us8k_cnn_baseline.ipynb

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  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "colab": {
      "name": "us8k-cnn-baseline.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "accelerator": "GPU"
  },
  "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": "896ba8ff-229f-4c71-b815-54042ccadc61",
        "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": 1,
      "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",
            "UrbanSound8K/audio/fold9/94182-9-0-20.wav\n",
            "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": "883d76db-ecd8-4bc0-e1bc-b070c15ec99e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        }
      },
      "source": [
        "# get latest librosa version for audio preprocessing\n",
        "!pip install --upgrade librosa"
      ],
      "execution_count": 2,
      "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 8.2MB/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=451a1c5df5e4c74a4a6596ffae76ca366814bec83376781aa7aa19dbe180fa12\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": "NiWBuWAn2ZWy",
        "colab_type": "code",
        "outputId": "4ec4bfa8-bf07-46b3-a3da-2b577f8db1c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        }
      },
      "source": [
        "!git clone https://github.com/grudloff/Salomon2017Replication.git\n",
        "!mv Salomon2017Replication/*.py .\n",
        "!rm Salomon2017Replication -r"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Salomon2017Replication'...\n",
            "remote: Enumerating objects: 116, done.\u001b[K\n",
            "remote: Counting objects: 100% (116/116), done.\u001b[K\n",
            "remote: Compressing objects: 100% (115/115), done.\u001b[K\n",
            "remote: Total 116 (delta 67), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (116/116), 30.86 KiB | 501.00 KiB/s, done.\n",
            "Resolving deltas: 100% (67/67), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81_TXQD2Ek4z",
        "colab_type": "code",
        "outputId": "2371fa31-9836-44c5-a5ff-306071e462a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "import numpy as np\n",
        "from preprocessing_multi import save_folds, load_folds \n",
        "from preprocessing_multi import load_folds\n",
        "from model import build_model_multi\n",
        "from evaluation import evaluate\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "import math\n",
        "\n",
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "data_dir = \"UrbanSound8K/audio\" # Where you have saved the UrbanSound8K data set\"       \n",
        "save_dir = \"data/us8k\"\n",
        "load_dir = save_dir\n",
        "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']"
      ],
      "execution_count": 3,
      "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": "code",
      "metadata": {
        "id": "c7LdNAtXHhhH",
        "colab_type": "code",
        "outputId": "613b69ea-7833-4ce7-eb15-3a872a281912",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 925
        }
      },
      "source": [
        "save_folds(data_dir, save_dir)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Saving fold1\n",
            "Number of samples in  fold1 :  873\n",
            "Saved data/us8k/fold1_x.npy\n",
            "Saved data/us8k/fold1_y.npy\n",
            "\n",
            "Saving fold2\n",
            "Number of samples in  fold2 :  888\n",
            "Saved data/us8k/fold2_x.npy\n",
            "Saved data/us8k/fold2_y.npy\n",
            "\n",
            "Saving fold3\n",
            "Number of samples in  fold3 :  925\n",
            "Saved data/us8k/fold3_x.npy\n",
            "Saved data/us8k/fold3_y.npy\n",
            "\n",
            "Saving fold4\n",
            "Number of samples in  fold4 :  990\n",
            "Saved data/us8k/fold4_x.npy\n",
            "Saved data/us8k/fold4_y.npy\n",
            "\n",
            "Saving fold5\n",
            "Number of samples in  fold5 :  936\n",
            "Saved data/us8k/fold5_x.npy\n",
            "Saved data/us8k/fold5_y.npy\n",
            "\n",
            "Saving fold6\n",
            "Number of samples in  fold6 :  823\n",
            "Saved data/us8k/fold6_x.npy\n",
            "Saved data/us8k/fold6_y.npy\n",
            "\n",
            "Saving fold7\n",
            "Number of samples in  fold7 :  838\n",
            "Saved data/us8k/fold7_x.npy\n",
            "Saved data/us8k/fold7_y.npy\n",
            "\n",
            "Saving fold8\n",
            "Number of samples in  fold8 :  806\n",
            "Saved data/us8k/fold8_x.npy\n",
            "Saved data/us8k/fold8_y.npy\n",
            "\n",
            "Saving fold9\n",
            "Number of samples in  fold9 :  816\n",
            "Saved data/us8k/fold9_x.npy\n",
            "Saved data/us8k/fold9_y.npy\n",
            "\n",
            "Saving fold10\n",
            "Number of samples in  fold10 :  837\n",
            "Saved data/us8k/fold10_x.npy\n",
            "Saved data/us8k/fold10_y.npy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mxie_r1Wsyjv",
        "colab_type": "text"
      },
      "source": [
        "## Custom generators"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3HrS7Up9tHb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "frames = 128\n",
        "bands=128\n",
        "channels = 1\n",
        "def random_slice(imgs, indexes):\n",
        "    new_shape=(len(indexes), frames, bands, channels)\n",
        "    new_imgs = np.empty(new_shape)\n",
        "    for i, idx in enumerate(indexes):\n",
        "        img = imgs[idx]\n",
        "        L = img.shape[0]\n",
        "        N = np.random.randint(0,1 + L - frames)\n",
        "        new_imgs[i] = img[N:N+frames, ...]\n",
        "    return new_imgs[:,np.newaxis,...]\n",
        "\n",
        "\n",
        "def all_slices(img):\n",
        "    N = img.shape[0]\n",
        "    n_slices = N - frames + 1\n",
        "    new_shape=(n_slices, frames, bands, channels)\n",
        "    new_imgs = np.empty(new_shape)\n",
        "    for i in range(n_slices):\n",
        "        new_imgs[i] = img[i:i+frames,...]\n",
        "\n",
        "    return new_imgs\n",
        "    \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8-0E4awsxxw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.utils import Sequence\n",
        "\n",
        "class trainGenerator(Sequence):\n",
        "\n",
        "    def __init__(self, x_set, y_set, batch_size, shuffle):\n",
        "        self.x, self.y = x_set, np.array(y_set)\n",
        "        self.batch_size = batch_size\n",
        "        self.shuffle = shuffle\n",
        "        self.indexes = np.arange(len(self.x))\n",
        "\n",
        "    def __len__(self):\n",
        "        return math.ceil(len(self.x) / self.batch_size)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        indexes = self.indexes[idx * self.batch_size: (idx + 1) * self.batch_size]\n",
        "        batch_x = random_slice(self.x, indexes)\n",
        "        batch_y = self.y[indexes]\n",
        "\n",
        "        return batch_x , batch_y\n",
        "\n",
        "    def on_epoch_end(self):\n",
        "        \"\"\"Updates indexes after each epoch\n",
        "        \"\"\"\n",
        "        if self.shuffle == True:\n",
        "            np.random.shuffle(self.indexes)\n",
        "            \n",
        "class testGenerator(Sequence):\n",
        "\n",
        "    def __init__(self, x_set, y_set):\n",
        "        self.x, self.y = x_set, np.array(y_set)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.x)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        batch_x = all_slices(np.array(self.x[idx]))\n",
        "        batch_y = self.y[idx:idx+1]\n",
        "\n",
        "        return batch_x[np.newaxis,:] , batch_y[np.newaxis,:] #"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "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": "0ca69d3a-64c6-462f-95db-c2320c4c9543",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "\n",
        "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",
        "    #train_datagen = ImageDataGenerator(preprocessing_function = random_slice)\n",
        "    #test_val_datagen = ImageDataGenerator(preprocessing_function = all_slices)\n",
        "\n",
        "    #train_data = train_datagen.flow(train_x, train_y , batch_size=100)\n",
        "    #test_data = test_val_datagen.flow(test_x, test_y)\n",
        "    #val_data = test_val_datagen.flow(val_x, val_y)\n",
        "\n",
        "    train_data = trainGenerator(train_x, train_y , batch_size=100, shuffle=True)\n",
        "    test_data = testGenerator(test_x, test_y)\n",
        "    val_data = testGenerator(val_x, val_y)\n",
        "    \n",
        "    print(\"Building model...\")\n",
        "    model = build_model_multi()\n",
        "\n",
        "    # now fit the model to the training data, evaluating loss against the validation data\n",
        "    print(\"Training model...\")\n",
        "    model.fit(train_data, validation_data=test_data, \n",
        "              callbacks=[EarlyStopping(restore_best_weights=True, patience=15)],\n",
        "              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_data, val_y)\n",
        "\n",
        "    CM += cm\n",
        "\n",
        "    #del model\n",
        "    #tf.clear_session()\n",
        "    #gc.collect()\n",
        "\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": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "*** Train on {2, 3, 5, 6, 7, 8, 9, 10} Validate on 1 Test on 4 ***\n",
            "val len:  873\n",
            "test len:  990\n",
            "train len:  6869\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "69/69 [==============================] - 9s 125ms/step - loss: 2.6485 - accuracy: 0.1226 - val_loss: 2.4419 - val_accuracy: 0.0747\n",
            "Epoch 2/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 2.4262 - accuracy: 0.1316 - val_loss: 2.4149 - val_accuracy: 0.1313\n",
            "Epoch 3/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 2.4040 - accuracy: 0.1521 - val_loss: 2.3670 - val_accuracy: 0.2141\n",
            "Epoch 4/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 2.3837 - accuracy: 0.1617 - val_loss: 2.3675 - val_accuracy: 0.1717\n",
            "Epoch 5/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 2.3410 - accuracy: 0.1855 - val_loss: 2.1383 - val_accuracy: 0.3404\n",
            "Epoch 6/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 2.2457 - accuracy: 0.2358 - val_loss: 1.9859 - val_accuracy: 0.3515\n",
            "Epoch 7/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 2.3629 - accuracy: 0.1571 - val_loss: 2.4086 - val_accuracy: 0.1333\n",
            "Epoch 8/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 2.3425 - accuracy: 0.1670 - val_loss: 2.3588 - val_accuracy: 0.1586\n",
            "Epoch 9/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 2.2838 - accuracy: 0.2144 - val_loss: 2.0412 - val_accuracy: 0.3030\n",
            "Epoch 10/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 2.1475 - accuracy: 0.2670 - val_loss: 1.9784 - val_accuracy: 0.3434\n",
            "Epoch 11/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 2.0911 - accuracy: 0.2896 - val_loss: 1.9215 - val_accuracy: 0.3606\n",
            "Epoch 12/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 2.1733 - accuracy: 0.2760 - val_loss: 1.9535 - val_accuracy: 0.3404\n",
            "Epoch 13/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 2.0138 - accuracy: 0.3309 - val_loss: 1.9595 - val_accuracy: 0.3434\n",
            "Epoch 14/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 1.9739 - accuracy: 0.3478 - val_loss: 1.8366 - val_accuracy: 0.4172\n",
            "Epoch 15/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 1.9259 - accuracy: 0.3672 - val_loss: 1.7944 - val_accuracy: 0.4475\n",
            "Epoch 16/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 1.8523 - accuracy: 0.3871 - val_loss: 1.9761 - val_accuracy: 0.3263\n",
            "Epoch 17/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.8166 - accuracy: 0.3986 - val_loss: 1.6807 - val_accuracy: 0.4899\n",
            "Epoch 18/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 1.7257 - accuracy: 0.4383 - val_loss: 1.6317 - val_accuracy: 0.4717\n",
            "Epoch 19/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.6730 - accuracy: 0.4532 - val_loss: 1.7175 - val_accuracy: 0.4333\n",
            "Epoch 20/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.6344 - accuracy: 0.4798 - val_loss: 1.7159 - val_accuracy: 0.4263\n",
            "Epoch 21/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.5982 - accuracy: 0.4887 - val_loss: 1.5779 - val_accuracy: 0.5394\n",
            "Epoch 22/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.5426 - accuracy: 0.5039 - val_loss: 1.5183 - val_accuracy: 0.5525\n",
            "Epoch 23/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.5480 - accuracy: 0.5095 - val_loss: 1.5143 - val_accuracy: 0.5505\n",
            "Epoch 24/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 1.4900 - accuracy: 0.5276 - val_loss: 1.4743 - val_accuracy: 0.5768\n",
            "Epoch 25/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 1.4757 - accuracy: 0.5357 - val_loss: 1.4353 - val_accuracy: 0.5818\n",
            "Epoch 26/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.4478 - accuracy: 0.5419 - val_loss: 1.5070 - val_accuracy: 0.5515\n",
            "Epoch 27/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.4364 - accuracy: 0.5551 - val_loss: 1.3431 - val_accuracy: 0.6273\n",
            "Epoch 28/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.3950 - accuracy: 0.5640 - val_loss: 1.3806 - val_accuracy: 0.6172\n",
            "Epoch 29/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.3463 - accuracy: 0.5842 - val_loss: 1.3746 - val_accuracy: 0.6172\n",
            "Epoch 30/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.3613 - accuracy: 0.5815 - val_loss: 1.4363 - val_accuracy: 0.5879\n",
            "Epoch 31/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 1.3239 - accuracy: 0.5899 - val_loss: 1.5701 - val_accuracy: 0.4939\n",
            "Epoch 32/200\n",
            "69/69 [==============================] - 8s 111ms/step - loss: 1.2895 - accuracy: 0.6036 - val_loss: 1.3877 - val_accuracy: 0.6232\n",
            "Epoch 33/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.2700 - accuracy: 0.6136 - val_loss: 1.3791 - val_accuracy: 0.6020\n",
            "Epoch 34/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.2885 - accuracy: 0.6039 - val_loss: 1.3634 - val_accuracy: 0.6071\n",
            "Epoch 35/200\n",
            "69/69 [==============================] - 7s 109ms/step - loss: 1.2438 - accuracy: 0.6178 - val_loss: 1.2442 - val_accuracy: 0.6606\n",
            "Epoch 36/200\n",
            "69/69 [==============================] - 7s 109ms/step - loss: 1.2084 - accuracy: 0.6411 - val_loss: 1.3068 - val_accuracy: 0.6424\n",
            "Epoch 37/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.2291 - accuracy: 0.6215 - val_loss: 1.2972 - val_accuracy: 0.6545\n",
            "Epoch 38/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.1872 - accuracy: 0.6397 - val_loss: 1.2840 - val_accuracy: 0.6283\n",
            "Epoch 39/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.1891 - accuracy: 0.6413 - val_loss: 1.3296 - val_accuracy: 0.6121\n",
            "Epoch 40/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.1690 - accuracy: 0.6499 - val_loss: 1.2130 - val_accuracy: 0.6556\n",
            "Epoch 41/200\n",
            "69/69 [==============================] - 8s 110ms/step - loss: 1.1561 - accuracy: 0.6518 - val_loss: 1.5534 - val_accuracy: 0.5071\n",
            "Epoch 42/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.1407 - accuracy: 0.6585 - val_loss: 1.3660 - val_accuracy: 0.6010\n",
            "Epoch 43/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 1.1403 - accuracy: 0.6585 - val_loss: 1.2840 - val_accuracy: 0.6323\n",
            "Epoch 44/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.1084 - accuracy: 0.6716 - val_loss: 1.1970 - val_accuracy: 0.6677\n",
            "Epoch 45/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.0941 - accuracy: 0.6694 - val_loss: 1.2116 - val_accuracy: 0.6505\n",
            "Epoch 46/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.0773 - accuracy: 0.6768 - val_loss: 1.2835 - val_accuracy: 0.6051\n",
            "Epoch 47/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.0520 - accuracy: 0.6874 - val_loss: 1.2366 - val_accuracy: 0.6343\n",
            "Epoch 48/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 1.0346 - accuracy: 0.6883 - val_loss: 1.1636 - val_accuracy: 0.6515\n",
            "Epoch 49/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 1.0541 - accuracy: 0.6851 - val_loss: 1.1784 - val_accuracy: 0.6556\n",
            "Epoch 50/200\n",
            "69/69 [==============================] - 7s 109ms/step - loss: 1.0640 - accuracy: 0.6853 - val_loss: 1.1455 - val_accuracy: 0.6727\n",
            "Epoch 51/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.9958 - accuracy: 0.7096 - val_loss: 1.3024 - val_accuracy: 0.6222\n",
            "Epoch 52/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 1.0070 - accuracy: 0.6969 - val_loss: 1.3115 - val_accuracy: 0.5889\n",
            "Epoch 53/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.9963 - accuracy: 0.7085 - val_loss: 1.2385 - val_accuracy: 0.6354\n",
            "Epoch 54/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 0.9732 - accuracy: 0.7168 - val_loss: 1.1863 - val_accuracy: 0.6475\n",
            "Epoch 55/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.9729 - accuracy: 0.7152 - val_loss: 1.2321 - val_accuracy: 0.6384\n",
            "Epoch 56/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.9684 - accuracy: 0.7126 - val_loss: 1.2053 - val_accuracy: 0.6414\n",
            "Epoch 57/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.9500 - accuracy: 0.7195 - val_loss: 1.2482 - val_accuracy: 0.6424\n",
            "Epoch 58/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 0.9285 - accuracy: 0.7251 - val_loss: 1.1580 - val_accuracy: 0.6586\n",
            "Epoch 59/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.9260 - accuracy: 0.7282 - val_loss: 1.3290 - val_accuracy: 0.6111\n",
            "Epoch 60/200\n",
            "69/69 [==============================] - 7s 109ms/step - loss: 0.9428 - accuracy: 0.7209 - val_loss: 1.2309 - val_accuracy: 0.6566\n",
            "Epoch 61/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.9603 - accuracy: 0.7144 - val_loss: 1.1643 - val_accuracy: 0.6444\n",
            "Epoch 62/200\n",
            "69/69 [==============================] - 7s 109ms/step - loss: 0.9079 - accuracy: 0.7410 - val_loss: 1.1474 - val_accuracy: 0.6808\n",
            "Epoch 63/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.9221 - accuracy: 0.7356 - val_loss: 1.2032 - val_accuracy: 0.6505\n",
            "Epoch 64/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.8828 - accuracy: 0.7438 - val_loss: 1.1311 - val_accuracy: 0.6788\n",
            "Epoch 65/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.8701 - accuracy: 0.7479 - val_loss: 1.1422 - val_accuracy: 0.6848\n",
            "Epoch 66/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.8873 - accuracy: 0.7451 - val_loss: 1.1158 - val_accuracy: 0.6727\n",
            "Epoch 67/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.8649 - accuracy: 0.7490 - val_loss: 1.1664 - val_accuracy: 0.6697\n",
            "Epoch 68/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.8390 - accuracy: 0.7576 - val_loss: 1.1205 - val_accuracy: 0.7020\n",
            "Epoch 69/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.8364 - accuracy: 0.7551 - val_loss: 1.1361 - val_accuracy: 0.6717\n",
            "Epoch 70/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.8572 - accuracy: 0.7502 - val_loss: 1.0823 - val_accuracy: 0.6990\n",
            "Epoch 71/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.8260 - accuracy: 0.7550 - val_loss: 1.1736 - val_accuracy: 0.6646\n",
            "Epoch 72/200\n",
            "69/69 [==============================] - 7s 109ms/step - loss: 0.8041 - accuracy: 0.7733 - val_loss: 1.1875 - val_accuracy: 0.6343\n",
            "Epoch 73/200\n",
            "69/69 [==============================] - 8s 110ms/step - loss: 0.8011 - accuracy: 0.7674 - val_loss: 1.2601 - val_accuracy: 0.6636\n",
            "Epoch 74/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 0.8354 - accuracy: 0.7633 - val_loss: 1.2276 - val_accuracy: 0.6293\n",
            "Epoch 75/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 0.8148 - accuracy: 0.7636 - val_loss: 1.2297 - val_accuracy: 0.6455\n",
            "Epoch 76/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 0.7940 - accuracy: 0.7745 - val_loss: 1.1602 - val_accuracy: 0.6889\n",
            "Epoch 77/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.7805 - accuracy: 0.7738 - val_loss: 1.2004 - val_accuracy: 0.6515\n",
            "Epoch 78/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.8174 - accuracy: 0.7694 - val_loss: 1.2099 - val_accuracy: 0.6606\n",
            "Epoch 79/200\n",
            "69/69 [==============================] - 7s 107ms/step - loss: 0.7949 - accuracy: 0.7759 - val_loss: 1.1434 - val_accuracy: 0.6747\n",
            "Epoch 80/200\n",
            "69/69 [==============================] - 8s 110ms/step - loss: 0.7780 - accuracy: 0.7754 - val_loss: 1.3564 - val_accuracy: 0.6040\n",
            "Epoch 81/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.7928 - accuracy: 0.7835 - val_loss: 1.1583 - val_accuracy: 0.6879\n",
            "Epoch 82/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.7681 - accuracy: 0.7825 - val_loss: 1.1683 - val_accuracy: 0.6657\n",
            "Epoch 83/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 0.7702 - accuracy: 0.7819 - val_loss: 1.3194 - val_accuracy: 0.6172\n",
            "Epoch 84/200\n",
            "69/69 [==============================] - 7s 109ms/step - loss: 0.7594 - accuracy: 0.7819 - val_loss: 1.1273 - val_accuracy: 0.6939\n",
            "Epoch 85/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.7501 - accuracy: 0.7872 - val_loss: 1.0553 - val_accuracy: 0.7000\n",
            "Epoch 86/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.7432 - accuracy: 0.7921 - val_loss: 1.2790 - val_accuracy: 0.6434\n",
            "Epoch 87/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.7420 - accuracy: 0.7885 - val_loss: 1.0590 - val_accuracy: 0.7051\n",
            "Epoch 88/200\n",
            "69/69 [==============================] - 7s 109ms/step - loss: 0.7367 - accuracy: 0.7997 - val_loss: 1.0386 - val_accuracy: 0.7162\n",
            "Epoch 89/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.7384 - accuracy: 0.7943 - val_loss: 1.0382 - val_accuracy: 0.7242\n",
            "Epoch 90/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 0.7075 - accuracy: 0.8022 - val_loss: 1.1457 - val_accuracy: 0.6818\n",
            "Epoch 91/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 0.7212 - accuracy: 0.7975 - val_loss: 1.1198 - val_accuracy: 0.6960\n",
            "Epoch 92/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.6920 - accuracy: 0.8032 - val_loss: 1.0907 - val_accuracy: 0.7030\n",
            "Epoch 93/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 0.7030 - accuracy: 0.8017 - val_loss: 1.0811 - val_accuracy: 0.7091\n",
            "Epoch 94/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 0.6867 - accuracy: 0.8023 - val_loss: 1.1089 - val_accuracy: 0.6859\n",
            "Epoch 95/200\n",
            "69/69 [==============================] - 8s 110ms/step - loss: 0.7263 - accuracy: 0.7946 - val_loss: 1.1988 - val_accuracy: 0.6414\n",
            "Epoch 96/200\n",
            "69/69 [==============================] - 7s 109ms/step - loss: 0.6885 - accuracy: 0.8087 - val_loss: 1.0826 - val_accuracy: 0.6899\n",
            "Epoch 97/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.6734 - accuracy: 0.8128 - val_loss: 1.1447 - val_accuracy: 0.6929\n",
            "Epoch 98/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 0.6909 - accuracy: 0.8061 - val_loss: 1.2533 - val_accuracy: 0.6515\n",
            "Epoch 99/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.6576 - accuracy: 0.8188 - val_loss: 1.1148 - val_accuracy: 0.6848\n",
            "Epoch 100/200\n",
            "69/69 [==============================] - 7s 109ms/step - loss: 0.6656 - accuracy: 0.8206 - val_loss: 1.1682 - val_accuracy: 0.6939\n",
            "Epoch 101/200\n",
            "69/69 [==============================] - 8s 109ms/step - loss: 0.6809 - accuracy: 0.8090 - val_loss: 1.1703 - val_accuracy: 0.6616\n",
            "Epoch 102/200\n",
            "69/69 [==============================] - 7s 108ms/step - loss: 0.6588 - accuracy: 0.8215 - val_loss: 1.1444 - val_accuracy: 0.6798\n",
            "Epoch 103/200\n",
            "69/69 [==============================] - 8s 110ms/step - loss: 0.6561 - accuracy: 0.8150 - val_loss: 1.1327 - val_accuracy: 0.7000\n",
            "Epoch 104/200\n",
            "69/69 [==============================] - 7s 109ms/step - loss: 0.6532 - accuracy: 0.8205 - val_loss: 1.0839 - val_accuracy: 0.6960\n",
            "Evaluating model...\n",
            "ROC: 0.932\n",
            "\n",
            "Accuracy = 0.65\n",
            "F-Score: 0.65\n",
            "\n",
            "*** Train on {1, 3, 4, 5, 6, 8, 9, 10} Validate on 2 Test on 7 ***\n",
            "val len:  888\n",
            "test len:  838\n",
            "train len:  7006\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "71/71 [==============================] - 8s 106ms/step - loss: 2.6821 - accuracy: 0.1329 - val_loss: 2.4284 - val_accuracy: 0.1635\n",
            "Epoch 2/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 2.4010 - accuracy: 0.1436 - val_loss: 2.3107 - val_accuracy: 0.1623\n",
            "Epoch 3/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 2.3462 - accuracy: 0.1831 - val_loss: 2.2894 - val_accuracy: 0.3437\n",
            "Epoch 4/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 2.2759 - accuracy: 0.2299 - val_loss: 2.1912 - val_accuracy: 0.3508\n",
            "Epoch 5/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 2.2192 - accuracy: 0.2572 - val_loss: 2.1879 - val_accuracy: 0.2780\n",
            "Epoch 6/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 2.2194 - accuracy: 0.2511 - val_loss: 2.0341 - val_accuracy: 0.3556\n",
            "Epoch 7/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 2.1066 - accuracy: 0.2965 - val_loss: 1.9471 - val_accuracy: 0.3902\n",
            "Epoch 8/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 2.0216 - accuracy: 0.3317 - val_loss: 1.8675 - val_accuracy: 0.3998\n",
            "Epoch 9/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.9642 - accuracy: 0.3476 - val_loss: 1.8312 - val_accuracy: 0.4069\n",
            "Epoch 10/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.9152 - accuracy: 0.3723 - val_loss: 1.7405 - val_accuracy: 0.4368\n",
            "Epoch 11/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.8473 - accuracy: 0.3935 - val_loss: 1.6719 - val_accuracy: 0.4415\n",
            "Epoch 12/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.9015 - accuracy: 0.3734 - val_loss: 1.9377 - val_accuracy: 0.3019\n",
            "Epoch 13/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.8251 - accuracy: 0.4034 - val_loss: 1.7031 - val_accuracy: 0.3974\n",
            "Epoch 14/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.7663 - accuracy: 0.4184 - val_loss: 1.6034 - val_accuracy: 0.4475\n",
            "Epoch 15/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.6595 - accuracy: 0.4660 - val_loss: 1.6332 - val_accuracy: 0.5143\n",
            "Epoch 16/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.6022 - accuracy: 0.4853 - val_loss: 1.5003 - val_accuracy: 0.5143\n",
            "Epoch 17/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.5518 - accuracy: 0.5101 - val_loss: 1.5768 - val_accuracy: 0.4487\n",
            "Epoch 18/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.5304 - accuracy: 0.5234 - val_loss: 1.4532 - val_accuracy: 0.5573\n",
            "Epoch 19/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.5054 - accuracy: 0.5390 - val_loss: 1.4065 - val_accuracy: 0.6193\n",
            "Epoch 20/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.4433 - accuracy: 0.5500 - val_loss: 1.3638 - val_accuracy: 0.6480\n",
            "Epoch 21/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.4404 - accuracy: 0.5658 - val_loss: 1.4087 - val_accuracy: 0.5716\n",
            "Epoch 22/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.4193 - accuracy: 0.5568 - val_loss: 1.3805 - val_accuracy: 0.6062\n",
            "Epoch 23/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.3437 - accuracy: 0.5929 - val_loss: 1.3063 - val_accuracy: 0.6360\n",
            "Epoch 24/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.3116 - accuracy: 0.5999 - val_loss: 1.3988 - val_accuracy: 0.5656\n",
            "Epoch 25/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.2678 - accuracy: 0.6182 - val_loss: 1.2536 - val_accuracy: 0.6897\n",
            "Epoch 26/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.2490 - accuracy: 0.6183 - val_loss: 1.2637 - val_accuracy: 0.6885\n",
            "Epoch 27/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.1973 - accuracy: 0.6487 - val_loss: 1.1855 - val_accuracy: 0.7160\n",
            "Epoch 28/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.2106 - accuracy: 0.6416 - val_loss: 1.2620 - val_accuracy: 0.6778\n",
            "Epoch 29/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.2379 - accuracy: 0.6365 - val_loss: 1.1756 - val_accuracy: 0.6742\n",
            "Epoch 30/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 11.7599 - accuracy: 0.2181 - val_loss: 14.3328 - val_accuracy: 0.1193\n",
            "Epoch 31/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 14.4286 - accuracy: 0.1119 - val_loss: 14.3357 - val_accuracy: 0.1193\n",
            "Epoch 32/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 14.3891 - accuracy: 0.1148 - val_loss: 14.3419 - val_accuracy: 0.1193\n",
            "Epoch 33/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 14.3824 - accuracy: 0.1139 - val_loss: 14.3459 - val_accuracy: 0.1193\n",
            "Epoch 34/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 14.2063 - accuracy: 0.1150 - val_loss: 14.3588 - val_accuracy: 0.1193\n",
            "Epoch 35/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 14.1132 - accuracy: 0.1140 - val_loss: 14.3597 - val_accuracy: 0.1193\n",
            "Epoch 36/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 14.0756 - accuracy: 0.1145 - val_loss: 14.3597 - val_accuracy: 0.1193\n",
            "Epoch 37/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 14.1433 - accuracy: 0.1126 - val_loss: 14.3595 - val_accuracy: 0.1193\n",
            "Epoch 38/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 14.0094 - accuracy: 0.1156 - val_loss: 14.3591 - val_accuracy: 0.1193\n",
            "Epoch 39/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 14.0815 - accuracy: 0.1146 - val_loss: 14.3589 - val_accuracy: 0.1193\n",
            "Epoch 40/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 14.0919 - accuracy: 0.1138 - val_loss: 14.3586 - val_accuracy: 0.1193\n",
            "Epoch 41/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 14.0591 - accuracy: 0.1169 - val_loss: 14.3581 - val_accuracy: 0.1193\n",
            "Epoch 42/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 14.0683 - accuracy: 0.1160 - val_loss: 14.3578 - val_accuracy: 0.1193\n",
            "Epoch 43/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 14.1353 - accuracy: 0.1129 - val_loss: 14.3578 - val_accuracy: 0.1193\n",
            "Epoch 44/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 14.0558 - accuracy: 0.1146 - val_loss: 14.3572 - val_accuracy: 0.1193\n",
            "Evaluating model...\n",
            "ROC: 0.929\n",
            "\n",
            "Accuracy = 0.62\n",
            "F-Score: 0.62\n",
            "\n",
            "*** Train on {1, 2, 4, 5, 6, 7, 8, 9} Validate on 3 Test on 10 ***\n",
            "val len:  925\n",
            "test len:  837\n",
            "train len:  6970\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 7s 105ms/step - loss: 2.7660 - accuracy: 0.1234 - val_loss: 2.4214 - val_accuracy: 0.1924\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 2.4039 - accuracy: 0.1521 - val_loss: 2.3697 - val_accuracy: 0.1864\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 2.3665 - accuracy: 0.1867 - val_loss: 2.3076 - val_accuracy: 0.2497\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 2.2764 - accuracy: 0.2273 - val_loss: 2.3243 - val_accuracy: 0.2521\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 2.2038 - accuracy: 0.2459 - val_loss: 2.2467 - val_accuracy: 0.2736\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 2.1454 - accuracy: 0.2671 - val_loss: 2.1045 - val_accuracy: 0.3751\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 2.0690 - accuracy: 0.3010 - val_loss: 1.9922 - val_accuracy: 0.4014\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 2.0248 - accuracy: 0.3263 - val_loss: 1.9661 - val_accuracy: 0.4241\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 7s 98ms/step - loss: 1.9791 - accuracy: 0.3425 - val_loss: 1.9433 - val_accuracy: 0.4182\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.9170 - accuracy: 0.3679 - val_loss: 1.8184 - val_accuracy: 0.4958\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.8627 - accuracy: 0.3839 - val_loss: 1.8082 - val_accuracy: 0.5090\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.8266 - accuracy: 0.4024 - val_loss: 1.6846 - val_accuracy: 0.5245\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.7581 - accuracy: 0.4291 - val_loss: 1.6488 - val_accuracy: 0.5066\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.7147 - accuracy: 0.4438 - val_loss: 1.6148 - val_accuracy: 0.5173\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.6858 - accuracy: 0.4627 - val_loss: 1.7166 - val_accuracy: 0.5544\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.6272 - accuracy: 0.4779 - val_loss: 1.4960 - val_accuracy: 0.6022\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.5954 - accuracy: 0.4956 - val_loss: 1.6611 - val_accuracy: 0.5412\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.5577 - accuracy: 0.5020 - val_loss: 1.4234 - val_accuracy: 0.5556\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.5606 - accuracy: 0.5082 - val_loss: 1.8758 - val_accuracy: 0.3381\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.4985 - accuracy: 0.5273 - val_loss: 1.4506 - val_accuracy: 0.5448\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.4491 - accuracy: 0.5522 - val_loss: 1.3420 - val_accuracy: 0.5950\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.4082 - accuracy: 0.5670 - val_loss: 1.3765 - val_accuracy: 0.5484\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.3871 - accuracy: 0.5717 - val_loss: 1.5006 - val_accuracy: 0.5400\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.3827 - accuracy: 0.5719 - val_loss: 1.3166 - val_accuracy: 0.6069\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.3397 - accuracy: 0.5890 - val_loss: 1.5216 - val_accuracy: 0.4958\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.3128 - accuracy: 0.6032 - val_loss: 1.3032 - val_accuracy: 0.6105\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.3957 - accuracy: 0.5829 - val_loss: 1.3514 - val_accuracy: 0.5639\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.3169 - accuracy: 0.6036 - val_loss: 1.2253 - val_accuracy: 0.6380\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.2800 - accuracy: 0.6149 - val_loss: 1.2377 - val_accuracy: 0.6165\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.2172 - accuracy: 0.6395 - val_loss: 1.2142 - val_accuracy: 0.6344\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.2154 - accuracy: 0.6377 - val_loss: 1.3002 - val_accuracy: 0.5830\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.2009 - accuracy: 0.6364 - val_loss: 1.3487 - val_accuracy: 0.5938\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.1947 - accuracy: 0.6379 - val_loss: 1.2679 - val_accuracy: 0.6213\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.1935 - accuracy: 0.6433 - val_loss: 1.2315 - val_accuracy: 0.6607\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.1319 - accuracy: 0.6587 - val_loss: 1.2703 - val_accuracy: 0.6045\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.1559 - accuracy: 0.6532 - val_loss: 1.4169 - val_accuracy: 0.5783\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.0928 - accuracy: 0.6788 - val_loss: 1.1885 - val_accuracy: 0.6440\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.1024 - accuracy: 0.6753 - val_loss: 1.2086 - val_accuracy: 0.6428\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.0986 - accuracy: 0.6782 - val_loss: 1.1760 - val_accuracy: 0.6607\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.0325 - accuracy: 0.7070 - val_loss: 1.2366 - val_accuracy: 0.6392\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.0460 - accuracy: 0.6895 - val_loss: 1.1795 - val_accuracy: 0.6487\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.0049 - accuracy: 0.7209 - val_loss: 1.1767 - val_accuracy: 0.6786\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.0078 - accuracy: 0.7077 - val_loss: 1.1847 - val_accuracy: 0.6535\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.9863 - accuracy: 0.7116 - val_loss: 1.1809 - val_accuracy: 0.6595\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.0182 - accuracy: 0.7044 - val_loss: 1.1275 - val_accuracy: 0.6631\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.9673 - accuracy: 0.7181 - val_loss: 1.1061 - val_accuracy: 0.6738\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.9449 - accuracy: 0.7287 - val_loss: 1.1003 - val_accuracy: 0.6511\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.9261 - accuracy: 0.7341 - val_loss: 1.1143 - val_accuracy: 0.6619\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.9072 - accuracy: 0.7382 - val_loss: 1.0972 - val_accuracy: 0.6679\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.9000 - accuracy: 0.7405 - val_loss: 1.1822 - val_accuracy: 0.6523\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.8730 - accuracy: 0.7494 - val_loss: 1.0925 - val_accuracy: 0.6714\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.9123 - accuracy: 0.7329 - val_loss: 1.2169 - val_accuracy: 0.6308\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.8420 - accuracy: 0.7636 - val_loss: 1.1036 - val_accuracy: 0.6822\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.8715 - accuracy: 0.7465 - val_loss: 1.0914 - val_accuracy: 0.6810\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 7s 98ms/step - loss: 0.8460 - accuracy: 0.7532 - val_loss: 1.0789 - val_accuracy: 0.6691\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.8335 - accuracy: 0.7647 - val_loss: 1.2287 - val_accuracy: 0.6595\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.8406 - accuracy: 0.7549 - val_loss: 1.1324 - val_accuracy: 0.6834\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.8204 - accuracy: 0.7641 - val_loss: 1.1911 - val_accuracy: 0.6953\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.7826 - accuracy: 0.7737 - val_loss: 1.1018 - val_accuracy: 0.6906\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.8199 - accuracy: 0.7699 - val_loss: 1.0876 - val_accuracy: 0.6834\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.7732 - accuracy: 0.7805 - val_loss: 1.1205 - val_accuracy: 0.6810\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.7915 - accuracy: 0.7720 - val_loss: 1.1024 - val_accuracy: 0.6930\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.7641 - accuracy: 0.7795 - val_loss: 1.1484 - val_accuracy: 0.6762\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.7575 - accuracy: 0.7869 - val_loss: 1.1503 - val_accuracy: 0.6930\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.7439 - accuracy: 0.7865 - val_loss: 1.1592 - val_accuracy: 0.6953\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.7415 - accuracy: 0.7878 - val_loss: 1.0484 - val_accuracy: 0.6918\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.7385 - accuracy: 0.7911 - val_loss: 1.0593 - val_accuracy: 0.6822\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.7087 - accuracy: 0.7970 - val_loss: 1.1365 - val_accuracy: 0.6786\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.6995 - accuracy: 0.8088 - val_loss: 1.1111 - val_accuracy: 0.7133\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.7142 - accuracy: 0.7958 - val_loss: 1.0672 - val_accuracy: 0.6977\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.7098 - accuracy: 0.7999 - val_loss: 1.1135 - val_accuracy: 0.6930\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.7230 - accuracy: 0.7986 - val_loss: 1.1127 - val_accuracy: 0.6738\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.7015 - accuracy: 0.8076 - val_loss: 1.0791 - val_accuracy: 0.7001\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 0.6653 - accuracy: 0.8131 - val_loss: 1.2275 - val_accuracy: 0.6607\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.7050 - accuracy: 0.8022 - val_loss: 1.3191 - val_accuracy: 0.6619\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.6705 - accuracy: 0.8115 - val_loss: 1.1655 - val_accuracy: 0.6858\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.6594 - accuracy: 0.8171 - val_loss: 1.1519 - val_accuracy: 0.6798\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.6552 - accuracy: 0.8166 - val_loss: 1.0893 - val_accuracy: 0.6965\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.6483 - accuracy: 0.8248 - val_loss: 1.0991 - val_accuracy: 0.7001\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.6594 - accuracy: 0.8188 - val_loss: 1.1829 - val_accuracy: 0.6989\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.6138 - accuracy: 0.8313 - val_loss: 1.1811 - val_accuracy: 0.6965\n",
            "Evaluating model...\n",
            "ROC: 0.912\n",
            "\n",
            "Accuracy = 0.60\n",
            "F-Score: 0.6\n",
            "\n",
            "*** Train on {1, 2, 3, 5, 6, 8, 9, 10} Validate on 4 Test on 7 ***\n",
            "val len:  990\n",
            "test len:  838\n",
            "train len:  6904\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 2.8522 - accuracy: 0.1191 - val_loss: 2.4333 - val_accuracy: 0.1265\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 2.4222 - accuracy: 0.1304 - val_loss: 2.4144 - val_accuracy: 0.1265\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 2.3922 - accuracy: 0.1469 - val_loss: 2.3657 - val_accuracy: 0.1265\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 2.3574 - accuracy: 0.1821 - val_loss: 2.3609 - val_accuracy: 0.1778\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 2.3164 - accuracy: 0.2158 - val_loss: 2.3783 - val_accuracy: 0.2005\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 2.2971 - accuracy: 0.2116 - val_loss: 2.2321 - val_accuracy: 0.3270\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 2.2181 - accuracy: 0.2406 - val_loss: 2.1092 - val_accuracy: 0.3317\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 2.1384 - accuracy: 0.2748 - val_loss: 1.9390 - val_accuracy: 0.4045\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 2.0433 - accuracy: 0.3126 - val_loss: 2.0281 - val_accuracy: 0.3258\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 7s 98ms/step - loss: 1.9810 - accuracy: 0.3418 - val_loss: 1.8198 - val_accuracy: 0.3831\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 2.1524 - accuracy: 0.3133 - val_loss: 2.0132 - val_accuracy: 0.4081\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 2.0007 - accuracy: 0.3431 - val_loss: 1.8768 - val_accuracy: 0.4284\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.9361 - accuracy: 0.3678 - val_loss: 1.7355 - val_accuracy: 0.4535\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.9244 - accuracy: 0.3798 - val_loss: 1.8021 - val_accuracy: 0.4284\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.8200 - accuracy: 0.4085 - val_loss: 1.6514 - val_accuracy: 0.4797\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.7078 - accuracy: 0.4508 - val_loss: 1.5387 - val_accuracy: 0.5107\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.6470 - accuracy: 0.4765 - val_loss: 1.5347 - val_accuracy: 0.5263\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 7s 100ms/step - loss: 1.6082 - accuracy: 0.4819 - val_loss: 1.5022 - val_accuracy: 0.5143\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.5346 - accuracy: 0.5178 - val_loss: 1.3863 - val_accuracy: 0.5895\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.5301 - accuracy: 0.5255 - val_loss: 1.3620 - val_accuracy: 0.5835\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.4921 - accuracy: 0.5372 - val_loss: 1.4364 - val_accuracy: 0.5346\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.4568 - accuracy: 0.5452 - val_loss: 1.4038 - val_accuracy: 0.5573\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.4400 - accuracy: 0.5466 - val_loss: 1.3425 - val_accuracy: 0.5740\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.3498 - accuracy: 0.5798 - val_loss: 1.2993 - val_accuracy: 0.6146\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.3637 - accuracy: 0.5778 - val_loss: 1.2578 - val_accuracy: 0.6086\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.3465 - accuracy: 0.5852 - val_loss: 1.2641 - val_accuracy: 0.6301\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.2928 - accuracy: 0.6065 - val_loss: 1.2976 - val_accuracy: 0.5931\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.2717 - accuracy: 0.6101 - val_loss: 1.2987 - val_accuracy: 0.5919\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.4876 - accuracy: 0.5429 - val_loss: 1.2470 - val_accuracy: 0.6301\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.2839 - accuracy: 0.6128 - val_loss: 1.3458 - val_accuracy: 0.5501\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.4164 - accuracy: 0.5647 - val_loss: 2.4346 - val_accuracy: 0.2697\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.3528 - accuracy: 0.5863 - val_loss: 1.4042 - val_accuracy: 0.5072\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.1898 - accuracy: 0.6348 - val_loss: 1.1696 - val_accuracy: 0.6659\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.3453 - accuracy: 0.6023 - val_loss: 1.4147 - val_accuracy: 0.5477\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.1956 - accuracy: 0.6421 - val_loss: 1.1740 - val_accuracy: 0.6683\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.1314 - accuracy: 0.6606 - val_loss: 1.2000 - val_accuracy: 0.6432\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.1590 - accuracy: 0.6531 - val_loss: 1.1639 - val_accuracy: 0.6885\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 7s 98ms/step - loss: 1.0584 - accuracy: 0.6835 - val_loss: 1.2192 - val_accuracy: 0.6086\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.0821 - accuracy: 0.6754 - val_loss: 1.1106 - val_accuracy: 0.6838\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 7s 98ms/step - loss: 1.0706 - accuracy: 0.6764 - val_loss: 1.1115 - val_accuracy: 0.6706\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.2164 - accuracy: 0.6577 - val_loss: 1.2630 - val_accuracy: 0.5943\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 7s 99ms/step - loss: 1.0476 - accuracy: 0.6925 - val_loss: 1.0908 - val_accuracy: 0.6742\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.0325 - accuracy: 0.6913 - val_loss: 1.1028 - val_accuracy: 0.6850\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.0456 - accuracy: 0.6890 - val_loss: 1.1158 - val_accuracy: 0.6754\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.9767 - accuracy: 0.7106 - val_loss: 1.1251 - val_accuracy: 0.6492\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.1593 - accuracy: 0.6550 - val_loss: 1.1210 - val_accuracy: 0.6790\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.0076 - accuracy: 0.7050 - val_loss: 1.0949 - val_accuracy: 0.6742\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.9558 - accuracy: 0.7161 - val_loss: 1.0615 - val_accuracy: 0.6790\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.9926 - accuracy: 0.7060 - val_loss: 1.0836 - val_accuracy: 0.6969\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.0081 - accuracy: 0.7079 - val_loss: 1.2438 - val_accuracy: 0.5895\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.9037 - accuracy: 0.7445 - val_loss: 1.5239 - val_accuracy: 0.5072\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.9369 - accuracy: 0.7284 - val_loss: 1.0519 - val_accuracy: 0.7112\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.0058 - accuracy: 0.7313 - val_loss: 1.5872 - val_accuracy: 0.5024\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.5460 - accuracy: 0.5794 - val_loss: 1.2678 - val_accuracy: 0.6337\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 1.0204 - accuracy: 0.7026 - val_loss: 1.0888 - val_accuracy: 0.6766\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.9137 - accuracy: 0.7436 - val_loss: 1.0620 - val_accuracy: 0.6802\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.8890 - accuracy: 0.7449 - val_loss: 1.0655 - val_accuracy: 0.7005\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.8765 - accuracy: 0.7529 - val_loss: 1.0458 - val_accuracy: 0.6945\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.8645 - accuracy: 0.7501 - val_loss: 1.1448 - val_accuracy: 0.6551\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.8632 - accuracy: 0.7561 - val_loss: 1.0208 - val_accuracy: 0.7064\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.9611 - accuracy: 0.7381 - val_loss: 1.0631 - val_accuracy: 0.6826\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.8425 - accuracy: 0.7612 - val_loss: 1.0283 - val_accuracy: 0.7470\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 7s 99ms/step - loss: 0.8154 - accuracy: 0.7717 - val_loss: 1.0074 - val_accuracy: 0.7220\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 7s 100ms/step - loss: 0.7997 - accuracy: 0.7722 - val_loss: 1.1571 - val_accuracy: 0.6527\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.8073 - accuracy: 0.7722 - val_loss: 0.9890 - val_accuracy: 0.7100\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.8242 - accuracy: 0.7674 - val_loss: 0.9973 - val_accuracy: 0.7041\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.7980 - accuracy: 0.7738 - val_loss: 0.9966 - val_accuracy: 0.7255\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.9350 - accuracy: 0.7619 - val_loss: 2.3628 - val_accuracy: 0.4105\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.0346 - accuracy: 0.6974 - val_loss: 1.2953 - val_accuracy: 0.5811\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.0102 - accuracy: 0.7184 - val_loss: 1.1473 - val_accuracy: 0.6504\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.8622 - accuracy: 0.7565 - val_loss: 1.0336 - val_accuracy: 0.7208\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.2091 - accuracy: 0.6896 - val_loss: 1.3551 - val_accuracy: 0.5430\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.2702 - accuracy: 0.6360 - val_loss: 1.2606 - val_accuracy: 0.5967\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.9956 - accuracy: 0.7081 - val_loss: 1.2709 - val_accuracy: 0.5990\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.8678 - accuracy: 0.7532 - val_loss: 1.0166 - val_accuracy: 0.6993\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 0.8032 - accuracy: 0.7775 - val_loss: 1.0277 - val_accuracy: 0.6874\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.7807 - accuracy: 0.7816 - val_loss: 1.0375 - val_accuracy: 0.7148\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.7479 - accuracy: 0.7866 - val_loss: 1.0162 - val_accuracy: 0.6909\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.7344 - accuracy: 0.7878 - val_loss: 1.0488 - val_accuracy: 0.7243\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 7s 97ms/step - loss: 0.7108 - accuracy: 0.8007 - val_loss: 0.9942 - val_accuracy: 0.7327\n",
            "Evaluating model...\n",
            "ROC: 0.937\n",
            "\n",
            "Accuracy = 0.68\n",
            "F-Score: 0.68\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 6, 7, 8, 10} Validate on 5 Test on 9 ***\n",
            "val len:  936\n",
            "test len:  816\n",
            "train len:  6980\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 2.7013 - accuracy: 0.1246 - val_loss: 2.4133 - val_accuracy: 0.1189\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 2.4126 - accuracy: 0.1491 - val_loss: 2.4041 - val_accuracy: 0.1213\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 2.3878 - accuracy: 0.1638 - val_loss: 2.3352 - val_accuracy: 0.1311\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 2.3541 - accuracy: 0.1824 - val_loss: 2.2861 - val_accuracy: 0.2132\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 2.3132 - accuracy: 0.2027 - val_loss: 2.2653 - val_accuracy: 0.2145\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 2.2503 - accuracy: 0.2378 - val_loss: 2.1656 - val_accuracy: 0.2512\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 2.1870 - accuracy: 0.2658 - val_loss: 2.0769 - val_accuracy: 0.2659\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 2.1254 - accuracy: 0.2887 - val_loss: 2.0076 - val_accuracy: 0.2966\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 2.0748 - accuracy: 0.3109 - val_loss: 1.9900 - val_accuracy: 0.3100\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 2.0392 - accuracy: 0.3128 - val_loss: 2.0296 - val_accuracy: 0.3150\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.9924 - accuracy: 0.3344 - val_loss: 1.9415 - val_accuracy: 0.3517\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.9372 - accuracy: 0.3623 - val_loss: 1.8375 - val_accuracy: 0.4093\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.9057 - accuracy: 0.3716 - val_loss: 1.7973 - val_accuracy: 0.4400\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.8592 - accuracy: 0.3897 - val_loss: 1.7678 - val_accuracy: 0.4363\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.8092 - accuracy: 0.4113 - val_loss: 1.7472 - val_accuracy: 0.4363\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.8151 - accuracy: 0.4054 - val_loss: 1.8644 - val_accuracy: 0.4412\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.7658 - accuracy: 0.4156 - val_loss: 1.6365 - val_accuracy: 0.5159\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.6807 - accuracy: 0.4526 - val_loss: 1.5598 - val_accuracy: 0.5257\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.7036 - accuracy: 0.4467 - val_loss: 1.7455 - val_accuracy: 0.4669\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.6857 - accuracy: 0.4420 - val_loss: 1.5614 - val_accuracy: 0.5000\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.6427 - accuracy: 0.4699 - val_loss: 1.6045 - val_accuracy: 0.4755\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.5776 - accuracy: 0.4915 - val_loss: 1.4611 - val_accuracy: 0.5429\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.5629 - accuracy: 0.5034 - val_loss: 1.4269 - val_accuracy: 0.5784\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.4840 - accuracy: 0.5324 - val_loss: 1.3799 - val_accuracy: 0.5993\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.4676 - accuracy: 0.5400 - val_loss: 1.3969 - val_accuracy: 0.5858\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.4580 - accuracy: 0.5486 - val_loss: 1.4416 - val_accuracy: 0.6042\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.4619 - accuracy: 0.5375 - val_loss: 1.4670 - val_accuracy: 0.5858\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 7s 96ms/step - loss: 1.3962 - accuracy: 0.5701 - val_loss: 1.3917 - val_accuracy: 0.6078\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.3700 - accuracy: 0.5775 - val_loss: 1.3588 - val_accuracy: 0.6385\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.3415 - accuracy: 0.5811 - val_loss: 1.3497 - val_accuracy: 0.6225\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 7s 93ms/step - loss: 1.2973 - accuracy: 0.6021 - val_loss: 1.5781 - val_accuracy: 0.5650\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.3224 - accuracy: 0.5907 - val_loss: 1.3564 - val_accuracy: 0.6250\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.2645 - accuracy: 0.6189 - val_loss: 1.3743 - val_accuracy: 0.6262\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.2418 - accuracy: 0.6168 - val_loss: 1.3179 - val_accuracy: 0.6593\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.2081 - accuracy: 0.6358 - val_loss: 1.2871 - val_accuracy: 0.6642\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.1743 - accuracy: 0.6423 - val_loss: 1.2983 - val_accuracy: 0.6691\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.1758 - accuracy: 0.6448 - val_loss: 1.2922 - val_accuracy: 0.6397\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.1561 - accuracy: 0.6553 - val_loss: 1.3426 - val_accuracy: 0.6446\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 1.1201 - accuracy: 0.6622 - val_loss: 1.3900 - val_accuracy: 0.6520\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.0842 - accuracy: 0.6788 - val_loss: 1.4506 - val_accuracy: 0.6140\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.0678 - accuracy: 0.6852 - val_loss: 1.5612 - val_accuracy: 0.5980\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.1819 - accuracy: 0.6484 - val_loss: 1.5091 - val_accuracy: 0.6299\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.0889 - accuracy: 0.6784 - val_loss: 1.3957 - val_accuracy: 0.6262\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.0308 - accuracy: 0.6956 - val_loss: 1.3823 - val_accuracy: 0.6605\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 1.0666 - accuracy: 0.6834 - val_loss: 1.3176 - val_accuracy: 0.6667\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.9759 - accuracy: 0.7099 - val_loss: 1.3283 - val_accuracy: 0.6654\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 0.9699 - accuracy: 0.7148 - val_loss: 1.4256 - val_accuracy: 0.6679\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 0.9521 - accuracy: 0.7196 - val_loss: 1.3378 - val_accuracy: 0.6752\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 7s 94ms/step - loss: 0.9379 - accuracy: 0.7289 - val_loss: 1.3591 - val_accuracy: 0.6569\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 7s 95ms/step - loss: 0.9179 - accuracy: 0.7365 - val_loss: 1.3793 - val_accuracy: 0.6385\n",
            "Evaluating model...\n",
            "ROC: 0.941\n",
            "\n",
            "Accuracy = 0.67\n",
            "F-Score: 0.67\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 5, 8, 9, 10} Validate on 6 Test on 7 ***\n",
            "val len:  823\n",
            "test len:  838\n",
            "train len:  7071\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "71/71 [==============================] - 7s 101ms/step - loss: 3.3869 - accuracy: 0.1171 - val_loss: 2.4377 - val_accuracy: 0.0895\n",
            "Epoch 2/200\n",
            "71/71 [==============================] - 7s 94ms/step - loss: 2.4195 - accuracy: 0.1277 - val_loss: 2.4182 - val_accuracy: 0.1158\n",
            "Epoch 3/200\n",
            "71/71 [==============================] - 7s 94ms/step - loss: 2.4045 - accuracy: 0.1393 - val_loss: 2.4022 - val_accuracy: 0.2291\n",
            "Epoch 4/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 2.3737 - accuracy: 0.1710 - val_loss: 2.3758 - val_accuracy: 0.2363\n",
            "Epoch 5/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 2.3096 - accuracy: 0.2004 - val_loss: 2.3814 - val_accuracy: 0.2148\n",
            "Epoch 6/200\n",
            "71/71 [==============================] - 7s 94ms/step - loss: 2.2696 - accuracy: 0.2249 - val_loss: 2.1299 - val_accuracy: 0.2768\n",
            "Epoch 7/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 2.1755 - accuracy: 0.2618 - val_loss: 2.0854 - val_accuracy: 0.3317\n",
            "Epoch 8/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 2.1155 - accuracy: 0.2862 - val_loss: 2.0254 - val_accuracy: 0.3675\n",
            "Epoch 9/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 2.0663 - accuracy: 0.3079 - val_loss: 1.9312 - val_accuracy: 0.4021\n",
            "Epoch 10/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.9955 - accuracy: 0.3340 - val_loss: 1.9415 - val_accuracy: 0.3854\n",
            "Epoch 11/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 2.0204 - accuracy: 0.3350 - val_loss: 1.8560 - val_accuracy: 0.4141\n",
            "Epoch 12/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.9191 - accuracy: 0.3712 - val_loss: 1.8185 - val_accuracy: 0.4391\n",
            "Epoch 13/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.8979 - accuracy: 0.3741 - val_loss: 1.8225 - val_accuracy: 0.4248\n",
            "Epoch 14/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.8210 - accuracy: 0.4022 - val_loss: 1.7110 - val_accuracy: 0.5024\n",
            "Epoch 15/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.7985 - accuracy: 0.4100 - val_loss: 1.6794 - val_accuracy: 0.5203\n",
            "Epoch 16/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.9403 - accuracy: 0.3606 - val_loss: 1.6742 - val_accuracy: 0.4940\n",
            "Epoch 17/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.7148 - accuracy: 0.4504 - val_loss: 1.6219 - val_accuracy: 0.5000\n",
            "Epoch 18/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.6666 - accuracy: 0.4562 - val_loss: 1.7088 - val_accuracy: 0.4200\n",
            "Epoch 19/200\n",
            "71/71 [==============================] - 7s 94ms/step - loss: 1.6297 - accuracy: 0.4680 - val_loss: 1.5477 - val_accuracy: 0.5167\n",
            "Epoch 20/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.6197 - accuracy: 0.4893 - val_loss: 1.6634 - val_accuracy: 0.4714\n",
            "Epoch 21/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.5982 - accuracy: 0.4913 - val_loss: 1.5210 - val_accuracy: 0.5334\n",
            "Epoch 22/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.5139 - accuracy: 0.5179 - val_loss: 1.4934 - val_accuracy: 0.5537\n",
            "Epoch 23/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.5397 - accuracy: 0.5104 - val_loss: 1.4935 - val_accuracy: 0.5430\n",
            "Epoch 24/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.4659 - accuracy: 0.5404 - val_loss: 1.4108 - val_accuracy: 0.5525\n",
            "Epoch 25/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.4436 - accuracy: 0.5438 - val_loss: 1.5083 - val_accuracy: 0.5274\n",
            "Epoch 26/200\n",
            "71/71 [==============================] - 7s 94ms/step - loss: 1.4351 - accuracy: 0.5506 - val_loss: 1.3497 - val_accuracy: 0.5752\n",
            "Epoch 27/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.4292 - accuracy: 0.5484 - val_loss: 1.3856 - val_accuracy: 0.5609\n",
            "Epoch 28/200\n",
            "71/71 [==============================] - 7s 94ms/step - loss: 1.3754 - accuracy: 0.5726 - val_loss: 1.3549 - val_accuracy: 0.5680\n",
            "Epoch 29/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.3373 - accuracy: 0.5825 - val_loss: 1.3926 - val_accuracy: 0.5644\n",
            "Epoch 30/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.3505 - accuracy: 0.5793 - val_loss: 1.4714 - val_accuracy: 0.5549\n",
            "Epoch 31/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.3179 - accuracy: 0.6008 - val_loss: 1.5735 - val_accuracy: 0.4749\n",
            "Epoch 32/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.2987 - accuracy: 0.6022 - val_loss: 1.3517 - val_accuracy: 0.5621\n",
            "Epoch 33/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.3097 - accuracy: 0.5950 - val_loss: 1.2999 - val_accuracy: 0.6014\n",
            "Epoch 34/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.2712 - accuracy: 0.6214 - val_loss: 1.5679 - val_accuracy: 0.4630\n",
            "Epoch 35/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.2409 - accuracy: 0.6286 - val_loss: 1.3122 - val_accuracy: 0.6086\n",
            "Epoch 36/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.2127 - accuracy: 0.6337 - val_loss: 1.4232 - val_accuracy: 0.5239\n",
            "Epoch 37/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.2199 - accuracy: 0.6303 - val_loss: 1.2977 - val_accuracy: 0.5990\n",
            "Epoch 38/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.1792 - accuracy: 0.6447 - val_loss: 1.3460 - val_accuracy: 0.5716\n",
            "Epoch 39/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.1993 - accuracy: 0.6370 - val_loss: 1.2581 - val_accuracy: 0.6014\n",
            "Epoch 40/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.2053 - accuracy: 0.6361 - val_loss: 1.2506 - val_accuracy: 0.6026\n",
            "Epoch 41/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.1600 - accuracy: 0.6487 - val_loss: 1.3609 - val_accuracy: 0.5680\n",
            "Epoch 42/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.1544 - accuracy: 0.6569 - val_loss: 1.2338 - val_accuracy: 0.6229\n",
            "Epoch 43/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.1193 - accuracy: 0.6691 - val_loss: 1.2379 - val_accuracy: 0.6169\n",
            "Epoch 44/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.1128 - accuracy: 0.6689 - val_loss: 1.2161 - val_accuracy: 0.6265\n",
            "Epoch 45/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.0774 - accuracy: 0.6805 - val_loss: 1.1550 - val_accuracy: 0.6384\n",
            "Epoch 46/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.0773 - accuracy: 0.6800 - val_loss: 1.1670 - val_accuracy: 0.6420\n",
            "Epoch 47/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 1.0875 - accuracy: 0.6744 - val_loss: 1.1357 - val_accuracy: 0.6444\n",
            "Epoch 48/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.0320 - accuracy: 0.6863 - val_loss: 1.2319 - val_accuracy: 0.6026\n",
            "Epoch 49/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.0363 - accuracy: 0.6896 - val_loss: 1.1446 - val_accuracy: 0.6396\n",
            "Epoch 50/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.0208 - accuracy: 0.7024 - val_loss: 1.1843 - val_accuracy: 0.6384\n",
            "Epoch 51/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.0039 - accuracy: 0.6996 - val_loss: 1.1190 - val_accuracy: 0.6492\n",
            "Epoch 52/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 1.0009 - accuracy: 0.7026 - val_loss: 1.1241 - val_accuracy: 0.6527\n",
            "Epoch 53/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.9663 - accuracy: 0.7189 - val_loss: 1.2058 - val_accuracy: 0.6169\n",
            "Epoch 54/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.9661 - accuracy: 0.7061 - val_loss: 1.1380 - val_accuracy: 0.6348\n",
            "Epoch 55/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.9471 - accuracy: 0.7205 - val_loss: 1.2281 - val_accuracy: 0.6396\n",
            "Epoch 56/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.9333 - accuracy: 0.7297 - val_loss: 1.1721 - val_accuracy: 0.6348\n",
            "Epoch 57/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.9356 - accuracy: 0.7218 - val_loss: 1.1274 - val_accuracy: 0.6456\n",
            "Epoch 58/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.9150 - accuracy: 0.7234 - val_loss: 1.2351 - val_accuracy: 0.6169\n",
            "Epoch 59/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.9178 - accuracy: 0.7319 - val_loss: 1.1128 - val_accuracy: 0.6611\n",
            "Epoch 60/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.9150 - accuracy: 0.7324 - val_loss: 1.1582 - val_accuracy: 0.6456\n",
            "Epoch 61/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.8839 - accuracy: 0.7364 - val_loss: 1.1211 - val_accuracy: 0.6504\n",
            "Epoch 62/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.8951 - accuracy: 0.7403 - val_loss: 1.2236 - val_accuracy: 0.6325\n",
            "Epoch 63/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.8709 - accuracy: 0.7494 - val_loss: 1.2008 - val_accuracy: 0.6360\n",
            "Epoch 64/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.8609 - accuracy: 0.7486 - val_loss: 1.0892 - val_accuracy: 0.6575\n",
            "Epoch 65/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.8626 - accuracy: 0.7464 - val_loss: 1.1594 - val_accuracy: 0.6420\n",
            "Epoch 66/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.8229 - accuracy: 0.7627 - val_loss: 1.0835 - val_accuracy: 0.6635\n",
            "Epoch 67/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.8181 - accuracy: 0.7642 - val_loss: 1.2469 - val_accuracy: 0.6193\n",
            "Epoch 68/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.8083 - accuracy: 0.7624 - val_loss: 1.1157 - val_accuracy: 0.6408\n",
            "Epoch 69/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.8302 - accuracy: 0.7569 - val_loss: 1.1113 - val_accuracy: 0.6599\n",
            "Epoch 70/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.8303 - accuracy: 0.7590 - val_loss: 1.0858 - val_accuracy: 0.6706\n",
            "Epoch 71/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.7931 - accuracy: 0.7758 - val_loss: 1.1058 - val_accuracy: 0.6492\n",
            "Epoch 72/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.7817 - accuracy: 0.7722 - val_loss: 1.0951 - val_accuracy: 0.6695\n",
            "Epoch 73/200\n",
            "71/71 [==============================] - 7s 94ms/step - loss: 0.8017 - accuracy: 0.7675 - val_loss: 1.2897 - val_accuracy: 0.5955\n",
            "Epoch 74/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.7917 - accuracy: 0.7723 - val_loss: 1.0680 - val_accuracy: 0.6766\n",
            "Epoch 75/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.7760 - accuracy: 0.7739 - val_loss: 1.0150 - val_accuracy: 0.6850\n",
            "Epoch 76/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.8067 - accuracy: 0.7652 - val_loss: 1.1440 - val_accuracy: 0.6372\n",
            "Epoch 77/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.7753 - accuracy: 0.7802 - val_loss: 1.0601 - val_accuracy: 0.6921\n",
            "Epoch 78/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.7606 - accuracy: 0.7792 - val_loss: 1.0258 - val_accuracy: 0.6933\n",
            "Epoch 79/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.7498 - accuracy: 0.7828 - val_loss: 1.0704 - val_accuracy: 0.6730\n",
            "Epoch 80/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.7795 - accuracy: 0.7808 - val_loss: 1.0732 - val_accuracy: 0.6695\n",
            "Epoch 81/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.7394 - accuracy: 0.7903 - val_loss: 1.1096 - val_accuracy: 0.6683\n",
            "Epoch 82/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.7369 - accuracy: 0.7873 - val_loss: 1.1247 - val_accuracy: 0.6659\n",
            "Epoch 83/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.7246 - accuracy: 0.7945 - val_loss: 1.0549 - val_accuracy: 0.6718\n",
            "Epoch 84/200\n",
            "71/71 [==============================] - 7s 94ms/step - loss: 0.7364 - accuracy: 0.7928 - val_loss: 1.0603 - val_accuracy: 0.6814\n",
            "Epoch 85/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.7055 - accuracy: 0.8038 - val_loss: 1.0927 - val_accuracy: 0.6862\n",
            "Epoch 86/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.7175 - accuracy: 0.7906 - val_loss: 1.0899 - val_accuracy: 0.6647\n",
            "Epoch 87/200\n",
            "71/71 [==============================] - 7s 96ms/step - loss: 0.7146 - accuracy: 0.7988 - val_loss: 1.0740 - val_accuracy: 0.6802\n",
            "Epoch 88/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.7048 - accuracy: 0.8016 - val_loss: 1.1176 - val_accuracy: 0.6551\n",
            "Epoch 89/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.7155 - accuracy: 0.7935 - val_loss: 1.1044 - val_accuracy: 0.6838\n",
            "Epoch 90/200\n",
            "71/71 [==============================] - 7s 95ms/step - loss: 0.7073 - accuracy: 0.8027 - val_loss: 1.0477 - val_accuracy: 0.6874\n",
            "Evaluating model...\n",
            "ROC: 0.909\n",
            "\n",
            "Accuracy = 0.64\n",
            "F-Score: 0.64\n",
            "\n",
            "*** Train on {1, 2, 4, 5, 6, 8, 9, 10} Validate on 7 Test on 3 ***\n",
            "val len:  838\n",
            "test len:  925\n",
            "train len:  6969\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 7s 107ms/step - loss: 2.6714 - accuracy: 0.1119 - val_loss: 2.4307 - val_accuracy: 0.1751\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 2.4235 - accuracy: 0.1267 - val_loss: 2.4186 - val_accuracy: 0.1459\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 2.4019 - accuracy: 0.1369 - val_loss: 2.3813 - val_accuracy: 0.1632\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 2.3617 - accuracy: 0.1741 - val_loss: 2.3251 - val_accuracy: 0.2573\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 2.3258 - accuracy: 0.2049 - val_loss: 2.2616 - val_accuracy: 0.2638\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 2.2670 - accuracy: 0.2317 - val_loss: 2.0541 - val_accuracy: 0.3708\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 2.2093 - accuracy: 0.2576 - val_loss: 2.0212 - val_accuracy: 0.3762\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 2.1673 - accuracy: 0.2805 - val_loss: 2.1192 - val_accuracy: 0.3319\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 2.1434 - accuracy: 0.2772 - val_loss: 2.1133 - val_accuracy: 0.3027\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 2.1313 - accuracy: 0.2787 - val_loss: 1.9027 - val_accuracy: 0.3330\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.9856 - accuracy: 0.3384 - val_loss: 1.7566 - val_accuracy: 0.4000\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.9194 - accuracy: 0.3672 - val_loss: 1.7119 - val_accuracy: 0.4368\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.8878 - accuracy: 0.3768 - val_loss: 1.7108 - val_accuracy: 0.4443\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.8227 - accuracy: 0.4039 - val_loss: 1.6571 - val_accuracy: 0.5059\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.7974 - accuracy: 0.4166 - val_loss: 1.6207 - val_accuracy: 0.4422\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.7297 - accuracy: 0.4397 - val_loss: 1.5624 - val_accuracy: 0.4951\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.6807 - accuracy: 0.4648 - val_loss: 1.5718 - val_accuracy: 0.4995\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.6548 - accuracy: 0.4730 - val_loss: 1.5630 - val_accuracy: 0.5319\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.6045 - accuracy: 0.4833 - val_loss: 1.5397 - val_accuracy: 0.5427\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.6036 - accuracy: 0.4930 - val_loss: 1.5293 - val_accuracy: 0.5135\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 8s 108ms/step - loss: 1.5518 - accuracy: 0.5081 - val_loss: 1.5496 - val_accuracy: 0.5081\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.4969 - accuracy: 0.5237 - val_loss: 1.4887 - val_accuracy: 0.4789\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.5045 - accuracy: 0.5315 - val_loss: 1.4756 - val_accuracy: 0.5114\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.4458 - accuracy: 0.5446 - val_loss: 1.4796 - val_accuracy: 0.5405\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.4206 - accuracy: 0.5652 - val_loss: 1.4382 - val_accuracy: 0.5503\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.3961 - accuracy: 0.5731 - val_loss: 1.5103 - val_accuracy: 0.4995\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.3660 - accuracy: 0.5856 - val_loss: 1.3991 - val_accuracy: 0.5589\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.3309 - accuracy: 0.5925 - val_loss: 1.3699 - val_accuracy: 0.5459\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.3460 - accuracy: 0.5913 - val_loss: 1.3549 - val_accuracy: 0.5838\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.2791 - accuracy: 0.6183 - val_loss: 1.3704 - val_accuracy: 0.5741\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.2616 - accuracy: 0.6196 - val_loss: 1.2875 - val_accuracy: 0.6173\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.2665 - accuracy: 0.6195 - val_loss: 1.4409 - val_accuracy: 0.5297\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.2433 - accuracy: 0.6238 - val_loss: 1.3371 - val_accuracy: 0.5924\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.2033 - accuracy: 0.6449 - val_loss: 1.3639 - val_accuracy: 0.6032\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.1997 - accuracy: 0.6394 - val_loss: 1.5975 - val_accuracy: 0.4454\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.1793 - accuracy: 0.6545 - val_loss: 1.3753 - val_accuracy: 0.5600\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.1389 - accuracy: 0.6550 - val_loss: 1.5736 - val_accuracy: 0.4746\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.1697 - accuracy: 0.6476 - val_loss: 1.2784 - val_accuracy: 0.6184\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.1363 - accuracy: 0.6579 - val_loss: 1.3270 - val_accuracy: 0.6573\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.2088 - accuracy: 0.6397 - val_loss: 1.3437 - val_accuracy: 0.5762\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.0915 - accuracy: 0.6760 - val_loss: 1.3093 - val_accuracy: 0.6032\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.0463 - accuracy: 0.6934 - val_loss: 1.3576 - val_accuracy: 0.6173\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.0899 - accuracy: 0.6870 - val_loss: 1.2777 - val_accuracy: 0.6346\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.0184 - accuracy: 0.7010 - val_loss: 1.3577 - val_accuracy: 0.6011\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.0281 - accuracy: 0.7023 - val_loss: 1.3583 - val_accuracy: 0.6205\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 1.0266 - accuracy: 0.6952 - val_loss: 1.2526 - val_accuracy: 0.6530\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 7s 105ms/step - loss: 1.0247 - accuracy: 0.7043 - val_loss: 1.2690 - val_accuracy: 0.6130\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 0.9946 - accuracy: 0.7136 - val_loss: 1.3620 - val_accuracy: 0.5805\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 1.1116 - accuracy: 0.6728 - val_loss: 1.3738 - val_accuracy: 0.6151\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 7s 102ms/step - loss: 0.9887 - accuracy: 0.7120 - val_loss: 1.2722 - val_accuracy: 0.6454\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.9753 - accuracy: 0.7146 - val_loss: 1.3815 - val_accuracy: 0.6011\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.9641 - accuracy: 0.7264 - val_loss: 1.2522 - val_accuracy: 0.6508\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.9240 - accuracy: 0.7330 - val_loss: 1.2201 - val_accuracy: 0.6486\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 7s 102ms/step - loss: 0.9413 - accuracy: 0.7274 - val_loss: 1.2881 - val_accuracy: 0.6422\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.9066 - accuracy: 0.7413 - val_loss: 1.2748 - val_accuracy: 0.6378\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 0.9179 - accuracy: 0.7317 - val_loss: 1.2632 - val_accuracy: 0.6573\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 0.9490 - accuracy: 0.7246 - val_loss: 1.3379 - val_accuracy: 0.6270\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.8915 - accuracy: 0.7424 - val_loss: 1.2584 - val_accuracy: 0.6422\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.9008 - accuracy: 0.7440 - val_loss: 1.2710 - val_accuracy: 0.6422\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.8773 - accuracy: 0.7486 - val_loss: 1.2364 - val_accuracy: 0.6270\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 7s 102ms/step - loss: 0.8715 - accuracy: 0.7523 - val_loss: 1.3512 - val_accuracy: 0.6173\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.8673 - accuracy: 0.7510 - val_loss: 1.2791 - val_accuracy: 0.6432\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 7s 105ms/step - loss: 0.8614 - accuracy: 0.7520 - val_loss: 1.2175 - val_accuracy: 0.6335\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 7s 106ms/step - loss: 0.8201 - accuracy: 0.7665 - val_loss: 1.2428 - val_accuracy: 0.6346\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 7s 102ms/step - loss: 0.8778 - accuracy: 0.7486 - val_loss: 1.3637 - val_accuracy: 0.5741\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.8344 - accuracy: 0.7598 - val_loss: 1.3639 - val_accuracy: 0.5978\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.8079 - accuracy: 0.7683 - val_loss: 1.2683 - val_accuracy: 0.6357\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 0.8232 - accuracy: 0.7625 - val_loss: 1.3834 - val_accuracy: 0.6119\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 0.7977 - accuracy: 0.7764 - val_loss: 1.3502 - val_accuracy: 0.6086\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 0.8090 - accuracy: 0.7683 - val_loss: 1.3552 - val_accuracy: 0.6508\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 0.8115 - accuracy: 0.7756 - val_loss: 1.2758 - val_accuracy: 0.6281\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.7982 - accuracy: 0.7792 - val_loss: 1.4466 - val_accuracy: 0.6378\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 0.7749 - accuracy: 0.7839 - val_loss: 1.3804 - val_accuracy: 0.6314\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.7618 - accuracy: 0.7894 - val_loss: 1.5396 - val_accuracy: 0.5903\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.7466 - accuracy: 0.7875 - val_loss: 1.3147 - val_accuracy: 0.6476\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 0.7624 - accuracy: 0.7848 - val_loss: 1.4324 - val_accuracy: 0.6141\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.7361 - accuracy: 0.7899 - val_loss: 1.4321 - val_accuracy: 0.6486\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 7s 104ms/step - loss: 0.7650 - accuracy: 0.7812 - val_loss: 1.4251 - val_accuracy: 0.6022\n",
            "Evaluating model...\n",
            "ROC: 0.937\n",
            "\n",
            "Accuracy = 0.65\n",
            "F-Score: 0.65\n",
            "\n",
            "*** Train on {1, 3, 4, 5, 6, 7, 9, 10} Validate on 8 Test on 2 ***\n",
            "val len:  806\n",
            "test len:  888\n",
            "train len:  7038\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "71/71 [==============================] - 7s 102ms/step - loss: 4.2742 - accuracy: 0.1202 - val_loss: 2.4103 - val_accuracy: 0.1892\n",
            "Epoch 2/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.4053 - accuracy: 0.1485 - val_loss: 2.3822 - val_accuracy: 0.1441\n",
            "Epoch 3/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.3709 - accuracy: 0.1687 - val_loss: 2.3196 - val_accuracy: 0.2320\n",
            "Epoch 4/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.3052 - accuracy: 0.2107 - val_loss: 2.1829 - val_accuracy: 0.2995\n",
            "Epoch 5/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 2.2333 - accuracy: 0.2549 - val_loss: 2.2532 - val_accuracy: 0.2151\n",
            "Epoch 6/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 2.2243 - accuracy: 0.2438 - val_loss: 2.1273 - val_accuracy: 0.2928\n",
            "Epoch 7/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.1118 - accuracy: 0.2916 - val_loss: 1.9991 - val_accuracy: 0.3547\n",
            "Epoch 8/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.0311 - accuracy: 0.3122 - val_loss: 1.9379 - val_accuracy: 0.3818\n",
            "Epoch 9/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.0194 - accuracy: 0.3176 - val_loss: 1.8388 - val_accuracy: 0.4144\n",
            "Epoch 10/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.9201 - accuracy: 0.3542 - val_loss: 1.8114 - val_accuracy: 0.4212\n",
            "Epoch 11/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.8432 - accuracy: 0.3806 - val_loss: 1.7767 - val_accuracy: 0.4347\n",
            "Epoch 12/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.9317 - accuracy: 0.3507 - val_loss: 1.8063 - val_accuracy: 0.4369\n",
            "Epoch 13/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.8466 - accuracy: 0.3806 - val_loss: 1.7683 - val_accuracy: 0.4313\n",
            "Epoch 14/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.8355 - accuracy: 0.3751 - val_loss: 1.7075 - val_accuracy: 0.4538\n",
            "Epoch 15/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.7585 - accuracy: 0.4128 - val_loss: 1.6659 - val_accuracy: 0.5045\n",
            "Epoch 16/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.7906 - accuracy: 0.4068 - val_loss: 1.7356 - val_accuracy: 0.4358\n",
            "Epoch 17/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.6414 - accuracy: 0.4497 - val_loss: 1.5592 - val_accuracy: 0.4887\n",
            "Epoch 18/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.6598 - accuracy: 0.4523 - val_loss: 1.5910 - val_accuracy: 0.5113\n",
            "Epoch 19/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.5924 - accuracy: 0.4737 - val_loss: 1.5733 - val_accuracy: 0.5023\n",
            "Epoch 20/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.5952 - accuracy: 0.4811 - val_loss: 2.0888 - val_accuracy: 0.3198\n",
            "Epoch 21/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.6300 - accuracy: 0.4699 - val_loss: 1.4798 - val_accuracy: 0.5642\n",
            "Epoch 22/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.5098 - accuracy: 0.5124 - val_loss: 1.4558 - val_accuracy: 0.5811\n",
            "Epoch 23/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.4664 - accuracy: 0.5354 - val_loss: 1.4203 - val_accuracy: 0.5484\n",
            "Epoch 24/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4251 - accuracy: 0.5413 - val_loss: 1.4502 - val_accuracy: 0.5755\n",
            "Epoch 25/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4089 - accuracy: 0.5578 - val_loss: 1.4051 - val_accuracy: 0.5327\n",
            "Epoch 26/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.3778 - accuracy: 0.5705 - val_loss: 1.3372 - val_accuracy: 0.5495\n",
            "Epoch 27/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.3602 - accuracy: 0.5769 - val_loss: 1.5942 - val_accuracy: 0.4831\n",
            "Epoch 28/200\n",
            "71/71 [==============================] - 7s 102ms/step - loss: 1.3558 - accuracy: 0.5824 - val_loss: 1.2779 - val_accuracy: 0.6047\n",
            "Epoch 29/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.2917 - accuracy: 0.6044 - val_loss: 1.4597 - val_accuracy: 0.5462\n",
            "Epoch 30/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.3078 - accuracy: 0.5982 - val_loss: 1.1626 - val_accuracy: 0.6430\n",
            "Epoch 31/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.2591 - accuracy: 0.6149 - val_loss: 1.2097 - val_accuracy: 0.6408\n",
            "Epoch 32/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.2771 - accuracy: 0.6019 - val_loss: 1.3202 - val_accuracy: 0.5946\n",
            "Epoch 33/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.3088 - accuracy: 0.6027 - val_loss: 1.3102 - val_accuracy: 0.6070\n",
            "Epoch 34/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.1967 - accuracy: 0.6375 - val_loss: 1.3052 - val_accuracy: 0.5709\n",
            "Epoch 35/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.2028 - accuracy: 0.6452 - val_loss: 1.1909 - val_accuracy: 0.6588\n",
            "Epoch 36/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1753 - accuracy: 0.6510 - val_loss: 1.1956 - val_accuracy: 0.6182\n",
            "Epoch 37/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1453 - accuracy: 0.6596 - val_loss: 1.3807 - val_accuracy: 0.5845\n",
            "Epoch 38/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1502 - accuracy: 0.6530 - val_loss: 1.2462 - val_accuracy: 0.5901\n",
            "Epoch 39/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1354 - accuracy: 0.6621 - val_loss: 1.1366 - val_accuracy: 0.6273\n",
            "Epoch 40/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0922 - accuracy: 0.6715 - val_loss: 1.1324 - val_accuracy: 0.6284\n",
            "Epoch 41/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0937 - accuracy: 0.6733 - val_loss: 1.2196 - val_accuracy: 0.6182\n",
            "Epoch 42/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.0696 - accuracy: 0.6809 - val_loss: 1.1247 - val_accuracy: 0.6250\n",
            "Epoch 43/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.0679 - accuracy: 0.6868 - val_loss: 1.4188 - val_accuracy: 0.5428\n",
            "Epoch 44/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0718 - accuracy: 0.6827 - val_loss: 1.1301 - val_accuracy: 0.6318\n",
            "Epoch 45/200\n",
            "71/71 [==============================] - 7s 100ms/step - loss: 1.0574 - accuracy: 0.6877 - val_loss: 1.3732 - val_accuracy: 0.5597\n",
            "Epoch 46/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0374 - accuracy: 0.6917 - val_loss: 1.2939 - val_accuracy: 0.5991\n",
            "Epoch 47/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0374 - accuracy: 0.6954 - val_loss: 1.0717 - val_accuracy: 0.6306\n",
            "Epoch 48/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0638 - accuracy: 0.6871 - val_loss: 1.3311 - val_accuracy: 0.5304\n",
            "Epoch 49/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.0133 - accuracy: 0.7012 - val_loss: 1.2063 - val_accuracy: 0.6025\n",
            "Epoch 50/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.9768 - accuracy: 0.7107 - val_loss: 1.1237 - val_accuracy: 0.6273\n",
            "Epoch 51/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.9772 - accuracy: 0.7114 - val_loss: 1.1428 - val_accuracy: 0.6137\n",
            "Epoch 52/200\n",
            "71/71 [==============================] - 7s 100ms/step - loss: 0.9612 - accuracy: 0.7219 - val_loss: 1.1819 - val_accuracy: 0.6014\n",
            "Epoch 53/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.9436 - accuracy: 0.7266 - val_loss: 1.0948 - val_accuracy: 0.6261\n",
            "Epoch 54/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.9206 - accuracy: 0.7327 - val_loss: 1.2115 - val_accuracy: 0.5957\n",
            "Epoch 55/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.9482 - accuracy: 0.7241 - val_loss: 1.3405 - val_accuracy: 0.5552\n",
            "Epoch 56/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.8997 - accuracy: 0.7420 - val_loss: 1.1346 - val_accuracy: 0.6137\n",
            "Epoch 57/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.9204 - accuracy: 0.7400 - val_loss: 1.3556 - val_accuracy: 0.5653\n",
            "Epoch 58/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.9282 - accuracy: 0.7326 - val_loss: 1.1603 - val_accuracy: 0.6115\n",
            "Epoch 59/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.9008 - accuracy: 0.7391 - val_loss: 1.0489 - val_accuracy: 0.6453\n",
            "Epoch 60/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.8952 - accuracy: 0.7461 - val_loss: 1.0436 - val_accuracy: 0.6430\n",
            "Epoch 61/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.8843 - accuracy: 0.7437 - val_loss: 1.1273 - val_accuracy: 0.6329\n",
            "Epoch 62/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.8506 - accuracy: 0.7531 - val_loss: 1.0925 - val_accuracy: 0.6182\n",
            "Epoch 63/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.8831 - accuracy: 0.7516 - val_loss: 1.1041 - val_accuracy: 0.6205\n",
            "Epoch 64/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.8392 - accuracy: 0.7610 - val_loss: 1.3704 - val_accuracy: 0.5867\n",
            "Epoch 65/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.8616 - accuracy: 0.7506 - val_loss: 1.0690 - val_accuracy: 0.6532\n",
            "Epoch 66/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.8237 - accuracy: 0.7604 - val_loss: 1.1158 - val_accuracy: 0.6216\n",
            "Epoch 67/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.8077 - accuracy: 0.7680 - val_loss: 1.2271 - val_accuracy: 0.6047\n",
            "Epoch 68/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1148 - accuracy: 0.7086 - val_loss: 1.4779 - val_accuracy: 0.5709\n",
            "Epoch 69/200\n",
            "71/71 [==============================] - 7s 103ms/step - loss: 0.9004 - accuracy: 0.7380 - val_loss: 1.1337 - val_accuracy: 0.6182\n",
            "Epoch 70/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.8498 - accuracy: 0.7540 - val_loss: 1.1907 - val_accuracy: 0.6160\n",
            "Epoch 71/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.8086 - accuracy: 0.7729 - val_loss: 1.1379 - val_accuracy: 0.6318\n",
            "Epoch 72/200\n",
            "71/71 [==============================] - 7s 101ms/step - loss: 0.7897 - accuracy: 0.7714 - val_loss: 1.1446 - val_accuracy: 0.6194\n",
            "Epoch 73/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.7905 - accuracy: 0.7774 - val_loss: 1.2591 - val_accuracy: 0.6081\n",
            "Epoch 74/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.7824 - accuracy: 0.7825 - val_loss: 1.1851 - val_accuracy: 0.6340\n",
            "Epoch 75/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.7875 - accuracy: 0.7731 - val_loss: 1.1314 - val_accuracy: 0.6374\n",
            "Evaluating model...\n",
            "ROC: 0.914\n",
            "\n",
            "Accuracy = 0.66\n",
            "F-Score: 0.66\n",
            "\n",
            "*** Train on {1, 3, 4, 5, 6, 7, 8, 10} Validate on 9 Test on 2 ***\n",
            "val len:  816\n",
            "test len:  888\n",
            "train len:  7028\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "71/71 [==============================] - 7s 103ms/step - loss: 2.6921 - accuracy: 0.1141 - val_loss: 2.4270 - val_accuracy: 0.1723\n",
            "Epoch 2/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 2.4034 - accuracy: 0.1589 - val_loss: 2.3774 - val_accuracy: 0.1982\n",
            "Epoch 3/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.3720 - accuracy: 0.1905 - val_loss: 2.3739 - val_accuracy: 0.2151\n",
            "Epoch 4/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 2.3853 - accuracy: 0.1726 - val_loss: 2.3688 - val_accuracy: 0.2624\n",
            "Epoch 5/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.3554 - accuracy: 0.1871 - val_loss: 2.2522 - val_accuracy: 0.2365\n",
            "Epoch 6/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.2721 - accuracy: 0.2235 - val_loss: 2.2155 - val_accuracy: 0.2793\n",
            "Epoch 7/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.3420 - accuracy: 0.2079 - val_loss: 2.1784 - val_accuracy: 0.2545\n",
            "Epoch 8/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 2.1813 - accuracy: 0.2568 - val_loss: 2.1370 - val_accuracy: 0.2804\n",
            "Epoch 9/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 2.0925 - accuracy: 0.2935 - val_loss: 1.9620 - val_accuracy: 0.3705\n",
            "Epoch 10/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 2.0758 - accuracy: 0.3081 - val_loss: 1.8650 - val_accuracy: 0.3851\n",
            "Epoch 11/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.9940 - accuracy: 0.3297 - val_loss: 1.7941 - val_accuracy: 0.4291\n",
            "Epoch 12/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.9247 - accuracy: 0.3526 - val_loss: 1.7931 - val_accuracy: 0.4009\n",
            "Epoch 13/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.8893 - accuracy: 0.3687 - val_loss: 1.7541 - val_accuracy: 0.4212\n",
            "Epoch 14/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.8584 - accuracy: 0.3919 - val_loss: 1.6702 - val_accuracy: 0.5023\n",
            "Epoch 15/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.7595 - accuracy: 0.4200 - val_loss: 1.6178 - val_accuracy: 0.5236\n",
            "Epoch 16/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.7262 - accuracy: 0.4276 - val_loss: 1.6669 - val_accuracy: 0.4459\n",
            "Epoch 17/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.7017 - accuracy: 0.4455 - val_loss: 1.5365 - val_accuracy: 0.5664\n",
            "Epoch 18/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.6500 - accuracy: 0.4678 - val_loss: 1.4831 - val_accuracy: 0.6002\n",
            "Epoch 19/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.5912 - accuracy: 0.4801 - val_loss: 1.5047 - val_accuracy: 0.5338\n",
            "Epoch 20/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.5758 - accuracy: 0.4898 - val_loss: 1.4527 - val_accuracy: 0.5619\n",
            "Epoch 21/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.5542 - accuracy: 0.5067 - val_loss: 1.5015 - val_accuracy: 0.5642\n",
            "Epoch 22/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4867 - accuracy: 0.5275 - val_loss: 1.4521 - val_accuracy: 0.5383\n",
            "Epoch 23/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4931 - accuracy: 0.5206 - val_loss: 1.4235 - val_accuracy: 0.5755\n",
            "Epoch 24/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4965 - accuracy: 0.5262 - val_loss: 1.3522 - val_accuracy: 0.5991\n",
            "Epoch 25/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4043 - accuracy: 0.5575 - val_loss: 1.4630 - val_accuracy: 0.5338\n",
            "Epoch 26/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4008 - accuracy: 0.5542 - val_loss: 1.4504 - val_accuracy: 0.5743\n",
            "Epoch 27/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.3599 - accuracy: 0.5737 - val_loss: 1.3300 - val_accuracy: 0.6047\n",
            "Epoch 28/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.3507 - accuracy: 0.5696 - val_loss: 1.3347 - val_accuracy: 0.6137\n",
            "Epoch 29/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.3327 - accuracy: 0.5845 - val_loss: 1.3379 - val_accuracy: 0.5800\n",
            "Epoch 30/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.3423 - accuracy: 0.5838 - val_loss: 1.3154 - val_accuracy: 0.6059\n",
            "Epoch 31/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.2910 - accuracy: 0.6016 - val_loss: 1.3156 - val_accuracy: 0.6385\n",
            "Epoch 32/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.2523 - accuracy: 0.6128 - val_loss: 1.3201 - val_accuracy: 0.6059\n",
            "Epoch 33/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.2298 - accuracy: 0.6266 - val_loss: 1.3759 - val_accuracy: 0.6047\n",
            "Epoch 34/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.4300 - accuracy: 0.5509 - val_loss: 1.4039 - val_accuracy: 0.6351\n",
            "Epoch 35/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.2274 - accuracy: 0.6215 - val_loss: 1.2954 - val_accuracy: 0.6250\n",
            "Epoch 36/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1611 - accuracy: 0.6450 - val_loss: 1.2676 - val_accuracy: 0.6227\n",
            "Epoch 37/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1615 - accuracy: 0.6424 - val_loss: 1.2481 - val_accuracy: 0.6351\n",
            "Epoch 38/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1598 - accuracy: 0.6490 - val_loss: 1.2740 - val_accuracy: 0.6340\n",
            "Epoch 39/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1273 - accuracy: 0.6578 - val_loss: 1.2893 - val_accuracy: 0.6194\n",
            "Epoch 40/200\n",
            "71/71 [==============================] - 7s 100ms/step - loss: 1.1236 - accuracy: 0.6587 - val_loss: 1.3488 - val_accuracy: 0.6104\n",
            "Epoch 41/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.1171 - accuracy: 0.6673 - val_loss: 1.1839 - val_accuracy: 0.6498\n",
            "Epoch 42/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.0955 - accuracy: 0.6716 - val_loss: 1.2457 - val_accuracy: 0.6284\n",
            "Epoch 43/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0559 - accuracy: 0.6826 - val_loss: 1.3272 - val_accuracy: 0.6385\n",
            "Epoch 44/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0775 - accuracy: 0.6763 - val_loss: 1.2542 - val_accuracy: 0.6273\n",
            "Epoch 45/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0405 - accuracy: 0.6828 - val_loss: 1.1899 - val_accuracy: 0.6498\n",
            "Epoch 46/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0074 - accuracy: 0.7048 - val_loss: 1.2621 - val_accuracy: 0.6464\n",
            "Epoch 47/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.0194 - accuracy: 0.6949 - val_loss: 1.4662 - val_accuracy: 0.5833\n",
            "Epoch 48/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0076 - accuracy: 0.6995 - val_loss: 1.3287 - val_accuracy: 0.5980\n",
            "Epoch 49/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0119 - accuracy: 0.6999 - val_loss: 1.3033 - val_accuracy: 0.6171\n",
            "Epoch 50/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.9737 - accuracy: 0.7106 - val_loss: 1.2868 - val_accuracy: 0.6351\n",
            "Epoch 51/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.9548 - accuracy: 0.7242 - val_loss: 1.3039 - val_accuracy: 0.6171\n",
            "Epoch 52/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.9693 - accuracy: 0.7193 - val_loss: 1.2587 - val_accuracy: 0.6194\n",
            "Epoch 53/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.9651 - accuracy: 0.7180 - val_loss: 1.3797 - val_accuracy: 0.6070\n",
            "Epoch 54/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.9629 - accuracy: 0.7119 - val_loss: 1.2148 - val_accuracy: 0.6520\n",
            "Epoch 55/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.9375 - accuracy: 0.7264 - val_loss: 1.3474 - val_accuracy: 0.6284\n",
            "Epoch 56/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.9337 - accuracy: 0.7287 - val_loss: 1.3807 - val_accuracy: 0.6137\n",
            "Evaluating model...\n",
            "ROC: 0.938\n",
            "\n",
            "Accuracy = 0.68\n",
            "F-Score: 0.68\n",
            "\n",
            "*** Train on {1, 3, 4, 5, 6, 7, 8, 9} Validate on 10 Test on 2 ***\n",
            "val len:  837\n",
            "test len:  888\n",
            "train len:  7007\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "71/71 [==============================] - 7s 101ms/step - loss: 2.7781 - accuracy: 0.1073 - val_loss: 2.4235 - val_accuracy: 0.1419\n",
            "Epoch 2/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.4037 - accuracy: 0.1371 - val_loss: 2.3956 - val_accuracy: 0.1768\n",
            "Epoch 3/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.3674 - accuracy: 0.1657 - val_loss: 2.3239 - val_accuracy: 0.2196\n",
            "Epoch 4/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 2.3183 - accuracy: 0.1900 - val_loss: 2.2903 - val_accuracy: 0.2489\n",
            "Epoch 5/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.2846 - accuracy: 0.2138 - val_loss: 2.2335 - val_accuracy: 0.2646\n",
            "Epoch 6/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 2.2452 - accuracy: 0.2273 - val_loss: 2.2208 - val_accuracy: 0.3108\n",
            "Epoch 7/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.1938 - accuracy: 0.2488 - val_loss: 2.1967 - val_accuracy: 0.2973\n",
            "Epoch 8/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 2.1607 - accuracy: 0.2672 - val_loss: 2.0498 - val_accuracy: 0.3322\n",
            "Epoch 9/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 2.1013 - accuracy: 0.2983 - val_loss: 1.9916 - val_accuracy: 0.3953\n",
            "Epoch 10/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 2.0456 - accuracy: 0.3234 - val_loss: 1.8955 - val_accuracy: 0.3863\n",
            "Epoch 11/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.9758 - accuracy: 0.3445 - val_loss: 1.8208 - val_accuracy: 0.3998\n",
            "Epoch 12/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.9423 - accuracy: 0.3561 - val_loss: 1.7769 - val_accuracy: 0.4673\n",
            "Epoch 13/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.8539 - accuracy: 0.3915 - val_loss: 1.7266 - val_accuracy: 0.4718\n",
            "Epoch 14/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.8207 - accuracy: 0.3989 - val_loss: 1.6935 - val_accuracy: 0.4662\n",
            "Epoch 15/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.7783 - accuracy: 0.4197 - val_loss: 1.7091 - val_accuracy: 0.3896\n",
            "Epoch 16/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.7278 - accuracy: 0.4380 - val_loss: 1.5542 - val_accuracy: 0.5428\n",
            "Epoch 17/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.7461 - accuracy: 0.4446 - val_loss: 1.9725 - val_accuracy: 0.3300\n",
            "Epoch 18/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.8048 - accuracy: 0.4127 - val_loss: 2.2490 - val_accuracy: 0.2703\n",
            "Epoch 19/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.6679 - accuracy: 0.4702 - val_loss: 1.5152 - val_accuracy: 0.5709\n",
            "Epoch 20/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.6047 - accuracy: 0.5005 - val_loss: 1.5929 - val_accuracy: 0.5056\n",
            "Epoch 21/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.5187 - accuracy: 0.5172 - val_loss: 1.4584 - val_accuracy: 0.5124\n",
            "Epoch 22/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4928 - accuracy: 0.5350 - val_loss: 1.7901 - val_accuracy: 0.4155\n",
            "Epoch 23/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4691 - accuracy: 0.5455 - val_loss: 1.4624 - val_accuracy: 0.5023\n",
            "Epoch 24/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4034 - accuracy: 0.5630 - val_loss: 1.3720 - val_accuracy: 0.5822\n",
            "Epoch 25/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4637 - accuracy: 0.5496 - val_loss: 1.4082 - val_accuracy: 0.5394\n",
            "Epoch 26/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.3670 - accuracy: 0.5887 - val_loss: 1.3776 - val_accuracy: 0.5394\n",
            "Epoch 27/200\n",
            "71/71 [==============================] - 7s 101ms/step - loss: 1.7525 - accuracy: 0.4651 - val_loss: 1.6435 - val_accuracy: 0.4550\n",
            "Epoch 28/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.6749 - accuracy: 0.4665 - val_loss: 1.6921 - val_accuracy: 0.4448\n",
            "Epoch 29/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.5726 - accuracy: 0.4985 - val_loss: 1.9074 - val_accuracy: 0.3277\n",
            "Epoch 30/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.5588 - accuracy: 0.5118 - val_loss: 1.4641 - val_accuracy: 0.5282\n",
            "Epoch 31/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.5100 - accuracy: 0.5230 - val_loss: 1.4340 - val_accuracy: 0.5417\n",
            "Epoch 32/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.4015 - accuracy: 0.5643 - val_loss: 1.3121 - val_accuracy: 0.5755\n",
            "Epoch 33/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4458 - accuracy: 0.5769 - val_loss: 1.6961 - val_accuracy: 0.4324\n",
            "Epoch 34/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.4689 - accuracy: 0.5346 - val_loss: 1.3401 - val_accuracy: 0.5619\n",
            "Epoch 35/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.3601 - accuracy: 0.5878 - val_loss: 1.3040 - val_accuracy: 0.5586\n",
            "Epoch 36/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.2753 - accuracy: 0.6172 - val_loss: 1.4219 - val_accuracy: 0.5191\n",
            "Epoch 37/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.2726 - accuracy: 0.6182 - val_loss: 1.5428 - val_accuracy: 0.4347\n",
            "Epoch 38/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.2571 - accuracy: 0.6204 - val_loss: 1.2969 - val_accuracy: 0.5991\n",
            "Epoch 39/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.2549 - accuracy: 0.6241 - val_loss: 1.2469 - val_accuracy: 0.5743\n",
            "Epoch 40/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1758 - accuracy: 0.6509 - val_loss: 1.2010 - val_accuracy: 0.6160\n",
            "Epoch 41/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1674 - accuracy: 0.6532 - val_loss: 1.5557 - val_accuracy: 0.4426\n",
            "Epoch 42/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1589 - accuracy: 0.6609 - val_loss: 1.2226 - val_accuracy: 0.6025\n",
            "Epoch 43/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.2532 - accuracy: 0.6386 - val_loss: 1.2321 - val_accuracy: 0.6092\n",
            "Epoch 44/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.1414 - accuracy: 0.6623 - val_loss: 1.2143 - val_accuracy: 0.6227\n",
            "Epoch 45/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.1144 - accuracy: 0.6692 - val_loss: 1.2283 - val_accuracy: 0.5935\n",
            "Epoch 46/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.0513 - accuracy: 0.6920 - val_loss: 1.2378 - val_accuracy: 0.6126\n",
            "Epoch 47/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.2875 - accuracy: 0.6185 - val_loss: 1.5740 - val_accuracy: 0.4572\n",
            "Epoch 48/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.3170 - accuracy: 0.5964 - val_loss: 1.4358 - val_accuracy: 0.5405\n",
            "Epoch 49/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 1.1281 - accuracy: 0.6622 - val_loss: 1.6152 - val_accuracy: 0.4966\n",
            "Epoch 50/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0979 - accuracy: 0.6756 - val_loss: 1.3505 - val_accuracy: 0.5743\n",
            "Epoch 51/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.0261 - accuracy: 0.6996 - val_loss: 1.2436 - val_accuracy: 0.6160\n",
            "Epoch 52/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.0180 - accuracy: 0.7049 - val_loss: 1.1835 - val_accuracy: 0.6250\n",
            "Epoch 53/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.9794 - accuracy: 0.7126 - val_loss: 1.1691 - val_accuracy: 0.6092\n",
            "Epoch 54/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.2022 - accuracy: 0.6521 - val_loss: 1.3327 - val_accuracy: 0.5552\n",
            "Epoch 55/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 1.1500 - accuracy: 0.6494 - val_loss: 1.2461 - val_accuracy: 0.5800\n",
            "Epoch 56/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 1.0462 - accuracy: 0.7030 - val_loss: 1.1865 - val_accuracy: 0.5980\n",
            "Epoch 57/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.9563 - accuracy: 0.7233 - val_loss: 1.2295 - val_accuracy: 0.6047\n",
            "Epoch 58/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.9335 - accuracy: 0.7256 - val_loss: 1.1658 - val_accuracy: 0.5991\n",
            "Epoch 59/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.9073 - accuracy: 0.7440 - val_loss: 1.3688 - val_accuracy: 0.5597\n",
            "Epoch 60/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.9032 - accuracy: 0.7385 - val_loss: 1.1843 - val_accuracy: 0.6250\n",
            "Epoch 61/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.9032 - accuracy: 0.7438 - val_loss: 1.2027 - val_accuracy: 0.6081\n",
            "Epoch 62/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.8668 - accuracy: 0.7542 - val_loss: 1.1686 - val_accuracy: 0.6329\n",
            "Epoch 63/200\n",
            "71/71 [==============================] - 7s 100ms/step - loss: 0.8953 - accuracy: 0.7410 - val_loss: 1.1579 - val_accuracy: 0.6137\n",
            "Epoch 64/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.8486 - accuracy: 0.7561 - val_loss: 1.1728 - val_accuracy: 0.6273\n",
            "Epoch 65/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.8499 - accuracy: 0.7552 - val_loss: 1.1414 - val_accuracy: 0.6329\n",
            "Epoch 66/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.9616 - accuracy: 0.7321 - val_loss: 1.1486 - val_accuracy: 0.6486\n",
            "Epoch 67/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.8559 - accuracy: 0.7508 - val_loss: 1.2501 - val_accuracy: 0.5946\n",
            "Epoch 68/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.8289 - accuracy: 0.7611 - val_loss: 1.1385 - val_accuracy: 0.6295\n",
            "Epoch 69/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.8089 - accuracy: 0.7732 - val_loss: 1.0784 - val_accuracy: 0.6351\n",
            "Epoch 70/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.8206 - accuracy: 0.7614 - val_loss: 1.2355 - val_accuracy: 0.6014\n",
            "Epoch 71/200\n",
            "71/71 [==============================] - 7s 101ms/step - loss: 0.8175 - accuracy: 0.7682 - val_loss: 1.1168 - val_accuracy: 0.6396\n",
            "Epoch 72/200\n",
            "71/71 [==============================] - 7s 100ms/step - loss: 0.7835 - accuracy: 0.7811 - val_loss: 1.2123 - val_accuracy: 0.6182\n",
            "Epoch 73/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.8009 - accuracy: 0.7737 - val_loss: 1.1959 - val_accuracy: 0.6227\n",
            "Epoch 74/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.7798 - accuracy: 0.7804 - val_loss: 1.1351 - val_accuracy: 0.6115\n",
            "Epoch 75/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.7731 - accuracy: 0.7822 - val_loss: 1.1172 - val_accuracy: 0.6408\n",
            "Epoch 76/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.7876 - accuracy: 0.7821 - val_loss: 1.1408 - val_accuracy: 0.6306\n",
            "Epoch 77/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.8545 - accuracy: 0.7642 - val_loss: 1.1115 - val_accuracy: 0.6374\n",
            "Epoch 78/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.7629 - accuracy: 0.7845 - val_loss: 1.1867 - val_accuracy: 0.6329\n",
            "Epoch 79/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.7906 - accuracy: 0.7834 - val_loss: 1.1515 - val_accuracy: 0.6329\n",
            "Epoch 80/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.7609 - accuracy: 0.7881 - val_loss: 1.3007 - val_accuracy: 0.5732\n",
            "Epoch 81/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.7494 - accuracy: 0.7886 - val_loss: 1.2842 - val_accuracy: 0.6002\n",
            "Epoch 82/200\n",
            "71/71 [==============================] - 7s 97ms/step - loss: 0.7240 - accuracy: 0.7902 - val_loss: 1.2359 - val_accuracy: 0.6126\n",
            "Epoch 83/200\n",
            "71/71 [==============================] - 7s 99ms/step - loss: 0.7253 - accuracy: 0.7981 - val_loss: 1.2858 - val_accuracy: 0.5923\n",
            "Epoch 84/200\n",
            "71/71 [==============================] - 7s 98ms/step - loss: 0.7385 - accuracy: 0.7953 - val_loss: 1.1506 - val_accuracy: 0.6329\n",
            "Evaluating model...\n",
            "ROC: 0.951\n",
            "\n",
            "Accuracy = 0.64\n",
            "F-Score: 0.64\n",
            "\n",
            "Average R.O.C: 0.9300376305586452\n",
            "Average Accuracy: 0.6491336524486542\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Z8IjgNVu5is",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "d84e4fa0-921d-496a-e49c-a6bbae705b10"
      },
      "source": [
        "plt.boxplot(acc)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boxes': [<matplotlib.lines.Line2D at 0x7f9fee849d30>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f9fee85c780>,\n",
              "  <matplotlib.lines.Line2D at 0x7f9fee85cb00>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f9fee866240>],\n",
              " 'means': [],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f9fee85ce80>],\n",
              " 'whiskers': [<matplotlib.lines.Line2D at 0x7f9fee85c080>,\n",
              "  <matplotlib.lines.Line2D at 0x7f9fee85c400>]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "e717b5a5-019b-4997-864f-26ff9be6ba46"
      },
      "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": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9fee8669b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "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": "FxO_yG-q0gBM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save(\"acc_baseline.npy\", acc)\n",
        "np.save(\"cm_baseline.npy\", CM)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wv5ifJTqQtYP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "acc_baseline=acc\n",
        "cm_baseline=CM"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_NR2z8onZ9Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del train_x, test_x, val_x, train_y, test_y, val_y\n",
        "del train_data, test_data, val_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4Oe0PWAyKwt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "frames = 68\n",
        "bands=68\n",
        "channels = 4\n",
        "def random_slice(imgs, indexes):\n",
        "    new_shape=(len(indexes), frames, bands, channels)\n",
        "    new_imgs = np.empty(new_shape)\n",
        "    for i, idx in enumerate(indexes):\n",
        "        img = imgs[idx]\n",
        "        L = img.shape[0]\n",
        "        N = np.random.randint(0,1 + L - frames)\n",
        "        new_imgs[i] = img[N:N+frames, ...]\n",
        "    return new_imgs[:,np.newaxis,...]\n",
        "\n",
        "\n",
        "def all_slices(img):\n",
        "    N = img.shape[0]\n",
        "    n_slices = N - frames + 1\n",
        "    new_shape=(n_slices, frames, bands, channels)\n",
        "    new_imgs = np.empty(new_shape)\n",
        "    for i in range(n_slices):\n",
        "        new_imgs[i] = img[i:i+frames,...]\n",
        "\n",
        "    return new_imgs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1A5IzJgyQCE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.utils import Sequence\n",
        "\n",
        "class trainGenerator(Sequence):\n",
        "\n",
        "    def __init__(self, x_set, y_set, batch_size, shuffle):\n",
        "        self.x, self.y = x_set, np.array(y_set)\n",
        "        self.batch_size = batch_size\n",
        "        self.shuffle = shuffle\n",
        "        self.indexes = np.arange(len(self.x))\n",
        "\n",
        "    def __len__(self):\n",
        "        return math.ceil(len(self.x) / self.batch_size)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        indexes = self.indexes[idx * self.batch_size: (idx + 1) * self.batch_size]\n",
        "        batch_x = random_slice(self.x, indexes)\n",
        "        batch_y = self.y[indexes]\n",
        "\n",
        "        return batch_x , batch_y\n",
        "\n",
        "    def on_epoch_end(self):\n",
        "        \"\"\"Updates indexes after each epoch\n",
        "        \"\"\"\n",
        "        if self.shuffle == True:\n",
        "            np.random.shuffle(self.indexes)\n",
        "            \n",
        "class testGenerator(Sequence):\n",
        "\n",
        "    def __init__(self, x_set, y_set):\n",
        "        self.x, self.y = x_set, np.array(y_set)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.x)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        batch_x = all_slices(np.array(self.x[idx]))\n",
        "        batch_y = self.y[idx:idx+1]\n",
        "\n",
        "        return batch_x[np.newaxis,:] , batch_y[np.newaxis,:] #"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVAZFd45P17E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 925
        },
        "outputId": "eca8a224-f3e1-40b5-94f1-9717a45cc6b7"
      },
      "source": [
        "save_folds(data_dir, save_dir, frames=68, bands=68, channels=4, normalize=True, wavelet='bior1.5')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Saving fold1\n",
            "Number of samples in  fold1 :  873\n",
            "Saved data/us8k/fold1_x.npy\n",
            "Saved data/us8k/fold1_y.npy\n",
            "\n",
            "Saving fold2\n",
            "Number of samples in  fold2 :  888\n",
            "Saved data/us8k/fold2_x.npy\n",
            "Saved data/us8k/fold2_y.npy\n",
            "\n",
            "Saving fold3\n",
            "Number of samples in  fold3 :  925\n",
            "Saved data/us8k/fold3_x.npy\n",
            "Saved data/us8k/fold3_y.npy\n",
            "\n",
            "Saving fold4\n",
            "Number of samples in  fold4 :  990\n",
            "Saved data/us8k/fold4_x.npy\n",
            "Saved data/us8k/fold4_y.npy\n",
            "\n",
            "Saving fold5\n",
            "Number of samples in  fold5 :  936\n",
            "Saved data/us8k/fold5_x.npy\n",
            "Saved data/us8k/fold5_y.npy\n",
            "\n",
            "Saving fold6\n",
            "Number of samples in  fold6 :  823\n",
            "Saved data/us8k/fold6_x.npy\n",
            "Saved data/us8k/fold6_y.npy\n",
            "\n",
            "Saving fold7\n",
            "Number of samples in  fold7 :  838\n",
            "Saved data/us8k/fold7_x.npy\n",
            "Saved data/us8k/fold7_y.npy\n",
            "\n",
            "Saving fold8\n",
            "Number of samples in  fold8 :  806\n",
            "Saved data/us8k/fold8_x.npy\n",
            "Saved data/us8k/fold8_y.npy\n",
            "\n",
            "Saving fold9\n",
            "Number of samples in  fold9 :  816\n",
            "Saved data/us8k/fold9_x.npy\n",
            "Saved data/us8k/fold9_y.npy\n",
            "\n",
            "Saving fold10\n",
            "Number of samples in  fold10 :  837\n",
            "Saved data/us8k/fold10_x.npy\n",
            "Saved data/us8k/fold10_y.npy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeRoRr4AQoZp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "715c8706-a31a-4883-8a05-f9fd90f3afd7"
      },
      "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",
        "    train_data = trainGenerator(train_x, train_y , batch_size=100, shuffle=True)\n",
        "    test_data = testGenerator(test_x, test_y)\n",
        "    val_data = testGenerator(val_x, val_y)\n",
        "    \n",
        "    print(\"Building model...\")\n",
        "    model = build_model_multi(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_data, validation_data=test_data, \n",
        "              callbacks=[EarlyStopping(restore_best_weights=True, patience=15)],\n",
        "              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_data, val_y)\n",
        "\n",
        "    CM += cm\n",
        "\n",
        "    #del model\n",
        "    #tf.clear_session()\n",
        "    #gc.collect()\n",
        "\n",
        "    \n",
        "print ('\\nAverage R.O.C:', np.mean(roc))\n",
        "print ('Average Accuracy:', np.mean(acc))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "*** Train on {3, 4, 5, 6, 7, 8, 9, 10} Validate on 1 Test on 2 ***\n",
            "val len:  873\n",
            "test len:  888\n",
            "train len:  6971\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 4s 62ms/step - loss: 2.4275 - accuracy: 0.1313 - val_loss: 2.3634 - val_accuracy: 0.2095\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 2.3731 - accuracy: 0.1635 - val_loss: 2.3282 - val_accuracy: 0.2624\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 2.3497 - accuracy: 0.1793 - val_loss: 2.2848 - val_accuracy: 0.3041\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 2.3051 - accuracy: 0.2038 - val_loss: 2.2354 - val_accuracy: 0.3187\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 2.2490 - accuracy: 0.2368 - val_loss: 2.1673 - val_accuracy: 0.3694\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 2.1805 - accuracy: 0.2705 - val_loss: 2.0903 - val_accuracy: 0.3716\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 2.1126 - accuracy: 0.2878 - val_loss: 2.0342 - val_accuracy: 0.3716\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 2.0476 - accuracy: 0.3226 - val_loss: 1.9632 - val_accuracy: 0.4054\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.9831 - accuracy: 0.3466 - val_loss: 1.9284 - val_accuracy: 0.3975\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.9359 - accuracy: 0.3572 - val_loss: 1.8594 - val_accuracy: 0.4257\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.8949 - accuracy: 0.3684 - val_loss: 1.7929 - val_accuracy: 0.4392\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.8424 - accuracy: 0.3965 - val_loss: 1.7902 - val_accuracy: 0.4482\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.8076 - accuracy: 0.4100 - val_loss: 1.7081 - val_accuracy: 0.4730\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.7657 - accuracy: 0.4230 - val_loss: 1.6839 - val_accuracy: 0.5079\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.7249 - accuracy: 0.4372 - val_loss: 1.6995 - val_accuracy: 0.5034\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.6994 - accuracy: 0.4500 - val_loss: 1.6429 - val_accuracy: 0.5349\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 1.6603 - accuracy: 0.4559 - val_loss: 1.6193 - val_accuracy: 0.5169\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.6442 - accuracy: 0.4679 - val_loss: 1.5836 - val_accuracy: 0.5158\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.5994 - accuracy: 0.4778 - val_loss: 1.5793 - val_accuracy: 0.5169\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.5707 - accuracy: 0.4981 - val_loss: 1.5816 - val_accuracy: 0.5552\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.5536 - accuracy: 0.5018 - val_loss: 1.5109 - val_accuracy: 0.4854\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.5459 - accuracy: 0.5035 - val_loss: 1.5261 - val_accuracy: 0.5417\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.5005 - accuracy: 0.5270 - val_loss: 1.4311 - val_accuracy: 0.5394\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.4842 - accuracy: 0.5225 - val_loss: 1.4942 - val_accuracy: 0.5495\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.4657 - accuracy: 0.5367 - val_loss: 1.4467 - val_accuracy: 0.5248\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.4412 - accuracy: 0.5384 - val_loss: 1.3987 - val_accuracy: 0.5045\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.4455 - accuracy: 0.5444 - val_loss: 1.4377 - val_accuracy: 0.5282\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.3989 - accuracy: 0.5579 - val_loss: 1.3662 - val_accuracy: 0.5315\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.3967 - accuracy: 0.5590 - val_loss: 1.4226 - val_accuracy: 0.5450\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.3585 - accuracy: 0.5702 - val_loss: 1.3613 - val_accuracy: 0.5777\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.3658 - accuracy: 0.5686 - val_loss: 1.3411 - val_accuracy: 0.5721\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.3389 - accuracy: 0.5785 - val_loss: 1.3868 - val_accuracy: 0.5338\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.3272 - accuracy: 0.5849 - val_loss: 1.3129 - val_accuracy: 0.5777\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.3040 - accuracy: 0.5993 - val_loss: 1.3567 - val_accuracy: 0.5687\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.2880 - accuracy: 0.5894 - val_loss: 1.3342 - val_accuracy: 0.5619\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.2828 - accuracy: 0.6003 - val_loss: 1.3152 - val_accuracy: 0.5597\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.2827 - accuracy: 0.6011 - val_loss: 1.2722 - val_accuracy: 0.5495\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.2622 - accuracy: 0.6166 - val_loss: 1.2622 - val_accuracy: 0.5811\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.2384 - accuracy: 0.6177 - val_loss: 1.2473 - val_accuracy: 0.5766\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.2126 - accuracy: 0.6283 - val_loss: 1.2363 - val_accuracy: 0.5721\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.2133 - accuracy: 0.6246 - val_loss: 1.4672 - val_accuracy: 0.5698\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.2111 - accuracy: 0.6287 - val_loss: 1.2221 - val_accuracy: 0.5923\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.2199 - accuracy: 0.6260 - val_loss: 1.2335 - val_accuracy: 0.5890\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.1763 - accuracy: 0.6480 - val_loss: 1.2425 - val_accuracy: 0.5867\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.1629 - accuracy: 0.6494 - val_loss: 1.2052 - val_accuracy: 0.5878\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.1525 - accuracy: 0.6507 - val_loss: 1.2470 - val_accuracy: 0.5428\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.1539 - accuracy: 0.6501 - val_loss: 1.2589 - val_accuracy: 0.6047\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.1524 - accuracy: 0.6544 - val_loss: 1.2573 - val_accuracy: 0.5946\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.1408 - accuracy: 0.6567 - val_loss: 1.2393 - val_accuracy: 0.5968\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.1332 - accuracy: 0.6673 - val_loss: 1.2716 - val_accuracy: 0.6014\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.1344 - accuracy: 0.6603 - val_loss: 1.1495 - val_accuracy: 0.5980\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.1084 - accuracy: 0.6712 - val_loss: 1.1866 - val_accuracy: 0.5923\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.1152 - accuracy: 0.6679 - val_loss: 1.1789 - val_accuracy: 0.5755\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.0930 - accuracy: 0.6764 - val_loss: 1.1205 - val_accuracy: 0.6239\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.0824 - accuracy: 0.6886 - val_loss: 1.1745 - val_accuracy: 0.5901\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.0866 - accuracy: 0.6762 - val_loss: 1.1359 - val_accuracy: 0.6047\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.0732 - accuracy: 0.6825 - val_loss: 1.1355 - val_accuracy: 0.6002\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.0665 - accuracy: 0.6870 - val_loss: 1.1601 - val_accuracy: 0.6047\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.0678 - accuracy: 0.6873 - val_loss: 1.2824 - val_accuracy: 0.5878\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.0413 - accuracy: 0.6922 - val_loss: 1.1007 - val_accuracy: 0.5991\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.0537 - accuracy: 0.6897 - val_loss: 1.2083 - val_accuracy: 0.6036\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 1.0236 - accuracy: 0.7029 - val_loss: 1.1929 - val_accuracy: 0.5980\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.0271 - accuracy: 0.6924 - val_loss: 1.1054 - val_accuracy: 0.6070\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.0299 - accuracy: 0.7052 - val_loss: 1.1597 - val_accuracy: 0.6070\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9992 - accuracy: 0.7069 - val_loss: 1.0819 - val_accuracy: 0.6284\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 1.0012 - accuracy: 0.7102 - val_loss: 1.1779 - val_accuracy: 0.6036\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9947 - accuracy: 0.7127 - val_loss: 1.1170 - val_accuracy: 0.6149\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 0.9909 - accuracy: 0.7167 - val_loss: 1.1372 - val_accuracy: 0.5968\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.9979 - accuracy: 0.7111 - val_loss: 1.1642 - val_accuracy: 0.6182\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.9833 - accuracy: 0.7142 - val_loss: 1.0745 - val_accuracy: 0.6104\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9630 - accuracy: 0.7251 - val_loss: 1.2449 - val_accuracy: 0.6171\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.9708 - accuracy: 0.7167 - val_loss: 1.0595 - val_accuracy: 0.6295\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.9623 - accuracy: 0.7197 - val_loss: 1.1002 - val_accuracy: 0.6059\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9602 - accuracy: 0.7233 - val_loss: 1.0863 - val_accuracy: 0.6284\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.9457 - accuracy: 0.7247 - val_loss: 1.1596 - val_accuracy: 0.5777\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.9519 - accuracy: 0.7289 - val_loss: 1.0747 - val_accuracy: 0.6171\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 0.9492 - accuracy: 0.7266 - val_loss: 1.1724 - val_accuracy: 0.6126\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 0.9375 - accuracy: 0.7273 - val_loss: 1.1205 - val_accuracy: 0.6137\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.9283 - accuracy: 0.7340 - val_loss: 1.0635 - val_accuracy: 0.6126\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.8955 - accuracy: 0.7424 - val_loss: 1.0299 - val_accuracy: 0.6239\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.9217 - accuracy: 0.7352 - val_loss: 1.1776 - val_accuracy: 0.6160\n",
            "Epoch 82/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 0.9169 - accuracy: 0.7339 - val_loss: 1.0724 - val_accuracy: 0.6261\n",
            "Epoch 83/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 0.9118 - accuracy: 0.7409 - val_loss: 1.1236 - val_accuracy: 0.6171\n",
            "Epoch 84/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.8950 - accuracy: 0.7424 - val_loss: 1.0884 - val_accuracy: 0.6081\n",
            "Epoch 85/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9027 - accuracy: 0.7441 - val_loss: 1.1552 - val_accuracy: 0.6059\n",
            "Epoch 86/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.8835 - accuracy: 0.7488 - val_loss: 1.0318 - val_accuracy: 0.6306\n",
            "Epoch 87/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.8907 - accuracy: 0.7500 - val_loss: 1.1558 - val_accuracy: 0.6149\n",
            "Epoch 88/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.8705 - accuracy: 0.7477 - val_loss: 1.1430 - val_accuracy: 0.6149\n",
            "Epoch 89/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 0.8769 - accuracy: 0.7518 - val_loss: 1.0953 - val_accuracy: 0.6261\n",
            "Epoch 90/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.8763 - accuracy: 0.7583 - val_loss: 1.2248 - val_accuracy: 0.6295\n",
            "Epoch 91/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.8730 - accuracy: 0.7457 - val_loss: 1.0514 - val_accuracy: 0.6486\n",
            "Epoch 92/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.8508 - accuracy: 0.7597 - val_loss: 1.0743 - val_accuracy: 0.6160\n",
            "Epoch 93/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 0.8482 - accuracy: 0.7617 - val_loss: 1.1706 - val_accuracy: 0.6002\n",
            "Epoch 94/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.8538 - accuracy: 0.7580 - val_loss: 0.9954 - val_accuracy: 0.6374\n",
            "Epoch 95/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8501 - accuracy: 0.7627 - val_loss: 1.0035 - val_accuracy: 0.6239\n",
            "Epoch 96/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.8335 - accuracy: 0.7647 - val_loss: 1.0317 - val_accuracy: 0.6092\n",
            "Epoch 97/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 0.8317 - accuracy: 0.7627 - val_loss: 1.1433 - val_accuracy: 0.6171\n",
            "Epoch 98/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.8448 - accuracy: 0.7551 - val_loss: 0.9854 - val_accuracy: 0.6599\n",
            "Epoch 99/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 0.8168 - accuracy: 0.7643 - val_loss: 1.0474 - val_accuracy: 0.6205\n",
            "Epoch 100/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 0.8286 - accuracy: 0.7663 - val_loss: 1.0427 - val_accuracy: 0.6554\n",
            "Epoch 101/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 0.8166 - accuracy: 0.7675 - val_loss: 1.1628 - val_accuracy: 0.6216\n",
            "Epoch 102/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.8090 - accuracy: 0.7726 - val_loss: 1.0587 - val_accuracy: 0.6430\n",
            "Epoch 103/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 0.8129 - accuracy: 0.7683 - val_loss: 1.0736 - val_accuracy: 0.6396\n",
            "Epoch 104/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 0.8406 - accuracy: 0.7679 - val_loss: 1.0340 - val_accuracy: 0.6261\n",
            "Epoch 105/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.8012 - accuracy: 0.7756 - val_loss: 1.0123 - val_accuracy: 0.6295\n",
            "Epoch 106/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7987 - accuracy: 0.7735 - val_loss: 1.1953 - val_accuracy: 0.6194\n",
            "Epoch 107/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.7922 - accuracy: 0.7825 - val_loss: 1.0372 - val_accuracy: 0.6171\n",
            "Epoch 108/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.7998 - accuracy: 0.7718 - val_loss: 1.0395 - val_accuracy: 0.6250\n",
            "Epoch 109/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.7958 - accuracy: 0.7789 - val_loss: 1.0595 - val_accuracy: 0.6374\n",
            "Epoch 110/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 0.7981 - accuracy: 0.7723 - val_loss: 1.0594 - val_accuracy: 0.6250\n",
            "Epoch 111/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.7867 - accuracy: 0.7788 - val_loss: 1.0928 - val_accuracy: 0.6329\n",
            "Epoch 112/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.7913 - accuracy: 0.7837 - val_loss: 1.1857 - val_accuracy: 0.6329\n",
            "Epoch 113/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.7757 - accuracy: 0.7824 - val_loss: 1.1686 - val_accuracy: 0.6453\n",
            "Evaluating model...\n",
            "ROC: 0.956\n",
            "\n",
            "Accuracy = 0.71\n",
            "F-Score: 0.71\n",
            "\n",
            "*** Train on {1, 3, 5, 6, 7, 8, 9, 10} Validate on 2 Test on 4 ***\n",
            "val len:  888\n",
            "test len:  990\n",
            "train len:  6854\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "69/69 [==============================] - 5s 67ms/step - loss: 2.4429 - accuracy: 0.1300 - val_loss: 2.3767 - val_accuracy: 0.1414\n",
            "Epoch 2/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 2.3627 - accuracy: 0.1701 - val_loss: 2.3197 - val_accuracy: 0.1838\n",
            "Epoch 3/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 2.3132 - accuracy: 0.1897 - val_loss: 2.2638 - val_accuracy: 0.2747\n",
            "Epoch 4/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 2.2448 - accuracy: 0.2393 - val_loss: 2.1919 - val_accuracy: 0.3535\n",
            "Epoch 5/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 2.1933 - accuracy: 0.2638 - val_loss: 2.1178 - val_accuracy: 0.4000\n",
            "Epoch 6/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 2.1185 - accuracy: 0.2914 - val_loss: 2.0491 - val_accuracy: 0.4323\n",
            "Epoch 7/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 2.0686 - accuracy: 0.3134 - val_loss: 2.0001 - val_accuracy: 0.4232\n",
            "Epoch 8/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 2.0045 - accuracy: 0.3357 - val_loss: 1.9303 - val_accuracy: 0.4990\n",
            "Epoch 9/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.9572 - accuracy: 0.3547 - val_loss: 1.8803 - val_accuracy: 0.4404\n",
            "Epoch 10/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.9105 - accuracy: 0.3700 - val_loss: 1.8218 - val_accuracy: 0.4869\n",
            "Epoch 11/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.8531 - accuracy: 0.3913 - val_loss: 1.7566 - val_accuracy: 0.4818\n",
            "Epoch 12/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.8095 - accuracy: 0.4028 - val_loss: 1.6975 - val_accuracy: 0.5333\n",
            "Epoch 13/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.7757 - accuracy: 0.4252 - val_loss: 1.6485 - val_accuracy: 0.5394\n",
            "Epoch 14/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.7333 - accuracy: 0.4357 - val_loss: 1.6335 - val_accuracy: 0.5182\n",
            "Epoch 15/200\n",
            "69/69 [==============================] - 4s 58ms/step - loss: 1.6969 - accuracy: 0.4536 - val_loss: 1.5654 - val_accuracy: 0.5182\n",
            "Epoch 16/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.6741 - accuracy: 0.4537 - val_loss: 1.5461 - val_accuracy: 0.5545\n",
            "Epoch 17/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.6417 - accuracy: 0.4656 - val_loss: 1.5935 - val_accuracy: 0.5081\n",
            "Epoch 18/200\n",
            "69/69 [==============================] - 4s 59ms/step - loss: 1.6125 - accuracy: 0.4749 - val_loss: 1.4969 - val_accuracy: 0.5879\n",
            "Epoch 19/200\n",
            "69/69 [==============================] - 4s 58ms/step - loss: 1.5809 - accuracy: 0.4844 - val_loss: 1.4767 - val_accuracy: 0.5919\n",
            "Epoch 20/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.5595 - accuracy: 0.4945 - val_loss: 1.4426 - val_accuracy: 0.5919\n",
            "Epoch 21/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 1.5247 - accuracy: 0.5124 - val_loss: 1.4376 - val_accuracy: 0.5939\n",
            "Epoch 22/200\n",
            "69/69 [==============================] - 4s 58ms/step - loss: 1.5194 - accuracy: 0.5156 - val_loss: 1.3938 - val_accuracy: 0.6152\n",
            "Epoch 23/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.4804 - accuracy: 0.5293 - val_loss: 1.3476 - val_accuracy: 0.6263\n",
            "Epoch 24/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.4555 - accuracy: 0.5299 - val_loss: 1.3746 - val_accuracy: 0.6121\n",
            "Epoch 25/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.4490 - accuracy: 0.5427 - val_loss: 1.3308 - val_accuracy: 0.6404\n",
            "Epoch 26/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 1.4387 - accuracy: 0.5448 - val_loss: 1.3885 - val_accuracy: 0.5636\n",
            "Epoch 27/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.4036 - accuracy: 0.5566 - val_loss: 1.2728 - val_accuracy: 0.6394\n",
            "Epoch 28/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.4052 - accuracy: 0.5597 - val_loss: 1.3222 - val_accuracy: 0.6374\n",
            "Epoch 29/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 1.3718 - accuracy: 0.5664 - val_loss: 1.2947 - val_accuracy: 0.6343\n",
            "Epoch 30/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.3572 - accuracy: 0.5702 - val_loss: 1.2659 - val_accuracy: 0.6455\n",
            "Epoch 31/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.3350 - accuracy: 0.5900 - val_loss: 1.2783 - val_accuracy: 0.6354\n",
            "Epoch 32/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.3400 - accuracy: 0.5890 - val_loss: 1.2588 - val_accuracy: 0.6414\n",
            "Epoch 33/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.3098 - accuracy: 0.5922 - val_loss: 1.2236 - val_accuracy: 0.6424\n",
            "Epoch 34/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.3046 - accuracy: 0.5944 - val_loss: 1.2555 - val_accuracy: 0.6444\n",
            "Epoch 35/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.2853 - accuracy: 0.6061 - val_loss: 1.2240 - val_accuracy: 0.6556\n",
            "Epoch 36/200\n",
            "69/69 [==============================] - 4s 58ms/step - loss: 1.2686 - accuracy: 0.6090 - val_loss: 1.2203 - val_accuracy: 0.6576\n",
            "Epoch 37/200\n",
            "69/69 [==============================] - 4s 58ms/step - loss: 1.2571 - accuracy: 0.6106 - val_loss: 1.2259 - val_accuracy: 0.6535\n",
            "Epoch 38/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.2575 - accuracy: 0.6189 - val_loss: 1.2109 - val_accuracy: 0.6616\n",
            "Epoch 39/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.2296 - accuracy: 0.6252 - val_loss: 1.2095 - val_accuracy: 0.6586\n",
            "Epoch 40/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.2294 - accuracy: 0.6261 - val_loss: 1.2108 - val_accuracy: 0.6485\n",
            "Epoch 41/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.2140 - accuracy: 0.6325 - val_loss: 1.2015 - val_accuracy: 0.6556\n",
            "Epoch 42/200\n",
            "69/69 [==============================] - 4s 58ms/step - loss: 1.1874 - accuracy: 0.6411 - val_loss: 1.1610 - val_accuracy: 0.6586\n",
            "Epoch 43/200\n",
            "69/69 [==============================] - 4s 59ms/step - loss: 1.1842 - accuracy: 0.6373 - val_loss: 1.1852 - val_accuracy: 0.6525\n",
            "Epoch 44/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.1894 - accuracy: 0.6373 - val_loss: 1.2441 - val_accuracy: 0.6384\n",
            "Epoch 45/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 1.1689 - accuracy: 0.6427 - val_loss: 1.1569 - val_accuracy: 0.6727\n",
            "Epoch 46/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 1.1731 - accuracy: 0.6444 - val_loss: 1.1771 - val_accuracy: 0.6687\n",
            "Epoch 47/200\n",
            "69/69 [==============================] - 4s 58ms/step - loss: 1.1608 - accuracy: 0.6530 - val_loss: 1.2006 - val_accuracy: 0.6657\n",
            "Epoch 48/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 1.1398 - accuracy: 0.6574 - val_loss: 1.1537 - val_accuracy: 0.6677\n",
            "Epoch 49/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.1055 - accuracy: 0.6742 - val_loss: 1.1468 - val_accuracy: 0.6697\n",
            "Epoch 50/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.1167 - accuracy: 0.6687 - val_loss: 1.2094 - val_accuracy: 0.6414\n",
            "Epoch 51/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.1073 - accuracy: 0.6714 - val_loss: 1.1918 - val_accuracy: 0.6545\n",
            "Epoch 52/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 1.0984 - accuracy: 0.6713 - val_loss: 1.1993 - val_accuracy: 0.6616\n",
            "Epoch 53/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.0836 - accuracy: 0.6781 - val_loss: 1.1355 - val_accuracy: 0.6848\n",
            "Epoch 54/200\n",
            "69/69 [==============================] - 4s 59ms/step - loss: 1.0769 - accuracy: 0.6796 - val_loss: 1.1421 - val_accuracy: 0.6707\n",
            "Epoch 55/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.0675 - accuracy: 0.6795 - val_loss: 1.1439 - val_accuracy: 0.6636\n",
            "Epoch 56/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.0622 - accuracy: 0.6814 - val_loss: 1.2020 - val_accuracy: 0.6444\n",
            "Epoch 57/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.0527 - accuracy: 0.6886 - val_loss: 1.1511 - val_accuracy: 0.6737\n",
            "Epoch 58/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.0527 - accuracy: 0.6903 - val_loss: 1.1691 - val_accuracy: 0.6707\n",
            "Epoch 59/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.0498 - accuracy: 0.6959 - val_loss: 1.1208 - val_accuracy: 0.6869\n",
            "Epoch 60/200\n",
            "69/69 [==============================] - 4s 59ms/step - loss: 1.0187 - accuracy: 0.7029 - val_loss: 1.1378 - val_accuracy: 0.6798\n",
            "Epoch 61/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.0255 - accuracy: 0.6981 - val_loss: 1.1181 - val_accuracy: 0.6899\n",
            "Epoch 62/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.0311 - accuracy: 0.6923 - val_loss: 1.1253 - val_accuracy: 0.6828\n",
            "Epoch 63/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.9913 - accuracy: 0.7091 - val_loss: 1.1173 - val_accuracy: 0.6768\n",
            "Epoch 64/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9977 - accuracy: 0.7148 - val_loss: 1.1138 - val_accuracy: 0.6818\n",
            "Epoch 65/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.9776 - accuracy: 0.7162 - val_loss: 1.1125 - val_accuracy: 0.6808\n",
            "Epoch 66/200\n",
            "69/69 [==============================] - 4s 59ms/step - loss: 1.0020 - accuracy: 0.7070 - val_loss: 1.1150 - val_accuracy: 0.6848\n",
            "Epoch 67/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.9884 - accuracy: 0.7088 - val_loss: 1.0933 - val_accuracy: 0.6899\n",
            "Epoch 68/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9690 - accuracy: 0.7210 - val_loss: 1.1072 - val_accuracy: 0.6960\n",
            "Epoch 69/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9676 - accuracy: 0.7189 - val_loss: 1.0920 - val_accuracy: 0.6899\n",
            "Epoch 70/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.9713 - accuracy: 0.7181 - val_loss: 1.1134 - val_accuracy: 0.6707\n",
            "Epoch 71/200\n",
            "69/69 [==============================] - 4s 61ms/step - loss: 0.9434 - accuracy: 0.7266 - val_loss: 1.0755 - val_accuracy: 0.7051\n",
            "Epoch 72/200\n",
            "69/69 [==============================] - 4s 59ms/step - loss: 0.9446 - accuracy: 0.7276 - val_loss: 1.0990 - val_accuracy: 0.6828\n",
            "Epoch 73/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9396 - accuracy: 0.7315 - val_loss: 1.0971 - val_accuracy: 0.6919\n",
            "Epoch 74/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.9325 - accuracy: 0.7294 - val_loss: 1.1041 - val_accuracy: 0.6859\n",
            "Epoch 75/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9350 - accuracy: 0.7355 - val_loss: 1.0812 - val_accuracy: 0.6990\n",
            "Epoch 76/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9245 - accuracy: 0.7387 - val_loss: 1.1435 - val_accuracy: 0.6424\n",
            "Epoch 77/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9010 - accuracy: 0.7378 - val_loss: 1.0672 - val_accuracy: 0.6909\n",
            "Epoch 78/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.9067 - accuracy: 0.7381 - val_loss: 1.1089 - val_accuracy: 0.6889\n",
            "Epoch 79/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.8963 - accuracy: 0.7439 - val_loss: 1.0961 - val_accuracy: 0.6626\n",
            "Epoch 80/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.8869 - accuracy: 0.7437 - val_loss: 1.1091 - val_accuracy: 0.6758\n",
            "Epoch 81/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.8863 - accuracy: 0.7486 - val_loss: 1.0848 - val_accuracy: 0.6970\n",
            "Epoch 82/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.8905 - accuracy: 0.7442 - val_loss: 1.1066 - val_accuracy: 0.6596\n",
            "Epoch 83/200\n",
            "69/69 [==============================] - 4s 59ms/step - loss: 0.8681 - accuracy: 0.7514 - val_loss: 1.1009 - val_accuracy: 0.6889\n",
            "Epoch 84/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.8915 - accuracy: 0.7413 - val_loss: 1.0568 - val_accuracy: 0.7040\n",
            "Epoch 85/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.8678 - accuracy: 0.7501 - val_loss: 1.0873 - val_accuracy: 0.6818\n",
            "Epoch 86/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.8731 - accuracy: 0.7542 - val_loss: 1.0379 - val_accuracy: 0.7101\n",
            "Epoch 87/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.8744 - accuracy: 0.7483 - val_loss: 1.0616 - val_accuracy: 0.6939\n",
            "Epoch 88/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.8681 - accuracy: 0.7540 - val_loss: 1.1050 - val_accuracy: 0.6818\n",
            "Epoch 89/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.8538 - accuracy: 0.7553 - val_loss: 1.0507 - val_accuracy: 0.6990\n",
            "Epoch 90/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.8391 - accuracy: 0.7632 - val_loss: 1.1397 - val_accuracy: 0.6475\n",
            "Epoch 91/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.8616 - accuracy: 0.7543 - val_loss: 1.0870 - val_accuracy: 0.6869\n",
            "Epoch 92/200\n",
            "69/69 [==============================] - 4s 61ms/step - loss: 0.8341 - accuracy: 0.7680 - val_loss: 1.0825 - val_accuracy: 0.6747\n",
            "Epoch 93/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.8313 - accuracy: 0.7683 - val_loss: 1.0272 - val_accuracy: 0.7010\n",
            "Epoch 94/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.8354 - accuracy: 0.7590 - val_loss: 1.0693 - val_accuracy: 0.6848\n",
            "Epoch 95/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.8219 - accuracy: 0.7722 - val_loss: 1.1287 - val_accuracy: 0.6768\n",
            "Epoch 96/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.8184 - accuracy: 0.7682 - val_loss: 1.0478 - val_accuracy: 0.6949\n",
            "Epoch 97/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.8079 - accuracy: 0.7724 - val_loss: 1.0779 - val_accuracy: 0.6990\n",
            "Epoch 98/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.8084 - accuracy: 0.7730 - val_loss: 1.0519 - val_accuracy: 0.6960\n",
            "Epoch 99/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.8001 - accuracy: 0.7787 - val_loss: 1.0450 - val_accuracy: 0.7040\n",
            "Epoch 100/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7937 - accuracy: 0.7790 - val_loss: 1.0505 - val_accuracy: 0.7010\n",
            "Epoch 101/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7926 - accuracy: 0.7730 - val_loss: 1.0377 - val_accuracy: 0.6990\n",
            "Epoch 102/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.7872 - accuracy: 0.7819 - val_loss: 1.0776 - val_accuracy: 0.6879\n",
            "Epoch 103/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.7957 - accuracy: 0.7774 - val_loss: 1.0494 - val_accuracy: 0.6889\n",
            "Epoch 104/200\n",
            "69/69 [==============================] - 4s 58ms/step - loss: 0.8110 - accuracy: 0.7695 - val_loss: 1.0556 - val_accuracy: 0.6828\n",
            "Epoch 105/200\n",
            "69/69 [==============================] - 4s 61ms/step - loss: 0.7808 - accuracy: 0.7813 - val_loss: 1.0242 - val_accuracy: 0.6919\n",
            "Epoch 106/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.7634 - accuracy: 0.7867 - val_loss: 1.1115 - val_accuracy: 0.6859\n",
            "Epoch 107/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.7853 - accuracy: 0.7838 - val_loss: 1.0962 - val_accuracy: 0.6758\n",
            "Epoch 108/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.7702 - accuracy: 0.7857 - val_loss: 1.0651 - val_accuracy: 0.7051\n",
            "Epoch 109/200\n",
            "69/69 [==============================] - 4s 59ms/step - loss: 0.7817 - accuracy: 0.7858 - val_loss: 1.0628 - val_accuracy: 0.6949\n",
            "Epoch 110/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7526 - accuracy: 0.7947 - val_loss: 1.0748 - val_accuracy: 0.6848\n",
            "Epoch 111/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.7711 - accuracy: 0.7877 - val_loss: 1.0861 - val_accuracy: 0.6848\n",
            "Epoch 112/200\n",
            "69/69 [==============================] - 4s 58ms/step - loss: 0.7416 - accuracy: 0.7924 - val_loss: 1.0410 - val_accuracy: 0.7030\n",
            "Epoch 113/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7413 - accuracy: 0.7981 - val_loss: 1.0362 - val_accuracy: 0.7071\n",
            "Epoch 114/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7397 - accuracy: 0.7960 - val_loss: 1.0173 - val_accuracy: 0.7000\n",
            "Epoch 115/200\n",
            "69/69 [==============================] - 4s 58ms/step - loss: 0.7522 - accuracy: 0.7943 - val_loss: 1.0866 - val_accuracy: 0.6838\n",
            "Epoch 116/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7346 - accuracy: 0.8022 - val_loss: 1.0395 - val_accuracy: 0.7101\n",
            "Epoch 117/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7395 - accuracy: 0.7940 - val_loss: 1.0338 - val_accuracy: 0.7040\n",
            "Epoch 118/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7427 - accuracy: 0.7922 - val_loss: 1.0365 - val_accuracy: 0.6970\n",
            "Epoch 119/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7122 - accuracy: 0.8023 - val_loss: 1.0349 - val_accuracy: 0.7040\n",
            "Epoch 120/200\n",
            "69/69 [==============================] - 4s 58ms/step - loss: 0.7214 - accuracy: 0.8029 - val_loss: 1.0734 - val_accuracy: 0.6909\n",
            "Epoch 121/200\n",
            "69/69 [==============================] - 4s 60ms/step - loss: 0.7319 - accuracy: 0.7957 - val_loss: 1.0222 - val_accuracy: 0.7010\n",
            "Epoch 122/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7144 - accuracy: 0.8025 - val_loss: 1.0286 - val_accuracy: 0.7061\n",
            "Epoch 123/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7066 - accuracy: 0.8004 - val_loss: 1.0663 - val_accuracy: 0.6929\n",
            "Epoch 124/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.6987 - accuracy: 0.8116 - val_loss: 1.0550 - val_accuracy: 0.6980\n",
            "Epoch 125/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7187 - accuracy: 0.8081 - val_loss: 1.0228 - val_accuracy: 0.6990\n",
            "Epoch 126/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.7089 - accuracy: 0.8045 - val_loss: 1.0272 - val_accuracy: 0.7010\n",
            "Epoch 127/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.6957 - accuracy: 0.8073 - val_loss: 1.0991 - val_accuracy: 0.7030\n",
            "Epoch 128/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.6943 - accuracy: 0.8096 - val_loss: 1.0682 - val_accuracy: 0.6818\n",
            "Epoch 129/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.6987 - accuracy: 0.8108 - val_loss: 1.0480 - val_accuracy: 0.7010\n",
            "Evaluating model...\n",
            "ROC: 0.953\n",
            "\n",
            "Accuracy = 0.65\n",
            "F-Score: 0.65\n",
            "\n",
            "*** Train on {1, 2, 4, 6, 7, 8, 9, 10} Validate on 3 Test on 5 ***\n",
            "val len:  925\n",
            "test len:  936\n",
            "train len:  6871\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 2.4327 - accuracy: 0.1218 - val_loss: 2.3757 - val_accuracy: 0.1571\n",
            "Epoch 2/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 2.3743 - accuracy: 0.1579 - val_loss: 2.3315 - val_accuracy: 0.3248\n",
            "Epoch 3/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 2.3378 - accuracy: 0.1806 - val_loss: 2.2762 - val_accuracy: 0.2938\n",
            "Epoch 4/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 2.2881 - accuracy: 0.2119 - val_loss: 2.2081 - val_accuracy: 0.3803\n",
            "Epoch 5/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 2.2233 - accuracy: 0.2318 - val_loss: 2.1303 - val_accuracy: 0.3835\n",
            "Epoch 6/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 2.1767 - accuracy: 0.2602 - val_loss: 2.0572 - val_accuracy: 0.3974\n",
            "Epoch 7/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 2.1222 - accuracy: 0.2834 - val_loss: 1.9757 - val_accuracy: 0.4006\n",
            "Epoch 8/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 2.0663 - accuracy: 0.3087 - val_loss: 1.9153 - val_accuracy: 0.4444\n",
            "Epoch 9/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 2.0217 - accuracy: 0.3177 - val_loss: 1.8424 - val_accuracy: 0.4103\n",
            "Epoch 10/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.9685 - accuracy: 0.3393 - val_loss: 1.7606 - val_accuracy: 0.4359\n",
            "Epoch 11/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.9162 - accuracy: 0.3525 - val_loss: 1.6923 - val_accuracy: 0.4786\n",
            "Epoch 12/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.8776 - accuracy: 0.3698 - val_loss: 1.6229 - val_accuracy: 0.5064\n",
            "Epoch 13/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.8276 - accuracy: 0.3899 - val_loss: 1.5816 - val_accuracy: 0.5160\n",
            "Epoch 14/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 1.7860 - accuracy: 0.4081 - val_loss: 1.5894 - val_accuracy: 0.4658\n",
            "Epoch 15/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.7484 - accuracy: 0.4250 - val_loss: 1.5174 - val_accuracy: 0.4925\n",
            "Epoch 16/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.7246 - accuracy: 0.4325 - val_loss: 1.4499 - val_accuracy: 0.5598\n",
            "Epoch 17/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.6950 - accuracy: 0.4443 - val_loss: 1.4237 - val_accuracy: 0.5588\n",
            "Epoch 18/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.6426 - accuracy: 0.4618 - val_loss: 1.4137 - val_accuracy: 0.5577\n",
            "Epoch 19/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.6089 - accuracy: 0.4800 - val_loss: 1.3509 - val_accuracy: 0.5545\n",
            "Epoch 20/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.6002 - accuracy: 0.4790 - val_loss: 1.3210 - val_accuracy: 0.5780\n",
            "Epoch 21/200\n",
            "69/69 [==============================] - 4s 59ms/step - loss: 1.5579 - accuracy: 0.5030 - val_loss: 1.3644 - val_accuracy: 0.5288\n",
            "Epoch 22/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.5465 - accuracy: 0.5066 - val_loss: 1.3125 - val_accuracy: 0.5780\n",
            "Epoch 23/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.5200 - accuracy: 0.5140 - val_loss: 1.2828 - val_accuracy: 0.5983\n",
            "Epoch 24/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.4972 - accuracy: 0.5260 - val_loss: 1.2282 - val_accuracy: 0.6229\n",
            "Epoch 25/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.4660 - accuracy: 0.5299 - val_loss: 1.2561 - val_accuracy: 0.6036\n",
            "Epoch 26/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.4589 - accuracy: 0.5343 - val_loss: 1.2005 - val_accuracy: 0.6400\n",
            "Epoch 27/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.4427 - accuracy: 0.5416 - val_loss: 1.2020 - val_accuracy: 0.6389\n",
            "Epoch 28/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.4152 - accuracy: 0.5535 - val_loss: 1.1858 - val_accuracy: 0.6506\n",
            "Epoch 29/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.3877 - accuracy: 0.5576 - val_loss: 1.1725 - val_accuracy: 0.6613\n",
            "Epoch 30/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.3602 - accuracy: 0.5698 - val_loss: 1.2323 - val_accuracy: 0.6165\n",
            "Epoch 31/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.3651 - accuracy: 0.5768 - val_loss: 1.1815 - val_accuracy: 0.6635\n",
            "Epoch 32/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.3486 - accuracy: 0.5824 - val_loss: 1.1620 - val_accuracy: 0.6731\n",
            "Epoch 33/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.3416 - accuracy: 0.5836 - val_loss: 1.1508 - val_accuracy: 0.6709\n",
            "Epoch 34/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.3031 - accuracy: 0.5985 - val_loss: 1.1564 - val_accuracy: 0.6859\n",
            "Epoch 35/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.2855 - accuracy: 0.5928 - val_loss: 1.0803 - val_accuracy: 0.6741\n",
            "Epoch 36/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.2731 - accuracy: 0.5996 - val_loss: 1.0950 - val_accuracy: 0.6656\n",
            "Epoch 37/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.2654 - accuracy: 0.6102 - val_loss: 1.1628 - val_accuracy: 0.6795\n",
            "Epoch 38/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.2401 - accuracy: 0.6132 - val_loss: 1.1087 - val_accuracy: 0.6987\n",
            "Epoch 39/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.2350 - accuracy: 0.6184 - val_loss: 1.0620 - val_accuracy: 0.7073\n",
            "Epoch 40/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.2046 - accuracy: 0.6312 - val_loss: 1.0868 - val_accuracy: 0.7009\n",
            "Epoch 41/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.2077 - accuracy: 0.6321 - val_loss: 1.0488 - val_accuracy: 0.6966\n",
            "Epoch 42/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.1968 - accuracy: 0.6388 - val_loss: 1.0274 - val_accuracy: 0.6923\n",
            "Epoch 43/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.1862 - accuracy: 0.6401 - val_loss: 1.0261 - val_accuracy: 0.7179\n",
            "Epoch 44/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.1704 - accuracy: 0.6452 - val_loss: 1.0480 - val_accuracy: 0.6923\n",
            "Epoch 45/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.1420 - accuracy: 0.6522 - val_loss: 1.0295 - val_accuracy: 0.7051\n",
            "Epoch 46/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.1503 - accuracy: 0.6543 - val_loss: 0.9941 - val_accuracy: 0.7169\n",
            "Epoch 47/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.1196 - accuracy: 0.6609 - val_loss: 0.9932 - val_accuracy: 0.7286\n",
            "Epoch 48/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.1000 - accuracy: 0.6711 - val_loss: 1.0775 - val_accuracy: 0.7062\n",
            "Epoch 49/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.1055 - accuracy: 0.6653 - val_loss: 1.0976 - val_accuracy: 0.6934\n",
            "Epoch 50/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.0985 - accuracy: 0.6750 - val_loss: 0.9844 - val_accuracy: 0.7244\n",
            "Epoch 51/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.0983 - accuracy: 0.6740 - val_loss: 1.0119 - val_accuracy: 0.7265\n",
            "Epoch 52/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.0629 - accuracy: 0.6786 - val_loss: 1.0709 - val_accuracy: 0.6741\n",
            "Epoch 53/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.0676 - accuracy: 0.6855 - val_loss: 0.9678 - val_accuracy: 0.7244\n",
            "Epoch 54/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.0574 - accuracy: 0.6835 - val_loss: 1.0179 - val_accuracy: 0.7201\n",
            "Epoch 55/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.0514 - accuracy: 0.6869 - val_loss: 0.9543 - val_accuracy: 0.7115\n",
            "Epoch 56/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.0522 - accuracy: 0.6862 - val_loss: 0.9163 - val_accuracy: 0.7553\n",
            "Epoch 57/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.0102 - accuracy: 0.7080 - val_loss: 0.9296 - val_accuracy: 0.7404\n",
            "Epoch 58/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 1.0234 - accuracy: 0.6974 - val_loss: 0.9708 - val_accuracy: 0.7382\n",
            "Epoch 59/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.0065 - accuracy: 0.7062 - val_loss: 0.9647 - val_accuracy: 0.7447\n",
            "Epoch 60/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.0087 - accuracy: 0.7021 - val_loss: 0.9321 - val_accuracy: 0.7500\n",
            "Epoch 61/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.9901 - accuracy: 0.7070 - val_loss: 0.9048 - val_accuracy: 0.7479\n",
            "Epoch 62/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9733 - accuracy: 0.7181 - val_loss: 1.0075 - val_accuracy: 0.7468\n",
            "Epoch 63/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9643 - accuracy: 0.7209 - val_loss: 0.9684 - val_accuracy: 0.7500\n",
            "Epoch 64/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.9749 - accuracy: 0.7182 - val_loss: 0.9863 - val_accuracy: 0.7361\n",
            "Epoch 65/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.9716 - accuracy: 0.7225 - val_loss: 0.9079 - val_accuracy: 0.7436\n",
            "Epoch 66/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9519 - accuracy: 0.7255 - val_loss: 0.8956 - val_accuracy: 0.7500\n",
            "Epoch 67/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.9534 - accuracy: 0.7236 - val_loss: 1.0201 - val_accuracy: 0.7404\n",
            "Epoch 68/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.9311 - accuracy: 0.7310 - val_loss: 0.8850 - val_accuracy: 0.7575\n",
            "Epoch 69/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.9351 - accuracy: 0.7294 - val_loss: 0.8911 - val_accuracy: 0.7425\n",
            "Epoch 70/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9303 - accuracy: 0.7329 - val_loss: 1.0597 - val_accuracy: 0.7083\n",
            "Epoch 71/200\n",
            "69/69 [==============================] - 4s 58ms/step - loss: 0.9313 - accuracy: 0.7322 - val_loss: 0.8791 - val_accuracy: 0.7553\n",
            "Epoch 72/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.8930 - accuracy: 0.7427 - val_loss: 0.8450 - val_accuracy: 0.7511\n",
            "Epoch 73/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9082 - accuracy: 0.7396 - val_loss: 0.8848 - val_accuracy: 0.7500\n",
            "Epoch 74/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.9045 - accuracy: 0.7369 - val_loss: 0.8738 - val_accuracy: 0.7564\n",
            "Epoch 75/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.8868 - accuracy: 0.7476 - val_loss: 0.8465 - val_accuracy: 0.7692\n",
            "Epoch 76/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.9013 - accuracy: 0.7425 - val_loss: 0.8589 - val_accuracy: 0.7372\n",
            "Epoch 77/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.8891 - accuracy: 0.7462 - val_loss: 0.8382 - val_accuracy: 0.7404\n",
            "Epoch 78/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.8835 - accuracy: 0.7520 - val_loss: 0.8231 - val_accuracy: 0.7671\n",
            "Epoch 79/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.8717 - accuracy: 0.7535 - val_loss: 0.8462 - val_accuracy: 0.7596\n",
            "Epoch 80/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.8752 - accuracy: 0.7482 - val_loss: 0.8646 - val_accuracy: 0.7607\n",
            "Epoch 81/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.8404 - accuracy: 0.7616 - val_loss: 0.8237 - val_accuracy: 0.7650\n",
            "Epoch 82/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.8504 - accuracy: 0.7540 - val_loss: 0.9081 - val_accuracy: 0.7650\n",
            "Epoch 83/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.8323 - accuracy: 0.7641 - val_loss: 0.8824 - val_accuracy: 0.7479\n",
            "Epoch 84/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.8310 - accuracy: 0.7641 - val_loss: 0.8396 - val_accuracy: 0.7682\n",
            "Epoch 85/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.8516 - accuracy: 0.7634 - val_loss: 0.8004 - val_accuracy: 0.7735\n",
            "Epoch 86/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.8295 - accuracy: 0.7667 - val_loss: 0.8130 - val_accuracy: 0.7628\n",
            "Epoch 87/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.8250 - accuracy: 0.7651 - val_loss: 0.7850 - val_accuracy: 0.7874\n",
            "Epoch 88/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.8172 - accuracy: 0.7684 - val_loss: 0.7825 - val_accuracy: 0.7724\n",
            "Epoch 89/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.8194 - accuracy: 0.7638 - val_loss: 0.8090 - val_accuracy: 0.7660\n",
            "Epoch 90/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.8112 - accuracy: 0.7724 - val_loss: 0.8492 - val_accuracy: 0.7628\n",
            "Epoch 91/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.8044 - accuracy: 0.7779 - val_loss: 0.8690 - val_accuracy: 0.7340\n",
            "Epoch 92/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.8012 - accuracy: 0.7785 - val_loss: 0.7928 - val_accuracy: 0.7906\n",
            "Epoch 93/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7910 - accuracy: 0.7775 - val_loss: 0.7668 - val_accuracy: 0.7650\n",
            "Epoch 94/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7918 - accuracy: 0.7811 - val_loss: 0.8460 - val_accuracy: 0.7735\n",
            "Epoch 95/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.7863 - accuracy: 0.7811 - val_loss: 0.7637 - val_accuracy: 0.7991\n",
            "Epoch 96/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.8002 - accuracy: 0.7712 - val_loss: 0.8084 - val_accuracy: 0.7618\n",
            "Epoch 97/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7866 - accuracy: 0.7823 - val_loss: 0.8186 - val_accuracy: 0.7692\n",
            "Epoch 98/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7593 - accuracy: 0.7879 - val_loss: 0.8722 - val_accuracy: 0.7671\n",
            "Epoch 99/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.7897 - accuracy: 0.7814 - val_loss: 0.7735 - val_accuracy: 0.7703\n",
            "Epoch 100/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7782 - accuracy: 0.7818 - val_loss: 0.8698 - val_accuracy: 0.7329\n",
            "Epoch 101/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7806 - accuracy: 0.7817 - val_loss: 0.8210 - val_accuracy: 0.7607\n",
            "Epoch 102/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7668 - accuracy: 0.7887 - val_loss: 0.7905 - val_accuracy: 0.7810\n",
            "Epoch 103/200\n",
            "69/69 [==============================] - 4s 60ms/step - loss: 0.7475 - accuracy: 0.7903 - val_loss: 0.8443 - val_accuracy: 0.7682\n",
            "Epoch 104/200\n",
            "69/69 [==============================] - 4s 59ms/step - loss: 0.7402 - accuracy: 0.7968 - val_loss: 0.7906 - val_accuracy: 0.7949\n",
            "Epoch 105/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7623 - accuracy: 0.7896 - val_loss: 0.7862 - val_accuracy: 0.7767\n",
            "Epoch 106/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.7459 - accuracy: 0.7928 - val_loss: 0.8348 - val_accuracy: 0.7575\n",
            "Epoch 107/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7222 - accuracy: 0.7983 - val_loss: 0.7761 - val_accuracy: 0.8045\n",
            "Epoch 108/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7368 - accuracy: 0.7941 - val_loss: 0.7554 - val_accuracy: 0.7778\n",
            "Epoch 109/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7392 - accuracy: 0.7949 - val_loss: 0.8165 - val_accuracy: 0.7746\n",
            "Epoch 110/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.7322 - accuracy: 0.7997 - val_loss: 0.7844 - val_accuracy: 0.7810\n",
            "Epoch 111/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7384 - accuracy: 0.7974 - val_loss: 0.7617 - val_accuracy: 0.7842\n",
            "Epoch 112/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7015 - accuracy: 0.8063 - val_loss: 0.7637 - val_accuracy: 0.7938\n",
            "Epoch 113/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7152 - accuracy: 0.8003 - val_loss: 0.7882 - val_accuracy: 0.7778\n",
            "Epoch 114/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7170 - accuracy: 0.8059 - val_loss: 0.7818 - val_accuracy: 0.8024\n",
            "Epoch 115/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.7131 - accuracy: 0.8035 - val_loss: 0.7761 - val_accuracy: 0.7714\n",
            "Epoch 116/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7169 - accuracy: 0.8029 - val_loss: 0.8131 - val_accuracy: 0.7682\n",
            "Epoch 117/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7164 - accuracy: 0.8029 - val_loss: 0.7488 - val_accuracy: 0.7799\n",
            "Epoch 118/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.6905 - accuracy: 0.8107 - val_loss: 0.7563 - val_accuracy: 0.7906\n",
            "Epoch 119/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.6896 - accuracy: 0.8077 - val_loss: 0.7846 - val_accuracy: 0.7703\n",
            "Epoch 120/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.6837 - accuracy: 0.8182 - val_loss: 0.7264 - val_accuracy: 0.8002\n",
            "Epoch 121/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.6955 - accuracy: 0.8120 - val_loss: 0.8144 - val_accuracy: 0.7703\n",
            "Epoch 122/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.6867 - accuracy: 0.8166 - val_loss: 0.7862 - val_accuracy: 0.7810\n",
            "Epoch 123/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.6890 - accuracy: 0.8131 - val_loss: 0.7718 - val_accuracy: 0.7895\n",
            "Epoch 124/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.6854 - accuracy: 0.8172 - val_loss: 0.8448 - val_accuracy: 0.7682\n",
            "Epoch 125/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.6762 - accuracy: 0.8165 - val_loss: 0.7871 - val_accuracy: 0.7799\n",
            "Epoch 126/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.6655 - accuracy: 0.8207 - val_loss: 0.7428 - val_accuracy: 0.7895\n",
            "Epoch 127/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.6931 - accuracy: 0.8083 - val_loss: 0.7286 - val_accuracy: 0.8013\n",
            "Epoch 128/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.6665 - accuracy: 0.8159 - val_loss: 0.7604 - val_accuracy: 0.8088\n",
            "Epoch 129/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.6629 - accuracy: 0.8222 - val_loss: 0.7918 - val_accuracy: 0.7895\n",
            "Epoch 130/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.6638 - accuracy: 0.8143 - val_loss: 0.7986 - val_accuracy: 0.7543\n",
            "Epoch 131/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.6772 - accuracy: 0.8123 - val_loss: 0.7480 - val_accuracy: 0.8024\n",
            "Epoch 132/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.6533 - accuracy: 0.8255 - val_loss: 0.7682 - val_accuracy: 0.7735\n",
            "Epoch 133/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.6712 - accuracy: 0.8227 - val_loss: 0.8062 - val_accuracy: 0.7671\n",
            "Epoch 134/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.6510 - accuracy: 0.8214 - val_loss: 0.7462 - val_accuracy: 0.7810\n",
            "Epoch 135/200\n",
            "69/69 [==============================] - 4s 59ms/step - loss: 0.6421 - accuracy: 0.8227 - val_loss: 0.7625 - val_accuracy: 0.8077\n",
            "Evaluating model...\n",
            "ROC: 0.938\n",
            "\n",
            "Accuracy = 0.63\n",
            "F-Score: 0.63\n",
            "\n",
            "*** Train on {1, 3, 5, 6, 7, 8, 9, 10} Validate on 4 Test on 2 ***\n",
            "val len:  990\n",
            "test len:  888\n",
            "train len:  6854\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 2.4231 - accuracy: 0.1290 - val_loss: 2.3711 - val_accuracy: 0.1284\n",
            "Epoch 2/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 2.3736 - accuracy: 0.1601 - val_loss: 2.3245 - val_accuracy: 0.1903\n",
            "Epoch 3/200\n",
            "69/69 [==============================] - 3s 51ms/step - loss: 2.3313 - accuracy: 0.1919 - val_loss: 2.2695 - val_accuracy: 0.2466\n",
            "Epoch 4/200\n",
            "69/69 [==============================] - 3s 51ms/step - loss: 2.2884 - accuracy: 0.2029 - val_loss: 2.2025 - val_accuracy: 0.3390\n",
            "Epoch 5/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 2.2313 - accuracy: 0.2343 - val_loss: 2.1385 - val_accuracy: 0.3806\n",
            "Epoch 6/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 2.1668 - accuracy: 0.2594 - val_loss: 2.0705 - val_accuracy: 0.3930\n",
            "Epoch 7/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 2.0953 - accuracy: 0.2905 - val_loss: 2.0112 - val_accuracy: 0.4257\n",
            "Epoch 8/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 2.0477 - accuracy: 0.3093 - val_loss: 1.9571 - val_accuracy: 0.4718\n",
            "Epoch 9/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.9952 - accuracy: 0.3309 - val_loss: 1.9023 - val_accuracy: 0.4505\n",
            "Epoch 10/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.9222 - accuracy: 0.3556 - val_loss: 1.8391 - val_accuracy: 0.5146\n",
            "Epoch 11/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.8846 - accuracy: 0.3748 - val_loss: 1.8302 - val_accuracy: 0.4955\n",
            "Epoch 12/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.8438 - accuracy: 0.3945 - val_loss: 1.7872 - val_accuracy: 0.5034\n",
            "Epoch 13/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 1.8071 - accuracy: 0.4084 - val_loss: 1.7392 - val_accuracy: 0.4921\n",
            "Epoch 14/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.7668 - accuracy: 0.4202 - val_loss: 1.6910 - val_accuracy: 0.4831\n",
            "Epoch 15/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.7419 - accuracy: 0.4288 - val_loss: 1.6996 - val_accuracy: 0.5225\n",
            "Epoch 16/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.7220 - accuracy: 0.4422 - val_loss: 1.6684 - val_accuracy: 0.5462\n",
            "Epoch 17/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.6631 - accuracy: 0.4479 - val_loss: 1.6738 - val_accuracy: 0.5360\n",
            "Epoch 18/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 1.6612 - accuracy: 0.4666 - val_loss: 1.6580 - val_accuracy: 0.5338\n",
            "Epoch 19/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.6217 - accuracy: 0.4790 - val_loss: 1.5870 - val_accuracy: 0.5484\n",
            "Epoch 20/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.5948 - accuracy: 0.4818 - val_loss: 1.5415 - val_accuracy: 0.5428\n",
            "Epoch 21/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.5709 - accuracy: 0.5012 - val_loss: 1.5547 - val_accuracy: 0.5180\n",
            "Epoch 22/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 1.5540 - accuracy: 0.5025 - val_loss: 1.5130 - val_accuracy: 0.5586\n",
            "Epoch 23/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.5283 - accuracy: 0.5146 - val_loss: 1.4992 - val_accuracy: 0.5664\n",
            "Epoch 24/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 1.4998 - accuracy: 0.5181 - val_loss: 1.4985 - val_accuracy: 0.5563\n",
            "Epoch 25/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 1.5111 - accuracy: 0.5168 - val_loss: 1.4825 - val_accuracy: 0.5743\n",
            "Epoch 26/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.4754 - accuracy: 0.5306 - val_loss: 1.4690 - val_accuracy: 0.5698\n",
            "Epoch 27/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.4375 - accuracy: 0.5416 - val_loss: 1.4667 - val_accuracy: 0.5721\n",
            "Epoch 28/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.4393 - accuracy: 0.5454 - val_loss: 1.4733 - val_accuracy: 0.5732\n",
            "Epoch 29/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.4138 - accuracy: 0.5550 - val_loss: 1.4356 - val_accuracy: 0.5473\n",
            "Epoch 30/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.3939 - accuracy: 0.5658 - val_loss: 1.3718 - val_accuracy: 0.5923\n",
            "Epoch 31/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.3755 - accuracy: 0.5655 - val_loss: 1.3836 - val_accuracy: 0.5923\n",
            "Epoch 32/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.3635 - accuracy: 0.5681 - val_loss: 1.3540 - val_accuracy: 0.5923\n",
            "Epoch 33/200\n",
            "69/69 [==============================] - 3s 51ms/step - loss: 1.3437 - accuracy: 0.5848 - val_loss: 1.3513 - val_accuracy: 0.5619\n",
            "Epoch 34/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.3232 - accuracy: 0.5849 - val_loss: 1.3621 - val_accuracy: 0.5980\n",
            "Epoch 35/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.3005 - accuracy: 0.5931 - val_loss: 1.2937 - val_accuracy: 0.6137\n",
            "Epoch 36/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.3214 - accuracy: 0.5893 - val_loss: 1.3400 - val_accuracy: 0.5811\n",
            "Epoch 37/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.2947 - accuracy: 0.5953 - val_loss: 1.5631 - val_accuracy: 0.5428\n",
            "Epoch 38/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.2867 - accuracy: 0.5967 - val_loss: 1.2850 - val_accuracy: 0.6227\n",
            "Epoch 39/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.2528 - accuracy: 0.6167 - val_loss: 1.2460 - val_accuracy: 0.6273\n",
            "Epoch 40/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.2564 - accuracy: 0.6131 - val_loss: 1.2444 - val_accuracy: 0.6340\n",
            "Epoch 41/200\n",
            "69/69 [==============================] - 3s 51ms/step - loss: 1.2510 - accuracy: 0.6199 - val_loss: 1.2364 - val_accuracy: 0.6059\n",
            "Epoch 42/200\n",
            "69/69 [==============================] - 3s 51ms/step - loss: 1.2348 - accuracy: 0.6214 - val_loss: 1.2514 - val_accuracy: 0.5867\n",
            "Epoch 43/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.2034 - accuracy: 0.6316 - val_loss: 1.2254 - val_accuracy: 0.6486\n",
            "Epoch 44/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.1998 - accuracy: 0.6352 - val_loss: 1.2655 - val_accuracy: 0.6250\n",
            "Epoch 45/200\n",
            "69/69 [==============================] - 3s 51ms/step - loss: 1.1838 - accuracy: 0.6479 - val_loss: 1.2269 - val_accuracy: 0.6430\n",
            "Epoch 46/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.1739 - accuracy: 0.6415 - val_loss: 1.2086 - val_accuracy: 0.6385\n",
            "Epoch 47/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.1795 - accuracy: 0.6395 - val_loss: 1.2563 - val_accuracy: 0.6396\n",
            "Epoch 48/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.1498 - accuracy: 0.6522 - val_loss: 1.1918 - val_accuracy: 0.6419\n",
            "Epoch 49/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.1453 - accuracy: 0.6568 - val_loss: 1.2365 - val_accuracy: 0.6453\n",
            "Epoch 50/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.1355 - accuracy: 0.6522 - val_loss: 1.2049 - val_accuracy: 0.6430\n",
            "Epoch 51/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.1260 - accuracy: 0.6555 - val_loss: 1.2547 - val_accuracy: 0.6318\n",
            "Epoch 52/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 1.1196 - accuracy: 0.6634 - val_loss: 1.2353 - val_accuracy: 0.6239\n",
            "Epoch 53/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.1055 - accuracy: 0.6679 - val_loss: 1.1894 - val_accuracy: 0.6351\n",
            "Epoch 54/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.1062 - accuracy: 0.6643 - val_loss: 1.1593 - val_accuracy: 0.6498\n",
            "Epoch 55/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.0944 - accuracy: 0.6729 - val_loss: 1.1434 - val_accuracy: 0.6340\n",
            "Epoch 56/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.0689 - accuracy: 0.6827 - val_loss: 1.1507 - val_accuracy: 0.6239\n",
            "Epoch 57/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 1.0729 - accuracy: 0.6824 - val_loss: 1.1350 - val_accuracy: 0.6385\n",
            "Epoch 58/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.0732 - accuracy: 0.6805 - val_loss: 1.1610 - val_accuracy: 0.6498\n",
            "Epoch 59/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.0687 - accuracy: 0.6779 - val_loss: 1.1402 - val_accuracy: 0.6363\n",
            "Epoch 60/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 1.0763 - accuracy: 0.6711 - val_loss: 1.1837 - val_accuracy: 0.6441\n",
            "Epoch 61/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.0511 - accuracy: 0.6907 - val_loss: 1.1528 - val_accuracy: 0.6532\n",
            "Epoch 62/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 1.0361 - accuracy: 0.6948 - val_loss: 1.2407 - val_accuracy: 0.6182\n",
            "Epoch 63/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.0251 - accuracy: 0.6942 - val_loss: 1.2192 - val_accuracy: 0.5811\n",
            "Epoch 64/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 1.0100 - accuracy: 0.7006 - val_loss: 1.1664 - val_accuracy: 0.6464\n",
            "Epoch 65/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 1.0138 - accuracy: 0.7013 - val_loss: 1.1395 - val_accuracy: 0.6340\n",
            "Epoch 66/200\n",
            "69/69 [==============================] - 3s 51ms/step - loss: 1.0031 - accuracy: 0.7021 - val_loss: 1.2279 - val_accuracy: 0.6059\n",
            "Epoch 67/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.0003 - accuracy: 0.7015 - val_loss: 1.0837 - val_accuracy: 0.6486\n",
            "Epoch 68/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 1.0144 - accuracy: 0.7048 - val_loss: 1.1313 - val_accuracy: 0.6363\n",
            "Epoch 69/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 0.9924 - accuracy: 0.7101 - val_loss: 1.1354 - val_accuracy: 0.6408\n",
            "Epoch 70/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 0.9835 - accuracy: 0.7127 - val_loss: 1.1244 - val_accuracy: 0.6182\n",
            "Epoch 71/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.9737 - accuracy: 0.7114 - val_loss: 1.1822 - val_accuracy: 0.6430\n",
            "Epoch 72/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.9642 - accuracy: 0.7213 - val_loss: 1.1957 - val_accuracy: 0.6261\n",
            "Epoch 73/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.9701 - accuracy: 0.7177 - val_loss: 1.2577 - val_accuracy: 0.6149\n",
            "Epoch 74/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.9694 - accuracy: 0.7168 - val_loss: 1.1093 - val_accuracy: 0.6441\n",
            "Epoch 75/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.9802 - accuracy: 0.7171 - val_loss: 1.2628 - val_accuracy: 0.5991\n",
            "Epoch 76/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.9517 - accuracy: 0.7270 - val_loss: 1.1130 - val_accuracy: 0.6408\n",
            "Epoch 77/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 0.9482 - accuracy: 0.7238 - val_loss: 1.2096 - val_accuracy: 0.6430\n",
            "Epoch 78/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 0.9562 - accuracy: 0.7207 - val_loss: 1.0845 - val_accuracy: 0.6385\n",
            "Epoch 79/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 0.9384 - accuracy: 0.7311 - val_loss: 1.1228 - val_accuracy: 0.6374\n",
            "Epoch 80/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 0.9196 - accuracy: 0.7304 - val_loss: 1.2685 - val_accuracy: 0.5946\n",
            "Epoch 81/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.9179 - accuracy: 0.7301 - val_loss: 1.1008 - val_accuracy: 0.6239\n",
            "Epoch 82/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.9213 - accuracy: 0.7308 - val_loss: 1.0817 - val_accuracy: 0.6419\n",
            "Epoch 83/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 0.9080 - accuracy: 0.7358 - val_loss: 1.1360 - val_accuracy: 0.6385\n",
            "Epoch 84/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.9127 - accuracy: 0.7399 - val_loss: 1.0757 - val_accuracy: 0.6408\n",
            "Epoch 85/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 0.8954 - accuracy: 0.7426 - val_loss: 1.0961 - val_accuracy: 0.6453\n",
            "Epoch 86/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.8954 - accuracy: 0.7469 - val_loss: 1.1405 - val_accuracy: 0.6396\n",
            "Epoch 87/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.8890 - accuracy: 0.7474 - val_loss: 1.0795 - val_accuracy: 0.6396\n",
            "Epoch 88/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.8845 - accuracy: 0.7523 - val_loss: 1.0566 - val_accuracy: 0.6385\n",
            "Epoch 89/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.8917 - accuracy: 0.7472 - val_loss: 1.0726 - val_accuracy: 0.6543\n",
            "Epoch 90/200\n",
            "69/69 [==============================] - 3s 51ms/step - loss: 0.8914 - accuracy: 0.7469 - val_loss: 1.2009 - val_accuracy: 0.6464\n",
            "Epoch 91/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 0.8664 - accuracy: 0.7561 - val_loss: 1.0878 - val_accuracy: 0.6273\n",
            "Epoch 92/200\n",
            "69/69 [==============================] - 3s 51ms/step - loss: 0.8811 - accuracy: 0.7499 - val_loss: 1.0602 - val_accuracy: 0.6396\n",
            "Epoch 93/200\n",
            "69/69 [==============================] - 3s 51ms/step - loss: 0.8643 - accuracy: 0.7527 - val_loss: 1.1367 - val_accuracy: 0.6340\n",
            "Epoch 94/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 0.8534 - accuracy: 0.7530 - val_loss: 1.1067 - val_accuracy: 0.6284\n",
            "Epoch 95/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.8618 - accuracy: 0.7509 - val_loss: 1.0724 - val_accuracy: 0.6453\n",
            "Epoch 96/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.8421 - accuracy: 0.7613 - val_loss: 1.0475 - val_accuracy: 0.6250\n",
            "Epoch 97/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 0.8410 - accuracy: 0.7652 - val_loss: 1.1339 - val_accuracy: 0.6396\n",
            "Epoch 98/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.8538 - accuracy: 0.7603 - val_loss: 1.0743 - val_accuracy: 0.6419\n",
            "Epoch 99/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.8400 - accuracy: 0.7642 - val_loss: 1.1037 - val_accuracy: 0.6509\n",
            "Epoch 100/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.8041 - accuracy: 0.7762 - val_loss: 1.2054 - val_accuracy: 0.6351\n",
            "Epoch 101/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.8244 - accuracy: 0.7660 - val_loss: 1.2039 - val_accuracy: 0.6464\n",
            "Epoch 102/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.8257 - accuracy: 0.7693 - val_loss: 1.0808 - val_accuracy: 0.6464\n",
            "Epoch 103/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.8107 - accuracy: 0.7736 - val_loss: 1.0489 - val_accuracy: 0.6441\n",
            "Epoch 104/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 0.8267 - accuracy: 0.7664 - val_loss: 1.0944 - val_accuracy: 0.6430\n",
            "Epoch 105/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 0.8017 - accuracy: 0.7774 - val_loss: 1.0441 - val_accuracy: 0.6453\n",
            "Epoch 106/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7876 - accuracy: 0.7809 - val_loss: 1.0700 - val_accuracy: 0.6363\n",
            "Epoch 107/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.8091 - accuracy: 0.7701 - val_loss: 1.0700 - val_accuracy: 0.6475\n",
            "Epoch 108/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.7931 - accuracy: 0.7772 - val_loss: 1.1277 - val_accuracy: 0.6396\n",
            "Epoch 109/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7965 - accuracy: 0.7741 - val_loss: 1.0622 - val_accuracy: 0.6419\n",
            "Epoch 110/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.8050 - accuracy: 0.7714 - val_loss: 1.0782 - val_accuracy: 0.6396\n",
            "Epoch 111/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7885 - accuracy: 0.7759 - val_loss: 1.0415 - val_accuracy: 0.6419\n",
            "Epoch 112/200\n",
            "69/69 [==============================] - 4s 56ms/step - loss: 0.7796 - accuracy: 0.7788 - val_loss: 1.0817 - val_accuracy: 0.6453\n",
            "Epoch 113/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7830 - accuracy: 0.7820 - val_loss: 1.1115 - val_accuracy: 0.6532\n",
            "Epoch 114/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7644 - accuracy: 0.7886 - val_loss: 1.0336 - val_accuracy: 0.6453\n",
            "Epoch 115/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7953 - accuracy: 0.7721 - val_loss: 1.0702 - val_accuracy: 0.6453\n",
            "Epoch 116/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7593 - accuracy: 0.7912 - val_loss: 1.0344 - val_accuracy: 0.6565\n",
            "Epoch 117/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7651 - accuracy: 0.7893 - val_loss: 1.0830 - val_accuracy: 0.6532\n",
            "Epoch 118/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7524 - accuracy: 0.7886 - val_loss: 1.1082 - val_accuracy: 0.6182\n",
            "Epoch 119/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7515 - accuracy: 0.7931 - val_loss: 1.0772 - val_accuracy: 0.6295\n",
            "Epoch 120/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7714 - accuracy: 0.7844 - val_loss: 1.0583 - val_accuracy: 0.6667\n",
            "Epoch 121/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7473 - accuracy: 0.7917 - val_loss: 1.0965 - val_accuracy: 0.6520\n",
            "Epoch 122/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7360 - accuracy: 0.7933 - val_loss: 1.0129 - val_accuracy: 0.6441\n",
            "Epoch 123/200\n",
            "69/69 [==============================] - 4s 52ms/step - loss: 0.7452 - accuracy: 0.7879 - val_loss: 1.0583 - val_accuracy: 0.6622\n",
            "Epoch 124/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7408 - accuracy: 0.7971 - val_loss: 1.0816 - val_accuracy: 0.6498\n",
            "Epoch 125/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7257 - accuracy: 0.8004 - val_loss: 1.0900 - val_accuracy: 0.6498\n",
            "Epoch 126/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.7338 - accuracy: 0.7978 - val_loss: 1.1148 - val_accuracy: 0.6543\n",
            "Epoch 127/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 0.7244 - accuracy: 0.7959 - val_loss: 1.0421 - val_accuracy: 0.6543\n",
            "Epoch 128/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 0.7162 - accuracy: 0.7989 - val_loss: 1.0133 - val_accuracy: 0.6498\n",
            "Epoch 129/200\n",
            "69/69 [==============================] - 3s 50ms/step - loss: 0.7308 - accuracy: 0.7941 - val_loss: 1.0827 - val_accuracy: 0.6295\n",
            "Epoch 130/200\n",
            "69/69 [==============================] - 4s 51ms/step - loss: 0.7140 - accuracy: 0.8065 - val_loss: 1.0245 - val_accuracy: 0.6543\n",
            "Epoch 131/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7252 - accuracy: 0.7982 - val_loss: 1.1600 - val_accuracy: 0.6351\n",
            "Epoch 132/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7303 - accuracy: 0.7984 - val_loss: 1.0959 - val_accuracy: 0.6385\n",
            "Epoch 133/200\n",
            "69/69 [==============================] - 4s 54ms/step - loss: 0.7281 - accuracy: 0.7994 - val_loss: 1.0149 - val_accuracy: 0.6498\n",
            "Epoch 134/200\n",
            "69/69 [==============================] - 4s 53ms/step - loss: 0.6991 - accuracy: 0.8127 - val_loss: 1.1171 - val_accuracy: 0.6577\n",
            "Epoch 135/200\n",
            "69/69 [==============================] - 4s 55ms/step - loss: 0.7076 - accuracy: 0.8046 - val_loss: 1.0584 - val_accuracy: 0.6543\n",
            "Epoch 136/200\n",
            "69/69 [==============================] - 4s 59ms/step - loss: 0.7136 - accuracy: 0.8029 - val_loss: 1.0298 - val_accuracy: 0.6689\n",
            "Epoch 137/200\n",
            "69/69 [==============================] - 4s 57ms/step - loss: 0.7153 - accuracy: 0.8026 - val_loss: 1.0758 - val_accuracy: 0.6475\n",
            "Evaluating model...\n",
            "ROC: 0.947\n",
            "\n",
            "Accuracy = 0.73\n",
            "F-Score: 0.73\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 6, 7, 8, 10} Validate on 5 Test on 9 ***\n",
            "val len:  936\n",
            "test len:  816\n",
            "train len:  6980\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 2.4228 - accuracy: 0.1319 - val_loss: 2.3451 - val_accuracy: 0.1924\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 2.3631 - accuracy: 0.1683 - val_loss: 2.2810 - val_accuracy: 0.3297\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 3s 48ms/step - loss: 2.3085 - accuracy: 0.2020 - val_loss: 2.1851 - val_accuracy: 0.2672\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 2.2387 - accuracy: 0.2378 - val_loss: 2.1015 - val_accuracy: 0.3603\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 2.1639 - accuracy: 0.2698 - val_loss: 2.0082 - val_accuracy: 0.3456\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 2.1096 - accuracy: 0.2951 - val_loss: 1.9475 - val_accuracy: 0.3444\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 2.0285 - accuracy: 0.3248 - val_loss: 1.8364 - val_accuracy: 0.3713\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.9703 - accuracy: 0.3400 - val_loss: 1.7747 - val_accuracy: 0.3897\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.9177 - accuracy: 0.3681 - val_loss: 1.7305 - val_accuracy: 0.4485\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 1.8594 - accuracy: 0.3927 - val_loss: 1.8292 - val_accuracy: 0.3444\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.8062 - accuracy: 0.4093 - val_loss: 1.6733 - val_accuracy: 0.4522\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.7879 - accuracy: 0.4119 - val_loss: 1.6420 - val_accuracy: 0.4853\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 1.7337 - accuracy: 0.4418 - val_loss: 1.5635 - val_accuracy: 0.5061\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.6953 - accuracy: 0.4494 - val_loss: 1.5550 - val_accuracy: 0.4975\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 1.6549 - accuracy: 0.4668 - val_loss: 1.5371 - val_accuracy: 0.5086\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.6231 - accuracy: 0.4735 - val_loss: 1.4763 - val_accuracy: 0.5380\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 1.6128 - accuracy: 0.4804 - val_loss: 1.4632 - val_accuracy: 0.5417\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.5677 - accuracy: 0.4994 - val_loss: 1.4101 - val_accuracy: 0.5662\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.5252 - accuracy: 0.5115 - val_loss: 1.4129 - val_accuracy: 0.5735\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.5193 - accuracy: 0.5143 - val_loss: 1.3877 - val_accuracy: 0.5600\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.5003 - accuracy: 0.5242 - val_loss: 1.3979 - val_accuracy: 0.5919\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.4697 - accuracy: 0.5367 - val_loss: 1.3536 - val_accuracy: 0.5809\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.4599 - accuracy: 0.5426 - val_loss: 1.3482 - val_accuracy: 0.5993\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.4416 - accuracy: 0.5375 - val_loss: 1.3128 - val_accuracy: 0.6091\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 3s 48ms/step - loss: 1.4062 - accuracy: 0.5562 - val_loss: 1.3167 - val_accuracy: 0.6176\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 3s 48ms/step - loss: 1.3867 - accuracy: 0.5648 - val_loss: 1.3278 - val_accuracy: 0.5919\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 3s 48ms/step - loss: 1.3721 - accuracy: 0.5682 - val_loss: 1.3283 - val_accuracy: 0.5686\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 3s 48ms/step - loss: 1.3522 - accuracy: 0.5721 - val_loss: 1.2616 - val_accuracy: 0.6324\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 3s 48ms/step - loss: 1.3209 - accuracy: 0.5834 - val_loss: 1.2567 - val_accuracy: 0.6238\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 1.3194 - accuracy: 0.5807 - val_loss: 1.2959 - val_accuracy: 0.5919\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.3026 - accuracy: 0.5951 - val_loss: 1.2016 - val_accuracy: 0.6458\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 1.2828 - accuracy: 0.5977 - val_loss: 1.2062 - val_accuracy: 0.6471\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.2660 - accuracy: 0.6036 - val_loss: 1.1905 - val_accuracy: 0.6385\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 1.2669 - accuracy: 0.6138 - val_loss: 1.1983 - val_accuracy: 0.6299\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.2340 - accuracy: 0.6206 - val_loss: 1.2265 - val_accuracy: 0.6238\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.2397 - accuracy: 0.6188 - val_loss: 1.2323 - val_accuracy: 0.6189\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.2118 - accuracy: 0.6234 - val_loss: 1.1565 - val_accuracy: 0.6569\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.2135 - accuracy: 0.6261 - val_loss: 1.1620 - val_accuracy: 0.6483\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.2025 - accuracy: 0.6354 - val_loss: 1.1477 - val_accuracy: 0.6544\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.1654 - accuracy: 0.6486 - val_loss: 1.1088 - val_accuracy: 0.6630\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.1663 - accuracy: 0.6374 - val_loss: 1.1400 - val_accuracy: 0.6495\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.1543 - accuracy: 0.6486 - val_loss: 1.1342 - val_accuracy: 0.6299\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 1.1366 - accuracy: 0.6547 - val_loss: 1.1077 - val_accuracy: 0.6630\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.1161 - accuracy: 0.6576 - val_loss: 1.0880 - val_accuracy: 0.6777\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.1232 - accuracy: 0.6585 - val_loss: 1.0710 - val_accuracy: 0.6814\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.1215 - accuracy: 0.6633 - val_loss: 1.0998 - val_accuracy: 0.6789\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.1130 - accuracy: 0.6703 - val_loss: 1.0680 - val_accuracy: 0.6752\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 1.0931 - accuracy: 0.6603 - val_loss: 1.0918 - val_accuracy: 0.6654\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 1.0849 - accuracy: 0.6681 - val_loss: 1.1039 - val_accuracy: 0.6605\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.0629 - accuracy: 0.6848 - val_loss: 1.0797 - val_accuracy: 0.6740\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.0548 - accuracy: 0.6825 - val_loss: 1.0738 - val_accuracy: 0.6973\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.0641 - accuracy: 0.6852 - val_loss: 1.0913 - val_accuracy: 0.6998\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 1.0414 - accuracy: 0.6914 - val_loss: 1.1652 - val_accuracy: 0.6373\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 1.0242 - accuracy: 0.6941 - val_loss: 1.0416 - val_accuracy: 0.6961\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 1.0257 - accuracy: 0.6984 - val_loss: 1.0565 - val_accuracy: 0.6691\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.0262 - accuracy: 0.6987 - val_loss: 1.0299 - val_accuracy: 0.6691\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 1.0139 - accuracy: 0.6993 - val_loss: 1.0433 - val_accuracy: 0.7083\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.0027 - accuracy: 0.7034 - val_loss: 1.0231 - val_accuracy: 0.6826\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.0020 - accuracy: 0.7092 - val_loss: 1.0337 - val_accuracy: 0.7059\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9814 - accuracy: 0.7059 - val_loss: 1.0117 - val_accuracy: 0.7132\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.9842 - accuracy: 0.7130 - val_loss: 0.9991 - val_accuracy: 0.7047\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.9667 - accuracy: 0.7183 - val_loss: 0.9993 - val_accuracy: 0.6949\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.9637 - accuracy: 0.7149 - val_loss: 1.0514 - val_accuracy: 0.7120\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 0.9643 - accuracy: 0.7143 - val_loss: 1.0112 - val_accuracy: 0.7132\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 0.9547 - accuracy: 0.7149 - val_loss: 1.0003 - val_accuracy: 0.6887\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.9510 - accuracy: 0.7159 - val_loss: 0.9861 - val_accuracy: 0.7010\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.9475 - accuracy: 0.7254 - val_loss: 0.9911 - val_accuracy: 0.7022\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9313 - accuracy: 0.7238 - val_loss: 1.0119 - val_accuracy: 0.7108\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9230 - accuracy: 0.7321 - val_loss: 1.0095 - val_accuracy: 0.6985\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9230 - accuracy: 0.7251 - val_loss: 1.0163 - val_accuracy: 0.7108\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9275 - accuracy: 0.7308 - val_loss: 1.0055 - val_accuracy: 0.7390\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.9181 - accuracy: 0.7390 - val_loss: 0.9839 - val_accuracy: 0.7292\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 0.9107 - accuracy: 0.7407 - val_loss: 0.9817 - val_accuracy: 0.7022\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 0.8962 - accuracy: 0.7370 - val_loss: 1.0147 - val_accuracy: 0.6912\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 0.8783 - accuracy: 0.7486 - val_loss: 0.9650 - val_accuracy: 0.7169\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.8907 - accuracy: 0.7430 - val_loss: 1.0329 - val_accuracy: 0.7071\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9002 - accuracy: 0.7387 - val_loss: 0.9856 - val_accuracy: 0.7377\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 0.8673 - accuracy: 0.7532 - val_loss: 1.0227 - val_accuracy: 0.7279\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 0.8677 - accuracy: 0.7533 - val_loss: 0.9699 - val_accuracy: 0.7365\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8578 - accuracy: 0.7501 - val_loss: 1.0012 - val_accuracy: 0.7255\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 0.8555 - accuracy: 0.7517 - val_loss: 0.9671 - val_accuracy: 0.7071\n",
            "Epoch 82/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.8515 - accuracy: 0.7559 - val_loss: 0.9777 - val_accuracy: 0.7059\n",
            "Epoch 83/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.8494 - accuracy: 0.7580 - val_loss: 0.9577 - val_accuracy: 0.7145\n",
            "Epoch 84/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8483 - accuracy: 0.7597 - val_loss: 0.9894 - val_accuracy: 0.7304\n",
            "Epoch 85/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 0.8305 - accuracy: 0.7655 - val_loss: 0.9885 - val_accuracy: 0.7402\n",
            "Epoch 86/200\n",
            "70/70 [==============================] - 3s 48ms/step - loss: 0.8123 - accuracy: 0.7695 - val_loss: 0.9877 - val_accuracy: 0.7316\n",
            "Epoch 87/200\n",
            "70/70 [==============================] - 3s 48ms/step - loss: 0.8286 - accuracy: 0.7658 - val_loss: 0.9835 - val_accuracy: 0.7132\n",
            "Epoch 88/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.8412 - accuracy: 0.7570 - val_loss: 0.9994 - val_accuracy: 0.7328\n",
            "Epoch 89/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.8295 - accuracy: 0.7609 - val_loss: 0.9780 - val_accuracy: 0.7034\n",
            "Epoch 90/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.8157 - accuracy: 0.7695 - val_loss: 0.9946 - val_accuracy: 0.7341\n",
            "Epoch 91/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 0.8117 - accuracy: 0.7668 - val_loss: 0.9456 - val_accuracy: 0.7341\n",
            "Epoch 92/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 0.8087 - accuracy: 0.7688 - val_loss: 0.9471 - val_accuracy: 0.7243\n",
            "Epoch 93/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 0.7992 - accuracy: 0.7736 - val_loss: 0.9536 - val_accuracy: 0.7365\n",
            "Epoch 94/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7849 - accuracy: 0.7801 - val_loss: 0.9727 - val_accuracy: 0.7108\n",
            "Epoch 95/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.7963 - accuracy: 0.7799 - val_loss: 0.9570 - val_accuracy: 0.7267\n",
            "Epoch 96/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 0.7838 - accuracy: 0.7798 - val_loss: 0.9736 - val_accuracy: 0.6936\n",
            "Epoch 97/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.7688 - accuracy: 0.7850 - val_loss: 0.9983 - val_accuracy: 0.7267\n",
            "Epoch 98/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 0.7730 - accuracy: 0.7821 - val_loss: 0.9528 - val_accuracy: 0.7169\n",
            "Epoch 99/200\n",
            "70/70 [==============================] - 3s 48ms/step - loss: 0.7743 - accuracy: 0.7807 - val_loss: 0.9631 - val_accuracy: 0.7230\n",
            "Epoch 100/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 0.7749 - accuracy: 0.7822 - val_loss: 0.9619 - val_accuracy: 0.7316\n",
            "Epoch 101/200\n",
            "70/70 [==============================] - 3s 50ms/step - loss: 0.7728 - accuracy: 0.7897 - val_loss: 0.9892 - val_accuracy: 0.7194\n",
            "Epoch 102/200\n",
            "70/70 [==============================] - 4s 51ms/step - loss: 0.7574 - accuracy: 0.7894 - val_loss: 0.9731 - val_accuracy: 0.7181\n",
            "Epoch 103/200\n",
            "70/70 [==============================] - 3s 49ms/step - loss: 0.7785 - accuracy: 0.7818 - val_loss: 0.9462 - val_accuracy: 0.7279\n",
            "Epoch 104/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.7653 - accuracy: 0.7867 - val_loss: 0.9838 - val_accuracy: 0.7010\n",
            "Epoch 105/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 0.7518 - accuracy: 0.7890 - val_loss: 0.9563 - val_accuracy: 0.7451\n",
            "Epoch 106/200\n",
            "70/70 [==============================] - 4s 50ms/step - loss: 0.7566 - accuracy: 0.7834 - val_loss: 0.9588 - val_accuracy: 0.7292\n",
            "Evaluating model...\n",
            "ROC: 0.969\n",
            "\n",
            "Accuracy = 0.78\n",
            "F-Score: 0.78\n",
            "\n",
            "*** Train on {1, 3, 4, 5, 7, 8, 9, 10} Validate on 6 Test on 2 ***\n",
            "val len:  823\n",
            "test len:  888\n",
            "train len:  7021\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "71/71 [==============================] - 4s 58ms/step - loss: 2.4282 - accuracy: 0.1265 - val_loss: 2.3799 - val_accuracy: 0.1689\n",
            "Epoch 2/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 2.3775 - accuracy: 0.1561 - val_loss: 2.3275 - val_accuracy: 0.2151\n",
            "Epoch 3/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 2.3185 - accuracy: 0.1921 - val_loss: 2.2523 - val_accuracy: 0.3086\n",
            "Epoch 4/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 2.2667 - accuracy: 0.2240 - val_loss: 2.1779 - val_accuracy: 0.4122\n",
            "Epoch 5/200\n",
            "71/71 [==============================] - 4s 55ms/step - loss: 2.2023 - accuracy: 0.2522 - val_loss: 2.1161 - val_accuracy: 0.4741\n",
            "Epoch 6/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 2.1441 - accuracy: 0.2786 - val_loss: 2.0379 - val_accuracy: 0.4088\n",
            "Epoch 7/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 2.0726 - accuracy: 0.3112 - val_loss: 1.9730 - val_accuracy: 0.4595\n",
            "Epoch 8/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 2.0246 - accuracy: 0.3227 - val_loss: 1.9395 - val_accuracy: 0.4606\n",
            "Epoch 9/200\n",
            "71/71 [==============================] - 4s 54ms/step - loss: 1.9637 - accuracy: 0.3467 - val_loss: 1.8490 - val_accuracy: 0.4921\n",
            "Epoch 10/200\n",
            "71/71 [==============================] - 4s 54ms/step - loss: 1.8949 - accuracy: 0.3838 - val_loss: 1.7809 - val_accuracy: 0.4899\n",
            "Epoch 11/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 1.8441 - accuracy: 0.3850 - val_loss: 1.7280 - val_accuracy: 0.5113\n",
            "Epoch 12/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 1.8136 - accuracy: 0.3977 - val_loss: 1.7106 - val_accuracy: 0.4977\n",
            "Epoch 13/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 1.7755 - accuracy: 0.4233 - val_loss: 1.6998 - val_accuracy: 0.5462\n",
            "Epoch 14/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 1.7206 - accuracy: 0.4303 - val_loss: 1.7305 - val_accuracy: 0.4617\n",
            "Epoch 15/200\n",
            "71/71 [==============================] - 4s 54ms/step - loss: 1.6961 - accuracy: 0.4398 - val_loss: 1.6437 - val_accuracy: 0.5191\n",
            "Epoch 16/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 1.6648 - accuracy: 0.4552 - val_loss: 1.5857 - val_accuracy: 0.5158\n",
            "Epoch 17/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 1.6217 - accuracy: 0.4780 - val_loss: 1.5447 - val_accuracy: 0.5394\n",
            "Epoch 18/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 1.5873 - accuracy: 0.4857 - val_loss: 1.5223 - val_accuracy: 0.5405\n",
            "Epoch 19/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 1.5638 - accuracy: 0.4924 - val_loss: 1.4581 - val_accuracy: 0.5552\n",
            "Epoch 20/200\n",
            "71/71 [==============================] - 4s 56ms/step - loss: 1.5262 - accuracy: 0.5129 - val_loss: 1.5178 - val_accuracy: 0.5462\n",
            "Epoch 21/200\n",
            "71/71 [==============================] - 4s 57ms/step - loss: 1.5101 - accuracy: 0.5125 - val_loss: 1.4579 - val_accuracy: 0.5439\n",
            "Epoch 22/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 1.4949 - accuracy: 0.5243 - val_loss: 1.3983 - val_accuracy: 0.5642\n",
            "Epoch 23/200\n",
            "71/71 [==============================] - 4s 54ms/step - loss: 1.4647 - accuracy: 0.5320 - val_loss: 1.4159 - val_accuracy: 0.5698\n",
            "Epoch 24/200\n",
            "71/71 [==============================] - 4s 54ms/step - loss: 1.4561 - accuracy: 0.5367 - val_loss: 1.3735 - val_accuracy: 0.5552\n",
            "Epoch 25/200\n",
            "71/71 [==============================] - 4s 54ms/step - loss: 1.4135 - accuracy: 0.5550 - val_loss: 1.4213 - val_accuracy: 0.5642\n",
            "Epoch 26/200\n",
            "71/71 [==============================] - 4s 54ms/step - loss: 1.3948 - accuracy: 0.5522 - val_loss: 1.3648 - val_accuracy: 0.5755\n",
            "Epoch 27/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 1.3800 - accuracy: 0.5616 - val_loss: 1.4750 - val_accuracy: 0.5597\n",
            "Epoch 28/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 1.3603 - accuracy: 0.5754 - val_loss: 1.3234 - val_accuracy: 0.5822\n",
            "Epoch 29/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 1.3367 - accuracy: 0.5805 - val_loss: 1.2926 - val_accuracy: 0.5946\n",
            "Epoch 30/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 1.3253 - accuracy: 0.5852 - val_loss: 1.2993 - val_accuracy: 0.5935\n",
            "Epoch 31/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 1.3129 - accuracy: 0.5925 - val_loss: 1.3159 - val_accuracy: 0.5676\n",
            "Epoch 32/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 1.3032 - accuracy: 0.5904 - val_loss: 1.3345 - val_accuracy: 0.5766\n",
            "Epoch 33/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 1.2869 - accuracy: 0.5918 - val_loss: 1.2843 - val_accuracy: 0.5653\n",
            "Epoch 34/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 1.2653 - accuracy: 0.6107 - val_loss: 1.3784 - val_accuracy: 0.5079\n",
            "Epoch 35/200\n",
            "71/71 [==============================] - 4s 55ms/step - loss: 1.2763 - accuracy: 0.6030 - val_loss: 1.3245 - val_accuracy: 0.5912\n",
            "Epoch 36/200\n",
            "71/71 [==============================] - 4s 54ms/step - loss: 1.2393 - accuracy: 0.6250 - val_loss: 1.2384 - val_accuracy: 0.6104\n",
            "Epoch 37/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 1.2095 - accuracy: 0.6277 - val_loss: 1.3337 - val_accuracy: 0.6025\n",
            "Epoch 38/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 1.2096 - accuracy: 0.6308 - val_loss: 1.2136 - val_accuracy: 0.6104\n",
            "Epoch 39/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 1.1918 - accuracy: 0.6301 - val_loss: 1.2030 - val_accuracy: 0.6126\n",
            "Epoch 40/200\n",
            "71/71 [==============================] - 4s 55ms/step - loss: 1.2122 - accuracy: 0.6291 - val_loss: 1.2210 - val_accuracy: 0.6070\n",
            "Epoch 41/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 1.1681 - accuracy: 0.6408 - val_loss: 1.1831 - val_accuracy: 0.6002\n",
            "Epoch 42/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 1.1714 - accuracy: 0.6398 - val_loss: 1.2555 - val_accuracy: 0.6160\n",
            "Epoch 43/200\n",
            "71/71 [==============================] - 4s 54ms/step - loss: 1.1641 - accuracy: 0.6421 - val_loss: 1.4109 - val_accuracy: 0.5901\n",
            "Epoch 44/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 1.1588 - accuracy: 0.6463 - val_loss: 1.2746 - val_accuracy: 0.5957\n",
            "Epoch 45/200\n",
            "71/71 [==============================] - 4s 56ms/step - loss: 1.1535 - accuracy: 0.6478 - val_loss: 1.2111 - val_accuracy: 0.6126\n",
            "Epoch 46/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 1.1320 - accuracy: 0.6575 - val_loss: 1.2277 - val_accuracy: 0.5980\n",
            "Epoch 47/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 1.1238 - accuracy: 0.6603 - val_loss: 1.1809 - val_accuracy: 0.6036\n",
            "Epoch 48/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 1.1169 - accuracy: 0.6623 - val_loss: 1.2019 - val_accuracy: 0.6002\n",
            "Epoch 49/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 1.1059 - accuracy: 0.6666 - val_loss: 1.1594 - val_accuracy: 0.6104\n",
            "Epoch 50/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 1.1016 - accuracy: 0.6653 - val_loss: 1.1523 - val_accuracy: 0.6261\n",
            "Epoch 51/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 1.0880 - accuracy: 0.6764 - val_loss: 1.1571 - val_accuracy: 0.6284\n",
            "Epoch 52/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 1.0672 - accuracy: 0.6847 - val_loss: 1.2014 - val_accuracy: 0.6216\n",
            "Epoch 53/200\n",
            "71/71 [==============================] - 4s 55ms/step - loss: 1.0862 - accuracy: 0.6780 - val_loss: 1.1586 - val_accuracy: 0.6306\n",
            "Epoch 54/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 1.0619 - accuracy: 0.6741 - val_loss: 1.1087 - val_accuracy: 0.6374\n",
            "Epoch 55/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 1.0435 - accuracy: 0.6869 - val_loss: 1.1618 - val_accuracy: 0.6284\n",
            "Epoch 56/200\n",
            "71/71 [==============================] - 4s 54ms/step - loss: 1.0763 - accuracy: 0.6832 - val_loss: 1.2392 - val_accuracy: 0.6205\n",
            "Epoch 57/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 1.0397 - accuracy: 0.6868 - val_loss: 1.1866 - val_accuracy: 0.6160\n",
            "Epoch 58/200\n",
            "71/71 [==============================] - 4s 57ms/step - loss: 1.0411 - accuracy: 0.6931 - val_loss: 1.1217 - val_accuracy: 0.6374\n",
            "Epoch 59/200\n",
            "71/71 [==============================] - 4s 59ms/step - loss: 1.0047 - accuracy: 0.7076 - val_loss: 1.1613 - val_accuracy: 0.6284\n",
            "Epoch 60/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 1.0168 - accuracy: 0.7056 - val_loss: 1.1099 - val_accuracy: 0.6374\n",
            "Epoch 61/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 1.0100 - accuracy: 0.7022 - val_loss: 1.0791 - val_accuracy: 0.6261\n",
            "Epoch 62/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 1.0088 - accuracy: 0.7099 - val_loss: 1.1057 - val_accuracy: 0.6261\n",
            "Epoch 63/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 0.9977 - accuracy: 0.7057 - val_loss: 1.1246 - val_accuracy: 0.6520\n",
            "Epoch 64/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 0.9787 - accuracy: 0.7131 - val_loss: 1.1293 - val_accuracy: 0.6396\n",
            "Epoch 65/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 0.9745 - accuracy: 0.7131 - val_loss: 1.1838 - val_accuracy: 0.6408\n",
            "Epoch 66/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 0.9693 - accuracy: 0.7161 - val_loss: 1.0682 - val_accuracy: 0.6408\n",
            "Epoch 67/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 0.9726 - accuracy: 0.7211 - val_loss: 1.1987 - val_accuracy: 0.6363\n",
            "Epoch 68/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 0.9514 - accuracy: 0.7213 - val_loss: 1.1058 - val_accuracy: 0.6318\n",
            "Epoch 69/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 0.9459 - accuracy: 0.7262 - val_loss: 1.2568 - val_accuracy: 0.6295\n",
            "Epoch 70/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 0.9557 - accuracy: 0.7250 - val_loss: 1.0893 - val_accuracy: 0.6273\n",
            "Epoch 71/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 0.9495 - accuracy: 0.7275 - val_loss: 1.1013 - val_accuracy: 0.6588\n",
            "Epoch 72/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 0.9397 - accuracy: 0.7275 - val_loss: 1.2586 - val_accuracy: 0.6363\n",
            "Epoch 73/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 0.9453 - accuracy: 0.7267 - val_loss: 1.1141 - val_accuracy: 0.6250\n",
            "Epoch 74/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 0.9248 - accuracy: 0.7352 - val_loss: 1.1314 - val_accuracy: 0.6532\n",
            "Epoch 75/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 0.9067 - accuracy: 0.7418 - val_loss: 1.0857 - val_accuracy: 0.6678\n",
            "Epoch 76/200\n",
            "71/71 [==============================] - 4s 52ms/step - loss: 0.9103 - accuracy: 0.7369 - val_loss: 1.0735 - val_accuracy: 0.6520\n",
            "Epoch 77/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 0.9093 - accuracy: 0.7436 - val_loss: 1.0708 - val_accuracy: 0.6678\n",
            "Epoch 78/200\n",
            "71/71 [==============================] - 4s 50ms/step - loss: 0.8978 - accuracy: 0.7439 - val_loss: 1.2144 - val_accuracy: 0.6509\n",
            "Epoch 79/200\n",
            "71/71 [==============================] - 4s 51ms/step - loss: 0.9101 - accuracy: 0.7409 - val_loss: 1.1298 - val_accuracy: 0.6520\n",
            "Epoch 80/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 0.9174 - accuracy: 0.7341 - val_loss: 1.1530 - val_accuracy: 0.6633\n",
            "Epoch 81/200\n",
            "71/71 [==============================] - 4s 53ms/step - loss: 0.8945 - accuracy: 0.7419 - val_loss: 1.0839 - val_accuracy: 0.6689\n",
            "Evaluating model...\n",
            "ROC: 0.958\n",
            "\n",
            "Accuracy = 0.74\n",
            "F-Score: 0.74\n",
            "\n",
            "*** Train on {1, 2, 3, 5, 6, 8, 9, 10} Validate on 7 Test on 4 ***\n",
            "val len:  838\n",
            "test len:  990\n",
            "train len:  6904\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 2.4470 - accuracy: 0.1207 - val_loss: 2.3867 - val_accuracy: 0.1303\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 2.3891 - accuracy: 0.1480 - val_loss: 2.3587 - val_accuracy: 0.1636\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 2.3548 - accuracy: 0.1609 - val_loss: 2.3328 - val_accuracy: 0.2495\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 2.3146 - accuracy: 0.1989 - val_loss: 2.2575 - val_accuracy: 0.2576\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 2.2599 - accuracy: 0.2252 - val_loss: 2.1893 - val_accuracy: 0.2919\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 2.1886 - accuracy: 0.2493 - val_loss: 2.0929 - val_accuracy: 0.4303\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 4s 62ms/step - loss: 2.1195 - accuracy: 0.2772 - val_loss: 2.0066 - val_accuracy: 0.4323\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 2.0468 - accuracy: 0.3158 - val_loss: 1.9499 - val_accuracy: 0.4111\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.9910 - accuracy: 0.3366 - val_loss: 1.8826 - val_accuracy: 0.4485\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.9445 - accuracy: 0.3523 - val_loss: 1.8268 - val_accuracy: 0.4586\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 1.9088 - accuracy: 0.3621 - val_loss: 1.7742 - val_accuracy: 0.5000\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 4s 61ms/step - loss: 1.8581 - accuracy: 0.3792 - val_loss: 1.7123 - val_accuracy: 0.5091\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.8208 - accuracy: 0.4031 - val_loss: 1.7045 - val_accuracy: 0.4465\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.7764 - accuracy: 0.4200 - val_loss: 1.6560 - val_accuracy: 0.5374\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.7350 - accuracy: 0.4295 - val_loss: 1.6176 - val_accuracy: 0.5101\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.7059 - accuracy: 0.4402 - val_loss: 1.6220 - val_accuracy: 0.4899\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.6779 - accuracy: 0.4638 - val_loss: 1.5586 - val_accuracy: 0.5182\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 4s 62ms/step - loss: 1.6494 - accuracy: 0.4644 - val_loss: 1.5187 - val_accuracy: 0.5525\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 1.6156 - accuracy: 0.4817 - val_loss: 1.4966 - val_accuracy: 0.5535\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.5807 - accuracy: 0.4870 - val_loss: 2.6347 - val_accuracy: 0.2111\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.5791 - accuracy: 0.4968 - val_loss: 1.6838 - val_accuracy: 0.4717\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 4s 61ms/step - loss: 1.5424 - accuracy: 0.5161 - val_loss: 1.4149 - val_accuracy: 0.5949\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 4s 63ms/step - loss: 1.5170 - accuracy: 0.5294 - val_loss: 1.4379 - val_accuracy: 0.5525\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.4989 - accuracy: 0.5225 - val_loss: 1.4295 - val_accuracy: 0.5737\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 4s 60ms/step - loss: 1.4941 - accuracy: 0.5288 - val_loss: 1.4015 - val_accuracy: 0.5566\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.4507 - accuracy: 0.5474 - val_loss: 1.3664 - val_accuracy: 0.5859\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 4s 62ms/step - loss: 1.4137 - accuracy: 0.5626 - val_loss: 1.3279 - val_accuracy: 0.6182\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.4085 - accuracy: 0.5575 - val_loss: 1.3395 - val_accuracy: 0.6040\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.3867 - accuracy: 0.5614 - val_loss: 1.3501 - val_accuracy: 0.5889\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 1.3719 - accuracy: 0.5695 - val_loss: 1.4133 - val_accuracy: 0.6010\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.3661 - accuracy: 0.5743 - val_loss: 1.3082 - val_accuracy: 0.6273\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.3537 - accuracy: 0.5739 - val_loss: 1.3029 - val_accuracy: 0.6475\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.3123 - accuracy: 0.5911 - val_loss: 1.2612 - val_accuracy: 0.6263\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.3921 - accuracy: 0.5756 - val_loss: 1.3315 - val_accuracy: 0.5869\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.3144 - accuracy: 0.5865 - val_loss: 1.3361 - val_accuracy: 0.6152\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 1.2919 - accuracy: 0.6070 - val_loss: 1.2406 - val_accuracy: 0.6354\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 4s 60ms/step - loss: 1.2688 - accuracy: 0.6070 - val_loss: 1.2990 - val_accuracy: 0.6121\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.2624 - accuracy: 0.6110 - val_loss: 1.2303 - val_accuracy: 0.6495\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.2960 - accuracy: 0.5981 - val_loss: 1.2606 - val_accuracy: 0.6313\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.2732 - accuracy: 0.6046 - val_loss: 1.2908 - val_accuracy: 0.6111\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.2409 - accuracy: 0.6078 - val_loss: 1.2404 - val_accuracy: 0.6263\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.2389 - accuracy: 0.6154 - val_loss: 1.3139 - val_accuracy: 0.6040\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.2213 - accuracy: 0.6221 - val_loss: 1.2389 - val_accuracy: 0.6172\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.1850 - accuracy: 0.6340 - val_loss: 1.1907 - val_accuracy: 0.6394\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.2013 - accuracy: 0.6334 - val_loss: 1.1866 - val_accuracy: 0.6232\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.1539 - accuracy: 0.6499 - val_loss: 1.1801 - val_accuracy: 0.6606\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.1784 - accuracy: 0.6431 - val_loss: 1.1661 - val_accuracy: 0.6606\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.1557 - accuracy: 0.6415 - val_loss: 1.1744 - val_accuracy: 0.6444\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.1836 - accuracy: 0.6466 - val_loss: 1.2323 - val_accuracy: 0.6172\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.1254 - accuracy: 0.6618 - val_loss: 1.1658 - val_accuracy: 0.6485\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 1.1286 - accuracy: 0.6543 - val_loss: 1.1522 - val_accuracy: 0.6697\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.1253 - accuracy: 0.6535 - val_loss: 1.1351 - val_accuracy: 0.6424\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 1.0934 - accuracy: 0.6716 - val_loss: 1.1459 - val_accuracy: 0.6657\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 4s 63ms/step - loss: 1.1175 - accuracy: 0.6650 - val_loss: 1.1611 - val_accuracy: 0.6444\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 4s 61ms/step - loss: 1.0970 - accuracy: 0.6706 - val_loss: 1.1279 - val_accuracy: 0.6747\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 4s 60ms/step - loss: 1.0850 - accuracy: 0.6760 - val_loss: 1.1296 - val_accuracy: 0.6667\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.1116 - accuracy: 0.6682 - val_loss: 1.1278 - val_accuracy: 0.6525\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 4s 60ms/step - loss: 1.0693 - accuracy: 0.6795 - val_loss: 1.1178 - val_accuracy: 0.6737\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.0479 - accuracy: 0.6829 - val_loss: 1.1254 - val_accuracy: 0.6545\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.0439 - accuracy: 0.6883 - val_loss: 1.1037 - val_accuracy: 0.6606\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.0211 - accuracy: 0.6925 - val_loss: 1.0972 - val_accuracy: 0.6768\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.0316 - accuracy: 0.6941 - val_loss: 1.1183 - val_accuracy: 0.6636\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.0317 - accuracy: 0.6896 - val_loss: 1.0817 - val_accuracy: 0.6747\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.9879 - accuracy: 0.7035 - val_loss: 1.0687 - val_accuracy: 0.6667\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.0108 - accuracy: 0.7044 - val_loss: 1.1428 - val_accuracy: 0.6636\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.0429 - accuracy: 0.6958 - val_loss: 1.0872 - val_accuracy: 0.6657\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.0060 - accuracy: 0.7097 - val_loss: 1.0854 - val_accuracy: 0.6808\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.9969 - accuracy: 0.7093 - val_loss: 1.0637 - val_accuracy: 0.6818\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.9713 - accuracy: 0.7154 - val_loss: 1.0935 - val_accuracy: 0.6869\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.9836 - accuracy: 0.7134 - val_loss: 1.0849 - val_accuracy: 0.6778\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.9753 - accuracy: 0.7134 - val_loss: 1.0677 - val_accuracy: 0.6576\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.9463 - accuracy: 0.7239 - val_loss: 1.0479 - val_accuracy: 0.6960\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.9517 - accuracy: 0.7215 - val_loss: 1.0552 - val_accuracy: 0.6758\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.9436 - accuracy: 0.7213 - val_loss: 1.0389 - val_accuracy: 0.6889\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.9529 - accuracy: 0.7173 - val_loss: 1.0341 - val_accuracy: 0.6949\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.9371 - accuracy: 0.7254 - val_loss: 1.0782 - val_accuracy: 0.6869\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.9212 - accuracy: 0.7313 - val_loss: 1.0418 - val_accuracy: 0.6899\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.9211 - accuracy: 0.7290 - val_loss: 1.0335 - val_accuracy: 0.6778\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.9113 - accuracy: 0.7403 - val_loss: 1.0351 - val_accuracy: 0.7040\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.9052 - accuracy: 0.7397 - val_loss: 1.1000 - val_accuracy: 0.6838\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 4s 60ms/step - loss: 0.9247 - accuracy: 0.7306 - val_loss: 1.0355 - val_accuracy: 0.6939\n",
            "Epoch 82/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.9027 - accuracy: 0.7439 - val_loss: 1.0374 - val_accuracy: 0.7030\n",
            "Epoch 83/200\n",
            "70/70 [==============================] - 4s 61ms/step - loss: 0.9129 - accuracy: 0.7341 - val_loss: 1.0361 - val_accuracy: 0.6949\n",
            "Epoch 84/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.9025 - accuracy: 0.7374 - val_loss: 1.0435 - val_accuracy: 0.6939\n",
            "Epoch 85/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.8961 - accuracy: 0.7388 - val_loss: 1.0249 - val_accuracy: 0.6939\n",
            "Epoch 86/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.8869 - accuracy: 0.7409 - val_loss: 1.0663 - val_accuracy: 0.6828\n",
            "Epoch 87/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.8869 - accuracy: 0.7451 - val_loss: 1.0131 - val_accuracy: 0.6990\n",
            "Epoch 88/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.8793 - accuracy: 0.7500 - val_loss: 1.0128 - val_accuracy: 0.7040\n",
            "Epoch 89/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.8602 - accuracy: 0.7509 - val_loss: 1.0629 - val_accuracy: 0.6879\n",
            "Epoch 90/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.8801 - accuracy: 0.7481 - val_loss: 1.0229 - val_accuracy: 0.7061\n",
            "Epoch 91/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.8523 - accuracy: 0.7652 - val_loss: 1.0366 - val_accuracy: 0.7141\n",
            "Epoch 92/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.8426 - accuracy: 0.7603 - val_loss: 0.9957 - val_accuracy: 0.7051\n",
            "Epoch 93/200\n",
            "70/70 [==============================] - 4s 64ms/step - loss: 0.8348 - accuracy: 0.7568 - val_loss: 1.0242 - val_accuracy: 0.7040\n",
            "Epoch 94/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.8797 - accuracy: 0.7468 - val_loss: 1.0631 - val_accuracy: 0.6838\n",
            "Epoch 95/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.8401 - accuracy: 0.7610 - val_loss: 1.0252 - val_accuracy: 0.6939\n",
            "Epoch 96/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.8550 - accuracy: 0.7564 - val_loss: 1.0586 - val_accuracy: 0.6929\n",
            "Epoch 97/200\n",
            "70/70 [==============================] - 4s 60ms/step - loss: 0.8506 - accuracy: 0.7600 - val_loss: 0.9974 - val_accuracy: 0.7121\n",
            "Epoch 98/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.8104 - accuracy: 0.7720 - val_loss: 1.0093 - val_accuracy: 0.7020\n",
            "Epoch 99/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.8047 - accuracy: 0.7755 - val_loss: 0.9813 - val_accuracy: 0.7152\n",
            "Epoch 100/200\n",
            "70/70 [==============================] - 4s 60ms/step - loss: 0.8157 - accuracy: 0.7654 - val_loss: 1.0130 - val_accuracy: 0.7051\n",
            "Epoch 101/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.8664 - accuracy: 0.7629 - val_loss: 1.0823 - val_accuracy: 0.6657\n",
            "Epoch 102/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.8261 - accuracy: 0.7574 - val_loss: 0.9827 - val_accuracy: 0.7152\n",
            "Epoch 103/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.8049 - accuracy: 0.7719 - val_loss: 1.0101 - val_accuracy: 0.7101\n",
            "Epoch 104/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.7972 - accuracy: 0.7762 - val_loss: 1.0207 - val_accuracy: 0.6970\n",
            "Epoch 105/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.7891 - accuracy: 0.7753 - val_loss: 0.9846 - val_accuracy: 0.7222\n",
            "Epoch 106/200\n",
            "70/70 [==============================] - 4s 60ms/step - loss: 0.7906 - accuracy: 0.7806 - val_loss: 1.0435 - val_accuracy: 0.6939\n",
            "Epoch 107/200\n",
            "70/70 [==============================] - 4s 62ms/step - loss: 0.7787 - accuracy: 0.7827 - val_loss: 0.9786 - val_accuracy: 0.7111\n",
            "Epoch 108/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.7770 - accuracy: 0.7762 - val_loss: 0.9787 - val_accuracy: 0.7131\n",
            "Epoch 109/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.7831 - accuracy: 0.7758 - val_loss: 1.0044 - val_accuracy: 0.7111\n",
            "Epoch 110/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.7588 - accuracy: 0.7837 - val_loss: 1.0310 - val_accuracy: 0.7101\n",
            "Epoch 111/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.7792 - accuracy: 0.7778 - val_loss: 0.9739 - val_accuracy: 0.7141\n",
            "Epoch 112/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.7727 - accuracy: 0.7877 - val_loss: 0.9967 - val_accuracy: 0.7071\n",
            "Epoch 113/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.7636 - accuracy: 0.7865 - val_loss: 1.0039 - val_accuracy: 0.7000\n",
            "Epoch 114/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.8057 - accuracy: 0.7794 - val_loss: 0.9589 - val_accuracy: 0.7222\n",
            "Epoch 115/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.7623 - accuracy: 0.7816 - val_loss: 0.9892 - val_accuracy: 0.7101\n",
            "Epoch 116/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.7450 - accuracy: 0.7893 - val_loss: 1.0242 - val_accuracy: 0.6980\n",
            "Epoch 117/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.7329 - accuracy: 0.7962 - val_loss: 0.9821 - val_accuracy: 0.7101\n",
            "Epoch 118/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.7574 - accuracy: 0.7924 - val_loss: 0.9799 - val_accuracy: 0.7222\n",
            "Epoch 119/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.7344 - accuracy: 0.7923 - val_loss: 0.9874 - val_accuracy: 0.7152\n",
            "Epoch 120/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.7275 - accuracy: 0.7956 - val_loss: 1.0430 - val_accuracy: 0.7061\n",
            "Epoch 121/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.7287 - accuracy: 0.7939 - val_loss: 0.9870 - val_accuracy: 0.7283\n",
            "Epoch 122/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.7317 - accuracy: 0.7953 - val_loss: 0.9511 - val_accuracy: 0.7293\n",
            "Epoch 123/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.7236 - accuracy: 0.7969 - val_loss: 0.9643 - val_accuracy: 0.7141\n",
            "Epoch 124/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.7331 - accuracy: 0.7945 - val_loss: 0.9600 - val_accuracy: 0.7374\n",
            "Epoch 125/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.7499 - accuracy: 0.7940 - val_loss: 0.9836 - val_accuracy: 0.7364\n",
            "Epoch 126/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.7321 - accuracy: 0.7945 - val_loss: 0.9996 - val_accuracy: 0.7020\n",
            "Epoch 127/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.6949 - accuracy: 0.8103 - val_loss: 1.1224 - val_accuracy: 0.6980\n",
            "Epoch 128/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.7059 - accuracy: 0.8040 - val_loss: 0.9700 - val_accuracy: 0.7111\n",
            "Epoch 129/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.7011 - accuracy: 0.8052 - val_loss: 1.1122 - val_accuracy: 0.6949\n",
            "Epoch 130/200\n",
            "70/70 [==============================] - 4s 60ms/step - loss: 0.7498 - accuracy: 0.7982 - val_loss: 1.0253 - val_accuracy: 0.7141\n",
            "Epoch 131/200\n",
            "70/70 [==============================] - 5s 65ms/step - loss: 0.7106 - accuracy: 0.8037 - val_loss: 0.9806 - val_accuracy: 0.7242\n",
            "Epoch 132/200\n",
            "70/70 [==============================] - 4s 61ms/step - loss: 0.7051 - accuracy: 0.8024 - val_loss: 0.9837 - val_accuracy: 0.7101\n",
            "Epoch 133/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.6945 - accuracy: 0.8110 - val_loss: 0.9920 - val_accuracy: 0.7192\n",
            "Epoch 134/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.6944 - accuracy: 0.8094 - val_loss: 0.9814 - val_accuracy: 0.7051\n",
            "Epoch 135/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.7063 - accuracy: 0.8037 - val_loss: 0.9560 - val_accuracy: 0.7263\n",
            "Epoch 136/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.6857 - accuracy: 0.8071 - val_loss: 0.9772 - val_accuracy: 0.7222\n",
            "Epoch 137/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.7020 - accuracy: 0.8085 - val_loss: 0.9517 - val_accuracy: 0.7313\n",
            "Evaluating model...\n",
            "ROC: 0.97\n",
            "\n",
            "Accuracy = 0.74\n",
            "F-Score: 0.74\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 5, 6, 7, 10} Validate on 8 Test on 9 ***\n",
            "val len:  806\n",
            "test len:  816\n",
            "train len:  7110\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "72/72 [==============================] - 4s 54ms/step - loss: 2.4438 - accuracy: 0.1195 - val_loss: 2.3882 - val_accuracy: 0.2120\n",
            "Epoch 2/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 2.3918 - accuracy: 0.1433 - val_loss: 2.3458 - val_accuracy: 0.2917\n",
            "Epoch 3/200\n",
            "72/72 [==============================] - 3s 49ms/step - loss: 2.3561 - accuracy: 0.1740 - val_loss: 2.2964 - val_accuracy: 0.2941\n",
            "Epoch 4/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 2.3097 - accuracy: 0.1969 - val_loss: 2.2218 - val_accuracy: 0.3591\n",
            "Epoch 5/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 2.2563 - accuracy: 0.2241 - val_loss: 2.1623 - val_accuracy: 0.3603\n",
            "Epoch 6/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 2.1818 - accuracy: 0.2641 - val_loss: 2.0424 - val_accuracy: 0.4056\n",
            "Epoch 7/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 2.1170 - accuracy: 0.2972 - val_loss: 1.9640 - val_accuracy: 0.4203\n",
            "Epoch 8/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 2.0560 - accuracy: 0.3203 - val_loss: 1.8806 - val_accuracy: 0.4265\n",
            "Epoch 9/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 1.9968 - accuracy: 0.3430 - val_loss: 1.8586 - val_accuracy: 0.4510\n",
            "Epoch 10/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.9345 - accuracy: 0.3622 - val_loss: 1.7976 - val_accuracy: 0.4890\n",
            "Epoch 11/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.8917 - accuracy: 0.3807 - val_loss: 1.6998 - val_accuracy: 0.5147\n",
            "Epoch 12/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.8477 - accuracy: 0.4023 - val_loss: 1.6879 - val_accuracy: 0.5233\n",
            "Epoch 13/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.8149 - accuracy: 0.4049 - val_loss: 1.6466 - val_accuracy: 0.5098\n",
            "Epoch 14/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 1.7711 - accuracy: 0.4276 - val_loss: 1.6414 - val_accuracy: 0.5539\n",
            "Epoch 15/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 1.7333 - accuracy: 0.4408 - val_loss: 1.5999 - val_accuracy: 0.5282\n",
            "Epoch 16/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.7050 - accuracy: 0.4464 - val_loss: 1.5679 - val_accuracy: 0.5809\n",
            "Epoch 17/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 1.6837 - accuracy: 0.4591 - val_loss: 1.5608 - val_accuracy: 0.5404\n",
            "Epoch 18/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 1.6476 - accuracy: 0.4817 - val_loss: 1.5204 - val_accuracy: 0.5576\n",
            "Epoch 19/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.6314 - accuracy: 0.4817 - val_loss: 1.5359 - val_accuracy: 0.5748\n",
            "Epoch 20/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 1.5916 - accuracy: 0.4837 - val_loss: 1.5020 - val_accuracy: 0.5613\n",
            "Epoch 21/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.5531 - accuracy: 0.5131 - val_loss: 1.4571 - val_accuracy: 0.6005\n",
            "Epoch 22/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 1.5425 - accuracy: 0.5125 - val_loss: 1.4756 - val_accuracy: 0.5662\n",
            "Epoch 23/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.5232 - accuracy: 0.5200 - val_loss: 1.4476 - val_accuracy: 0.6238\n",
            "Epoch 24/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 1.5044 - accuracy: 0.5255 - val_loss: 1.4025 - val_accuracy: 0.5919\n",
            "Epoch 25/200\n",
            "72/72 [==============================] - 3s 49ms/step - loss: 1.4667 - accuracy: 0.5428 - val_loss: 1.3928 - val_accuracy: 0.5686\n",
            "Epoch 26/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 1.4573 - accuracy: 0.5354 - val_loss: 1.4350 - val_accuracy: 0.5380\n",
            "Epoch 27/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 1.4495 - accuracy: 0.5480 - val_loss: 1.3989 - val_accuracy: 0.5735\n",
            "Epoch 28/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 1.4173 - accuracy: 0.5632 - val_loss: 1.3778 - val_accuracy: 0.5760\n",
            "Epoch 29/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 1.4040 - accuracy: 0.5627 - val_loss: 1.3446 - val_accuracy: 0.5993\n",
            "Epoch 30/200\n",
            "72/72 [==============================] - 4s 52ms/step - loss: 1.3603 - accuracy: 0.5771 - val_loss: 1.3243 - val_accuracy: 0.6140\n",
            "Epoch 31/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 1.3712 - accuracy: 0.5738 - val_loss: 1.2869 - val_accuracy: 0.6324\n",
            "Epoch 32/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.3600 - accuracy: 0.5813 - val_loss: 1.3129 - val_accuracy: 0.6238\n",
            "Epoch 33/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 1.3521 - accuracy: 0.5883 - val_loss: 1.2771 - val_accuracy: 0.6544\n",
            "Epoch 34/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 1.3216 - accuracy: 0.5917 - val_loss: 1.2597 - val_accuracy: 0.6581\n",
            "Epoch 35/200\n",
            "72/72 [==============================] - 4s 51ms/step - loss: 1.2870 - accuracy: 0.6077 - val_loss: 1.2650 - val_accuracy: 0.6532\n",
            "Epoch 36/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 1.2893 - accuracy: 0.6063 - val_loss: 1.2579 - val_accuracy: 0.6164\n",
            "Epoch 37/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.2715 - accuracy: 0.6091 - val_loss: 1.2130 - val_accuracy: 0.6691\n",
            "Epoch 38/200\n",
            "72/72 [==============================] - 3s 49ms/step - loss: 1.2520 - accuracy: 0.6229 - val_loss: 1.2760 - val_accuracy: 0.6201\n",
            "Epoch 39/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.2381 - accuracy: 0.6228 - val_loss: 1.2056 - val_accuracy: 0.6801\n",
            "Epoch 40/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 1.2130 - accuracy: 0.6283 - val_loss: 1.2239 - val_accuracy: 0.6544\n",
            "Epoch 41/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 1.2232 - accuracy: 0.6259 - val_loss: 1.1932 - val_accuracy: 0.6863\n",
            "Epoch 42/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 1.2093 - accuracy: 0.6311 - val_loss: 1.1981 - val_accuracy: 0.6630\n",
            "Epoch 43/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.2022 - accuracy: 0.6387 - val_loss: 1.1775 - val_accuracy: 0.6949\n",
            "Epoch 44/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.1788 - accuracy: 0.6436 - val_loss: 1.1547 - val_accuracy: 0.6863\n",
            "Epoch 45/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 1.1622 - accuracy: 0.6496 - val_loss: 1.1568 - val_accuracy: 0.7120\n",
            "Epoch 46/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 1.1493 - accuracy: 0.6578 - val_loss: 1.2021 - val_accuracy: 0.6765\n",
            "Epoch 47/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.1549 - accuracy: 0.6558 - val_loss: 1.1522 - val_accuracy: 0.7034\n",
            "Epoch 48/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 1.1527 - accuracy: 0.6570 - val_loss: 1.1639 - val_accuracy: 0.6949\n",
            "Epoch 49/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.1137 - accuracy: 0.6674 - val_loss: 1.1928 - val_accuracy: 0.6605\n",
            "Epoch 50/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.1044 - accuracy: 0.6696 - val_loss: 1.1271 - val_accuracy: 0.7132\n",
            "Epoch 51/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 1.0886 - accuracy: 0.6743 - val_loss: 1.1241 - val_accuracy: 0.7059\n",
            "Epoch 52/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 1.0851 - accuracy: 0.6797 - val_loss: 1.1192 - val_accuracy: 0.7047\n",
            "Epoch 53/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 1.0900 - accuracy: 0.6713 - val_loss: 1.1345 - val_accuracy: 0.6912\n",
            "Epoch 54/200\n",
            "72/72 [==============================] - 4s 51ms/step - loss: 1.0778 - accuracy: 0.6833 - val_loss: 1.0965 - val_accuracy: 0.7230\n",
            "Epoch 55/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.0564 - accuracy: 0.6906 - val_loss: 1.1556 - val_accuracy: 0.7034\n",
            "Epoch 56/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 1.0612 - accuracy: 0.6847 - val_loss: 1.0995 - val_accuracy: 0.7181\n",
            "Epoch 57/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 1.0434 - accuracy: 0.6968 - val_loss: 1.1461 - val_accuracy: 0.7010\n",
            "Epoch 58/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 1.0512 - accuracy: 0.6890 - val_loss: 1.0750 - val_accuracy: 0.7341\n",
            "Epoch 59/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.0193 - accuracy: 0.6931 - val_loss: 1.0991 - val_accuracy: 0.7071\n",
            "Epoch 60/200\n",
            "72/72 [==============================] - 3s 46ms/step - loss: 1.0159 - accuracy: 0.6994 - val_loss: 1.1170 - val_accuracy: 0.6998\n",
            "Epoch 61/200\n",
            "72/72 [==============================] - 3s 46ms/step - loss: 1.0084 - accuracy: 0.7038 - val_loss: 1.0828 - val_accuracy: 0.7267\n",
            "Epoch 62/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 1.0003 - accuracy: 0.7068 - val_loss: 1.1084 - val_accuracy: 0.7243\n",
            "Epoch 63/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 0.9678 - accuracy: 0.7190 - val_loss: 1.1495 - val_accuracy: 0.6520\n",
            "Epoch 64/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 0.9957 - accuracy: 0.7073 - val_loss: 1.0280 - val_accuracy: 0.7488\n",
            "Epoch 65/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 0.9810 - accuracy: 0.7160 - val_loss: 1.0727 - val_accuracy: 0.7267\n",
            "Epoch 66/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 0.9758 - accuracy: 0.7134 - val_loss: 1.0917 - val_accuracy: 0.7181\n",
            "Epoch 67/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 0.9519 - accuracy: 0.7238 - val_loss: 1.0424 - val_accuracy: 0.7328\n",
            "Epoch 68/200\n",
            "72/72 [==============================] - 3s 49ms/step - loss: 0.9372 - accuracy: 0.7278 - val_loss: 1.0865 - val_accuracy: 0.7120\n",
            "Epoch 69/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 0.9466 - accuracy: 0.7197 - val_loss: 1.0411 - val_accuracy: 0.7390\n",
            "Epoch 70/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 0.9390 - accuracy: 0.7198 - val_loss: 1.0638 - val_accuracy: 0.7304\n",
            "Epoch 71/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 0.9474 - accuracy: 0.7167 - val_loss: 1.0600 - val_accuracy: 0.7390\n",
            "Epoch 72/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 0.9439 - accuracy: 0.7283 - val_loss: 1.0425 - val_accuracy: 0.7463\n",
            "Epoch 73/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 0.9363 - accuracy: 0.7297 - val_loss: 1.1214 - val_accuracy: 0.6924\n",
            "Epoch 74/200\n",
            "72/72 [==============================] - 4s 51ms/step - loss: 0.9169 - accuracy: 0.7373 - val_loss: 1.0539 - val_accuracy: 0.7537\n",
            "Epoch 75/200\n",
            "72/72 [==============================] - 4s 51ms/step - loss: 0.8813 - accuracy: 0.7428 - val_loss: 1.0268 - val_accuracy: 0.7328\n",
            "Epoch 76/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 0.8924 - accuracy: 0.7399 - val_loss: 1.0593 - val_accuracy: 0.7365\n",
            "Epoch 77/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 0.8990 - accuracy: 0.7402 - val_loss: 1.0707 - val_accuracy: 0.7206\n",
            "Epoch 78/200\n",
            "72/72 [==============================] - 4s 52ms/step - loss: 0.9058 - accuracy: 0.7426 - val_loss: 1.0540 - val_accuracy: 0.7341\n",
            "Epoch 79/200\n",
            "72/72 [==============================] - 4s 54ms/step - loss: 0.8803 - accuracy: 0.7466 - val_loss: 1.0346 - val_accuracy: 0.7255\n",
            "Epoch 80/200\n",
            "72/72 [==============================] - 4s 51ms/step - loss: 0.8880 - accuracy: 0.7488 - val_loss: 1.0279 - val_accuracy: 0.7402\n",
            "Epoch 81/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 0.8758 - accuracy: 0.7495 - val_loss: 1.0388 - val_accuracy: 0.7426\n",
            "Epoch 82/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 0.8701 - accuracy: 0.7488 - val_loss: 1.0449 - val_accuracy: 0.7243\n",
            "Epoch 83/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 0.8588 - accuracy: 0.7544 - val_loss: 1.0298 - val_accuracy: 0.7414\n",
            "Epoch 84/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 0.8608 - accuracy: 0.7516 - val_loss: 1.0478 - val_accuracy: 0.7083\n",
            "Epoch 85/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 0.8573 - accuracy: 0.7547 - val_loss: 1.0711 - val_accuracy: 0.7047\n",
            "Epoch 86/200\n",
            "72/72 [==============================] - 4s 49ms/step - loss: 0.8357 - accuracy: 0.7695 - val_loss: 1.0359 - val_accuracy: 0.7365\n",
            "Epoch 87/200\n",
            "72/72 [==============================] - 3s 47ms/step - loss: 0.8307 - accuracy: 0.7681 - val_loss: 1.0629 - val_accuracy: 0.7267\n",
            "Epoch 88/200\n",
            "72/72 [==============================] - 4s 50ms/step - loss: 0.8408 - accuracy: 0.7616 - val_loss: 1.0321 - val_accuracy: 0.7304\n",
            "Epoch 89/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 0.8131 - accuracy: 0.7729 - val_loss: 1.0372 - val_accuracy: 0.7316\n",
            "Epoch 90/200\n",
            "72/72 [==============================] - 3s 48ms/step - loss: 0.8120 - accuracy: 0.7726 - val_loss: 1.0377 - val_accuracy: 0.7353\n",
            "Evaluating model...\n",
            "ROC: 0.942\n",
            "\n",
            "Accuracy = 0.65\n",
            "F-Score: 0.65\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 6, 7, 8, 10} Validate on 9 Test on 5 ***\n",
            "val len:  816\n",
            "test len:  936\n",
            "train len:  6980\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 2.4308 - accuracy: 0.1277 - val_loss: 2.3854 - val_accuracy: 0.2137\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 2.3821 - accuracy: 0.1566 - val_loss: 2.3419 - val_accuracy: 0.2938\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 2.3388 - accuracy: 0.1897 - val_loss: 2.2920 - val_accuracy: 0.3301\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 2.2826 - accuracy: 0.2185 - val_loss: 2.2273 - val_accuracy: 0.3333\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 2.2315 - accuracy: 0.2362 - val_loss: 2.1585 - val_accuracy: 0.3675\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 2.1617 - accuracy: 0.2658 - val_loss: 2.0893 - val_accuracy: 0.3814\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 2.1012 - accuracy: 0.2921 - val_loss: 2.0489 - val_accuracy: 0.3889\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 2.0524 - accuracy: 0.3073 - val_loss: 1.9659 - val_accuracy: 0.4071\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 2.0108 - accuracy: 0.3284 - val_loss: 1.9000 - val_accuracy: 0.3793\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.9658 - accuracy: 0.3390 - val_loss: 1.8420 - val_accuracy: 0.4209\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.9286 - accuracy: 0.3540 - val_loss: 1.7898 - val_accuracy: 0.4380\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.8864 - accuracy: 0.3744 - val_loss: 1.7380 - val_accuracy: 0.4444\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.8582 - accuracy: 0.3762 - val_loss: 1.7079 - val_accuracy: 0.4444\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.8174 - accuracy: 0.3979 - val_loss: 1.6641 - val_accuracy: 0.4562\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.7694 - accuracy: 0.4211 - val_loss: 1.6921 - val_accuracy: 0.4487\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.7525 - accuracy: 0.4328 - val_loss: 1.5884 - val_accuracy: 0.4936\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.7303 - accuracy: 0.4384 - val_loss: 1.5600 - val_accuracy: 0.5021\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.6974 - accuracy: 0.4444 - val_loss: 1.6236 - val_accuracy: 0.4509\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.6688 - accuracy: 0.4585 - val_loss: 1.5162 - val_accuracy: 0.5150\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.6432 - accuracy: 0.4630 - val_loss: 1.4747 - val_accuracy: 0.5235\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.6204 - accuracy: 0.4748 - val_loss: 1.4702 - val_accuracy: 0.5331\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.6060 - accuracy: 0.4851 - val_loss: 1.4940 - val_accuracy: 0.4957\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.5682 - accuracy: 0.5013 - val_loss: 1.3903 - val_accuracy: 0.5876\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.5527 - accuracy: 0.4968 - val_loss: 1.4350 - val_accuracy: 0.5192\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.5356 - accuracy: 0.5105 - val_loss: 1.3800 - val_accuracy: 0.5705\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.4993 - accuracy: 0.5198 - val_loss: 1.3397 - val_accuracy: 0.5705\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.4991 - accuracy: 0.5223 - val_loss: 1.3550 - val_accuracy: 0.5780\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.4776 - accuracy: 0.5325 - val_loss: 1.3056 - val_accuracy: 0.6068\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.4485 - accuracy: 0.5433 - val_loss: 1.2598 - val_accuracy: 0.6154\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.4407 - accuracy: 0.5375 - val_loss: 1.2587 - val_accuracy: 0.6186\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.4079 - accuracy: 0.5630 - val_loss: 1.2589 - val_accuracy: 0.6100\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.4009 - accuracy: 0.5648 - val_loss: 1.2831 - val_accuracy: 0.6047\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.3824 - accuracy: 0.5683 - val_loss: 1.2596 - val_accuracy: 0.6122\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.3525 - accuracy: 0.5798 - val_loss: 1.2229 - val_accuracy: 0.6197\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.3421 - accuracy: 0.5831 - val_loss: 1.2343 - val_accuracy: 0.6036\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.3285 - accuracy: 0.5801 - val_loss: 1.2631 - val_accuracy: 0.6154\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.3199 - accuracy: 0.5907 - val_loss: 1.3100 - val_accuracy: 0.5865\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.3110 - accuracy: 0.5898 - val_loss: 1.1640 - val_accuracy: 0.6378\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.2941 - accuracy: 0.6000 - val_loss: 1.1810 - val_accuracy: 0.6378\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.2806 - accuracy: 0.6033 - val_loss: 1.1245 - val_accuracy: 0.6571\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.2510 - accuracy: 0.6109 - val_loss: 1.1372 - val_accuracy: 0.6442\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 4s 60ms/step - loss: 1.2544 - accuracy: 0.6162 - val_loss: 1.1400 - val_accuracy: 0.6410\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.2409 - accuracy: 0.6209 - val_loss: 1.1879 - val_accuracy: 0.6239\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.2210 - accuracy: 0.6175 - val_loss: 1.1624 - val_accuracy: 0.6389\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.2059 - accuracy: 0.6262 - val_loss: 1.1247 - val_accuracy: 0.6571\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.2046 - accuracy: 0.6295 - val_loss: 1.1116 - val_accuracy: 0.6464\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.2003 - accuracy: 0.6370 - val_loss: 1.1462 - val_accuracy: 0.6581\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.1702 - accuracy: 0.6385 - val_loss: 1.0902 - val_accuracy: 0.6496\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.1636 - accuracy: 0.6413 - val_loss: 1.0892 - val_accuracy: 0.6741\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.1559 - accuracy: 0.6410 - val_loss: 1.1074 - val_accuracy: 0.6603\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.1540 - accuracy: 0.6444 - val_loss: 1.0799 - val_accuracy: 0.6496\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.1313 - accuracy: 0.6583 - val_loss: 1.0594 - val_accuracy: 0.6624\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 1.1245 - accuracy: 0.6574 - val_loss: 1.0366 - val_accuracy: 0.6806\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.1295 - accuracy: 0.6569 - val_loss: 1.0597 - val_accuracy: 0.6774\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.1267 - accuracy: 0.6576 - val_loss: 1.0787 - val_accuracy: 0.6549\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.0851 - accuracy: 0.6785 - val_loss: 1.0893 - val_accuracy: 0.6613\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.0913 - accuracy: 0.6659 - val_loss: 1.0700 - val_accuracy: 0.6656\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.0833 - accuracy: 0.6758 - val_loss: 1.0313 - val_accuracy: 0.6656\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.0804 - accuracy: 0.6725 - val_loss: 1.0612 - val_accuracy: 0.6912\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.0797 - accuracy: 0.6736 - val_loss: 1.0031 - val_accuracy: 0.6827\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.0572 - accuracy: 0.6805 - val_loss: 1.0913 - val_accuracy: 0.6506\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.0379 - accuracy: 0.6926 - val_loss: 1.0052 - val_accuracy: 0.6976\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.0520 - accuracy: 0.6827 - val_loss: 1.0367 - val_accuracy: 0.6763\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.0439 - accuracy: 0.6862 - val_loss: 1.0468 - val_accuracy: 0.6870\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.0240 - accuracy: 0.6967 - val_loss: 1.0062 - val_accuracy: 0.6699\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.0264 - accuracy: 0.6974 - val_loss: 0.9984 - val_accuracy: 0.6912\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 4s 60ms/step - loss: 1.0104 - accuracy: 0.6987 - val_loss: 0.9712 - val_accuracy: 0.7233\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 1.0192 - accuracy: 0.7009 - val_loss: 1.0172 - val_accuracy: 0.6806\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.9972 - accuracy: 0.7044 - val_loss: 1.0119 - val_accuracy: 0.7009\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.0012 - accuracy: 0.7023 - val_loss: 0.9793 - val_accuracy: 0.6955\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.9878 - accuracy: 0.7047 - val_loss: 0.9527 - val_accuracy: 0.7158\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.9884 - accuracy: 0.7112 - val_loss: 0.9392 - val_accuracy: 0.7179\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.9719 - accuracy: 0.7140 - val_loss: 1.0195 - val_accuracy: 0.6944\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.9690 - accuracy: 0.7130 - val_loss: 0.9977 - val_accuracy: 0.6891\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.9652 - accuracy: 0.7129 - val_loss: 0.9999 - val_accuracy: 0.6859\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.9527 - accuracy: 0.7219 - val_loss: 0.9894 - val_accuracy: 0.7062\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.9532 - accuracy: 0.7288 - val_loss: 0.9804 - val_accuracy: 0.6987\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.9284 - accuracy: 0.7350 - val_loss: 0.9699 - val_accuracy: 0.7083\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.9351 - accuracy: 0.7249 - val_loss: 0.9420 - val_accuracy: 0.7094\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.9318 - accuracy: 0.7279 - val_loss: 0.9236 - val_accuracy: 0.7276\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.9274 - accuracy: 0.7264 - val_loss: 0.9305 - val_accuracy: 0.7233\n",
            "Epoch 82/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.9338 - accuracy: 0.7311 - val_loss: 0.9591 - val_accuracy: 0.7041\n",
            "Epoch 83/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.9086 - accuracy: 0.7371 - val_loss: 0.9083 - val_accuracy: 0.7382\n",
            "Epoch 84/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.9128 - accuracy: 0.7322 - val_loss: 0.9363 - val_accuracy: 0.7179\n",
            "Epoch 85/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.9005 - accuracy: 0.7312 - val_loss: 0.9343 - val_accuracy: 0.7169\n",
            "Epoch 86/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.8991 - accuracy: 0.7394 - val_loss: 0.9142 - val_accuracy: 0.7105\n",
            "Epoch 87/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.8923 - accuracy: 0.7450 - val_loss: 0.9296 - val_accuracy: 0.7212\n",
            "Epoch 88/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.8813 - accuracy: 0.7451 - val_loss: 0.9432 - val_accuracy: 0.7158\n",
            "Epoch 89/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.8729 - accuracy: 0.7490 - val_loss: 0.9085 - val_accuracy: 0.7105\n",
            "Epoch 90/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.8834 - accuracy: 0.7526 - val_loss: 0.9259 - val_accuracy: 0.7115\n",
            "Epoch 91/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.8903 - accuracy: 0.7444 - val_loss: 0.9866 - val_accuracy: 0.7201\n",
            "Epoch 92/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.8555 - accuracy: 0.7454 - val_loss: 0.9433 - val_accuracy: 0.7169\n",
            "Epoch 93/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.8656 - accuracy: 0.7532 - val_loss: 0.9212 - val_accuracy: 0.7265\n",
            "Epoch 94/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8696 - accuracy: 0.7490 - val_loss: 0.9241 - val_accuracy: 0.7126\n",
            "Epoch 95/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8670 - accuracy: 0.7519 - val_loss: 0.9349 - val_accuracy: 0.7276\n",
            "Epoch 96/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.8472 - accuracy: 0.7559 - val_loss: 0.9083 - val_accuracy: 0.7329\n",
            "Epoch 97/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.8481 - accuracy: 0.7607 - val_loss: 0.8805 - val_accuracy: 0.7276\n",
            "Epoch 98/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.8529 - accuracy: 0.7546 - val_loss: 0.8799 - val_accuracy: 0.7382\n",
            "Epoch 99/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.8294 - accuracy: 0.7625 - val_loss: 0.9158 - val_accuracy: 0.7318\n",
            "Epoch 100/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.8320 - accuracy: 0.7639 - val_loss: 0.9245 - val_accuracy: 0.7350\n",
            "Epoch 101/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8320 - accuracy: 0.7663 - val_loss: 0.8616 - val_accuracy: 0.7468\n",
            "Epoch 102/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.8382 - accuracy: 0.7629 - val_loss: 0.8729 - val_accuracy: 0.7361\n",
            "Epoch 103/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.8158 - accuracy: 0.7715 - val_loss: 0.8863 - val_accuracy: 0.7340\n",
            "Epoch 104/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.8104 - accuracy: 0.7712 - val_loss: 0.8950 - val_accuracy: 0.7276\n",
            "Epoch 105/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.8232 - accuracy: 0.7682 - val_loss: 0.8698 - val_accuracy: 0.7340\n",
            "Epoch 106/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.7952 - accuracy: 0.7715 - val_loss: 0.9389 - val_accuracy: 0.7382\n",
            "Epoch 107/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7849 - accuracy: 0.7729 - val_loss: 0.8906 - val_accuracy: 0.7372\n",
            "Epoch 108/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.8063 - accuracy: 0.7699 - val_loss: 0.8803 - val_accuracy: 0.7318\n",
            "Epoch 109/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7943 - accuracy: 0.7777 - val_loss: 0.8448 - val_accuracy: 0.7468\n",
            "Epoch 110/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7894 - accuracy: 0.7825 - val_loss: 0.8999 - val_accuracy: 0.7137\n",
            "Epoch 111/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.7775 - accuracy: 0.7828 - val_loss: 0.8552 - val_accuracy: 0.7415\n",
            "Epoch 112/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.7726 - accuracy: 0.7789 - val_loss: 0.8411 - val_accuracy: 0.7393\n",
            "Epoch 113/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7665 - accuracy: 0.7840 - val_loss: 0.9225 - val_accuracy: 0.7201\n",
            "Epoch 114/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.7646 - accuracy: 0.7827 - val_loss: 0.7963 - val_accuracy: 0.7671\n",
            "Epoch 115/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.7661 - accuracy: 0.7864 - val_loss: 0.8784 - val_accuracy: 0.7500\n",
            "Epoch 116/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.7714 - accuracy: 0.7848 - val_loss: 0.9613 - val_accuracy: 0.7329\n",
            "Epoch 117/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.7731 - accuracy: 0.7895 - val_loss: 0.9398 - val_accuracy: 0.7415\n",
            "Epoch 118/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7631 - accuracy: 0.7872 - val_loss: 0.8485 - val_accuracy: 0.7521\n",
            "Epoch 119/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.7430 - accuracy: 0.7928 - val_loss: 0.9226 - val_accuracy: 0.7308\n",
            "Epoch 120/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.7619 - accuracy: 0.7861 - val_loss: 0.8583 - val_accuracy: 0.7404\n",
            "Epoch 121/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.7528 - accuracy: 0.7911 - val_loss: 0.8345 - val_accuracy: 0.7489\n",
            "Epoch 122/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7394 - accuracy: 0.7941 - val_loss: 0.8104 - val_accuracy: 0.7447\n",
            "Epoch 123/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7540 - accuracy: 0.7878 - val_loss: 0.8034 - val_accuracy: 0.7575\n",
            "Epoch 124/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.7276 - accuracy: 0.7954 - val_loss: 0.8419 - val_accuracy: 0.7457\n",
            "Epoch 125/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.7274 - accuracy: 0.7991 - val_loss: 0.8493 - val_accuracy: 0.7489\n",
            "Epoch 126/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7334 - accuracy: 0.7981 - val_loss: 1.0069 - val_accuracy: 0.7361\n",
            "Epoch 127/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.7388 - accuracy: 0.7951 - val_loss: 0.8791 - val_accuracy: 0.7521\n",
            "Epoch 128/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.7112 - accuracy: 0.8000 - val_loss: 0.8026 - val_accuracy: 0.7596\n",
            "Epoch 129/200\n",
            "70/70 [==============================] - 4s 59ms/step - loss: 0.7303 - accuracy: 0.7947 - val_loss: 0.8215 - val_accuracy: 0.7489\n",
            "Evaluating model...\n",
            "ROC: 0.965\n",
            "\n",
            "Accuracy = 0.72\n",
            "F-Score: 0.72\n",
            "\n",
            "*** Train on {1, 2, 3, 4, 6, 7, 8, 9} Validate on 10 Test on 5 ***\n",
            "val len:  837\n",
            "test len:  936\n",
            "train len:  6959\n",
            "Building model...\n",
            "Training model...\n",
            "Epoch 1/200\n",
            "70/70 [==============================] - 4s 60ms/step - loss: 2.4301 - accuracy: 0.1270 - val_loss: 2.3742 - val_accuracy: 0.2051\n",
            "Epoch 2/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 2.3658 - accuracy: 0.1594 - val_loss: 2.3157 - val_accuracy: 0.2479\n",
            "Epoch 3/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 2.3170 - accuracy: 0.1931 - val_loss: 2.2484 - val_accuracy: 0.2842\n",
            "Epoch 4/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 2.2682 - accuracy: 0.2115 - val_loss: 2.1654 - val_accuracy: 0.2853\n",
            "Epoch 5/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 2.1859 - accuracy: 0.2594 - val_loss: 2.0554 - val_accuracy: 0.3611\n",
            "Epoch 6/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 2.1075 - accuracy: 0.2861 - val_loss: 1.9626 - val_accuracy: 0.4135\n",
            "Epoch 7/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 2.0445 - accuracy: 0.3101 - val_loss: 1.8577 - val_accuracy: 0.4519\n",
            "Epoch 8/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.9927 - accuracy: 0.3294 - val_loss: 1.7776 - val_accuracy: 0.4882\n",
            "Epoch 9/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.9326 - accuracy: 0.3523 - val_loss: 1.7269 - val_accuracy: 0.4765\n",
            "Epoch 10/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.8815 - accuracy: 0.3719 - val_loss: 1.6734 - val_accuracy: 0.5214\n",
            "Epoch 11/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.8358 - accuracy: 0.3910 - val_loss: 1.6220 - val_accuracy: 0.5246\n",
            "Epoch 12/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.8070 - accuracy: 0.3980 - val_loss: 1.5589 - val_accuracy: 0.5470\n",
            "Epoch 13/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.7767 - accuracy: 0.4126 - val_loss: 1.5138 - val_accuracy: 0.5577\n",
            "Epoch 14/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.7352 - accuracy: 0.4193 - val_loss: 1.4680 - val_accuracy: 0.5513\n",
            "Epoch 15/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.7051 - accuracy: 0.4371 - val_loss: 1.4642 - val_accuracy: 0.5769\n",
            "Epoch 16/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 1.6614 - accuracy: 0.4548 - val_loss: 1.3715 - val_accuracy: 0.6047\n",
            "Epoch 17/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.6402 - accuracy: 0.4659 - val_loss: 1.3899 - val_accuracy: 0.5876\n",
            "Epoch 18/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.6106 - accuracy: 0.4771 - val_loss: 1.3332 - val_accuracy: 0.6175\n",
            "Epoch 19/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.5868 - accuracy: 0.4890 - val_loss: 1.3214 - val_accuracy: 0.6207\n",
            "Epoch 20/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.5604 - accuracy: 0.4923 - val_loss: 1.2830 - val_accuracy: 0.6464\n",
            "Epoch 21/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.5542 - accuracy: 0.4914 - val_loss: 1.2522 - val_accuracy: 0.6699\n",
            "Epoch 22/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.5267 - accuracy: 0.5015 - val_loss: 1.4820 - val_accuracy: 0.4936\n",
            "Epoch 23/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.5059 - accuracy: 0.5255 - val_loss: 1.2522 - val_accuracy: 0.6442\n",
            "Epoch 24/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.4571 - accuracy: 0.5277 - val_loss: 1.2318 - val_accuracy: 0.6400\n",
            "Epoch 25/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.4514 - accuracy: 0.5308 - val_loss: 1.2131 - val_accuracy: 0.6549\n",
            "Epoch 26/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.4409 - accuracy: 0.5337 - val_loss: 1.1481 - val_accuracy: 0.6859\n",
            "Epoch 27/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.4270 - accuracy: 0.5574 - val_loss: 1.1814 - val_accuracy: 0.6613\n",
            "Epoch 28/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.4143 - accuracy: 0.5577 - val_loss: 1.1193 - val_accuracy: 0.6880\n",
            "Epoch 29/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.3885 - accuracy: 0.5574 - val_loss: 1.1722 - val_accuracy: 0.6581\n",
            "Epoch 30/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.3578 - accuracy: 0.5715 - val_loss: 1.1569 - val_accuracy: 0.6774\n",
            "Epoch 31/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.3515 - accuracy: 0.5731 - val_loss: 1.1380 - val_accuracy: 0.6784\n",
            "Epoch 32/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.3398 - accuracy: 0.5817 - val_loss: 1.0713 - val_accuracy: 0.6934\n",
            "Epoch 33/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.3171 - accuracy: 0.5900 - val_loss: 1.1463 - val_accuracy: 0.6806\n",
            "Epoch 34/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.3003 - accuracy: 0.5896 - val_loss: 1.0762 - val_accuracy: 0.7094\n",
            "Epoch 35/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.2971 - accuracy: 0.5913 - val_loss: 1.1315 - val_accuracy: 0.6731\n",
            "Epoch 36/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.2794 - accuracy: 0.5994 - val_loss: 1.0885 - val_accuracy: 0.7062\n",
            "Epoch 37/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.2606 - accuracy: 0.6060 - val_loss: 1.0667 - val_accuracy: 0.7009\n",
            "Epoch 38/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.2654 - accuracy: 0.6068 - val_loss: 1.0365 - val_accuracy: 0.7308\n",
            "Epoch 39/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.2501 - accuracy: 0.6120 - val_loss: 1.0536 - val_accuracy: 0.7222\n",
            "Epoch 40/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.2318 - accuracy: 0.6182 - val_loss: 1.2284 - val_accuracy: 0.6218\n",
            "Epoch 41/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.2111 - accuracy: 0.6291 - val_loss: 1.0102 - val_accuracy: 0.7308\n",
            "Epoch 42/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.1986 - accuracy: 0.6274 - val_loss: 1.0826 - val_accuracy: 0.6998\n",
            "Epoch 43/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.1943 - accuracy: 0.6287 - val_loss: 1.0530 - val_accuracy: 0.7233\n",
            "Epoch 44/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.1702 - accuracy: 0.6472 - val_loss: 1.0904 - val_accuracy: 0.6944\n",
            "Epoch 45/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.1806 - accuracy: 0.6362 - val_loss: 1.0036 - val_accuracy: 0.7190\n",
            "Epoch 46/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.1607 - accuracy: 0.6441 - val_loss: 1.0121 - val_accuracy: 0.7222\n",
            "Epoch 47/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.1427 - accuracy: 0.6495 - val_loss: 1.0249 - val_accuracy: 0.7286\n",
            "Epoch 48/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.1479 - accuracy: 0.6468 - val_loss: 0.9683 - val_accuracy: 0.7521\n",
            "Epoch 49/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.1196 - accuracy: 0.6547 - val_loss: 1.0241 - val_accuracy: 0.7179\n",
            "Epoch 50/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.1248 - accuracy: 0.6544 - val_loss: 0.9772 - val_accuracy: 0.7244\n",
            "Epoch 51/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 1.0952 - accuracy: 0.6714 - val_loss: 1.0068 - val_accuracy: 0.7169\n",
            "Epoch 52/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.1074 - accuracy: 0.6632 - val_loss: 0.9823 - val_accuracy: 0.7329\n",
            "Epoch 53/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.0937 - accuracy: 0.6678 - val_loss: 0.9663 - val_accuracy: 0.7425\n",
            "Epoch 54/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.0738 - accuracy: 0.6787 - val_loss: 0.9413 - val_accuracy: 0.7714\n",
            "Epoch 55/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.0788 - accuracy: 0.6757 - val_loss: 0.9819 - val_accuracy: 0.7511\n",
            "Epoch 56/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.0630 - accuracy: 0.6780 - val_loss: 0.9435 - val_accuracy: 0.7425\n",
            "Epoch 57/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.0606 - accuracy: 0.6787 - val_loss: 0.9215 - val_accuracy: 0.7821\n",
            "Epoch 58/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 1.0509 - accuracy: 0.6892 - val_loss: 0.9855 - val_accuracy: 0.7041\n",
            "Epoch 59/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 1.0540 - accuracy: 0.6853 - val_loss: 0.9605 - val_accuracy: 0.7553\n",
            "Epoch 60/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.0148 - accuracy: 0.6965 - val_loss: 0.9364 - val_accuracy: 0.7650\n",
            "Epoch 61/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 1.0287 - accuracy: 0.6882 - val_loss: 0.9072 - val_accuracy: 0.7692\n",
            "Epoch 62/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.0132 - accuracy: 0.7020 - val_loss: 0.9809 - val_accuracy: 0.7222\n",
            "Epoch 63/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.0164 - accuracy: 0.6926 - val_loss: 0.8985 - val_accuracy: 0.7596\n",
            "Epoch 64/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 1.0067 - accuracy: 0.6995 - val_loss: 1.0925 - val_accuracy: 0.6624\n",
            "Epoch 65/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 1.0100 - accuracy: 0.7079 - val_loss: 0.9624 - val_accuracy: 0.7212\n",
            "Epoch 66/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.9904 - accuracy: 0.7004 - val_loss: 0.9133 - val_accuracy: 0.7372\n",
            "Epoch 67/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9744 - accuracy: 0.7119 - val_loss: 0.9034 - val_accuracy: 0.7489\n",
            "Epoch 68/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.9723 - accuracy: 0.7106 - val_loss: 0.9475 - val_accuracy: 0.7233\n",
            "Epoch 69/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9592 - accuracy: 0.7184 - val_loss: 0.9377 - val_accuracy: 0.7147\n",
            "Epoch 70/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9605 - accuracy: 0.7219 - val_loss: 0.8810 - val_accuracy: 0.7575\n",
            "Epoch 71/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9715 - accuracy: 0.7227 - val_loss: 0.8990 - val_accuracy: 0.7682\n",
            "Epoch 72/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.9397 - accuracy: 0.7280 - val_loss: 0.8670 - val_accuracy: 0.7724\n",
            "Epoch 73/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.9301 - accuracy: 0.7301 - val_loss: 0.8515 - val_accuracy: 0.7767\n",
            "Epoch 74/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.9184 - accuracy: 0.7293 - val_loss: 0.8840 - val_accuracy: 0.7521\n",
            "Epoch 75/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.9372 - accuracy: 0.7175 - val_loss: 0.8786 - val_accuracy: 0.7703\n",
            "Epoch 76/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.9163 - accuracy: 0.7317 - val_loss: 0.8745 - val_accuracy: 0.7543\n",
            "Epoch 77/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.9185 - accuracy: 0.7320 - val_loss: 0.8295 - val_accuracy: 0.7917\n",
            "Epoch 78/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.9366 - accuracy: 0.7273 - val_loss: 0.9132 - val_accuracy: 0.7618\n",
            "Epoch 79/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.9001 - accuracy: 0.7390 - val_loss: 0.8633 - val_accuracy: 0.7553\n",
            "Epoch 80/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8964 - accuracy: 0.7330 - val_loss: 0.8907 - val_accuracy: 0.7543\n",
            "Epoch 81/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.8941 - accuracy: 0.7392 - val_loss: 0.8956 - val_accuracy: 0.7618\n",
            "Epoch 82/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.8882 - accuracy: 0.7412 - val_loss: 0.9001 - val_accuracy: 0.7500\n",
            "Epoch 83/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.8991 - accuracy: 0.7431 - val_loss: 0.9181 - val_accuracy: 0.7318\n",
            "Epoch 84/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.8788 - accuracy: 0.7441 - val_loss: 0.8687 - val_accuracy: 0.7489\n",
            "Epoch 85/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8840 - accuracy: 0.7457 - val_loss: 0.8591 - val_accuracy: 0.7585\n",
            "Epoch 86/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8712 - accuracy: 0.7495 - val_loss: 0.8197 - val_accuracy: 0.7917\n",
            "Epoch 87/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8697 - accuracy: 0.7475 - val_loss: 0.8740 - val_accuracy: 0.7575\n",
            "Epoch 88/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8601 - accuracy: 0.7556 - val_loss: 0.8933 - val_accuracy: 0.7585\n",
            "Epoch 89/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8543 - accuracy: 0.7554 - val_loss: 0.8688 - val_accuracy: 0.7500\n",
            "Epoch 90/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8411 - accuracy: 0.7610 - val_loss: 0.8230 - val_accuracy: 0.7906\n",
            "Epoch 91/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8469 - accuracy: 0.7541 - val_loss: 0.8292 - val_accuracy: 0.7682\n",
            "Epoch 92/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.8523 - accuracy: 0.7521 - val_loss: 0.8551 - val_accuracy: 0.7500\n",
            "Epoch 93/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.8409 - accuracy: 0.7573 - val_loss: 0.8219 - val_accuracy: 0.7799\n",
            "Epoch 94/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.8401 - accuracy: 0.7625 - val_loss: 0.8700 - val_accuracy: 0.7639\n",
            "Epoch 95/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.8305 - accuracy: 0.7668 - val_loss: 0.8416 - val_accuracy: 0.7457\n",
            "Epoch 96/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.8290 - accuracy: 0.7672 - val_loss: 0.8258 - val_accuracy: 0.7917\n",
            "Epoch 97/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.8241 - accuracy: 0.7628 - val_loss: 0.8022 - val_accuracy: 0.7874\n",
            "Epoch 98/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.8282 - accuracy: 0.7635 - val_loss: 1.0330 - val_accuracy: 0.6784\n",
            "Epoch 99/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.8358 - accuracy: 0.7638 - val_loss: 0.8095 - val_accuracy: 0.7927\n",
            "Epoch 100/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.8117 - accuracy: 0.7718 - val_loss: 0.8234 - val_accuracy: 0.7842\n",
            "Epoch 101/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.8056 - accuracy: 0.7707 - val_loss: 0.8251 - val_accuracy: 0.7831\n",
            "Epoch 102/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7993 - accuracy: 0.7770 - val_loss: 0.8129 - val_accuracy: 0.7874\n",
            "Epoch 103/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7954 - accuracy: 0.7712 - val_loss: 0.8326 - val_accuracy: 0.7778\n",
            "Epoch 104/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.7881 - accuracy: 0.7744 - val_loss: 0.8581 - val_accuracy: 0.7543\n",
            "Epoch 105/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7815 - accuracy: 0.7801 - val_loss: 0.8320 - val_accuracy: 0.7692\n",
            "Epoch 106/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7883 - accuracy: 0.7730 - val_loss: 0.7994 - val_accuracy: 0.7821\n",
            "Epoch 107/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.7919 - accuracy: 0.7780 - val_loss: 0.8275 - val_accuracy: 0.7639\n",
            "Epoch 108/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.7622 - accuracy: 0.7829 - val_loss: 0.8194 - val_accuracy: 0.7842\n",
            "Epoch 109/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.7644 - accuracy: 0.7870 - val_loss: 0.8126 - val_accuracy: 0.7735\n",
            "Epoch 110/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.7648 - accuracy: 0.7778 - val_loss: 0.8415 - val_accuracy: 0.7585\n",
            "Epoch 111/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.7596 - accuracy: 0.7845 - val_loss: 0.8089 - val_accuracy: 0.7607\n",
            "Epoch 112/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.7638 - accuracy: 0.7845 - val_loss: 0.7853 - val_accuracy: 0.7949\n",
            "Epoch 113/200\n",
            "70/70 [==============================] - 4s 58ms/step - loss: 0.7573 - accuracy: 0.7880 - val_loss: 0.7759 - val_accuracy: 0.7938\n",
            "Epoch 114/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7526 - accuracy: 0.7882 - val_loss: 0.8335 - val_accuracy: 0.7650\n",
            "Epoch 115/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.7519 - accuracy: 0.7879 - val_loss: 0.8266 - val_accuracy: 0.7532\n",
            "Epoch 116/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7603 - accuracy: 0.7806 - val_loss: 0.8965 - val_accuracy: 0.7553\n",
            "Epoch 117/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.7543 - accuracy: 0.7896 - val_loss: 0.8311 - val_accuracy: 0.7628\n",
            "Epoch 118/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7450 - accuracy: 0.7948 - val_loss: 0.9067 - val_accuracy: 0.7404\n",
            "Epoch 119/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.7247 - accuracy: 0.7962 - val_loss: 0.7815 - val_accuracy: 0.7970\n",
            "Epoch 120/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.7443 - accuracy: 0.7902 - val_loss: 0.8032 - val_accuracy: 0.7874\n",
            "Epoch 121/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.7302 - accuracy: 0.7993 - val_loss: 0.8446 - val_accuracy: 0.7596\n",
            "Epoch 122/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.7311 - accuracy: 0.7880 - val_loss: 0.8282 - val_accuracy: 0.7735\n",
            "Epoch 123/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7244 - accuracy: 0.7985 - val_loss: 0.7640 - val_accuracy: 0.8045\n",
            "Epoch 124/200\n",
            "70/70 [==============================] - 4s 56ms/step - loss: 0.7120 - accuracy: 0.8008 - val_loss: 0.7904 - val_accuracy: 0.7821\n",
            "Epoch 125/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.7243 - accuracy: 0.7964 - val_loss: 0.9441 - val_accuracy: 0.7489\n",
            "Epoch 126/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.7243 - accuracy: 0.7957 - val_loss: 0.8087 - val_accuracy: 0.7682\n",
            "Epoch 127/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.7269 - accuracy: 0.7935 - val_loss: 0.7976 - val_accuracy: 0.7660\n",
            "Epoch 128/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.7123 - accuracy: 0.8014 - val_loss: 0.8287 - val_accuracy: 0.7650\n",
            "Epoch 129/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.6985 - accuracy: 0.8092 - val_loss: 0.8218 - val_accuracy: 0.7564\n",
            "Epoch 130/200\n",
            "70/70 [==============================] - 4s 53ms/step - loss: 0.7065 - accuracy: 0.8043 - val_loss: 0.7795 - val_accuracy: 0.7821\n",
            "Epoch 131/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.6985 - accuracy: 0.8051 - val_loss: 0.8245 - val_accuracy: 0.7767\n",
            "Epoch 132/200\n",
            "70/70 [==============================] - 4s 57ms/step - loss: 0.6996 - accuracy: 0.8069 - val_loss: 0.8888 - val_accuracy: 0.7308\n",
            "Epoch 133/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.7087 - accuracy: 0.8073 - val_loss: 0.7644 - val_accuracy: 0.8066\n",
            "Epoch 134/200\n",
            "70/70 [==============================] - 4s 54ms/step - loss: 0.6856 - accuracy: 0.8017 - val_loss: 0.7680 - val_accuracy: 0.7970\n",
            "Epoch 135/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.6833 - accuracy: 0.8082 - val_loss: 0.8374 - val_accuracy: 0.7671\n",
            "Epoch 136/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.6794 - accuracy: 0.8139 - val_loss: 0.7669 - val_accuracy: 0.7853\n",
            "Epoch 137/200\n",
            "70/70 [==============================] - 4s 52ms/step - loss: 0.6755 - accuracy: 0.8139 - val_loss: 0.7919 - val_accuracy: 0.7778\n",
            "Epoch 138/200\n",
            "70/70 [==============================] - 4s 55ms/step - loss: 0.6751 - accuracy: 0.8171 - val_loss: 0.9029 - val_accuracy: 0.7318\n",
            "Evaluating model...\n",
            "ROC: 0.964\n",
            "\n",
            "Accuracy = 0.74\n",
            "F-Score: 0.74\n",
            "\n",
            "Average R.O.C: 0.9561658178684989\n",
            "Average Accuracy: 0.7085139274597168\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Sw4jTpVrQlO-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "7a6bb190-ed3b-4009-c672-c469e43dc954"
      },
      "source": [
        "plt.boxplot(acc)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boxes': [<matplotlib.lines.Line2D at 0x7f4555ff0da0>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f45560077f0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f4556007b70>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f45560122b0>],\n",
              " 'means': [],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f4556007ef0>],\n",
              " 'whiskers': [<matplotlib.lines.Line2D at 0x7f45560070f0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f4556007470>]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "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": {
        "colab_type": "code",
        "id": "Nvfu3qXMQmio",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "outputId": "24d77ff5-405e-455d-b45c-cd09cf64833e"
      },
      "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": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4555fe93c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "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": {
        "colab_type": "code",
        "id": "kSAxXuCwQnfJ",
        "colab": {}
      },
      "source": [
        "np.save(\"acc_baseline.npy\", acc)\n",
        "np.save(\"cm_baseline.npy\", cm)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJa7ilwmQz_O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "acc_wavelet=acc\n",
        "cm_wavelet=CM"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQz-IBhEik6w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download(\"acc_baseline.npy\") \n",
        "files.download(\"cm_baseline.npy\")"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}