val_loss: 1.4659 - val_accuracy: 0.5972\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.6268 - accuracy: 0.4785 - val_loss: 1.4411 - val_accuracy: 0.5726\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5934 - accuracy: 0.4896 - val_loss: 1.3935 - val_accuracy: 0.6015\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5733 - accuracy: 0.4933 - val_loss: 1.5129 - val_accuracy: 0.5192\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5531 - accuracy: 0.5012 - val_loss: 1.4048 - val_accuracy: 0.6004\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5385 - accuracy: 0.5111 - val_loss: 1.3597 - val_accuracy: 0.6015\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.5143 - accuracy: 0.5216 - val_loss: 1.2984 - val_accuracy: 0.6218\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4806 - accuracy: 0.5291 - val_loss: 1.3340 - val_accuracy: 0.6036\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4580 - accuracy: 0.5426 - val_loss: 1.3971 - val_accuracy: 0.5556\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4425 - accuracy: 0.5439 - val_loss: 1.2882 - val_accuracy: 0.5962\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.4328 - accuracy: 0.5465 - val_loss: 1.2686 - val_accuracy: 0.6378\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3950 - accuracy: 0.5655 - val_loss: 1.1937 - val_accuracy: 0.6335\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3927 - accuracy: 0.5657 - val_loss: 1.2650 - val_accuracy: 0.6165\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3557 - accuracy: 0.5758 - val_loss: 1.2940 - val_accuracy: 0.6325\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3421 - accuracy: 0.5843 - val_loss: 1.2316 - val_accuracy: 0.6506\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3320 - accuracy: 0.5861 - val_loss: 1.1801 - val_accuracy: 0.6571\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 1s 12ms/step - loss: 1.3154 - accuracy: 0.5948 - val_loss: 1.1637 - val_accuracy: 0.6421\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.3057 - accuracy: 0.5928 - val_loss: 1.2571 - val_accuracy: 0.6357\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2913 - accuracy: 0.5998 - val_loss: 1.1308 - val_accuracy: 0.6795\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2820 - accuracy: 0.5981 - val_loss: 1.2030 - val_accuracy: 0.6506\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2453 - accuracy: 0.6129 - val_loss: 1.1313 - val_accuracy: 0.6891\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2361 - accuracy: 0.6182 - val_loss: 1.1616 - val_accuracy: 0.6848\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2119 - accuracy: 0.6231 - val_loss: 1.1776 - val_accuracy: 0.6368\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.2146 - accuracy: 0.6307 - val_loss: 1.1661 - val_accuracy: 0.6774\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1969 - accuracy: 0.6330 - val_loss: 1.1502 - val_accuracy: 0.6784\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1804 - accuracy: 0.6397 - val_loss: 1.1430 - val_accuracy: 0.6549\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1879 - accuracy: 0.6392 - val_loss: 1.0905 - val_accuracy: 0.7041\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1609 - accuracy: 0.6504 - val_loss: 1.1849 - val_accuracy: 0.6624\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 1s 12ms/step - loss: 1.1783 - accuracy: 0.6402 - val_loss: 1.0895 - val_accuracy: 0.6838\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1476 - accuracy: 0.6558 - val_loss: 1.0922 - val_accuracy: 0.7073\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1292 - accuracy: 0.6602 - val_loss: 1.1565 - val_accuracy: 0.6806\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1227 - accuracy: 0.6632 - val_loss: 1.0785 - val_accuracy: 0.6976\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 1s 12ms/step - loss: 1.1001 - accuracy: 0.6682 - val_loss: 1.0256 - val_accuracy: 0.7030\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.1009 - accuracy: 0.6701 - val_loss: 1.0784 - val_accuracy: 0.7094\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0938 - accuracy: 0.6738 - val_loss: 1.0402 - val_accuracy: 0.6987\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0767 - accuracy: 0.6798 - val_loss: 1.1479 - val_accuracy: 0.6934\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0672 - accuracy: 0.6829 - val_loss: 1.1624 - val_accuracy: 0.6944\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0730 - accuracy: 0.6787 - val_loss: 1.0663 - val_accuracy: 0.7062\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0608 - accuracy: 0.6794 - val_loss: 1.0004 - val_accuracy: 0.7094\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0531 - accuracy: 0.6857 - val_loss: 1.0770 - val_accuracy: 0.6731\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0520 - accuracy: 0.6900 - val_loss: 1.1167 - val_accuracy: 0.6784\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0019 - accuracy: 0.6931 - val_loss: 1.0161 - val_accuracy: 0.7233\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0288 - accuracy: 0.6916 - val_loss: 1.1325 - val_accuracy: 0.6902\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0052 - accuracy: 0.7047 - val_loss: 1.0998 - val_accuracy: 0.7244\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 1.0115 - accuracy: 0.7030 - val_loss: 1.1530 - val_accuracy: 0.6624\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9968 - accuracy: 0.7139 - val_loss: 1.0467 - val_accuracy: 0.7297\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 1s 12ms/step - loss: 0.9755 - accuracy: 0.7054 - val_loss: 1.1509 - val_accuracy: 0.6549\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9970 - accuracy: 0.7060 - val_loss: 0.9793 - val_accuracy: 0.7457\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9687 - accuracy: 0.7048 - val_loss: 1.0357 - val_accuracy: 0.7350\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9720 - accuracy: 0.7166 - val_loss: 1.0400 - val_accuracy: 0.7340\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9771 - accuracy: 0.7069 - val_loss: 1.0064 - val_accuracy: 0.7297\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9534 - accuracy: 0.7204 - val_loss: 1.0082 - val_accuracy: 0.7457\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9426 - accuracy: 0.7250 - val_loss: 1.0665 - val_accuracy: 0.7254\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9365 - accuracy: 0.7251 - val_loss: 0.9663 - val_accuracy: 0.7543\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9467 - accuracy: 0.7166 - val_loss: 1.2253 - val_accuracy: 0.6688\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9307 - accuracy: 0.7296 - val_loss: 0.9429 - val_accuracy: 0.7521\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9258 - accuracy: 0.7297 - val_loss: 1.0037 - val_accuracy: 0.7479\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9137 - accuracy: 0.7321 - val_loss: 0.9933 - val_accuracy: 0.7276\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9109 - accuracy: 0.7386 - val_loss: 0.9794 - val_accuracy: 0.7297\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9021 - accuracy: 0.7388 - val_loss: 0.9801 - val_accuracy: 0.7361\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.9045 - accuracy: 0.7373 - val_loss: 0.9663 - val_accuracy: 0.7489\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8822 - accuracy: 0.7402 - val_loss: 1.0080 - val_accuracy: 0.7361\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8788 - accuracy: 0.7515 - val_loss: 1.0196 - val_accuracy: 0.7340\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8623 - accuracy: 0.7527 - val_loss: 0.9689 - val_accuracy: 0.7532\n",
            "Epoch 82/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8783 - accuracy: 0.7449 - val_loss: 0.9919 - val_accuracy: 0.7457\n",
            "Epoch 83/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8679 - accuracy: 0.7511 - val_loss: 0.9596 - val_accuracy: 0.7404\n",
            "Epoch 84/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8581 - accuracy: 0.7513 - val_loss: 0.9501 - val_accuracy: 0.7511\n",
            "Epoch 85/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8596 - accuracy: 0.7540 - val_loss: 0.9958 - val_accuracy: 0.7404\n",
            "Epoch 86/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8444 - accuracy: 0.7569 - val_loss: 0.9413 - val_accuracy: 0.7436\n",
            "Epoch 87/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8294 - accuracy: 0.7612 - val_loss: 1.0326 - val_accuracy: 0.7425\n",
            "Epoch 88/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8270 - accuracy: 0.7645 - val_loss: 0.9424 - val_accuracy: 0.7468\n",
            "Epoch 89/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8333 - accuracy: 0.7662 - val_loss: 0.9561 - val_accuracy: 0.7479\n",
            "Epoch 90/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8337 - accuracy: 0.7571 - val_loss: 0.9707 - val_accuracy: 0.7596\n",
            "Epoch 91/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8201 - accuracy: 0.7662 - val_loss: 1.0656 - val_accuracy: 0.7105\n",
            "Epoch 92/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8233 - accuracy: 0.7662 - val_loss: 1.1042 - val_accuracy: 0.6987\n",
            "Epoch 93/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8047 - accuracy: 0.7651 - val_loss: 0.9962 - val_accuracy: 0.7532\n",
            "Epoch 94/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8058 - accuracy: 0.7646 - val_loss: 0.9125 - val_accuracy: 0.7628\n",
            "Epoch 95/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8096 - accuracy: 0.7689 - val_loss: 0.9133 - val_accuracy: 0.7575\n",
            "Epoch 96/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.8099 - accuracy: 0.7725 - val_loss: 0.9725 - val_accuracy: 0.7457\n",
            "Epoch 97/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7859 - accuracy: 0.7781 - val_loss: 0.9379 - val_accuracy: 0.7372\n",
            "Epoch 98/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7916 - accuracy: 0.7731 - val_loss: 0.8825 - val_accuracy: 0.7682\n",
            "Epoch 99/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7800 - accuracy: 0.7849 - val_loss: 0.9868 - val_accuracy: 0.7511\n",
            "Epoch 100/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7861 - accuracy: 0.7763 - val_loss: 0.9526 - val_accuracy: 0.7500\n",
            "Epoch 101/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7766 - accuracy: 0.7791 - val_loss: 1.0553 - val_accuracy: 0.7222\n",
            "Epoch 102/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7636 - accuracy: 0.7761 - val_loss: 0.9683 - val_accuracy: 0.7489\n",
            "Epoch 103/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7658 - accuracy: 0.7842 - val_loss: 0.9506 - val_accuracy: 0.7575\n",
            "Epoch 104/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7609 - accuracy: 0.7843 - val_loss: 0.9728 - val_accuracy: 0.7404\n",
            "Epoch 105/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7640 - accuracy: 0.7916 - val_loss: 0.9327 - val_accuracy: 0.7596\n",
            "Epoch 106/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7501 - accuracy: 0.7938 - val_loss: 0.8729 - val_accuracy: 0.7671\n",
            "Epoch 107/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7397 - accuracy: 0.7918 - val_loss: 0.9829 - val_accuracy: 0.7415\n",
            "Epoch 108/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7524 - accuracy: 0.7875 - val_loss: 0.9286 - val_accuracy: 0.7479\n",
            "Epoch 109/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7375 - accuracy: 0.7886 - val_loss: 0.9495 - val_accuracy: 0.7532\n",
            "Epoch 110/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7360 - accuracy: 0.7954 - val_loss: 0.9317 - val_accuracy: 0.7329\n",
            "Epoch 111/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7369 - accuracy: 0.7885 - val_loss: 0.9313 - val_accuracy: 0.7585\n",
            "Epoch 112/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7326 - accuracy: 0.7947 - val_loss: 0.9090 - val_accuracy: 0.7639\n",
            "Epoch 113/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7298 - accuracy: 0.7925 - val_loss: 0.9353 - val_accuracy: 0.7575\n",
            "Epoch 114/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7117 - accuracy: 0.8039 - val_loss: 0.9145 - val_accuracy: 0.7682\n",
            "Epoch 115/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7113 - accuracy: 0.8036 - val_loss: 0.9419 - val_accuracy: 0.7489\n",
            "Epoch 116/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7120 - accuracy: 0.8005 - val_loss: 0.9784 - val_accuracy: 0.7415\n",
            "Epoch 117/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7060 - accuracy: 0.8030 - val_loss: 0.8954 - val_accuracy: 0.7639\n",
            "Epoch 118/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7028 - accuracy: 0.8046 - val_loss: 0.9592 - val_accuracy: 0.7489\n",
            "Epoch 119/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7155 - accuracy: 0.8021 - val_loss: 0.9086 - val_accuracy: 0.7564\n",
            "Epoch 120/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.6967 - accuracy: 0.8049 - val_loss: 0.9082 - val_accuracy: 0.7703\n",
            "Epoch 121/200\n",
            "70/70 [==============================] - 1s 11ms/step - loss: 0.7010 - accuracy: 0.8023 - val_loss: 0.8894 - val_accuracy: 0.7746\n",
            "Evaluating model...\n",
            "ROC: 0.959\n",
            "\n",
            "Accuracy = 0.72\n",
            "F-Score: 0.72\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwjVaP-_YrnO",
        "colab_type": "code",
        "outputId": "abe06ed3-a49d-41f1-b196-d5a7026e937f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "plt.boxplot(acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boxes': [<matplotlib.lines.Line2D at 0x7fe258dc82e8>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7fe258dc8940>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe2c0428630>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7fe2c0428e80>],\n",
              " 'means': [],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7fe2c0428588>],\n",
              " 'whiskers': [<matplotlib.lines.Line2D at 0x7fe258dc83c8>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe258dc8e48>]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZDJWAjhYv0I",
        "colab_type": "code",
        "outputId": "1dcf7adb-eb90-4ad0-8008-83da0c40fe6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        }
      },
      "source": [
        "df_cm = pd.DataFrame(CM, index = classes,\n",
        "                  columns = classes)\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(df_cm, annot=True,  fmt='d')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2c02ce898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MmJYcakYxXK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save(\"acc_baseline_wavelet.npy\", acc)\n",
        "np.save(\"cm_baseline_wavelet.npy\", CM)\n",
        "acc_baseline_wavelet = acc\n",
        "CM_baseline_wavelet = CM"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjKSTo648Nbp",
        "colab_type": "code",
        "outputId": "88df3c1f-23ea-425d-d7f5-a7b1e4db0d4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        }
      },
      "source": [
        "plt.boxplot([acc_baseline, acc_baseline_wavelet])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boxes': [<matplotlib.lines.Line2D at 0x7fe1c513d668>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe258a861d0>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7fe258bf8c88>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe258bf8208>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe2586582b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe2586585f8>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7fe258bf8f28>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe258658c88>],\n",
              " 'means': [],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7fe258bf8160>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe258658940>],\n",
              " 'whiskers': [<matplotlib.lines.Line2D at 0x7fe1c513d748>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe1c513dba8>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe258a86be0>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe258a86f28>]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXw8w7Vw8d9r",
        "colab_type": "code",
        "outputId": "c59d288d-1c6c-4b38-d2c2-6e3e29e95f2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        }
      },
      "source": [
        "df_cm = pd.DataFrame(CM_baseline_wavelet-CM_baseline, index = classes,\n",
        "                  columns = classes)\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(df_cm, annot=True,  fmt='d')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe27018b9e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}