Copy_of_project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of project.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rahulkashyap14298/AI-Project/blob/master/Copy_of_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pAqD4qv3Spek"
      },
      "source": [
        "**Extracting MFCC's For every audio file**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SQKjYQd2X56E"
      },
      "source": [
        " Importing the important libraries. Reading the csv file and creating the file path\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zg_RTVOtZNu",
        "outputId": "37837769-d156-465a-a8ef-1b9bfdea70f8"
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuZYA1XJtqqj"
      },
      "source": [
        "\n",
        "from zipfile import ZipFile\n",
        "# Create a ZipFile Object and load sample.zip in it\n",
        "with ZipFile('/content/gdrive/MyDrive/Audio-Classification-master.zip', 'r') as zipObj:\n",
        "   # Extract all the contents of zip file in current directory\n",
        "   zipObj.extractall()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QHC1_73G0HDl"
      },
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import librosa\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aoXyLMADUA0V"
      },
      "source": [
        "def features_extractor(file):\n",
        "    audio, sample_rate = librosa.load(file, res_type='kaiser_fast') \n",
        "    mfccs_features = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40)\n",
        "    mfccs_scaled_features = np.mean(mfccs_features.T,axis=0)\n",
        "    \n",
        "    return mfccs_scaled_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-QwXOJC1VwL"
      },
      "source": [
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GgdjhrucxC76"
      },
      "source": [
        "import os\n",
        "import IPython.display as ipd\n",
        "extracted_features=[]\n",
        "directory =\"/content/Audio-Classification-master/wavfiles\"\n",
        "for files in os.listdir(directory):\n",
        "  for audio in os.listdir(directory+\"/\"+files):\n",
        "    final_class_labels=files\n",
        "    data=features_extractor(directory+\"/\"+files+\"/\"+audio)\n",
        "    extracted_features.append([data,final_class_labels])\n",
        "    \n",
        "\n",
        "\n",
        "    \n",
        "    \n",
        "    \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5i1nbKxRS6kI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8968810d-7394-4fb4-c00a-42e0a678356d"
      },
      "source": [
        "print(extracted_features)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "         -2.9316394,   29.533422 ,   12.399391 ,   10.099918 ,\n",
            "        -14.980867 ,   -7.102765 ,    2.2680883,   21.175205 ,\n",
            "        -11.832846 ,   -7.1997333,  -16.43158  ,   -7.4697604,\n",
            "         -1.2796584,    7.2820563,   -5.87565  ,    1.904466 ,\n",
            "          5.2456093,    5.956822 ,  -14.035757 ,  -17.352228 ,\n",
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            "         -1.5791967,    1.5150236,   -5.1248007,   -1.9046451],\n",
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            "       -1.4235315e+00, -1.8626957e-01, -1.7225434e+00,  1.9667313e+00,\n",
            "       -4.7850199e+00, -6.2566977e+00,  1.3287680e+00,  8.6565542e+00,\n",
            "        2.1211474e+00, -5.8762975e+00, -1.1530007e+01,  9.6216142e-01],\n",
            "      dtype=float32), 'Hi_hat'], [array([-3.58069611e+02, -4.83942360e-01, -5.43701897e+01,  3.82621727e+01,\n",
            "       -2.83146038e+01,  8.87485504e+00, -2.53888664e+01,  7.36328220e+00,\n",
            "       -1.29495888e+01,  1.80807419e+01,  4.45183945e+00,  1.82232838e+01,\n",
            "       -8.64242935e+00,  1.72386436e+01, -3.81151271e+00,  1.43095422e+00,\n",
            "       -1.50475912e+01, -1.37226543e+01, -3.02417207e+00,  7.91333199e+00,\n",
            "       -1.22532167e+01, -8.35313606e+00, -1.20466385e+01,  3.07005310e+00,\n",
            "        8.26799393e+00, -3.66828585e+00, -7.61021233e+00, -4.22592783e+00,\n",
            "       -9.88842621e-02,  2.19205308e+00, -9.65139008e+00, -8.41592026e+00,\n",
            "       -5.92616272e+00,  7.19181681e+00, -9.33891392e+00, -1.70843067e+01,\n",
            "       -3.10751987e+00, -8.80835533e+00, -1.05693102e+01, -7.01862907e+00],\n",
            "      dtype=float32), 'Hi_hat'], [array([-384.6667    ,  -15.985008  ,  -56.397312  ,   38.662266  ,\n",
            "        -19.484789  ,    6.821888  ,  -25.3787    ,   10.070614  ,\n",
            "        -16.124256  ,   22.848717  ,   11.902367  ,   14.170259  ,\n",
            "         -8.428546  ,   13.424152  ,   -5.2097516 ,    1.3437309 ,\n",
            "        -21.88921   ,  -11.300383  ,   -6.2022986 ,    9.8155155 ,\n",
            "        -10.681691  ,   -7.660835  ,  -13.887624  ,    0.72942734,\n",
            "          5.0938373 ,   -4.268163  ,  -15.111562  ,   -1.519977  ,\n",
            "         -3.3201132 ,   -8.482904  ,   -9.0882435 ,   -3.167683  ,\n",
            "         -0.64902943,    7.8816934 ,   -7.030816  ,  -10.399217  ,\n",
            "         -5.5000257 ,   -2.3422866 ,   -5.814086  ,   -6.7880287 ],\n",
            "      dtype=float32), 'Hi_hat'], [array([-6.3901257e+02, -2.7371635e+01, -4.6262779e+01,  2.0898947e+01,\n",
            "       -1.0066810e+01,  1.6292618e+01, -1.5317215e-01,  1.4856296e+00,\n",
            "       -6.9561892e+00,  9.0196705e+00,  1.7316543e+00,  6.4080777e+00,\n",
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            "       -3.1416907e+00,  6.8437991e+00, -3.0993152e+00, -1.7060182e+00,\n",
            "       -8.1010714e+00, -1.5859745e+00,  1.1167302e+01,  8.5955620e+00,\n",
            "       -2.4782434e+00, -1.2839167e+00,  2.3973410e+00,  1.0311122e+01,\n",
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            "        2.5741208e+00, -3.1932514e+00, -3.8914096e+00,  2.9753190e-01],\n",
            "      dtype=float32), 'Hi_hat'], [array([-4.4446512e+02,  2.6533560e+01, -8.7149956e+01,  1.6727970e+01,\n",
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            "       -1.4018123e+01, -5.0497279e+00, -1.1792584e+01,  8.2811270e+00,\n",
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            "        2.8542714e+00,  1.0549897e-01,  4.7609906e+00,  8.0888939e+00,\n",
            "        3.5349636e+00,  2.5250981e+00, -1.3358411e+00,  2.1870077e+00,\n",
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            "      dtype=float32), 'Hi_hat'], [array([-3.4611377e+02, -1.4995301e+01, -5.3133530e+01,  3.4613144e+01,\n",
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            "       -5.5816889e+00, -4.8356605e+00, -4.8761311e+00,  9.4685560e-01,\n",
            "        5.0485330e+00, -7.0196538e+00, -6.9215875e+00,  2.2101066e+00],\n",
            "      dtype=float32), 'Hi_hat'], [array([-6.2636523e+02, -1.9778450e+01, -3.1134655e+01,  1.4827870e+01,\n",
            "       -9.5886450e+00,  1.4676500e+01, -5.9248576e+00,  2.7346210e+00,\n",
            "       -1.4234552e+01,  4.0033011e+00, -3.4691784e+00,  3.0474818e+00,\n",
            "       -1.3533879e+01,  1.0739462e+00, -7.0593429e+00, -3.4078548e+00,\n",
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            "       -2.4095349e+00,  8.7202191e-01,  3.2519999e+00,  4.0137186e+00,\n",
            "       -1.4224125e+00,  2.2631934e+00,  3.3274453e+00,  5.7852492e+00,\n",
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            "          5.2999754 ,   10.598904  ,   11.4968    ,   11.554639  ,\n",
            "          3.1975286 ,   -1.7041768 ,   -3.7939754 ,    6.649171  ,\n",
            "          6.864196  ,   -1.3985243 ,    1.6537286 ,    6.3217473 ,\n",
            "          5.572512  ,    7.942669  ,   10.721757  ,    3.441712  ,\n",
            "         -4.4902425 ,    2.5755386 ,    7.083697  ,    6.8965454 ,\n",
            "          6.434278  ,    1.0046475 ,   -1.9030524 ,   -2.44598   ,\n",
            "         -2.325415  ,    2.1689088 ,    1.3470116 ,   -5.832336  ,\n",
            "         -3.3802526 ,    0.508221  ,    3.5904226 ,    3.3108914 ,\n",
            "          0.83844405,   -3.0733705 ,   -7.641645  ,   -4.168804  ],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-210.37294   ,   67.591225  ,  -70.5585    ,   30.52592   ,\n",
            "        -35.53069   ,  -21.258356  ,  -19.673405  ,   -5.5853357 ,\n",
            "        -28.107132  ,    1.1368382 ,  -19.427822  ,    0.6928927 ,\n",
            "        -13.2505865 ,    6.661539  ,   -2.98493   ,   10.035354  ,\n",
            "         -3.4880528 ,    7.465315  ,   -2.589826  ,   -1.9058675 ,\n",
            "          4.278617  ,    5.3611426 ,   18.898302  ,   31.685234  ,\n",
            "         22.92039   ,   20.717802  ,   10.002732  ,   -0.22882694,\n",
            "          1.8891908 ,   14.655501  ,    6.095908  ,    1.1559241 ,\n",
            "          0.8195271 ,   -3.4404225 ,   -1.9147251 ,    4.929198  ,\n",
            "         -4.3140574 ,   -4.593194  ,   -6.393867  ,   -1.2149726 ],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-215.97849   ,   51.93912   ,  -35.642113  ,   10.14073   ,\n",
            "        -20.801607  ,   14.907165  ,    5.751213  ,   14.14893   ,\n",
            "        -27.732359  ,   18.335354  ,    5.939735  ,   -4.7885723 ,\n",
            "        -14.246993  ,   23.620867  ,   16.11294   ,    4.763764  ,\n",
            "         -0.69621754,   -2.1428902 ,    8.855887  ,   33.092915  ,\n",
            "        -31.032011  ,   -7.799434  ,   20.285433  ,   16.67961   ,\n",
            "         15.628126  ,  -19.350542  ,    4.9911966 ,   -0.7051943 ,\n",
            "         11.910637  ,   14.009944  ,  -34.742054  ,   20.00361   ,\n",
            "          0.88902116,    1.0440187 ,   -2.524567  ,    1.045692  ,\n",
            "         -9.188629  ,   16.947937  ,    5.4097204 ,  -33.884212  ],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-4.7471573e+02,  5.9534000e+01,  2.4623240e+01,  1.6753166e+01,\n",
            "        6.8284340e+00,  7.0105143e+00,  7.4801040e+00,  4.2873917e+00,\n",
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            "       -1.9310070e+00, -3.2328269e+00, -4.3371905e-02,  2.0661232e+00,\n",
            "        6.1041522e+00,  1.0533923e+01,  1.1756406e+01,  1.4292401e+01,\n",
            "        1.4161377e+01,  9.0205240e+00,  2.9815731e+00, -2.4446495e+00,\n",
            "       -2.9812269e+00, -3.4733450e-01, -1.4193119e+00, -5.0472722e+00,\n",
            "       -5.9308457e+00, -4.2119870e+00, -1.2705064e+00, -4.5037973e-01,\n",
            "       -1.8965952e+00, -3.3628801e-01,  2.5813122e+00,  4.9166040e+00,\n",
            "        5.1500602e+00,  4.2546167e+00,  6.3004446e+00,  7.2257280e+00],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-4.2041867e+02,  9.3098402e+00, -3.3431580e+01,  2.2366276e+01,\n",
            "       -2.2025730e+01, -9.0652685e+00, -2.6112282e+01, -2.7361121e+00,\n",
            "       -1.9470617e+01, -6.2745851e-01, -5.0512662e+00,  7.2376509e+00,\n",
            "       -4.0482941e+00,  1.1375612e+01,  1.4726884e+01,  1.9804188e+01,\n",
            "        4.3028860e+00,  9.3053961e+00,  2.7601647e+00, -1.5880240e+00,\n",
            "       -7.2889147e+00,  8.0402889e+00,  1.6045752e+01,  3.4574077e+00,\n",
            "        6.1102142e+00,  1.1684041e+01,  4.6435204e+00,  9.1569414e+00,\n",
            "        4.5785065e+00,  3.4522986e+00,  1.0847652e+01,  1.1463074e+01,\n",
            "       -1.8629843e-02, -6.4195027e+00,  2.8325992e+00, -7.0425540e-01,\n",
            "       -9.1128582e-01,  2.5615640e+00, -6.8786058e+00, -1.0130833e+01],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-5.0748508e+02,  7.5353157e+01,  2.2441957e+01,  2.4466917e+01,\n",
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            "       -3.2891181e+00, -6.0085735e+00, -4.3437343e+00,  2.8195262e+00,\n",
            "        7.0879707e+00,  7.8717823e+00,  6.1634603e+00,  2.9086626e+00,\n",
            "       -4.5383029e+00, -7.5253506e+00, -1.7797162e-01,  7.6923337e+00,\n",
            "        4.1010079e+00, -2.7625444e+00,  2.5427349e+00,  1.0176592e+01,\n",
            "        1.0367165e+01,  6.8538718e+00,  6.1191998e+00,  3.3812478e+00,\n",
            "        1.1180800e+00, -1.6494709e+00, -4.9847784e+00, -2.5619133e+00,\n",
            "       -7.1353638e-01, -4.7969773e-01,  3.7213621e+00,  3.9100299e+00,\n",
            "        1.1651756e+00,  5.9572043e+00,  4.4845543e+00, -3.8796837e+00],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-405.3372    ,    6.3252835 ,  -31.879297  ,   20.900476  ,\n",
            "        -20.870974  ,  -12.410822  ,  -25.797684  ,   -3.391488  ,\n",
            "        -24.83189   ,   -1.7151988 ,    0.46421286,    8.765252  ,\n",
            "         -2.1166785 ,    5.6416464 ,   10.751107  ,   21.236961  ,\n",
            "          7.692833  ,    1.5045255 ,   -3.439341  ,    3.1688678 ,\n",
            "          6.641734  ,   12.341387  ,    6.810499  ,   -3.3000963 ,\n",
            "          4.9470844 ,    6.7464795 ,    2.2392628 ,   13.653593  ,\n",
            "          8.919581  ,    6.522971  ,   11.702763  ,   12.450554  ,\n",
            "          2.0768378 ,   -7.632405  ,   -2.7262905 ,   -2.8088315 ,\n",
            "         -2.0979934 ,    5.9328876 ,   -6.8198647 ,   -8.892833  ],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-3.7108273e+02,  3.8729663e+00, -4.3388992e+01,  5.4404193e-01,\n",
            "       -1.2821525e+01,  1.7358015e+01, -1.0591153e+01, -3.3965237e+00,\n",
            "       -1.1580883e+01,  1.3101554e+01, -1.8036371e+01, -1.4915656e+01,\n",
            "        1.1852090e+01,  2.8011683e+01, -7.6336403e+00,  6.8410492e+00,\n",
            "       -5.8547592e+00, -3.3372531e+00,  3.0256214e+01,  1.8933033e+01,\n",
            "       -2.0757311e+01,  5.1888943e+00,  5.8737469e+00,  7.9288077e+00,\n",
            "        2.7890563e+00,  1.3027554e+01,  4.8885460e+00,  4.7403793e+00,\n",
            "       -9.2387075e+00, -2.5752900e+00,  2.5317247e+01, -2.8002563e+00,\n",
            "       -7.8927865e+00,  3.9007714e+00,  2.1310313e-01,  5.2595463e+00,\n",
            "       -1.0232360e+00,  4.5739956e+00,  1.5719768e+01, -1.4407800e+01],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-4.0995822e+02,  2.5614391e+01, -1.6413681e+01,  2.9048536e+01,\n",
            "       -9.0668020e+00,  2.7835023e+00, -3.9865658e-01,  1.7729088e+00,\n",
            "       -1.1802094e+01, -7.0180097e+00, -8.4944124e+00,  8.5400314e+00,\n",
            "        1.9294838e+01,  3.6415154e+01,  3.0700363e+01,  1.7389677e+01,\n",
            "       -1.1951741e+01, -7.6506438e+00,  8.1777086e+00,  1.1486382e+01,\n",
            "        5.9548354e+00, -1.1628091e+01, -1.1243215e+01,  1.4312505e+01,\n",
            "        2.4946997e+01,  1.4639419e+01,  7.1103501e+00,  2.8554585e+01,\n",
            "        1.5989167e+01, -1.9415735e+01, -1.4734617e+01,  1.2719992e+01,\n",
            "        8.4819059e+00, -1.5410749e+01, -8.2562428e+00, -1.0067263e+00,\n",
            "       -7.6145344e+00,  9.1270018e+00,  2.5941853e+01,  1.9152893e-01],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-454.59503  ,   99.75184  ,    7.809574 ,   10.132192 ,\n",
            "         -5.3171926,   12.876115 ,    1.4283623,   12.0965805,\n",
            "          2.6677203,    0.5005774,   -3.1367142,   -4.599437 ,\n",
            "         -8.790563 ,    0.7261439,   13.004444 ,   22.636324 ,\n",
            "         21.489197 ,   12.337233 ,   -4.936282 ,  -12.57365  ,\n",
            "         -4.660408 ,    8.733203 ,    6.7686152,   -7.3232894,\n",
            "        -15.23764  ,   -6.4272513,   -2.18357  ,   -8.307184 ,\n",
            "        -11.219018 ,    9.73888  ,   27.901567 ,   22.161064 ,\n",
            "          3.2106154,   -4.3693767,   -1.4608134,   -8.128525 ,\n",
            "         -2.6101925,    8.999401 ,    4.0809336,   -7.259537 ],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-420.01593  ,   19.63952  ,  -40.195724 ,    6.176166 ,\n",
            "        -25.970324 ,   -1.0247611,  -17.200338 ,   19.532915 ,\n",
            "         -3.4457996,    1.9857861,  -27.470951 ,  -19.289093 ,\n",
            "        -19.895107 ,   16.113304 ,   15.814162 ,   10.099339 ,\n",
            "          7.8578362,   13.995265 ,  -17.757284 ,  -11.253816 ,\n",
            "         12.958168 ,   25.320505 ,    5.647411 ,   12.351745 ,\n",
            "          9.789654 ,   -8.667321 ,    8.141931 ,   10.260673 ,\n",
            "         -4.324648 ,   17.84121  ,   12.008389 ,  -22.390242 ,\n",
            "          5.8924093,   13.177755 ,   -3.8300595,    2.714581 ,\n",
            "         -5.437927 ,   13.193763 ,   -1.8805515,  -19.740263 ],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-3.6613962e+02,  1.0397208e+02,  1.1613069e+01,  1.7670944e+01,\n",
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            "       -8.3480062e+00,  8.2715454e+00,  8.2138710e+00,  1.6373167e+01,\n",
            "        5.0476068e-01, -1.1833073e+00, -1.5592321e+01,  5.8965874e+00,\n",
            "        8.5214806e+00, -2.9915841e+00, -5.6287174e+00, -2.4336123e+00,\n",
            "        1.1477418e+01,  1.5477752e+01, -5.0126843e+00, -1.1246425e+01,\n",
            "        8.0892026e-01, -3.1984884e-02, -6.0342407e+00,  6.2009521e+00,\n",
            "        1.0860419e+00, -1.4161705e+01,  3.7366178e+00,  1.7713894e+01,\n",
            "        1.9970628e+00, -6.9044042e+00, -4.8499875e+00, -7.1522145e+00,\n",
            "        6.5610514e+00,  1.0911618e+01, -3.1772819e+00, -2.8185534e+00],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-628.954     ,   38.56502   ,   -2.443148  ,    0.820267  ,\n",
            "         -9.520881  ,   -7.0634465 ,  -10.802159  ,   -8.523184  ,\n",
            "         -6.3832107 ,   -8.217939  ,   -9.3459215 ,   -9.449774  ,\n",
            "        -10.449807  ,   -9.629837  ,   -4.583651  ,    3.7678986 ,\n",
            "         12.636116  ,   17.14033   ,   16.663874  ,   12.451105  ,\n",
            "          2.8563912 ,   -2.3244908 ,   -3.0824432 ,   -2.07151   ,\n",
            "         -0.70734614,   -4.646134  ,   -9.796434  ,   -7.340003  ,\n",
            "         -1.3297955 ,   -1.3468469 ,   -7.257042  ,   -7.037484  ,\n",
            "          3.809852  ,   10.042985  ,   10.119444  ,    9.457206  ,\n",
            "          9.870775  ,    5.2827444 ,   -2.5341697 ,   -4.8282294 ],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-3.51875916e+02,  5.62599411e+01, -6.23153954e+01,  4.03004303e+01,\n",
            "       -2.50177193e+01,  3.10784221e+00, -1.72725353e+01, -1.33889389e+00,\n",
            "       -2.65283070e+01, -5.15593481e+00, -3.85988283e+00,  6.32877827e+00,\n",
            "       -1.55897455e+01, -1.48142684e+00,  1.21048200e+00, -1.34236753e-01,\n",
            "       -4.53703737e+00, -2.90304852e+00, -4.61761236e+00,  4.45190096e+00,\n",
            "        1.60736980e+01,  4.78153381e+01,  4.86269302e+01,  4.06040611e+01,\n",
            "        3.37927461e+00, -2.70041676e+01, -6.62174511e+00,  1.14687004e+01,\n",
            "        1.62190552e+01,  3.28238773e+00, -1.52679472e+01, -1.70575790e+01,\n",
            "        1.53660345e+00,  1.92957191e+01, -1.09895468e+00, -2.46982517e+01,\n",
            "       -1.72569847e+01, -2.64386392e+00,  8.27645421e-01, -2.22470932e+01],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-375.05463   ,   19.015753  ,  -11.193822  ,   17.977362  ,\n",
            "        -23.869448  ,   -4.299939  ,    2.1891997 ,   20.344913  ,\n",
            "         11.734854  ,   12.39037   ,  -22.069864  ,  -15.448579  ,\n",
            "         -7.597849  ,   25.595482  ,   29.119368  ,   17.005539  ,\n",
            "         12.110857  ,   15.183547  ,  -18.51717   ,  -26.993338  ,\n",
            "         23.777622  ,   43.02954   ,    6.8537154 ,   12.48746   ,\n",
            "          8.95306   ,  -20.971853  ,    1.4147167 ,   17.416971  ,\n",
            "         -6.958687  ,   33.52221   ,   21.698317  ,  -24.784706  ,\n",
            "         -0.55507815,   -2.2379074 ,   -5.695271  ,    9.533166  ,\n",
            "         -3.1956801 ,   21.911102  ,    2.3689349 ,  -41.889267  ],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-316.6692    ,   55.513718  ,  -38.291603  ,   56.21931   ,\n",
            "        -15.925461  ,    6.783094  ,  -24.960215  ,  -13.117817  ,\n",
            "         -9.889703  ,   -2.647311  ,   -4.8247147 ,  -10.930525  ,\n",
            "        -15.33944   ,   -7.337804  ,  -12.866005  ,    0.74064124,\n",
            "          3.085848  ,   -1.9144822 ,   -4.046122  ,  -10.050761  ,\n",
            "         -8.740668  ,   -1.3292747 ,   11.1284485 ,   34.11503   ,\n",
            "         37.49475   ,   32.049664  ,   35.707546  ,    3.9423935 ,\n",
            "        -16.462158  ,  -15.487328  ,    6.5463543 ,    9.776207  ,\n",
            "         -1.2585733 ,   -6.1583886 ,  -14.736383  ,   -3.6920729 ,\n",
            "          5.842695  ,  -10.006113  ,  -18.891293  ,  -13.0405445 ],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-3.8405371e+02,  6.4372620e+01, -2.5646334e+01,  3.4392479e+01,\n",
            "       -1.0522286e-01, -1.5004027e+00,  4.9131455e+00, -9.8517466e+00,\n",
            "       -1.6365627e+01, -8.2311726e+00, -6.9555683e+00,  9.0354300e+00,\n",
            "        5.8740382e+00,  8.3228159e+00,  1.1187437e+01,  1.9995959e+00,\n",
            "       -8.5210699e-01,  1.0150420e+01,  5.8747959e+00, -1.2185739e-01,\n",
            "        6.5036340e+00,  4.0095339e+00,  3.5744355e+00,  7.9334521e+00,\n",
            "        1.0459781e+00, -6.1085849e+00, -8.9679422e+00, -8.0012989e+00,\n",
            "       -5.5281281e+00, -1.0965432e+01, -1.4585295e+01, -8.9929848e+00,\n",
            "        1.1608026e+01,  2.9378687e+01,  4.4368690e+01,  4.9141506e+01,\n",
            "        2.9755825e+01,  2.2152889e+00, -1.6184313e+01, -1.1295141e+01],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-6.9937573e+02,  4.8112518e+01,  1.5726909e+01,  8.4768686e+00,\n",
            "        6.5238774e-01, -3.4303074e+00, -5.2782860e+00, -6.8743773e+00,\n",
            "       -7.9391761e+00, -9.4948359e+00, -1.1057933e+01, -1.0140978e+01,\n",
            "       -9.2233934e+00, -1.0225286e+01, -1.0035607e+01, -9.7210693e+00,\n",
            "       -6.0918474e+00, -7.5712883e-01,  6.9610524e+00,  1.8470642e+01,\n",
            "        2.4873281e+01,  2.4139114e+01,  1.8579855e+01,  9.0855761e+00,\n",
            "        1.1937635e+00, -1.1868939e+00, -5.4742223e-01,  8.5066706e-01,\n",
            "       -1.4621745e+00, -7.9892869e+00, -1.0382197e+01, -6.1747851e+00,\n",
            "       -2.3379972e+00, -4.2745543e+00, -7.3861275e+00, -5.9992089e+00,\n",
            "       -2.3904903e+00, -1.3545904e+00, -7.2308147e-01,  5.3450804e+00],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-4.0156650e+02,  2.6743017e+01, -3.8255062e+01,  2.8937250e+01,\n",
            "       -1.9307283e+01, -1.1478702e+01, -1.5489244e+01, -8.1635590e+00,\n",
            "       -2.6594501e+01, -1.3829652e+01, -1.1368397e+01, -9.8077387e-01,\n",
            "       -5.0451317e+00,  4.0016575e+00,  7.0224376e+00,  1.4619231e+01,\n",
            "        2.7159190e+00,  7.8568044e+00,  7.0245533e+00,  1.1966778e+00,\n",
            "        8.1032104e+00,  1.3118318e+01,  2.4825109e+01,  1.2942995e+01,\n",
            "        1.0641085e+01,  8.2342424e+00,  1.5624470e+00,  3.6920848e+00,\n",
            "       -3.6991939e-01,  7.2389688e+00,  5.7055326e+00,  2.5204861e+00,\n",
            "        1.9150575e+00, -6.6047626e+00, -4.0509009e+00,  2.9374871e+00,\n",
            "       -1.3801297e+00,  1.3867733e+00, -5.3956628e+00, -8.9394751e+00],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-4.1118762e+02,  2.4189177e+01, -2.8194160e+01,  1.4417472e+01,\n",
            "       -1.7331884e+01,  1.1794154e+01, -6.7047849e+00,  2.4739433e+01,\n",
            "       -1.1430678e+01, -2.0538723e+01, -3.0151047e+01,  2.0868176e+01,\n",
            "        1.2437718e+01,  1.2405548e+01,  4.7534504e+00,  3.7792103e+00,\n",
            "        5.2798171e+00, -2.5560343e+00,  7.6834726e+00,  4.2833542e+01,\n",
            "       -9.8453465e+00, -1.1932206e+01,  1.5739654e+01,  1.2544589e+01,\n",
            "        1.3651398e+01,  1.7185575e+01, -5.9483466e+00,  6.2167816e+00,\n",
            "       -9.7593117e+00, -1.7213535e+01,  3.8670017e+01,  1.7989213e+00,\n",
            "       -1.4076752e+01,  1.1605622e+01, -1.2172123e+01,  2.3245903e-02,\n",
            "       -6.7482285e+00,  1.2259404e+01,  9.1717997e+00, -3.5582645e+01],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-4.3864877e+02,  3.5835232e+01, -3.8677361e+01,  3.1388334e+01,\n",
            "       -9.6227303e+00,  9.6230412e+00, -9.4456625e+00,  5.3879743e+00,\n",
            "       -1.5507080e+01,  2.9483898e+00, -8.5745020e+00,  1.3038429e+00,\n",
            "       -1.0400357e+01, -4.4821973e+00, -5.0542130e+00,  5.9846439e+00,\n",
            "        1.1550576e+01,  3.7705219e+01,  3.3124969e+01,  1.5108927e+01,\n",
            "        9.0697443e-01, -1.1792187e+01, -1.1812985e+01,  2.6135336e+01,\n",
            "        1.3815529e+01, -8.0574293e+00, -1.5120789e+01, -6.6062486e-01,\n",
            "        1.1132465e+01,  2.2772953e-01, -1.6970123e+01, -1.6899498e+01,\n",
            "        1.1218434e+01,  2.7508718e+01,  1.0525194e+01,  6.5252256e-01,\n",
            "        1.8469374e+01,  4.2030830e+00, -2.0760302e+01, -1.6355192e+01],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-1.8736475e+02,  6.3109753e+01, -8.2135765e+01,  3.7277355e+01,\n",
            "       -3.4473171e+01, -1.8587242e+01, -2.0893213e+01, -8.9620237e+00,\n",
            "       -2.9279648e+01, -9.6721286e-01, -2.6706293e+01, -6.4663897e+00,\n",
            "       -7.3098073e+00,  1.2486449e+01,  6.3661165e+00,  1.0497539e+01,\n",
            "       -2.1481915e-01,  1.2850980e+01,  9.8170519e+00,  1.0574458e+00,\n",
            "        1.1354744e+01,  1.7537864e+00, -3.8582013e+00,  3.5959961e+00,\n",
            "        4.8978639e+00,  8.4635344e+00,  3.9103107e+00, -1.9642680e-03,\n",
            "       -2.2596540e-01, -2.9685872e+00, -2.7574298e+00,  1.5073385e+01,\n",
            "        3.2209644e+01,  2.7863136e+01,  2.0086742e+01,  9.8782272e+00,\n",
            "       -5.7878151e+00, -7.5748692e+00, -2.6727514e+00,  5.1020894e+00],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-6.16505188e+02,  2.12366161e+01, -2.40541553e+01, -7.62158537e+00,\n",
            "       -3.23746262e+01, -1.09821262e+01, -1.32985544e+01,  6.67806816e+00,\n",
            "       -5.71745825e+00, -1.09605293e+01, -2.29032555e+01, -1.45251722e+01,\n",
            "       -1.15507257e+00,  1.16786585e+01,  5.52935982e+00,  5.12636232e+00,\n",
            "        6.90765095e+00,  1.31683159e+00, -1.27963572e+01,  1.27227414e+00,\n",
            "        1.30401087e+01,  4.36119318e+00, -5.67344332e+00,  1.47303557e+00,\n",
            "       -5.05830002e+00, -2.34085488e+00,  5.73508644e+00,  1.06793725e+00,\n",
            "        6.89030409e+00,  7.75805664e+00, -1.15064917e+01, -8.14636648e-01,\n",
            "        6.34815121e+00,  9.58898515e-02,  5.26849174e+00,  2.30181199e-02,\n",
            "        2.70774937e+00, -2.82496738e+00, -9.63102913e+00,  8.10507584e+00],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-541.31885   ,   11.829619  ,  -14.454312  ,    9.975287  ,\n",
            "        -10.130244  ,    6.9807124 ,   -5.5227194 ,   21.37313   ,\n",
            "          7.6412363 ,    5.48835   ,  -19.103355  ,  -14.553295  ,\n",
            "         -6.220454  ,   20.854507  ,   21.920885  ,   11.972437  ,\n",
            "         13.003044  ,    9.216597  ,  -13.06115   ,  -14.114232  ,\n",
            "         21.485992  ,   27.011208  ,    5.836322  ,   15.840657  ,\n",
            "          4.821385  ,  -13.955838  ,    4.635138  ,   12.070501  ,\n",
            "         -6.1077414 ,   25.425655  ,   10.880163  ,  -27.39302   ,\n",
            "          5.745806  ,    7.748964  ,   -4.0687675 ,    4.694035  ,\n",
            "         -5.0158544 ,   19.345467  ,   -0.62011623,  -31.00907   ],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-396.9638   ,   27.23556  ,  -15.056654 ,   23.348837 ,\n",
            "        -16.700161 ,    6.458374 ,    1.3052709,   23.717999 ,\n",
            "        -13.756101 ,  -14.724265 ,  -15.464813 ,   31.366611 ,\n",
            "         18.521103 ,    3.0879593,   -7.3586736,   24.359512 ,\n",
            "         -4.171895 ,   15.524814 ,   30.21211  ,   -0.9321968,\n",
            "        -24.343216 ,   25.860842 ,   19.473661 ,    6.9896374,\n",
            "         20.52875  ,   -4.2649074,   -5.374887 ,   18.689915 ,\n",
            "         -6.291611 ,   30.765703 ,    4.4227085,  -23.852234 ,\n",
            "         12.585485 ,   -0.9421164,    1.9405189,    3.9938507,\n",
            "          6.551543 ,    3.2824802,  -37.127495 ,   19.502028 ],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-445.68854   ,   20.188736  ,  -21.43163   ,   10.814642  ,\n",
            "        -12.155104  ,   13.988104  ,    4.690296  ,   20.669033  ,\n",
            "         -2.180045  ,  -14.547375  ,  -30.640682  ,   12.684224  ,\n",
            "         24.334364  ,   13.167314  ,   -4.2068076 ,    8.711395  ,\n",
            "         -1.9106576 ,   -4.7846794 ,   23.550549  ,   34.551495  ,\n",
            "        -20.141478  ,  -11.452479  ,    8.937404  ,   13.692601  ,\n",
            "         16.446262  ,   17.537802  ,  -12.243219  ,    5.498541  ,\n",
            "        -14.032234  ,   -8.708043  ,   40.168213  ,   -7.7902856 ,\n",
            "         -6.515733  ,    7.650223  ,  -12.6762    ,    3.5619318 ,\n",
            "         -0.48471797,   19.387226  ,   -4.729642  ,  -33.27437   ],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-5.5551074e+02,  9.3830734e+01,  4.3587742e+00,  1.7790253e+01,\n",
            "        6.9364327e-01,  1.3598794e+01,  3.0546803e+00,  1.0046648e+01,\n",
            "        4.3692331e+00,  6.4414706e+00,  2.7590556e+00,  1.4068372e+00,\n",
            "       -6.4037852e+00,  4.5865574e+00,  6.0508475e+00,  5.4956570e+00,\n",
            "        8.2727699e+00,  5.4779811e+00,  7.0289364e+00,  8.0438013e+00,\n",
            "        9.5428503e-01,  3.5510132e+00,  7.3581905e+00,  6.2731562e+00,\n",
            "        8.0277338e+00,  7.8800124e-01,  2.2851974e-01, -2.1348190e-01,\n",
            "        1.0505924e+00,  1.8002669e+00, -2.5719092e+00,  5.8594292e-01,\n",
            "        2.2104906e-01,  4.6748371e+00,  6.1670280e+00,  4.5735192e+00,\n",
            "        9.5531368e-01,  1.5049608e+00, -8.5967875e-01, -5.2825780e+00],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-4.9110989e+02,  3.2390835e+01,  1.5499081e+01,  1.7054960e+01,\n",
            "        3.3467944e+00,  6.7623067e+00,  1.0426849e+01,  1.8236353e+01,\n",
            "        1.7092224e+01,  1.7963531e+01,  8.4040203e+00,  2.5816450e+00,\n",
            "       -1.5909048e+00,  3.0241702e+00,  1.0793138e+01,  1.1360247e+01,\n",
            "        8.2963066e+00,  1.2479922e+01,  1.1821335e+01,  2.1708124e+00,\n",
            "       -6.5165787e+00,  8.0822754e-01,  9.0085688e+00,  4.7533360e+00,\n",
            "        5.6372390e+00,  9.1608191e+00, -8.7843841e-01,  3.9458711e-02,\n",
            "        3.3174186e+00, -6.5061021e+00, -4.0069036e+00,  7.3164325e+00,\n",
            "        1.7980125e+00, -7.4395645e-01,  3.2276795e+00, -1.7699705e+00,\n",
            "        2.5248625e+00, -8.0933899e-01, -7.3442149e+00,  1.6261461e+00],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-3.8706796e+02,  2.7320656e+01, -1.9150053e+01,  2.3152918e+01,\n",
            "       -4.9818602e+00,  5.3293552e+00, -4.6133294e+00,  1.6776226e+01,\n",
            "       -6.2409628e-02,  3.4117393e+00, -1.9452311e+01, -7.8849444e+00,\n",
            "       -1.6995127e+01,  2.2955315e+01,  1.9833340e+01,  1.3535440e+01,\n",
            "        5.5748882e+00,  1.1763091e+01, -2.7148132e+01, -1.6246361e+01,\n",
            "        9.5230722e+00,  3.1628424e+01,  3.4406273e+00,  1.2062391e+01,\n",
            "        1.2850901e-01, -2.1275103e+01,  1.8146414e+00,  4.9167008e+00,\n",
            "       -2.8364778e+00,  2.3082226e+01,  1.8201159e+01, -3.3789871e+01,\n",
            "       -4.9544091e+00,  1.2076029e+01, -6.1645899e+00,  5.5378642e+00,\n",
            "       -6.1054311e+00,  2.4510050e+01,  1.8069836e+00, -3.6408142e+01],\n",
            "      dtype=float32), 'Violin_or_fiddle'], [array([-415.2413   ,   76.33563  ,   51.534245 ,   47.191723 ,\n",
            "         34.644665 ,   30.732124 ,   24.119814 ,   23.056953 ,\n",
            "         19.654488 ,   18.057808 ,   16.121567 ,   13.941448 ,\n",
            "         10.20385  ,    9.814475 ,    8.2683   ,    7.80336  ,\n",
            "          7.199503 ,    7.374092 ,    3.8855982,    4.414874 ,\n",
            "          3.1397607,    2.7633176,    3.3142807,    1.602581 ,\n",
            "          1.760555 ,    1.8278741,    1.657062 ,    1.7543097,\n",
            "          1.4562092,    2.0311441,    1.8283533,    1.3408177,\n",
            "          1.4790208,    1.033124 ,    1.206687 ,    1.6886334,\n",
            "          1.2784002,    1.1041226,    0.9002285,    0.5944892],\n",
            "      dtype=float32), 'Bass_drum'], [array([-463.70544   ,   16.036503  ,   10.257094  ,   10.386723  ,\n",
            "          9.444088  ,    9.723481  ,    7.9435897 ,    8.122712  ,\n",
            "          7.137816  ,    7.4011564 ,    7.7330213 ,    7.2403636 ,\n",
            "          5.513518  ,    6.034238  ,    4.3648725 ,    5.1175714 ,\n",
            "          4.2318473 ,    2.9577658 ,    3.3326523 ,    4.485955  ,\n",
            "          3.5688384 ,    4.1556234 ,    3.6578157 ,    3.2758148 ,\n",
            "          2.888132  ,    1.8982192 ,    2.6413312 ,    1.9677967 ,\n",
            "          2.4393828 ,    1.9810761 ,    1.6940669 ,    1.3735207 ,\n",
            "          1.6795006 ,    1.6327393 ,    1.02454   ,    1.3597194 ,\n",
            "          1.155032  ,    0.8093037 ,    0.91316915,    0.6970517 ],\n",
            "      dtype=float32), 'Bass_drum'], [array([-4.5285269e+02,  1.8673824e+01,  1.3460702e+01,  1.1573175e+01,\n",
            "        9.7319422e+00,  8.8058300e+00,  8.7443371e+00,  7.0459037e+00,\n",
            "        5.9269495e+00,  6.3337131e+00,  5.7252774e+00,  4.7045279e+00,\n",
            "        4.4378581e+00,  3.9220684e+00,  3.3293850e+00,  2.9593902e+00,\n",
            "        2.5055859e+00,  2.2288451e+00,  1.8543425e+00,  1.4251572e+00,\n",
            "        1.3236476e+00,  1.0795081e+00,  7.2069782e-01,  5.8198708e-01,\n",
            "        3.5866016e-01,  1.0339378e-01,  9.6574225e-02,  1.0415747e-01,\n",
            "       -3.8057890e-02, -1.5330651e-01, -9.6732728e-02, -5.0002363e-02,\n",
            "       -2.0232625e-01, -2.4458362e-01, -1.5467180e-01, -2.1203433e-01,\n",
            "       -3.0878025e-01, -2.9580894e-01, -7.1470514e-02, -1.2793158e-02],\n",
            "      dtype=float32), 'Bass_drum'], [array([-4.5769293e+02,  1.9541824e+01,  8.7735233e+00,  1.0901648e+01,\n",
            "        6.3490992e+00,  7.3025661e+00,  7.9792981e+00,  8.0059566e+00,\n",
            "        7.3609667e+00,  5.8800645e+00,  5.8358250e+00,  4.8916140e+00,\n",
            "        4.8166146e+00,  4.5517011e+00,  4.3671074e+00,  3.8203776e+00,\n",
            "        3.0063715e+00,  2.4753304e+00,  2.0061331e+00,  1.9833415e+00,\n",
            "        1.4171182e+00,  9.4318509e-01,  4.2743394e-01,  2.7451012e-01,\n",
            "        4.0910578e-01,  4.0443581e-02, -9.9347711e-02, -3.6347941e-01,\n",
            "       -3.7030792e-01, -2.3117924e-01, -2.8634685e-01, -2.8989342e-01,\n",
            "       -3.9465883e-01, -1.8972538e-01,  3.3286020e-02,  1.9110824e-01,\n",
            "        3.2174003e-01,  5.0005996e-01,  5.6611961e-01,  4.8547065e-01],\n",
            "      dtype=float32), 'Bass_drum'], [array([-4.0175610e+02,  3.8222099e+01,  1.6665901e+01,  9.7915268e+00,\n",
            "        6.3090463e+00,  6.6505842e+00,  5.4859562e+00,  5.3055730e+00,\n",
            "        4.6510439e+00,  4.0352988e+00,  2.5542772e+00,  1.9488883e+00,\n",
            "        1.1786560e+00,  1.4966724e+00,  1.7473541e+00,  1.9194682e+00,\n",
            "        1.7301588e+00,  1.5670713e+00,  1.6966150e+00,  1.9073188e+00,\n",
            "        2.4175150e+00,  2.7343564e+00,  2.7752457e+00,  2.4116268e+00,\n",
            "        2.1611936e+00,  2.0258908e+00,  2.0588686e+00,  2.0396168e+00,\n",
            "        2.0877881e+00,  1.9522393e+00,  1.6574167e+00,  1.1038327e+00,\n",
            "        7.1322721e-01,  5.3632593e-01,  7.1424079e-01,  9.0255475e-01,\n",
            "        8.3346599e-01,  5.7328081e-01,  3.6616194e-01,  2.5654331e-01],\n",
            "      dtype=float32), 'Bass_drum'], [array([-4.9539450e+02,  3.5356613e+01,  1.0956502e+01,  1.7192309e+00,\n",
            "        2.3426266e-01,  1.2297704e+00,  2.8407371e-01, -6.5553355e-01,\n",
            "       -1.1699699e+00, -9.7791088e-01, -1.4566159e+00, -1.2321651e+00,\n",
            "       -1.6205155e+00, -1.0740139e+00,  3.2195136e-02,  1.5098126e+00,\n",
            "        1.5367607e+00,  2.7430478e-01, -2.1473680e-01,  4.9644393e-01,\n",
            "        1.3682131e+00,  1.3533292e+00,  1.5607255e+00,  2.0450418e+00,\n",
            "        1.9416173e+00,  9.6280891e-01, -2.6121548e-01, -6.9853830e-01,\n",
            "        3.6632344e-01,  1.5796170e+00,  1.4824138e+00,  4.9742836e-01,\n",
            "        1.2600239e-01, -2.1116400e-01, -8.7184948e-01, -1.4272383e+00,\n",
            "       -1.0351638e+00,  5.1371998e-01,  1.6479499e+00,  6.2303048e-01],\n",
            "      dtype=float32), 'Bass_drum'], [array([-5.13622986e+02,  1.55546627e+01,  8.37276363e+00,  7.87132549e+00,\n",
            "        5.54658699e+00,  6.02542639e+00,  6.31824684e+00,  5.56999969e+00,\n",
            "        5.37357903e+00,  4.58837700e+00,  4.65074921e+00,  4.31277800e+00,\n",
            "        4.00713348e+00,  3.87606812e+00,  3.21588945e+00,  3.14126182e+00,\n",
            "        3.02852583e+00,  2.47716880e+00,  2.03426123e+00,  1.71778309e+00,\n",
            "        1.32265031e+00,  8.15573812e-01,  2.56972969e-01, -9.41664800e-02,\n",
            "       -1.07859731e-01, -8.06178525e-02, -2.53198355e-01, -5.09224057e-01,\n",
            "       -3.20439011e-01, -4.23826501e-02, -1.30452290e-01, -1.49628401e-01,\n",
            "       -2.10826918e-01, -2.96754926e-01, -1.15042619e-01,  1.19245455e-01,\n",
            "        3.84599298e-01,  3.52050364e-01,  4.11220128e-03,  2.70668679e-04],\n",
            "      dtype=float32), 'Bass_drum'], [array([-5.1688812e+02,  1.5520589e+01,  8.3609066e+00,  8.0092087e+00,\n",
            "        5.7434149e+00,  6.3582325e+00,  6.4344525e+00,  5.4858146e+00,\n",
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            "         -4.424526  ,   -8.993736  ,  -14.934579  ,   -9.305546  ,\n",
            "          6.0050707 ,   24.172102  ,   31.96309   ,   19.067741  ,\n",
            "         -9.628967  ,  -28.702082  ,  -19.374985  ,    6.069752  ,\n",
            "         11.17208   ,   -9.089848  ,  -29.772985  ,  -27.489437  ,\n",
            "        -10.530684  ,    0.69974387,    9.515111  ,   26.435326  ,\n",
            "         37.144474  ,   22.80046   ,   -7.0777683 ,  -22.053024  ,\n",
            "        -10.346446  ,    2.9423656 ,    1.8748903 ,   -6.4210215 ,\n",
            "        -17.75539   ,  -21.91987   ,   -6.4801674 ,   13.11972   ],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-2.1355525e+02,  1.0364144e+02,  1.8248293e+01,  2.4165234e+01,\n",
            "       -5.9177189e+00,  2.9075947e+01, -3.8614960e+01,  3.1781249e+00,\n",
            "       -2.1478634e+01, -7.2651238e+00, -1.9626860e+01, -2.2713785e+01,\n",
            "       -1.8616789e+01, -2.0836485e+01, -3.9578562e+00, -1.1293378e+00,\n",
            "        8.8198578e-01,  8.5589495e+00,  5.2567034e+00,  8.1535530e+00,\n",
            "       -1.3278853e+01, -1.7417551e+01, -4.1785808e+00, -2.8397921e-01,\n",
            "        6.2119322e+00, -3.5135534e+00, -7.5655165e+00, -1.1666251e+01,\n",
            "       -1.9165748e+00,  8.3102465e+00,  1.1909724e+01, -7.6757026e-01,\n",
            "        1.1942604e-01,  1.0390764e+01,  7.2981133e+00,  1.2639936e+00,\n",
            "       -6.6707897e+00,  1.7244997e+00,  4.2608431e-01, -1.5742726e+01],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-5.26004883e+02,  2.50525284e+01,  2.02500629e+01,  1.60710430e+01,\n",
            "        1.22250748e+01,  9.09250259e+00,  5.86847687e+00,  2.64115047e+00,\n",
            "       -4.35586482e-01, -2.79444814e+00, -4.55857801e+00, -5.84026051e+00,\n",
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            "       -1.17103395e+01, -1.14554167e+01, -1.07355614e+01, -1.01389971e+01,\n",
            "       -9.74454021e+00, -9.18677330e+00, -8.09268093e+00, -6.39324331e+00,\n",
            "       -4.43061733e+00, -2.60729194e+00, -6.04174316e-01,  1.95665789e+00,\n",
            "        5.08904934e+00,  8.55951214e+00,  1.13655014e+01,  1.23153105e+01,\n",
            "        1.11301889e+01,  8.37684345e+00,  5.44458818e+00,  3.43358254e+00,\n",
            "        2.30668807e+00,  1.26647151e+00, -2.93570817e-01, -2.08121777e+00],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-4.27745178e+02,  7.07359238e+01,  2.06401501e+01, -1.69307148e+00,\n",
            "       -9.05254555e+00, -1.07720814e+01, -1.28795176e+01, -1.43476095e+01,\n",
            "       -1.59967041e+01, -1.72383251e+01, -1.64056110e+01, -1.28900623e+01,\n",
            "       -3.51843071e+00,  5.00046539e+00,  5.32640266e+00,  4.49782997e-01,\n",
            "       -3.61648059e+00, -3.12974858e+00,  4.95390624e-01,  2.60744143e+00,\n",
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            "       -1.44519138e+01, -1.44771490e+01, -1.21526852e+01, -1.37635221e+01,\n",
            "       -1.90991211e+01, -2.22482128e+01, -2.07418709e+01, -1.10124578e+01,\n",
            "        9.77484989e+00,  3.45800858e+01,  4.96221581e+01,  4.31373291e+01,\n",
            "        2.01590004e+01, -1.94048333e+00, -1.19409370e+01, -1.02395315e+01],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-4.4471118e+02,  1.0385034e+02,  4.8283730e+01,  3.2923195e+01,\n",
            "        1.7860998e+01,  1.4537703e+01,  6.3824039e+00,  8.8552672e-01,\n",
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            "       -8.3040828e-01,  1.6619639e+00,  3.9629591e+00,  5.1123328e+00,\n",
            "        2.7884893e+00,  3.9878646e-01, -2.4449255e+00, -2.5295496e+00,\n",
            "        2.6521218e+00,  1.4738761e+00,  1.2061566e+00,  4.1734114e-01,\n",
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            "       -9.5793772e-01,  2.9884062e+00,  1.6672820e+00, -1.7618127e+00,\n",
            "        1.2620915e-01,  4.8667445e+00,  4.2667322e+00,  2.3565934e+00,\n",
            "        6.7550582e-01, -1.2091694e+00, -1.1541485e+00, -2.1611559e+00],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-4.9603952e+02,  9.9832268e+00,  3.5162930e+00,  2.4178138e+00,\n",
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            "       -3.2723162e-01, -1.5339839e+00, -2.1086431e+00, -1.6915386e+00,\n",
            "       -1.8094893e+00, -2.4929137e+00, -2.9145284e+00, -2.9882910e+00,\n",
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            "       -2.8426440e+00, -2.6288481e+00, -2.4513288e+00, -2.2719433e+00,\n",
            "       -1.9284146e+00, -1.7765810e+00, -1.3632821e+00, -8.1350929e-01,\n",
            "       -8.4421819e-01, -1.0166364e+00, -8.2945770e-01,  2.0296988e-03,\n",
            "        1.9660281e+00,  4.8412552e+00,  7.0577145e+00,  6.9513044e+00,\n",
            "        4.5627675e+00,  1.7197677e+00,  2.1013109e-01,  5.2995992e-01],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-4.2874301e+02,  1.3044485e+02,  5.4055054e+01,  3.2524918e+01,\n",
            "        2.0928816e+01,  1.6554422e+01,  6.1308007e+00,  5.5277400e+00,\n",
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            "       -3.0260780e+00,  2.1890192e+00,  5.2035427e+00,  9.7446280e+00,\n",
            "        8.8707266e+00,  5.1152244e+00,  4.6823969e+00,  2.1701288e+00,\n",
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            "       -5.9965820e+00, -6.1598191e+00, -9.1555614e+00, -8.4992208e+00,\n",
            "       -2.0420401e-01,  1.0800499e+01,  9.3107824e+00,  2.3894942e-01,\n",
            "       -3.0790958e+00, -3.0837827e+00, -7.7905488e+00, -1.2947730e+01,\n",
            "       -7.2794890e+00, -3.6873552e-01, -2.7847981e+00, -1.0569779e+01],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-3.9381573e+02,  9.4472298e+01,  4.2721844e+01,  2.3051016e+01,\n",
            "        5.8369498e+00,  4.3793411e+00,  2.3739672e+00,  8.3898002e-01,\n",
            "       -6.1183143e+00, -8.2442408e+00, -9.3811302e+00, -2.5960200e+00,\n",
            "       -1.5815665e+00,  2.6644251e-01,  6.1907783e+00,  9.5823736e+00,\n",
            "        8.5995436e+00,  9.2695713e+00,  5.2004051e+00,  1.5016321e+00,\n",
            "       -2.1661050e+00, -7.0657153e+00, -2.8941658e-01,  7.8519976e-01,\n",
            "        2.4839704e-01,  9.6454316e-01, -6.7828804e-01,  7.5817070e+00,\n",
            "        1.3795038e+01,  1.1921911e+01, -1.7128125e+00, -8.1428747e+00,\n",
            "       -3.8155212e+00,  5.0591526e+00,  6.7309508e+00,  5.4277074e-01,\n",
            "       -2.4778411e+00, -5.7703266e+00, -6.3842278e+00, -4.0706434e+00],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-4.0680042e+02,  1.0369489e+02,  3.8280788e+01,  1.6420982e+01,\n",
            "       -2.5634214e-01,  6.4952893e+00,  8.0410681e+00,  1.8721049e+00,\n",
            "       -1.0180480e+00,  4.4785943e+00,  1.4390992e+00, -3.8796785e-01,\n",
            "       -9.7097158e+00, -5.8329349e+00, -1.4256101e+00,  9.0716248e+00,\n",
            "        1.5373498e+01,  8.9359913e+00,  6.0368938e+00,  4.3406839e+00,\n",
            "        4.5635586e+00,  9.0124226e+00,  4.5281262e+00, -2.0504472e+00,\n",
            "        1.5248653e-01,  2.5788951e+00,  1.2938144e+00,  1.7567916e-01,\n",
            "        1.0049343e-01, -5.6997901e-01,  3.4994349e+00,  1.1857372e-01,\n",
            "       -4.5097742e+00, -6.1952620e+00, -3.3286204e+00,  1.8535216e+00,\n",
            "       -1.3012664e-01, -3.5171056e+00,  2.9659986e+00,  3.3790202e+00],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-5.9986554e+02,  3.5051880e+01, -1.2550729e+01, -2.4228477e-01,\n",
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            "       -1.4418114e+01, -1.3961953e+01, -9.1308517e+00,  8.0786145e-01,\n",
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            "       -4.3724117e+00, -1.5677140e+01, -1.3182132e+01, -8.5153288e-01,\n",
            "       -4.6382709e+00, -1.9200357e+01, -2.7844824e+01, -1.4769584e+01,\n",
            "        3.4451642e+00,  9.7198601e+00,  2.4267559e+00,  3.5484419e+00,\n",
            "        1.6965919e+01,  1.4407336e+01, -4.8298841e+00, -1.2754212e+01,\n",
            "       -3.3274300e+00, -7.0505953e+00, -1.4881788e+01, -7.1313291e+00,\n",
            "       -3.4879282e+00, -1.0957364e+01, -3.7014465e+00,  1.4780531e+01],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-244.55826  ,   92.48606  ,   22.984156 ,   31.369919 ,\n",
            "        -11.367614 ,   14.880275 ,  -18.484829 ,   11.427686 ,\n",
            "        -10.1631365,   -6.0474467,  -16.38255  ,  -15.595661 ,\n",
            "        -24.908392 ,  -25.480362 ,  -28.278563 ,  -16.047281 ,\n",
            "        -13.415101 ,   -1.4269149,   -7.250849 ,   -1.0468769,\n",
            "         -7.506798 ,   -7.3023095,  -10.428977 ,   -7.446107 ,\n",
            "          1.2005365,    4.2459035,   12.167086 ,    5.0130434,\n",
            "         -3.2725859,   -6.565952 ,  -10.020938 ,  -10.22626  ,\n",
            "         -6.3182054,   -8.421112 ,  -12.291465 ,  -11.435996 ,\n",
            "          1.0941826,    7.8811164,   18.677439 ,   20.004772 ],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-5.1662848e+02,  9.7876266e+01,  6.8063759e+01,  5.1189144e+01,\n",
            "        4.0574924e+01,  3.3012711e+01,  2.3989040e+01,  1.7688744e+01,\n",
            "        1.3652303e+01,  9.7597027e+00,  4.5651884e+00,  2.2386420e+00,\n",
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            "       -8.3251057e+00, -6.5213304e+00, -5.6437626e+00, -4.8079486e+00,\n",
            "       -2.7922931e+00, -5.7331181e-01, -1.5685335e-01, -1.2581834e+00,\n",
            "       -9.8793775e-01, -9.7368136e-02,  7.7554494e-01,  1.5538852e+00,\n",
            "        2.2844913e+00,  2.6225941e+00,  2.3913183e+00,  1.6757083e+00],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-431.29556  ,   83.90537  ,   44.73182  ,   41.241035 ,\n",
            "         27.05243  ,   30.552584 ,   14.633906 ,   10.824264 ,\n",
            "         -2.2140405,   -3.7362182,  -10.539722 ,   -7.3538346,\n",
            "         -6.4892464,   -3.970929 ,   -2.936236 ,   -2.196667 ,\n",
            "         -5.829605 ,  -10.607891 ,  -14.137871 ,  -14.659521 ,\n",
            "        -13.45113  ,  -16.39215  ,  -16.800035 ,  -16.912455 ,\n",
            "        -12.311359 ,  -10.446178 ,  -10.8143835,  -12.933701 ,\n",
            "        -11.433225 ,   -9.715901 ,   -7.6092596,   -8.525128 ,\n",
            "        -10.565044 ,  -10.542764 ,   -8.5606165,   -5.8855715,\n",
            "         -3.0820172,   -1.7604197,   -3.5826962,   -2.673202 ],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-417.40317   ,   94.10797   ,   21.722124  ,   22.37704   ,\n",
            "          0.9449971 ,    9.003719  ,   -0.6716676 ,    4.523406  ,\n",
            "          2.0738332 ,   13.383422  ,    6.845544  ,    0.50342965,\n",
            "         -3.2227929 ,    3.8508415 ,   -3.9836981 ,    5.086893  ,\n",
            "          7.154966  ,    4.2241044 ,    9.041904  ,    5.933067  ,\n",
            "         -8.290719  ,   -2.2279024 ,    1.5300664 ,    4.355008  ,\n",
            "         13.461388  ,   18.97979   ,   25.198292  ,   25.215841  ,\n",
            "         13.592063  ,    1.4760107 ,   -7.9296393 ,   -7.424526  ,\n",
            "          9.817263  ,   10.041723  ,    0.43712503,   -8.745141  ,\n",
            "         -4.9129744 ,    2.4981558 ,   -0.9241528 ,   -8.050274  ],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-4.11092529e+02,  7.46026611e+01,  1.56480732e+01,  1.85562115e+01,\n",
            "       -7.19644451e+00,  3.45134854e+00, -5.19531202e+00,  1.01187158e+00,\n",
            "       -1.90354085e+00,  1.09307575e+01,  4.65976238e+00, -5.95458925e-01,\n",
            "       -3.65946817e+00,  1.23637724e+01, -1.18578076e+00, -1.80683362e+00,\n",
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            "        6.39857026e-03,  9.95570087e+00,  2.59563770e+01,  4.55529900e+01,\n",
            "        4.37692986e+01,  2.56774597e+01,  8.83809280e+00, -1.65533199e+01,\n",
            "       -1.13933744e+01,  1.37332737e+00,  1.20793333e+01,  5.24237871e+00,\n",
            "       -1.30275583e+01, -1.38903313e+01, -3.93736887e+00,  1.08180819e+01],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-1.4900250e+02,  1.2777417e+02,  5.2441726e+00,  2.1196711e+01,\n",
            "       -4.6134353e+00,  3.7157814e+01, -2.3494759e+01,  8.5227060e-01,\n",
            "       -2.2322277e+01, -2.4045427e+00, -9.0896006e+00, -1.5023769e+01,\n",
            "       -9.4903336e+00, -1.2050997e+01, -5.8956370e+00, -5.3661070e+00,\n",
            "       -1.4581981e+01, -9.7042170e+00, -1.2593046e+01, -5.4497566e+00,\n",
            "       -1.0699369e+01, -1.2066915e+01, -1.0772517e+01, -1.2936512e+01,\n",
            "       -1.2749271e+01, -6.2860579e+00, -5.2299628e+00, -1.2151297e+01,\n",
            "       -1.2582387e+01, -3.5862389e+00, -4.3418965e+00, -5.1918612e+00,\n",
            "       -1.3236790e+00, -2.2069052e-01, -5.4969442e-01, -3.2479234e+00,\n",
            "        6.3758111e-01, -1.8626739e-01,  1.4764519e-01,  7.6891422e-02],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-4.69416779e+02,  3.19716454e+01, -2.24513283e+01, -2.34360199e+01,\n",
            "       -1.96190834e+01, -3.28502703e+00,  1.77964840e+01,  3.44294090e+01,\n",
            "        1.81125679e+01, -1.28475971e+01, -3.63126144e+01, -2.06711559e+01,\n",
            "        1.36714983e+01,  2.69801922e+01,  1.03427610e+01,  5.56262359e-02,\n",
            "       -6.28637981e+00, -2.40423603e+01, -2.62069130e+01,  1.15054674e+01,\n",
            "        3.30960732e+01,  9.59664917e+00, -1.02802305e+01, -1.19865351e+01,\n",
            "       -1.47695370e+01, -7.41243410e+00,  4.46940756e+00,  1.00529804e+01,\n",
            "        1.57308092e+01,  1.12749469e+00, -2.33593426e+01, -9.67272186e+00,\n",
            "        1.29120140e+01,  8.21331787e+00, -9.69633222e-01,  2.17344642e+00,\n",
            "        2.79081535e+00, -1.42326088e+01, -1.74222527e+01,  6.06392860e+00],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-452.3702   ,  103.95891  ,   25.48977  ,    5.254189 ,\n",
            "         -2.5121534,   -7.5342455,   -2.9399216,   10.340101 ,\n",
            "         10.609957 ,   -3.8583732,  -13.870406 ,  -14.3225155,\n",
            "        -16.27983  ,  -10.255124 ,   -1.6712174,   -1.7640021,\n",
            "         -3.5068781,   -4.0403566,   -7.3665347,   -6.8086643,\n",
            "         -4.878288 ,   -5.1144767,  -10.1482115,  -12.476751 ,\n",
            "         -8.827485 ,   -7.575169 ,  -10.952398 ,  -12.230627 ,\n",
            "         -8.337921 ,   -7.0923214,   -9.748908 ,  -10.109996 ,\n",
            "         -6.412236 ,   -2.182326 ,   -4.05134  ,   -9.573816 ,\n",
            "        -14.475494 ,  -15.589451 ,  -13.524464 ,   -9.465126 ],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-435.02716  ,  103.03535  ,   38.726788 ,    9.620431 ,\n",
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            "        -24.720919 ,  -20.945053 ,  -11.698895 ,   -9.119291 ,\n",
            "        -13.614817 ,  -15.903464 ,  -12.73386  ,   -6.834743 ,\n",
            "         -6.020926 ,  -13.828268 ,  -18.442225 ,  -10.343373 ,\n",
            "          5.763529 ,   22.257427 ,   34.258022 ,   36.712315 ,\n",
            "         27.16868  ,    7.0078793,  -11.840569 ,  -14.914642 ,\n",
            "         -3.4488394,    6.536621 ,    2.3553872,  -10.721572 ],\n",
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            "          0.7662161,   13.804101 ,   -0.9689825,  -20.629166 ],\n",
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            "       -5.1522388e+00, -4.8276992e+00, -4.6746392e+00, -3.9222429e+00],\n",
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            "        9.1817474e-01,  1.0303874e+00, -2.4250560e+00, -5.7222996e+00],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-5.4549426e+02,  3.4956062e+01,  2.3067053e+01,  1.9837181e+01,\n",
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            "       -1.2929851e+00, -1.3504272e-02,  7.7953088e-01,  9.6734637e-01],\n",
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            "         -9.886164 ,   -0.5792363,   -3.8188164,  -12.915999 ,\n",
            "        -11.8604   ,    6.95841  ,    2.8546767,    7.185091 ,\n",
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            "         13.532742 ,   11.37678  ,   10.68238  ,    7.713045 ,\n",
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            "          1.9167292,   -7.0229683,  -15.046935 ,   -9.146438 ,\n",
            "        -14.970697 ,  -16.193317 ,    8.632675 ,   17.695395 ,\n",
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            "        -13.616651 ,  -15.197895 ,   -4.542896 ,    2.5222194],\n",
            "      dtype=float32), 'Acoustic_guitar'], [array([-4.0460049e+02,  1.0432282e+02,  3.1367897e+01,  1.2088865e+01,\n",
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            "        7.0414209e-01, -9.1672391e-01,  7.4932127e+00,  4.5391731e+00],\n",
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            "       -7.41667557e+00, -1.59725161e+01, -1.22199116e+01, -6.33273423e-02],\n",
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            "        -16.717234  ,  -15.971438  ,   11.846953  ,   27.895794  ,\n",
            "         20.812344  ,   12.722469  ,   17.056871  ,   13.7797165 ,\n",
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            "        -13.82684   ,  -10.175884  ,    1.292744  ,    3.5796149 ,\n",
            "         15.884418  ,   21.12821   ,   -0.7427126 ,   -8.96914   ],\n",
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            "         -2.8566477,   -4.722835 ,  -18.77686  ,   -1.065944 ],\n",
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            "       -2.4745777e+00, -1.2738489e+00,  4.2483845e+00,  6.4092875e+00],\n",
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            "       -4.9337854e+00, -4.6445781e-01,  6.3134294e+00,  1.0931691e+01],\n",
            "      dtype=float32), 'Snare_drum'], [array([-405.82187   ,   11.711906  ,  -21.174002  ,    2.435182  ,\n",
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            "         -4.067504  ,   -1.1229678 ,    0.42557362,    3.362255  ,\n",
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            "       -2.5579751e+00,  1.7471054e+00,  1.6230392e-01,  9.7299224e-01,\n",
            "        1.4832807e-01,  2.6520476e+00,  4.1842341e+00,  5.3492517e+00,\n",
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            "       -2.5110962e+00, -5.4198956e-01,  2.1436846e+00,  2.9095614e+00,\n",
            "       -4.7939181e+00, -1.0533040e+01, -6.0532756e+00,  3.9627018e+00,\n",
            "        8.6882486e+00,  6.6241517e+00,  4.2520201e-01, -5.4371095e+00],\n",
            "      dtype=float32), 'Snare_drum'], [array([-5.1236462e+02,  7.6094032e+01,  7.9475965e+00,  2.9522979e-01,\n",
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            "        2.3400576e+00,  5.5892267e+00,  5.7434406e+00,  1.4628431e+00],\n",
            "      dtype=float32), 'Snare_drum'], [array([-2.1951456e+02,  9.5523788e+01, -3.1523657e+01,  4.3518181e+01,\n",
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            "       -1.2905623e+00, -2.1283174e+00, -1.1464611e+00, -5.0499147e-01],\n",
            "      dtype=float32), 'Snare_drum'], [array([-177.9463    ,  107.94948   ,  -36.57471   ,   56.86213   ,\n",
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            "        -19.28899   ,   -2.0707145 ,   -2.203524  ,   -7.5717115 ,\n",
            "         -8.134     ,  -14.67616   ,   -6.759765  ,   -5.9161654 ,\n",
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            "         -1.9909294 ,   -6.0692215 ,   -1.030253  ,   -6.537711  ,\n",
            "         -2.7104836 ,   -4.042297  ,   -0.40868288,   -1.0313158 ],\n",
            "      dtype=float32), 'Snare_drum'], [array([-4.6279312e+02,  2.1061424e+01,  1.6894167e+01,  2.0691950e+01,\n",
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            "      dtype=float32), 'Snare_drum'], [array([-4.7585654e+02,  2.7824118e+01,  1.2932084e+00,  6.2861600e+00,\n",
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            "        8.9476818e-01,  6.2420374e-01,  3.6389199e-01, -3.1753290e-01],\n",
            "      dtype=float32), 'Snare_drum'], [array([-5.03319824e+02,  5.91094093e+01, -1.40322485e+01,  1.35544434e+01,\n",
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            "      dtype=float32), 'Snare_drum'], [array([-4.33522736e+02,  1.14559929e+02,  8.91041234e-02,  3.33569489e+01,\n",
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            "      dtype=float32), 'Cello'], [array([-3.5009610e+02,  1.7290279e+02, -1.1489610e+01,  3.3377850e+01,\n",
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            "       -7.8147513e-01, -1.9956174e+00, -1.8112168e+00,  6.7414966e+00],\n",
            "      dtype=float32), 'Cello'], [array([-374.9925    ,   39.987507  ,  -20.859974  ,   37.889008  ,\n",
            "         -4.843957  ,   14.179785  ,   -9.174392  ,    5.5621905 ,\n",
            "         -9.580586  ,   -1.1076106 ,  -15.967052  ,   -1.6415331 ,\n",
            "         -9.663967  ,    0.5715164 ,    8.744042  ,   27.395977  ,\n",
            "         27.6654    ,   28.328156  ,    0.38532037,  -11.143001  ,\n",
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            "        -15.646577  ,  -13.313092  ,   -6.6159773 ,   -5.3178368 ,\n",
            "         -1.1591575 ,    2.659409  ,    6.8039026 ,    8.385806  ,\n",
            "         10.489341  ,    4.990801  ,   -1.6244763 ,    3.4853861 ,\n",
            "          1.4798422 ,  -12.988185  ,  -13.635893  ,   -4.3014145 ],\n",
            "      dtype=float32), 'Cello'], [array([-4.5777744e+02,  1.1784184e+02, -3.1586676e+01,  4.7431385e+01,\n",
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            "      dtype=float32), 'Cello'], [array([-317.20212   ,   94.64279   ,  -29.874008  ,   24.548687  ,\n",
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            "         -7.3416343 ,   13.18906   ,    1.6305071 ,    4.115048  ,\n",
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            "         -3.7695482 ,    0.47261834,   -6.1257863 ,   -2.8851225 ,\n",
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            "        -13.619917  ,   -0.33005047,   17.238201  ,    6.0609536 ,\n",
            "         -3.3754082 ,  -12.251331  ,   -1.3795037 ,   11.895796  ],\n",
            "      dtype=float32), 'Cello'], [array([-414.82767   ,   62.452377  ,  -13.47506   ,   39.331146  ,\n",
            "         -2.7407768 ,    2.4025977 ,  -15.316183  ,    5.6306877 ,\n",
            "         -5.3330483 ,   11.803995  ,   -4.2685347 ,    1.0930322 ,\n",
            "          1.6736437 ,    3.2654479 ,   -6.3852096 ,   10.191878  ,\n",
            "         10.35858   ,    6.021724  ,  -13.354796  ,  -15.8359785 ,\n",
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            "         34.699028  ,   41.28879   ,   37.23633   ,   16.942848  ,\n",
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            "         15.071473  ,   13.500258  ,   -8.636798  ,  -17.46421   ],\n",
            "      dtype=float32), 'Cello'], [array([-4.7985541e+02,  8.2484673e+01, -1.3039392e+01,  2.4667389e+01,\n",
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            "        2.9775248e+00,  4.7414550e-01,  1.1316700e+00,  5.0880485e+00],\n",
            "      dtype=float32), 'Cello'], [array([-5.5601569e+02,  9.8185432e+01, -1.1663711e+01,  3.0778814e+01,\n",
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            "       -4.9725657e+00, -1.4749466e+01, -5.1279540e+00,  1.0903091e+01],\n",
            "      dtype=float32), 'Cello'], [array([-4.9261884e+02,  1.2233678e+02,  8.9264297e+00,  4.0995785e+01,\n",
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            "       -8.8553077e-01, -4.0857892e+00,  3.5530934e-01,  4.5070949e+00,\n",
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            "        3.2812622e+00, -4.2740494e-01, -1.1888683e+00, -2.0531583e+00],\n",
            "      dtype=float32), 'Cello'], [array([-5.9570306e+02,  4.9181408e+01,  2.3958378e+01,  1.7478491e+01,\n",
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            "       -6.0588932e+00, -7.2891555e+00, -9.1874475e+00, -1.0460278e+01,\n",
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            "       -7.1528268e+00, -6.8369861e+00, -6.1476798e+00, -3.9574413e+00,\n",
            "        2.1331348e-01,  5.2166667e+00,  9.8767614e+00,  1.1960061e+01,\n",
            "        9.6544189e+00,  4.3137035e+00, -6.7587775e-01, -2.2253780e+00,\n",
            "       -7.0627052e-01,  5.8452535e-01, -8.8419354e-01, -4.2490792e+00],\n",
            "      dtype=float32), 'Cello'], [array([-4.1780273e+02,  1.1611266e+02,  1.3259629e+01,  4.2246708e+01,\n",
            "        2.8910597e+01,  1.1371495e+01,  7.2803469e+00,  1.3601263e+01,\n",
            "        5.4070368e+00,  1.1133131e+01,  8.7887793e+00,  4.4013762e+00,\n",
            "        2.1147492e+00, -4.3211919e-01, -1.1138872e+00,  3.2119944e+00,\n",
            "       -2.0243440e+00, -5.2154365e+00, -9.7248468e+00, -2.9267728e+00,\n",
            "        1.4298793e-02, -3.4019125e+00, -1.2245448e+00,  3.7337044e-01,\n",
            "        8.5505581e-01, -4.2524319e+00, -1.9518335e+00, -1.6921849e+00,\n",
            "       -7.8319364e+00, -9.6130981e+00, -6.4296875e+00, -3.4199603e+00,\n",
            "        2.4184754e-01, -2.5267217e+00, -3.4093533e+00, -2.7155869e+00,\n",
            "       -7.7393347e-01, -3.0829639e+00, -4.3483281e+00,  8.8765949e-02],\n",
            "      dtype=float32), 'Cello'], [array([-2.9052814e+02,  1.3552875e+02, -9.5397053e+00,  3.4540695e+01,\n",
            "        3.0075796e+01,  5.6588764e+00, -8.6793756e+00,  2.5837063e+01,\n",
            "       -5.5006361e+00, -6.6957068e+00, -1.9063318e+01, -4.9903378e+00,\n",
            "       -6.9879098e+00, -6.6096563e+00, -7.8313179e+00,  1.0691935e+00,\n",
            "       -1.2052039e+01, -2.4970300e+00, -5.1307993e+00, -1.2713069e+01,\n",
            "       -5.6434975e+00, -2.9047885e+00, -2.3200858e+00, -1.2476910e+01,\n",
            "       -4.9259787e+00, -2.0947125e+00, -4.4821959e+00, -3.9058068e+00,\n",
            "       -5.1770730e+00, -3.8168530e+00,  4.7209959e+00,  4.9363232e+00,\n",
            "       -1.6974270e+00, -5.2493086e+00, -8.4529072e-02, -3.9864709e+00,\n",
            "       -4.0427732e+00, -6.5587554e+00, -7.0879388e+00, -2.3181145e+00],\n",
            "      dtype=float32), 'Cello'], [array([-4.9363467e+02,  1.7169579e+02,  1.3085165e+01,  1.7933098e+01,\n",
            "        2.6881527e+01,  2.6046185e+01, -1.1941423e+00,  4.0765200e+00,\n",
            "        2.3977695e+00,  1.1132857e+01,  3.2927690e+00,  4.8620930e+00,\n",
            "       -9.2320194e+00, -6.7273436e+00, -2.2194667e+00,  3.8178968e+00,\n",
            "       -6.9579558e+00, -7.3517294e+00, -1.1709092e+01, -6.0191059e+00,\n",
            "        5.7158738e-02, -6.2642550e+00, -3.8865387e+00,  1.0502056e+00,\n",
            "       -4.0496202e+00, -1.3391877e+00, -1.4530470e+00,  3.9128026e-01,\n",
            "       -5.8580446e+00, -7.1512003e+00, -4.7676525e+00, -7.3011929e-01,\n",
            "       -2.1311523e-02,  1.2184592e+00,  2.6854126e+00, -2.1549246e+00,\n",
            "       -2.3897021e+00, -1.0864500e+01, -1.1486834e+01, -6.4942980e+00],\n",
            "      dtype=float32), 'Cello'], [array([-4.3252194e+02,  1.2144044e+02,  2.8018278e+01,  4.1614265e+01,\n",
            "        2.7725655e+01,  1.1505519e+01,  8.6145840e+00,  9.2679873e+00,\n",
            "       -1.6084892e+00, -2.1972823e+00, -5.0940175e+00, -3.7428319e+00,\n",
            "       -3.5502110e+00, -5.0194774e+00, -4.0398550e+00, -1.0128395e-01,\n",
            "       -4.5097127e+00, -1.8699354e+00, -2.4411006e+00, -2.5868993e-02,\n",
            "        1.5245664e-01, -3.4869096e+00, -2.6492305e+00, -2.9242462e-01,\n",
            "        1.4976951e+00, -1.4491068e+00, -6.1510640e-01, -4.3383557e-02,\n",
            "       -3.0190949e+00, -5.2693367e+00, -5.7613158e+00, -7.5464888e+00,\n",
            "       -4.3071795e+00, -4.9003884e-01,  3.3582258e+00,  7.3830932e-01,\n",
            "        1.5911191e+00,  2.8242834e+00,  2.0387907e+00,  2.9361401e+00],\n",
            "      dtype=float32), 'Cello'], [array([-409.60843   ,   58.787083  ,  -15.086895  ,   24.692669  ,\n",
            "          4.0364532 ,   15.580224  ,   -2.9452448 ,    7.3031497 ,\n",
            "        -11.943586  ,   -3.4371166 ,  -10.237692  ,   -9.116761  ,\n",
            "        -19.84327   ,   -3.4046834 ,   -8.379376  ,   -2.011545  ,\n",
            "        -16.168621  ,   -8.30017   ,   -9.921249  ,   -0.90549445,\n",
            "         21.404644  ,   32.142788  ,   44.826797  ,   31.988457  ,\n",
            "          5.101569  ,  -15.307526  ,  -14.7495775 ,    3.9449425 ,\n",
            "         12.821166  ,   -1.7825158 ,  -19.382607  ,  -17.703518  ,\n",
            "         -2.4322205 ,   10.26087   ,    1.0933974 ,  -19.14451   ,\n",
            "        -12.564853  ,    5.9409842 ,    3.9789712 ,  -14.186598  ],\n",
            "      dtype=float32), 'Cello'], [array([-463.34357   ,   57.16837   ,   41.491325  ,   31.761292  ,\n",
            "         23.965736  ,   16.707067  ,   11.536787  ,    9.229431  ,\n",
            "          7.759232  ,    5.154826  ,    2.9072669 ,    0.5130778 ,\n",
            "         -1.8170664 ,   -2.8671181 ,   -3.7505925 ,   -4.3659945 ,\n",
            "         -4.589954  ,   -4.9754124 ,   -5.1465387 ,   -5.1190724 ,\n",
            "         -5.2265215 ,   -5.566306  ,   -6.0486836 ,   -6.7618256 ,\n",
            "         -7.8645353 ,   -9.46539   ,  -11.147058  ,  -11.878769  ,\n",
            "        -11.94589   ,  -11.682292  ,  -10.666472  ,   -9.646103  ,\n",
            "         -8.932828  ,   -8.096873  ,   -6.8612404 ,   -4.0247054 ,\n",
            "          0.89887697,    6.301597  ,   10.295129  ,   11.878771  ],\n",
            "      dtype=float32), 'Cello'], [array([-496.63257  ,  105.90457  ,   10.942869 ,   42.815376 ,\n",
            "         25.61683  ,   19.111439 ,   -5.282932 ,    7.516923 ,\n",
            "          2.759148 ,    3.152379 ,   -8.564525 ,   -2.3875256,\n",
            "         -6.22184  ,   -1.6412222,   -2.367725 ,    2.1440077,\n",
            "        -10.754361 ,   -7.679603 ,   -7.696422 ,    1.1553038,\n",
            "          3.28291  ,   -7.6740646,   -2.6294637,    3.769046 ,\n",
            "          2.259674 ,   -0.99103  ,  -12.256633 ,   -2.883098 ,\n",
            "         -5.116517 ,   -4.024788 ,   -3.6098156,   -5.802301 ,\n",
            "         -2.3065982,   -1.5617718,   -1.7908334,   -6.656703 ,\n",
            "         -6.4474683,   -6.562029 ,   -3.8686662,   -4.364649 ],\n",
            "      dtype=float32), 'Cello'], [array([-4.38281097e+02,  1.17640648e+02, -6.35631561e+00,  3.39092636e+01,\n",
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            "       -1.63894951e+00,  5.81975162e-01, -1.02435055e+01, -1.81779659e+00,\n",
            "       -8.92112732e+00, -1.05695653e+00, -2.18883663e-01, -3.81511986e-01,\n",
            "       -1.21406136e+01, -5.20014477e+00, -9.97294235e+00, -4.55710262e-01,\n",
            "        2.11804557e+00, -8.89925289e+00, -4.31854534e+00, -8.23715389e-01,\n",
            "        2.19186568e+00, -4.27458382e+00, -7.25883341e+00, -5.61050987e+00,\n",
            "       -6.72169542e+00, -2.55645227e+00, -7.00104666e+00, -6.94117498e+00,\n",
            "       -1.99145317e+00, -9.69093740e-01, -3.90684056e+00, -1.03580151e+01,\n",
            "       -6.33706236e+00, -5.87361193e+00, -5.30021620e+00, -3.48693752e+00],\n",
            "      dtype=float32), 'Cello'], [array([-5.3714618e+02,  1.0973632e+02, -2.1712442e+01,  3.6907612e+01,\n",
            "        1.0242684e+01,  1.5076452e+01, -8.8051128e+00,  1.6254092e+00,\n",
            "       -6.8767252e+00,  9.0240755e+00, -4.5160918e+00,  3.7523196e+00,\n",
            "       -4.4175773e+00,  4.3415341e+00, -2.8938477e+00,  6.5560660e+00,\n",
            "       -1.3198545e+01, -2.9578338e+00, -2.6756620e+00,  7.4957552e+00,\n",
            "        5.5187392e+00, -8.7972565e+00, -4.9496098e+00,  2.6179883e+00,\n",
            "        1.4057786e+00, -6.5928497e+00, -1.6777374e+01, -8.5031395e+00,\n",
            "        3.3649378e+00,  2.0388796e+01,  3.5070042e+01,  4.2933060e+01,\n",
            "        3.1376398e+01,  4.0391779e+00, -5.7698007e+00, -1.0034860e+01,\n",
            "        5.6820014e-03,  1.4142491e+01,  9.3151283e+00, -7.7660465e+00],\n",
            "      dtype=float32), 'Cello'], [array([-4.09054535e+02,  7.28577576e+01, -1.40623093e+01,  2.91414700e+01,\n",
            "        2.56000042e-01,  1.14063406e+01, -1.53625221e+01, -2.34440041e+00,\n",
            "       -1.01368885e+01,  2.24985600e+00, -4.82559681e+00,  5.99615932e-01,\n",
            "       -6.57649755e+00, -1.19960773e+00, -1.14673529e+01, -3.12936306e+00,\n",
            "       -1.26698513e+01, -3.61196470e+00, -1.55112190e+01, -1.61253796e+01,\n",
            "       -1.23775692e+01, -1.29959631e+01,  1.00252628e+01,  3.67349396e+01,\n",
            "        5.42461853e+01,  4.72553864e+01,  1.63468933e+01, -9.82404900e+00,\n",
            "       -2.30277710e+01, -1.31787252e+01,  4.78176785e+00,  1.06731539e+01,\n",
            "       -6.10934258e+00, -2.20501022e+01, -1.11554108e+01,  1.03470078e+01,\n",
            "        1.60893173e+01, -6.40351200e+00, -2.49011230e+01, -4.59074640e+00],\n",
            "      dtype=float32), 'Cello'], [array([-4.1139890e+02,  1.4709705e+02,  2.3177069e+01,  4.3960865e+01,\n",
            "        2.1843143e+01,  3.1771574e+00, -1.3317066e+00,  4.4069591e+00,\n",
            "       -4.7115536e+00,  1.8637821e-01, -6.8460355e+00, -6.5435858e+00,\n",
            "       -6.2611346e+00,  9.7456509e-01, -2.4538815e+00,  2.4451126e-01,\n",
            "       -1.7321693e+00,  4.1426325e-01, -3.6079836e+00, -1.2982869e+00,\n",
            "       -3.8206596e+00, -4.5785236e+00, -3.6029294e+00,  1.4384565e+00,\n",
            "        3.2697005e+00, -2.4981453e+00, -1.5448105e+00, -1.1035895e+00,\n",
            "       -6.0110693e+00, -3.4938447e+00, -1.6553907e+00, -1.2383032e+00,\n",
            "        1.9667696e+00, -3.6050625e+00, -4.1889291e+00, -4.1961241e+00,\n",
            "        2.3082705e+00,  7.8330970e-01,  4.0800473e-01,  3.6647911e+00],\n",
            "      dtype=float32), 'Cello'], [array([-4.2109030e+02,  1.4003104e+02, -3.2151632e+00,  3.9905293e+01,\n",
            "        2.7029482e+01,  3.2612743e+00,  5.6889105e+00,  1.5302789e+01,\n",
            "        6.8832307e+00,  1.3410195e+01,  8.8940735e+00,  3.5061538e-01,\n",
            "       -5.4047799e+00, -1.3806949e+00,  3.3825951e+00,  5.9249330e+00,\n",
            "        1.3606900e-01, -4.4427934e+00, -6.5307627e+00, -3.4915173e+00,\n",
            "       -1.6489578e+00, -6.2893219e+00, -6.9515347e+00, -1.3142153e+00,\n",
            "        3.3179276e+00, -8.8776016e-01, -1.4271396e+00, -8.3297205e-01,\n",
            "       -8.6518412e+00, -9.2814970e+00, -1.0609952e+01, -7.4973521e+00,\n",
            "        1.3872830e+00, -9.7313297e-01, -2.9198709e+00, -3.3104067e+00,\n",
            "        9.9089742e-01, -7.1965528e-01, -2.3511782e+00,  2.4257693e+00],\n",
            "      dtype=float32), 'Cello'], [array([-3.6853461e+02,  1.9582452e+02, -1.0203328e+01,  1.4874803e+01,\n",
            "        2.1846846e+01,  7.5232029e+00, -1.9478357e+01, -1.7462456e+01,\n",
            "       -1.5057819e+01,  6.1754603e+00,  5.6449523e+00,  6.5099826e+00,\n",
            "       -8.8083887e+00, -9.7558842e+00, -8.6607426e-01,  1.4773530e-01,\n",
            "       -1.2900160e+01, -1.4373316e+01, -1.0318197e+01, -3.7240350e+00,\n",
            "        3.0227327e+00, -4.8341222e+00,  2.4285705e+00,  3.2055507e+00,\n",
            "       -2.4632099e+00, -5.2999196e+00, -5.7063556e+00,  2.0991068e+00,\n",
            "       -4.1950359e+00, -1.3187405e+01, -1.0369141e+01, -4.1098652e+00,\n",
            "       -9.1322794e+00, -6.6624794e+00,  4.2093083e-01, -3.3422058e+00,\n",
            "       -1.8638070e+00, -3.9595904e+00,  6.9750398e-01,  5.5399814e+00],\n",
            "      dtype=float32), 'Cello'], [array([-384.8875    ,   73.600136  ,  -31.825119  ,   51.49278   ,\n",
            "          9.416514  ,    9.716314  ,  -11.066058  ,    8.074966  ,\n",
            "        -14.493732  ,    1.613353  ,  -11.161603  ,    0.69688463,\n",
            "          1.3422558 ,    6.553903  ,  -10.297924  ,    7.416197  ,\n",
            "          7.501454  ,    4.3309317 ,   -7.3791504 ,   -9.45287   ,\n",
            "         -3.878211  ,   -8.937887  ,   -8.6377535 ,   -4.7895885 ,\n",
            "         -6.489918  ,   -1.9465961 ,    6.6612854 ,   32.851875  ,\n",
            "         39.46937   ,   34.64711   ,    4.4091096 ,  -17.77807   ,\n",
            "        -13.403658  ,   -4.0382504 ,    9.7701235 ,   11.782992  ,\n",
            "         -3.5269492 ,  -17.011673  ,   -7.928147  ,    8.994715  ],\n",
            "      dtype=float32), 'Cello'], [array([-454.8685   ,   68.68505  ,   -4.8869214,   42.00841  ,\n",
            "          6.2403493,    4.8601637,   -1.5924677,   13.5896   ,\n",
            "         -6.295189 ,   -0.5277604,  -11.445926 ,    4.960455 ,\n",
            "          5.593501 ,    2.7160184,  -11.848092 ,    2.7207694,\n",
            "          4.1891084,    3.1281753,   -9.278863 ,   -5.1827064,\n",
            "         -3.7127478,   -8.740733 ,  -11.039654 ,  -12.709506 ,\n",
            "        -12.3265085,    2.8717735,   24.74615  ,   53.04335  ,\n",
            "         47.007206 ,   26.06342  ,   -2.0897315,  -23.650599 ,\n",
            "        -11.068993 ,    1.9538391,    5.944678 ,    1.2143463,\n",
            "         -8.852901 ,  -19.026651 ,   -6.0687494,   16.558502 ],\n",
            "      dtype=float32), 'Cello'], [array([-3.9964893e+02,  1.1311856e+01, -2.4036175e+01, -2.0622351e+00,\n",
            "       -1.2020216e+01, -8.5558691e+00, -2.1838215e+01, -8.0894136e+00,\n",
            "       -2.5444183e+01, -6.8412393e-01, -1.6500793e+01, -1.6451340e+01,\n",
            "       -2.6840099e+01, -5.5501184e+00, -9.6474638e+00,  8.9271593e+00,\n",
            "        1.3055273e+01,  1.7474340e+01,  7.4875288e+00,  2.2180914e+01,\n",
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            "       -2.1174261e+01, -7.5739980e+00,  4.3418870e+00,  5.5435529e+00,\n",
            "       -2.1273632e+01, -1.5611756e+01,  2.7571578e+00,  2.1406408e+01,\n",
            "       -1.4736902e+00, -1.7646894e+01, -5.2124610e+00, -1.6239246e+00,\n",
            "        3.4898672e-01, -2.2572150e+00,  5.9215903e+00,  1.7199833e+01],\n",
            "      dtype=float32), 'Cello'], [array([-4.0627927e+02,  8.3517715e+01, -7.8759789e+00,  3.2436596e+01,\n",
            "       -9.9648154e-01,  1.4286595e+01, -7.6745582e+00,  3.8587568e+00,\n",
            "       -8.5294800e+00,  4.0000830e+00, -9.3835125e+00,  1.2306830e+00,\n",
            "       -1.4985301e+01, -5.9009943e+00, -1.3402276e+01,  9.2791188e-01,\n",
            "       -1.1403838e+01, -6.6065531e+00, -8.5376787e+00, -2.5901186e+00,\n",
            "        1.0579253e+00, -3.5835681e+00, -5.7550478e+00, -9.8302460e+00,\n",
            "       -1.2490150e+01, -1.4586650e+01, -5.5022311e+00,  1.7802382e+01,\n",
            "        3.5532505e+01,  4.8333046e+01,  3.6590351e+01,  1.2083193e+01,\n",
            "       -1.5332967e+01, -1.7394396e+01,  2.9093975e-01,  1.5414269e+01,\n",
            "        5.7855139e+00, -8.4705696e+00, -1.4580002e+01, -5.9869547e+00],\n",
            "      dtype=float32), 'Cello'], [array([-3.8863147e+02,  1.3637993e+02, -1.7752180e+01,  9.4760494e+00,\n",
            "        1.0775571e+01,  1.2391054e+01, -2.6141160e+00,  1.8097315e+00,\n",
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            "       -5.13229847e+00, -5.48726177e+00, -5.85614109e+00, -4.57836294e+00,\n",
            "       -2.63673568e+00, -2.35234141e+00, -4.46675253e+00, -7.29740572e+00,\n",
            "       -8.56820297e+00, -7.68346119e+00, -5.96188164e+00, -4.99678755e+00],\n",
            "      dtype=float32), 'Double_bass'], [array([-132.43022   ,  142.80931   ,  -29.431684  ,    5.17526   ,\n",
            "        -20.837463  ,   19.332178  ,  -12.733033  ,   14.52796   ,\n",
            "          0.794462  ,    6.1343884 ,   -6.9192924 ,    6.8581376 ,\n",
            "          2.994389  ,    7.314485  ,   -0.70940953,   12.837403  ,\n",
            "          0.52900344,    9.444767  ,    1.3058448 ,    3.5776489 ,\n",
            "         -4.6309686 ,   -3.1088521 ,    1.5941873 ,   -1.0969472 ,\n",
            "         -0.47659692,   -0.36567992,    1.1936532 ,   -3.3925583 ,\n",
            "         -8.117161  ,   -2.456143  ,  -11.677965  ,    0.33420852,\n",
            "         -2.18692   ,   -3.1085863 ,   -0.34548637,   -1.7651671 ,\n",
            "         -4.3926425 ,   -1.7926108 ,   -3.123796  ,   -1.2212822 ],\n",
            "      dtype=float32), 'Double_bass'], [array([-5.2448358e+02,  9.9508499e+01,  7.7441238e+01,  5.4314022e+01,\n",
            "        3.5533878e+01,  2.2117779e+01,  1.4087740e+01,  1.0222805e+01,\n",
            "        8.0409222e+00,  5.3778591e+00,  2.2893653e+00,  2.5157714e-01,\n",
            "       -3.3236858e-01, -1.6211936e-01,  2.6082376e-01,  8.3166695e-01,\n",
            "        1.6553701e+00,  2.6518497e+00,  3.5018365e+00,  4.3578048e+00,\n",
            "        5.3412509e+00,  5.5584126e+00,  4.0114422e+00,  8.4106272e-01,\n",
            "       -2.6217034e+00, -4.8545132e+00, -5.4988375e+00, -5.0268617e+00,\n",
            "       -3.7469015e+00, -2.1844370e+00, -1.1460795e+00, -1.0887434e+00,\n",
            "       -1.8580835e+00, -2.7084994e+00, -2.9704714e+00, -2.5735154e+00,\n",
            "       -1.7601287e+00, -9.4671100e-01, -6.6468132e-01, -1.3113847e+00],\n",
            "      dtype=float32), 'Double_bass'], [array([-5.1663147e+02,  6.3991432e+01,  3.0014284e+01,  1.9298630e+01,\n",
            "        1.9077492e+01,  1.5830491e+01,  1.0247239e+01,  7.4483690e+00,\n",
            "        6.5865803e+00,  4.9847426e+00,  4.4998970e+00,  5.0369406e+00,\n",
            "        4.6634851e+00,  3.9405606e+00,  2.1724865e+00, -1.7598057e-01,\n",
            "       -3.4608054e-01,  8.7073022e-01,  1.4792029e+00,  1.2565446e+00,\n",
            "        1.1039163e+00,  2.4981291e+00,  6.1737499e+00,  1.2174424e+01,\n",
            "        1.6939896e+01,  1.5052832e+01,  7.7802458e+00,  1.7213400e-01,\n",
            "       -4.3023429e+00, -4.2041411e+00, -4.9700698e-01,  9.0943217e-01,\n",
            "       -3.2973626e+00, -7.0303717e+00, -5.3237939e+00, -3.2203266e-01,\n",
            "        2.2382151e-01, -4.3281384e+00, -6.2309637e+00, -3.8863533e+00],\n",
            "      dtype=float32), 'Double_bass'], [array([-431.55112  ,  157.55064  ,   -9.413337 ,   13.71123  ,\n",
            "         12.722861 ,   27.194092 ,    6.2391386,   17.919792 ,\n",
            "          5.979501 ,   -6.3754425,  -16.36506  ,   -1.9608908,\n",
            "         -6.4652324,   -3.9340982,    9.796733 ,    4.828881 ,\n",
            "          4.536759 ,    4.12015  ,   -2.2240956,    1.7975883,\n",
            "         -5.123607 ,   -5.979074 ,   -7.333261 ,   -2.2060091,\n",
            "         -3.220327 ,  -10.749805 ,   -2.259016 ,   -3.5737748,\n",
            "         -8.544546 ,  -14.811532 ,   -0.5234577,   -0.7082945,\n",
            "         -2.182078 ,    1.0321923,   -2.3600075,   -8.025375 ,\n",
            "         -9.983761 ,   -8.842199 ,   -5.831466 ,   -3.2742715],\n",
            "      dtype=float32), 'Double_bass'], [array([-4.20168671e+02,  1.82308762e+02,  3.11918850e+01,  1.00585289e+01,\n",
            "        1.59385805e+01,  2.29630566e+01,  1.59648151e+01,  1.31696892e+01,\n",
            "        4.34622574e+00, -3.71484351e+00, -1.26628995e+00,  3.53613973e+00,\n",
            "       -3.23554420e+00,  2.10319847e-01,  4.55543900e+00, -3.06457019e+00,\n",
            "        4.83363152e+00,  8.17629623e+00, -2.95875144e+00,  2.31894779e+00,\n",
            "        4.17059374e+00, -2.18748406e-01, -4.95176315e+00, -8.20157909e+00,\n",
            "       -6.24414206e+00, -5.39497757e+00, -1.69788003e+00,  5.11687517e-01,\n",
            "       -1.96320283e+00, -8.00876713e+00, -5.77688217e+00, -1.45624340e+00,\n",
            "        3.85613108e+00, -6.15460098e-01, -8.35425472e+00, -4.46340466e+00,\n",
            "       -2.35486341e+00, -2.88437700e+00, -2.69834971e+00, -6.35549963e-01],\n",
            "      dtype=float32), 'Double_bass'], [array([-6.4065295e+02,  9.1054298e+01,  4.9033863e+01,  3.2194912e+01,\n",
            "        2.3182249e+01,  2.0883623e+01,  1.1786962e+01,  4.5617833e+00,\n",
            "       -2.8409390e+00, -3.8498816e+00, -1.0201800e+00,  1.2580390e+00,\n",
            "       -1.4469510e+00, -2.0195699e+00, -1.4273294e+00,  1.1956589e+00,\n",
            "        2.0425012e+00,  2.4801073e+00,  8.1510872e-01, -1.4102595e+00,\n",
            "        4.7999793e-01,  2.0246940e+00,  1.7234868e+00, -1.8649012e+00,\n",
            "       -3.7949755e+00, -3.5926232e+00, -3.1812179e+00, -2.7625546e+00,\n",
            "       -1.9107555e+00, -9.8513156e-01, -4.5528021e-02, -1.3820747e+00,\n",
            "       -2.4443293e+00, -3.2646356e+00, -3.6445391e+00, -3.7042267e+00,\n",
            "       -3.5755153e+00, -1.7819463e+00, -7.6185650e-01, -1.3007759e+00],\n",
            "      dtype=float32), 'Double_bass'], [array([-4.8878824e+02,  1.9241902e+02,  4.3694663e+00,  1.4333903e+01,\n",
            "        1.8321434e+01,  1.6172224e+01,  8.0029029e-01,  4.2384682e+00,\n",
            "        4.0579238e+00,  3.8140433e+00, -4.6886482e+00, -1.0033201e+01,\n",
            "       -2.5488923e+00,  6.1226735e+00,  8.6241360e+00,  6.5264311e+00,\n",
            "        4.9738832e+00,  5.8613801e+00,  8.1491470e-03,  4.0877476e+00,\n",
            "       -8.4408312e+00, -1.1021167e+01, -2.6008744e+00, -1.0340246e+01,\n",
            "       -1.8952268e+00, -4.9254360e+00, -3.8004258e+00, -5.3618755e+00,\n",
            "        2.6286879e+00,  4.7368336e+00,  2.1595118e+00,  3.1841366e+00,\n",
            "       -4.0703163e+00, -6.9431705e+00, -4.8630934e+00, -2.4125810e-01,\n",
            "       -5.2867241e+00, -1.1400650e+00, -4.7475243e+00, -7.9382515e+00],\n",
            "      dtype=float32), 'Double_bass'], [array([-4.7201416e+02,  4.6837490e+01,  3.9257008e+01,  3.0734945e+01,\n",
            "        2.3730148e+01,  1.8561941e+01,  1.4523025e+01,  1.1247746e+01,\n",
            "        8.8880053e+00,  7.5979061e+00,  7.1560187e+00,  6.9820480e+00,\n",
            "        6.5107493e+00,  5.5016961e+00,  4.0983729e+00,  2.7083707e+00,\n",
            "        1.7882845e+00,  1.6123593e+00,  2.0285347e+00,  2.5158165e+00,\n",
            "        2.6230564e+00,  2.3028340e+00,  1.8171051e+00,  1.3516382e+00,\n",
            "        8.3777279e-01,  1.7372192e-01, -4.8765534e-01, -7.5429446e-01,\n",
            "       -3.8098630e-01,  4.4926146e-01,  1.2683111e+00,  1.6320978e+00,\n",
            "        1.3067963e+00,  3.2638255e-01, -9.0774822e-01, -1.7405514e+00,\n",
            "       -1.7848872e+00, -1.2819864e+00, -8.6423427e-01, -9.5931351e-01],\n",
            "      dtype=float32), 'Double_bass'], [array([-5.7820441e+02,  9.7794395e+01,  7.1045555e+01,  4.8170879e+01,\n",
            "        3.0352301e+01,  1.9085463e+01,  1.2046403e+01,  7.9746556e+00,\n",
            "        4.9924817e+00,  2.4795363e+00,  7.4503851e-01,  2.7853088e+00,\n",
            "        7.9064474e+00,  1.2921701e+01,  1.4133924e+01,  1.1374861e+01,\n",
            "        7.1380043e+00,  2.7608800e+00, -4.4898033e-01, -1.3592436e+00,\n",
            "       -7.2083479e-01, -1.1340584e+00, -3.1799834e+00, -6.0415087e+00,\n",
            "       -9.2593489e+00, -1.1301467e+01, -1.1200168e+01, -9.6132021e+00,\n",
            "       -7.8204613e+00, -6.1758842e+00, -5.4790030e+00, -6.0824957e+00,\n",
            "       -7.6385636e+00, -8.0764475e+00, -5.9379892e+00, -2.6844671e+00,\n",
            "        7.8120816e-01,  2.7384186e+00,  2.7235301e+00,  4.2283261e-01],\n",
            "      dtype=float32), 'Double_bass'], [array([-3.0425787e+02,  1.5487695e+02,  5.3062996e+01,  2.3422800e+01,\n",
            "        4.8698726e+00,  1.9353701e+01,  8.9522533e+00,  7.3242788e+00,\n",
            "        3.2812500e+00,  5.1333485e+00,  5.8298898e+00,  6.5616283e+00,\n",
            "        6.2964659e+00,  8.7829323e+00,  6.2130260e+00,  4.1557279e+00,\n",
            "       -6.9675738e-01,  2.9849988e-01,  1.8066625e-01, -1.8378714e+00,\n",
            "        1.5566604e+00, -3.7409210e-01, -1.3097563e+00, -3.8830492e+00,\n",
            "       -3.5288024e+00,  2.6933342e-01, -7.0224470e-01, -8.9275247e-01,\n",
            "        2.7333319e-01, -1.5486811e+00, -1.6168152e+00, -4.6335855e+00,\n",
            "       -4.0535731e+00, -2.0308435e+00, -2.4733887e+00, -2.8804319e+00,\n",
            "       -6.1956439e+00, -6.2892542e+00, -5.2683339e+00, -7.2540464e+00],\n",
            "      dtype=float32), 'Double_bass'], [array([-252.53659   ,  190.88809   ,   49.601738  ,    5.9541183 ,\n",
            "          3.0249062 ,   21.087675  ,    3.900836  ,    0.81473994,\n",
            "          5.839022  ,    6.4166694 ,    7.7229457 ,    7.1601977 ,\n",
            "          7.266068  ,    5.402565  ,    3.69004   ,    1.8955413 ,\n",
            "         -2.095561  ,   -0.26857466,   -0.7056546 ,   -3.8283691 ,\n",
            "         -3.473632  ,   -5.460516  ,   -3.8726287 ,   -5.9795027 ,\n",
            "         -9.043195  ,   -6.9119463 ,   -6.2234106 ,   -5.5292006 ,\n",
            "         -2.484748  ,   -4.1232524 ,   -3.83325   ,   -3.5778363 ,\n",
            "         -3.8963804 ,   -2.510914  ,   -4.4013047 ,   -2.0121691 ,\n",
            "         -2.9617155 ,   -6.085304  ,   -3.6329079 ,   -5.0369096 ],\n",
            "      dtype=float32), 'Double_bass'], [array([-5.8488531e+02,  5.2098534e+01,  2.9423697e+01,  2.0030729e+01,\n",
            "        1.1110497e+01,  8.9304028e+00,  4.9833388e+00,  5.0768785e+00,\n",
            "        4.4424672e+00,  3.0087330e+00,  1.5165088e+00,  4.2959613e-01,\n",
            "       -3.8907415e-01,  2.9301876e-01, -6.6274875e-01, -5.4873115e-01,\n",
            "       -1.5107294e+00, -1.7467978e+00, -2.6974115e+00, -3.3012464e+00,\n",
            "       -3.7346928e+00, -4.1147294e+00, -4.6268587e+00, -4.9152617e+00,\n",
            "       -6.1834064e+00, -8.1612301e+00, -9.1026363e+00, -8.5361662e+00,\n",
            "       -8.4213400e+00, -7.9768057e+00, -8.5063829e+00, -8.8949041e+00,\n",
            "       -8.6420908e+00, -8.5654449e+00, -7.8491964e+00, -7.2623854e+00,\n",
            "       -6.6885810e+00, -5.5385146e+00, -4.9821529e+00, -5.0467687e+00],\n",
            "      dtype=float32), 'Double_bass'], [array([-3.9437024e+02,  1.2026833e+02, -9.3702030e+00,  2.1591413e+01,\n",
            "        1.4434458e+01,  2.2656406e+01,  2.1346672e+00,  1.1104035e+01,\n",
            "        5.7512712e+00, -1.4321425e+00, -1.9746099e+01, -6.6138177e+00,\n",
            "        2.8930938e-01,  2.7815795e+00,  1.6353595e+00, -9.2227173e+00,\n",
            "        8.2581115e-01,  2.0637302e+00, -1.2249936e+01, -4.8690448e+00,\n",
            "       -8.8825436e+00, -6.6908770e+00, -7.1407857e+00, -8.7113047e+00,\n",
            "       -1.1159957e+01, -1.3399483e+01, -1.6119437e+00, -2.8972766e+00,\n",
            "       -5.2442827e+00, -1.1817117e+01, -6.8921900e+00, -5.3568592e+00,\n",
            "       -3.6639602e+00, -5.1400032e+00, -6.2361269e+00, -5.1146760e+00,\n",
            "       -1.2238050e+01, -1.2336200e+01, -9.6270018e+00, -9.8264885e+00],\n",
            "      dtype=float32), 'Double_bass'], [array([-397.17053   ,  112.100235  ,  -10.273108  ,   26.447475  ,\n",
            "         12.849033  ,   21.547424  ,   12.208792  ,   21.671457  ,\n",
            "         10.919824  ,    6.440262  ,  -18.417814  ,   -8.24179   ,\n",
            "         -6.4948664 ,    3.5758994 ,    3.851864  ,   -2.2485847 ,\n",
            "         -2.4557629 ,    3.3094401 ,   -2.3602586 ,    2.0476127 ,\n",
            "         -8.180691  ,   -6.2827487 ,   -3.38491   ,   -7.65817   ,\n",
            "         -7.2866855 ,  -11.627787  ,   -4.879469  ,   -1.8902085 ,\n",
            "          4.4203143 ,   -4.333147  ,   -6.47253   ,   -8.81958   ,\n",
            "         -5.960651  ,   -5.728425  ,   -5.3082895 ,   -6.3656898 ,\n",
            "         -4.7904716 ,   -0.85626787,   -5.6215014 ,   -7.650069  ],\n",
            "      dtype=float32), 'Double_bass'], [array([-548.7565   ,   49.65     ,   30.753002 ,   16.972685 ,\n",
            "         10.987802 ,    9.179613 ,    8.415214 ,    7.1940455,\n",
            "          4.791541 ,    1.5074278,   -1.1117172,   -2.519569 ,\n",
            "         -3.7597067,   -5.2144623,   -6.4574966,   -7.116094 ,\n",
            "         -7.0549355,   -6.780061 ,   -6.4576993,   -6.0618067,\n",
            "         -6.051395 ,   -6.8288097,   -8.072362 ,   -8.801462 ,\n",
            "         -9.092025 ,   -9.460595 ,   -9.527391 ,   -8.962473 ,\n",
            "         -7.8778663,   -6.431496 ,   -4.810423 ,   -3.8814263,\n",
            "         -4.487997 ,   -5.9666677,   -6.084576 ,   -2.4887877,\n",
            "          5.174473 ,   14.398351 ,   20.98984  ,   21.745167 ],\n",
            "      dtype=float32), 'Double_bass'], [array([-4.4524863e+02,  7.9241005e+01,  5.8119801e+01,  3.7484413e+01,\n",
            "        2.2865709e+01,  1.4921844e+01,  1.0921435e+01,  8.5520086e+00,\n",
            "        6.5864248e+00,  4.4764700e+00,  2.8451126e+00,  1.9914906e+00,\n",
            "        1.8878593e+00,  1.4688066e+00, -5.7392614e-04, -1.9161870e+00,\n",
            "       -4.1553788e+00, -5.6760573e+00, -5.5098710e+00, -4.2319717e+00,\n",
            "       -2.4572318e+00, -1.0910761e+00, -2.1676485e-01,  6.3261515e-01,\n",
            "        8.8945109e-01,  4.1996863e-01, -4.7056440e-01, -1.7161556e+00,\n",
            "       -2.9855893e+00, -3.9742911e+00, -4.7199135e+00, -5.2887459e+00,\n",
            "       -5.6290946e+00, -5.8061943e+00, -5.6720595e+00, -5.1677055e+00,\n",
            "       -4.6109586e+00, -4.0922813e+00, -3.9947994e+00, -4.4667630e+00],\n",
            "      dtype=float32), 'Double_bass'], [array([-167.70149   ,  171.41718   ,  -18.088312  ,    1.5315666 ,\n",
            "        -11.556445  ,   18.826603  ,  -16.128944  ,   12.239514  ,\n",
            "          6.47056   ,    6.9516473 ,  -16.815142  ,    1.7948467 ,\n",
            "         -5.6505337 ,    3.0310965 ,    1.6726712 ,    0.72595125,\n",
            "         -5.364633  ,   -1.1756816 ,   -6.3727374 ,    2.9902945 ,\n",
            "          2.5752058 ,   -1.778627  ,   -2.4189563 ,   -7.27167   ,\n",
            "         -7.2952805 ,   -5.648772  ,    4.8208175 ,    1.0451671 ,\n",
            "         -5.7384257 ,   -5.995095  ,  -13.5875225 ,   -5.39395   ,\n",
            "         -0.93436366,   -7.148524  ,   -1.3921858 ,   -4.5353074 ,\n",
            "         -5.758212  ,   -5.6269503 ,   -6.89292   ,   -4.42041   ],\n",
            "      dtype=float32), 'Double_bass'], [array([-3.9830814e+02,  1.8717807e+02,  1.9534981e+01,  1.9660362e+01,\n",
            "        1.6170349e+01,  2.9364286e+01,  7.8442569e+00,  1.3714447e+01,\n",
            "        4.7221832e+00,  4.3777537e+00, -5.6824498e+00,  6.8993702e+00,\n",
            "       -2.0945511e+00, -3.4833393e+00, -4.8507781e+00, -9.1592207e+00,\n",
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            "         14.644811  ,   11.27746   ,   -9.028802  ,  -20.463154  ,\n",
            "        -12.424862  ,    1.6578428 ,   -1.7542382 ,  -18.251173  ],\n",
            "      dtype=float32), 'Saxophone']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VnplN0CATsLr"
      },
      "source": [
        "Creating the feature extractor function. Extracting the mfcc features from the discrete data points of a single audio file and then scaling it"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V4wz8eQuXxCq"
      },
      "source": [
        "Now we iterate through every audio file and extract features using Mel-Frequency Cepstral Coefficients"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "htJohcZIUSCX"
      },
      "source": [
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7wESQJdUTFQ"
      },
      "source": [
        "Converting extracted_features to Pandas dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTYR3CoHUXvL"
      },
      "source": [
        "extracted_features_df=pd.DataFrame(extracted_features,columns=['feature','class'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7No1iXmOUcWx"
      },
      "source": [
        "Split the dataset into independent and dependent dataset. Creating the dataset on which the model will undergo training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XczrLQgyUsoZ"
      },
      "source": [
        "X=np.array(extracted_features_df['feature'].tolist())\n",
        "y=np.array(extracted_features_df['class'].tolist())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJWs1CqhUzak"
      },
      "source": [
        " **TRAINING THE MODEL**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PW3hHMhLVLkt"
      },
      "source": [
        "Label Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BbONROFVRMy"
      },
      "source": [
        "y=np.array(pd.get_dummies(y))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aR9PWslIVeiU"
      },
      "source": [
        "TRAIN TEST SPLIT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whtY_vQoVjxw"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B5d-ybhzVpim"
      },
      "source": [
        "MODEL CREATION "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wso9BfDmVr1p"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense,Dropout,Activation,Flatten\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn import metrics\n",
        "\n",
        "num_labels=y.shape[1]\n",
        "\n",
        "\n",
        "\n",
        "model=Sequential()\n",
        "###first layer\n",
        "model.add(Dense(100,input_shape=(40,)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "###second layer\n",
        "model.add(Dense(200))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "###third layer\n",
        "model.add(Dense(100))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "###final layer\n",
        "model.add(Dense(num_labels))\n",
        "model.add(Activation('softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uD34jCRRBl3Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d6283d4-0eb2-4035-8ef7-cfd18b5bd505"
      },
      "source": [
        "print(y.shape[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "300\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-0tKEzxWa1c"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',metrics=['accuracy'],optimizer='adam')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YW0YzElEWeQb"
      },
      "source": [
        "Training my model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0VEyKaRrWdjt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7668007-d81a-461e-da50-6ec4f86d07c1"
      },
      "source": [
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from datetime import datetime \n",
        "\n",
        "num_epochs = 1000\n",
        "num_batch_size = 32\n",
        "\n",
        "checkpointer = ModelCheckpoint(filepath='saved_models/audio_classification.hdf5', \n",
        "                               verbose=1, save_best_only=True)\n",
        "start = datetime.now()\n",
        "\n",
        "model.fit(X_train, y_train, batch_size=num_batch_size, epochs=num_epochs, validation_data=(X_test, y_test), callbacks=[checkpointer], verbose=1)\n",
        "\n",
        "\n",
        "duration = datetime.now() - start\n",
        "print(\"Training completed in time: \", duration)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 1.6684 - accuracy: 0.4167 - val_loss: 1.6265 - val_accuracy: 0.4333\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 1.62645, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 2/1000\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5649 - accuracy: 0.4333 - val_loss: 1.6503 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 1.62645\n",
            "Epoch 3/1000\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.6126 - accuracy: 0.4167 - val_loss: 1.6655 - val_accuracy: 0.4833\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 1.62645\n",
            "Epoch 4/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.5093 - accuracy: 0.4417 - val_loss: 1.6335 - val_accuracy: 0.5167\n",
            "\n",
            "Epoch 00004: val_loss did not improve from 1.62645\n",
            "Epoch 5/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.5375 - accuracy: 0.4500 - val_loss: 1.5973 - val_accuracy: 0.4667\n",
            "\n",
            "Epoch 00005: val_loss improved from 1.62645 to 1.59732, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 6/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.4946 - accuracy: 0.4583 - val_loss: 1.5927 - val_accuracy: 0.4667\n",
            "\n",
            "Epoch 00006: val_loss improved from 1.59732 to 1.59273, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 7/1000\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5522 - accuracy: 0.3792 - val_loss: 1.6057 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 1.59273\n",
            "Epoch 8/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.4703 - accuracy: 0.4625 - val_loss: 1.6494 - val_accuracy: 0.5167\n",
            "\n",
            "Epoch 00008: val_loss did not improve from 1.59273\n",
            "Epoch 9/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.5448 - accuracy: 0.4500 - val_loss: 1.6511 - val_accuracy: 0.4667\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 1.59273\n",
            "Epoch 10/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5296 - accuracy: 0.4167 - val_loss: 1.6380 - val_accuracy: 0.4667\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 1.59273\n",
            "Epoch 11/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5215 - accuracy: 0.4500 - val_loss: 1.6219 - val_accuracy: 0.4667\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 1.59273\n",
            "Epoch 12/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.6596 - accuracy: 0.4083 - val_loss: 1.6379 - val_accuracy: 0.4167\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 1.59273\n",
            "Epoch 13/1000\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.5476 - accuracy: 0.4083 - val_loss: 1.6462 - val_accuracy: 0.4500\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 1.59273\n",
            "Epoch 14/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.5403 - accuracy: 0.4042 - val_loss: 1.6315 - val_accuracy: 0.4833\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 1.59273\n",
            "Epoch 15/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.4523 - accuracy: 0.4208 - val_loss: 1.6009 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 1.59273\n",
            "Epoch 16/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.4358 - accuracy: 0.4833 - val_loss: 1.5920 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00016: val_loss improved from 1.59273 to 1.59197, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 17/1000\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.4271 - accuracy: 0.4792 - val_loss: 1.5919 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00017: val_loss improved from 1.59197 to 1.59191, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 18/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.3708 - accuracy: 0.5250 - val_loss: 1.6038 - val_accuracy: 0.5500\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 1.59191\n",
            "Epoch 19/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.3981 - accuracy: 0.4833 - val_loss: 1.6012 - val_accuracy: 0.5500\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 1.59191\n",
            "Epoch 20/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.4715 - accuracy: 0.4750 - val_loss: 1.6053 - val_accuracy: 0.5333\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 1.59191\n",
            "Epoch 21/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.3340 - accuracy: 0.5167 - val_loss: 1.6035 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 1.59191\n",
            "Epoch 22/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.4385 - accuracy: 0.4417 - val_loss: 1.5746 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00022: val_loss improved from 1.59191 to 1.57455, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 23/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.5168 - accuracy: 0.4208 - val_loss: 1.5587 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00023: val_loss improved from 1.57455 to 1.55870, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 24/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.5076 - accuracy: 0.4500 - val_loss: 1.5725 - val_accuracy: 0.4833\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 1.55870\n",
            "Epoch 25/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.3070 - accuracy: 0.4833 - val_loss: 1.5872 - val_accuracy: 0.4833\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 1.55870\n",
            "Epoch 26/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.3717 - accuracy: 0.4917 - val_loss: 1.5632 - val_accuracy: 0.4833\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 1.55870\n",
            "Epoch 27/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2886 - accuracy: 0.5750 - val_loss: 1.5464 - val_accuracy: 0.5167\n",
            "\n",
            "Epoch 00027: val_loss improved from 1.55870 to 1.54638, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 28/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.4213 - accuracy: 0.5250 - val_loss: 1.5279 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00028: val_loss improved from 1.54638 to 1.52790, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 29/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.3547 - accuracy: 0.4750 - val_loss: 1.5238 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00029: val_loss improved from 1.52790 to 1.52384, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 30/1000\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 1.2574 - accuracy: 0.5333 - val_loss: 1.4517 - val_accuracy: 0.5333\n",
            "\n",
            "Epoch 00030: val_loss improved from 1.52384 to 1.45166, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 31/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2784 - accuracy: 0.5208 - val_loss: 1.4341 - val_accuracy: 0.5167\n",
            "\n",
            "Epoch 00031: val_loss improved from 1.45166 to 1.43410, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 32/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2883 - accuracy: 0.5125 - val_loss: 1.4410 - val_accuracy: 0.5333\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 1.43410\n",
            "Epoch 33/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.3049 - accuracy: 0.5208 - val_loss: 1.4689 - val_accuracy: 0.5833\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 1.43410\n",
            "Epoch 34/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.4346 - accuracy: 0.5208 - val_loss: 1.5277 - val_accuracy: 0.5500\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 1.43410\n",
            "Epoch 35/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.4232 - accuracy: 0.4917 - val_loss: 1.5410 - val_accuracy: 0.4667\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 1.43410\n",
            "Epoch 36/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2990 - accuracy: 0.5167 - val_loss: 1.5152 - val_accuracy: 0.4833\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 1.43410\n",
            "Epoch 37/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.3060 - accuracy: 0.5167 - val_loss: 1.4863 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 1.43410\n",
            "Epoch 38/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2548 - accuracy: 0.5458 - val_loss: 1.4535 - val_accuracy: 0.4667\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 1.43410\n",
            "Epoch 39/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.2397 - accuracy: 0.5500 - val_loss: 1.4844 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 1.43410\n",
            "Epoch 40/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2822 - accuracy: 0.5333 - val_loss: 1.5037 - val_accuracy: 0.5167\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 1.43410\n",
            "Epoch 41/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 1.1950 - accuracy: 0.5292 - val_loss: 1.5232 - val_accuracy: 0.4833\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 1.43410\n",
            "Epoch 42/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.1519 - accuracy: 0.5583 - val_loss: 1.5377 - val_accuracy: 0.4833\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 1.43410\n",
            "Epoch 43/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2554 - accuracy: 0.5458 - val_loss: 1.5261 - val_accuracy: 0.5167\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 1.43410\n",
            "Epoch 44/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2694 - accuracy: 0.5708 - val_loss: 1.4650 - val_accuracy: 0.5167\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 1.43410\n",
            "Epoch 45/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.1047 - accuracy: 0.5750 - val_loss: 1.4740 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 1.43410\n",
            "Epoch 46/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.1980 - accuracy: 0.5708 - val_loss: 1.4732 - val_accuracy: 0.4667\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 1.43410\n",
            "Epoch 47/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.2866 - accuracy: 0.5667 - val_loss: 1.4968 - val_accuracy: 0.5500\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 1.43410\n",
            "Epoch 48/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.3155 - accuracy: 0.5417 - val_loss: 1.5132 - val_accuracy: 0.5333\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 1.43410\n",
            "Epoch 49/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.2883 - accuracy: 0.5333 - val_loss: 1.5504 - val_accuracy: 0.4833\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 1.43410\n",
            "Epoch 50/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2311 - accuracy: 0.5208 - val_loss: 1.5878 - val_accuracy: 0.5500\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 1.43410\n",
            "Epoch 51/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2709 - accuracy: 0.5292 - val_loss: 1.6512 - val_accuracy: 0.5500\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 1.43410\n",
            "Epoch 52/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.1773 - accuracy: 0.5708 - val_loss: 1.6808 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 1.43410\n",
            "Epoch 53/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2129 - accuracy: 0.5042 - val_loss: 1.6230 - val_accuracy: 0.5000\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 1.43410\n",
            "Epoch 54/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2601 - accuracy: 0.5708 - val_loss: 1.5193 - val_accuracy: 0.5500\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 1.43410\n",
            "Epoch 55/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2076 - accuracy: 0.5750 - val_loss: 1.4700 - val_accuracy: 0.5333\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 1.43410\n",
            "Epoch 56/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.1566 - accuracy: 0.5917 - val_loss: 1.5201 - val_accuracy: 0.5500\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 1.43410\n",
            "Epoch 57/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.1366 - accuracy: 0.5958 - val_loss: 1.5300 - val_accuracy: 0.5333\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 1.43410\n",
            "Epoch 58/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2452 - accuracy: 0.5500 - val_loss: 1.5305 - val_accuracy: 0.5500\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 1.43410\n",
            "Epoch 59/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.2383 - accuracy: 0.5292 - val_loss: 1.5914 - val_accuracy: 0.5500\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 1.43410\n",
            "Epoch 60/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0898 - accuracy: 0.6167 - val_loss: 1.5867 - val_accuracy: 0.5167\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 1.43410\n",
            "Epoch 61/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.1633 - accuracy: 0.5667 - val_loss: 1.5593 - val_accuracy: 0.5500\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 1.43410\n",
            "Epoch 62/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 1.0478 - accuracy: 0.6500 - val_loss: 1.5875 - val_accuracy: 0.5833\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 1.43410\n",
            "Epoch 63/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.1754 - accuracy: 0.5875 - val_loss: 1.6043 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 1.43410\n",
            "Epoch 64/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.1689 - accuracy: 0.5542 - val_loss: 1.5903 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 1.43410\n",
            "Epoch 65/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9635 - accuracy: 0.6833 - val_loss: 1.4838 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 1.43410\n",
            "Epoch 66/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.0299 - accuracy: 0.6333 - val_loss: 1.4610 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 1.43410\n",
            "Epoch 67/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0521 - accuracy: 0.6375 - val_loss: 1.4842 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 1.43410\n",
            "Epoch 68/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0263 - accuracy: 0.6417 - val_loss: 1.5637 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 1.43410\n",
            "Epoch 69/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0887 - accuracy: 0.6333 - val_loss: 1.6144 - val_accuracy: 0.5833\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 1.43410\n",
            "Epoch 70/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0988 - accuracy: 0.6208 - val_loss: 1.6265 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 1.43410\n",
            "Epoch 71/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0321 - accuracy: 0.6417 - val_loss: 1.6130 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 1.43410\n",
            "Epoch 72/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.1403 - accuracy: 0.5917 - val_loss: 1.5673 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 1.43410\n",
            "Epoch 73/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0926 - accuracy: 0.6208 - val_loss: 1.5752 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 1.43410\n",
            "Epoch 74/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.1553 - accuracy: 0.6167 - val_loss: 1.5781 - val_accuracy: 0.5833\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 1.43410\n",
            "Epoch 75/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0532 - accuracy: 0.6083 - val_loss: 1.6107 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 1.43410\n",
            "Epoch 76/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.1629 - accuracy: 0.5833 - val_loss: 1.6195 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 1.43410\n",
            "Epoch 77/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0440 - accuracy: 0.6333 - val_loss: 1.6192 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 1.43410\n",
            "Epoch 78/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0490 - accuracy: 0.6208 - val_loss: 1.5714 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 1.43410\n",
            "Epoch 79/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9756 - accuracy: 0.6708 - val_loss: 1.5321 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 1.43410\n",
            "Epoch 80/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.9777 - accuracy: 0.6625 - val_loss: 1.5234 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 1.43410\n",
            "Epoch 81/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9893 - accuracy: 0.6417 - val_loss: 1.5253 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00081: val_loss did not improve from 1.43410\n",
            "Epoch 82/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9865 - accuracy: 0.6500 - val_loss: 1.5101 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00082: val_loss did not improve from 1.43410\n",
            "Epoch 83/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.9653 - accuracy: 0.6208 - val_loss: 1.4908 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00083: val_loss did not improve from 1.43410\n",
            "Epoch 84/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.9173 - accuracy: 0.6667 - val_loss: 1.5491 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00084: val_loss did not improve from 1.43410\n",
            "Epoch 85/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9295 - accuracy: 0.6750 - val_loss: 1.5033 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00085: val_loss did not improve from 1.43410\n",
            "Epoch 86/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0385 - accuracy: 0.6708 - val_loss: 1.4295 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00086: val_loss improved from 1.43410 to 1.42950, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 87/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9706 - accuracy: 0.6500 - val_loss: 1.4090 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00087: val_loss improved from 1.42950 to 1.40902, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 88/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9074 - accuracy: 0.6542 - val_loss: 1.3909 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00088: val_loss improved from 1.40902 to 1.39088, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 89/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9714 - accuracy: 0.6583 - val_loss: 1.4169 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00089: val_loss did not improve from 1.39088\n",
            "Epoch 90/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.0271 - accuracy: 0.6750 - val_loss: 1.4101 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00090: val_loss did not improve from 1.39088\n",
            "Epoch 91/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9876 - accuracy: 0.6583 - val_loss: 1.4150 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00091: val_loss did not improve from 1.39088\n",
            "Epoch 92/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0455 - accuracy: 0.6583 - val_loss: 1.4484 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00092: val_loss did not improve from 1.39088\n",
            "Epoch 93/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9395 - accuracy: 0.6792 - val_loss: 1.5172 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00093: val_loss did not improve from 1.39088\n",
            "Epoch 94/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.8826 - accuracy: 0.6792 - val_loss: 1.5304 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00094: val_loss did not improve from 1.39088\n",
            "Epoch 95/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9848 - accuracy: 0.6417 - val_loss: 1.5305 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00095: val_loss did not improve from 1.39088\n",
            "Epoch 96/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9918 - accuracy: 0.6667 - val_loss: 1.4973 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00096: val_loss did not improve from 1.39088\n",
            "Epoch 97/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7962 - accuracy: 0.6958 - val_loss: 1.5117 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00097: val_loss did not improve from 1.39088\n",
            "Epoch 98/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8488 - accuracy: 0.6958 - val_loss: 1.4821 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00098: val_loss did not improve from 1.39088\n",
            "Epoch 99/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8919 - accuracy: 0.6917 - val_loss: 1.4263 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00099: val_loss did not improve from 1.39088\n",
            "Epoch 100/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9633 - accuracy: 0.6417 - val_loss: 1.3833 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00100: val_loss improved from 1.39088 to 1.38330, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 101/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9743 - accuracy: 0.7333 - val_loss: 1.3806 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00101: val_loss improved from 1.38330 to 1.38065, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 102/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.9162 - accuracy: 0.6792 - val_loss: 1.4369 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00102: val_loss did not improve from 1.38065\n",
            "Epoch 103/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8421 - accuracy: 0.6792 - val_loss: 1.4592 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00103: val_loss did not improve from 1.38065\n",
            "Epoch 104/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.8989 - accuracy: 0.6833 - val_loss: 1.4894 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00104: val_loss did not improve from 1.38065\n",
            "Epoch 105/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8929 - accuracy: 0.6750 - val_loss: 1.5152 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00105: val_loss did not improve from 1.38065\n",
            "Epoch 106/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.8976 - accuracy: 0.6958 - val_loss: 1.5226 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00106: val_loss did not improve from 1.38065\n",
            "Epoch 107/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9178 - accuracy: 0.6708 - val_loss: 1.4814 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00107: val_loss did not improve from 1.38065\n",
            "Epoch 108/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9548 - accuracy: 0.6625 - val_loss: 1.4239 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00108: val_loss did not improve from 1.38065\n",
            "Epoch 109/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9470 - accuracy: 0.6708 - val_loss: 1.4306 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00109: val_loss did not improve from 1.38065\n",
            "Epoch 110/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9290 - accuracy: 0.6792 - val_loss: 1.4704 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00110: val_loss did not improve from 1.38065\n",
            "Epoch 111/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0204 - accuracy: 0.6500 - val_loss: 1.4726 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00111: val_loss did not improve from 1.38065\n",
            "Epoch 112/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.9127 - accuracy: 0.6917 - val_loss: 1.4335 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00112: val_loss did not improve from 1.38065\n",
            "Epoch 113/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9168 - accuracy: 0.7167 - val_loss: 1.4425 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00113: val_loss did not improve from 1.38065\n",
            "Epoch 114/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7678 - accuracy: 0.7083 - val_loss: 1.4562 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00114: val_loss did not improve from 1.38065\n",
            "Epoch 115/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.8778 - accuracy: 0.6917 - val_loss: 1.4382 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00115: val_loss did not improve from 1.38065\n",
            "Epoch 116/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.8902 - accuracy: 0.7000 - val_loss: 1.5134 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00116: val_loss did not improve from 1.38065\n",
            "Epoch 117/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9274 - accuracy: 0.7083 - val_loss: 1.6022 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00117: val_loss did not improve from 1.38065\n",
            "Epoch 118/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8869 - accuracy: 0.7000 - val_loss: 1.6005 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00118: val_loss did not improve from 1.38065\n",
            "Epoch 119/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9217 - accuracy: 0.6917 - val_loss: 1.4882 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00119: val_loss did not improve from 1.38065\n",
            "Epoch 120/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9436 - accuracy: 0.7208 - val_loss: 1.4576 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00120: val_loss did not improve from 1.38065\n",
            "Epoch 121/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.8716 - accuracy: 0.7042 - val_loss: 1.4660 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00121: val_loss did not improve from 1.38065\n",
            "Epoch 122/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.9193 - accuracy: 0.6792 - val_loss: 1.4573 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00122: val_loss did not improve from 1.38065\n",
            "Epoch 123/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8043 - accuracy: 0.7292 - val_loss: 1.4507 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00123: val_loss did not improve from 1.38065\n",
            "Epoch 124/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9348 - accuracy: 0.7167 - val_loss: 1.3703 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00124: val_loss improved from 1.38065 to 1.37027, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 125/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.8259 - accuracy: 0.7125 - val_loss: 1.3155 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00125: val_loss improved from 1.37027 to 1.31546, saving model to saved_models/audio_classification.hdf5\n",
            "Epoch 126/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7457 - accuracy: 0.7542 - val_loss: 1.3233 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00126: val_loss did not improve from 1.31546\n",
            "Epoch 127/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8544 - accuracy: 0.7250 - val_loss: 1.3636 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00127: val_loss did not improve from 1.31546\n",
            "Epoch 128/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7401 - accuracy: 0.7208 - val_loss: 1.4155 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00128: val_loss did not improve from 1.31546\n",
            "Epoch 129/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.0625 - accuracy: 0.6500 - val_loss: 1.4803 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00129: val_loss did not improve from 1.31546\n",
            "Epoch 130/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.8212 - accuracy: 0.7000 - val_loss: 1.5462 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00130: val_loss did not improve from 1.31546\n",
            "Epoch 131/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.0333 - accuracy: 0.6708 - val_loss: 1.5595 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00131: val_loss did not improve from 1.31546\n",
            "Epoch 132/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8876 - accuracy: 0.6958 - val_loss: 1.5301 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00132: val_loss did not improve from 1.31546\n",
            "Epoch 133/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7989 - accuracy: 0.7083 - val_loss: 1.4735 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00133: val_loss did not improve from 1.31546\n",
            "Epoch 134/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8240 - accuracy: 0.7208 - val_loss: 1.4398 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00134: val_loss did not improve from 1.31546\n",
            "Epoch 135/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.8400 - accuracy: 0.6750 - val_loss: 1.4182 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00135: val_loss did not improve from 1.31546\n",
            "Epoch 136/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.8326 - accuracy: 0.7375 - val_loss: 1.4271 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00136: val_loss did not improve from 1.31546\n",
            "Epoch 137/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7827 - accuracy: 0.7083 - val_loss: 1.4603 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00137: val_loss did not improve from 1.31546\n",
            "Epoch 138/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.8006 - accuracy: 0.7208 - val_loss: 1.4587 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00138: val_loss did not improve from 1.31546\n",
            "Epoch 139/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7844 - accuracy: 0.6917 - val_loss: 1.5169 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00139: val_loss did not improve from 1.31546\n",
            "Epoch 140/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7748 - accuracy: 0.7292 - val_loss: 1.5645 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00140: val_loss did not improve from 1.31546\n",
            "Epoch 141/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.9181 - accuracy: 0.7083 - val_loss: 1.6103 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00141: val_loss did not improve from 1.31546\n",
            "Epoch 142/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7684 - accuracy: 0.7417 - val_loss: 1.6482 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00142: val_loss did not improve from 1.31546\n",
            "Epoch 143/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.9369 - accuracy: 0.7292 - val_loss: 1.6845 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00143: val_loss did not improve from 1.31546\n",
            "Epoch 144/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7831 - accuracy: 0.7375 - val_loss: 1.6945 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00144: val_loss did not improve from 1.31546\n",
            "Epoch 145/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8069 - accuracy: 0.7375 - val_loss: 1.5754 - val_accuracy: 0.7333\n",
            "\n",
            "Epoch 00145: val_loss did not improve from 1.31546\n",
            "Epoch 146/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8515 - accuracy: 0.7167 - val_loss: 1.4777 - val_accuracy: 0.7333\n",
            "\n",
            "Epoch 00146: val_loss did not improve from 1.31546\n",
            "Epoch 147/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7347 - accuracy: 0.7458 - val_loss: 1.4181 - val_accuracy: 0.7500\n",
            "\n",
            "Epoch 00147: val_loss did not improve from 1.31546\n",
            "Epoch 148/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7079 - accuracy: 0.7750 - val_loss: 1.4164 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00148: val_loss did not improve from 1.31546\n",
            "Epoch 149/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8457 - accuracy: 0.6958 - val_loss: 1.4051 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00149: val_loss did not improve from 1.31546\n",
            "Epoch 150/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6809 - accuracy: 0.7583 - val_loss: 1.3863 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00150: val_loss did not improve from 1.31546\n",
            "Epoch 151/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7378 - accuracy: 0.7583 - val_loss: 1.4249 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00151: val_loss did not improve from 1.31546\n",
            "Epoch 152/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.8059 - accuracy: 0.7667 - val_loss: 1.3911 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00152: val_loss did not improve from 1.31546\n",
            "Epoch 153/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7986 - accuracy: 0.7333 - val_loss: 1.3623 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00153: val_loss did not improve from 1.31546\n",
            "Epoch 154/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6906 - accuracy: 0.7583 - val_loss: 1.3437 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00154: val_loss did not improve from 1.31546\n",
            "Epoch 155/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7197 - accuracy: 0.7500 - val_loss: 1.3656 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00155: val_loss did not improve from 1.31546\n",
            "Epoch 156/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.7423 - accuracy: 0.7708 - val_loss: 1.4301 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00156: val_loss did not improve from 1.31546\n",
            "Epoch 157/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7706 - accuracy: 0.7208 - val_loss: 1.4575 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00157: val_loss did not improve from 1.31546\n",
            "Epoch 158/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.8535 - accuracy: 0.6917 - val_loss: 1.4377 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00158: val_loss did not improve from 1.31546\n",
            "Epoch 159/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6431 - accuracy: 0.7917 - val_loss: 1.4055 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00159: val_loss did not improve from 1.31546\n",
            "Epoch 160/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6613 - accuracy: 0.7542 - val_loss: 1.3989 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00160: val_loss did not improve from 1.31546\n",
            "Epoch 161/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6485 - accuracy: 0.7792 - val_loss: 1.3825 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00161: val_loss did not improve from 1.31546\n",
            "Epoch 162/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6947 - accuracy: 0.7708 - val_loss: 1.3787 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00162: val_loss did not improve from 1.31546\n",
            "Epoch 163/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6867 - accuracy: 0.7500 - val_loss: 1.3696 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00163: val_loss did not improve from 1.31546\n",
            "Epoch 164/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7160 - accuracy: 0.7167 - val_loss: 1.3696 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00164: val_loss did not improve from 1.31546\n",
            "Epoch 165/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8534 - accuracy: 0.7042 - val_loss: 1.3727 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00165: val_loss did not improve from 1.31546\n",
            "Epoch 166/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.7070 - accuracy: 0.7500 - val_loss: 1.4221 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00166: val_loss did not improve from 1.31546\n",
            "Epoch 167/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7237 - accuracy: 0.7542 - val_loss: 1.4405 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00167: val_loss did not improve from 1.31546\n",
            "Epoch 168/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7854 - accuracy: 0.7500 - val_loss: 1.4263 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00168: val_loss did not improve from 1.31546\n",
            "Epoch 169/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5581 - accuracy: 0.7625 - val_loss: 1.4053 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00169: val_loss did not improve from 1.31546\n",
            "Epoch 170/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.7796 - accuracy: 0.7042 - val_loss: 1.4317 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00170: val_loss did not improve from 1.31546\n",
            "Epoch 171/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7397 - accuracy: 0.7458 - val_loss: 1.4416 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00171: val_loss did not improve from 1.31546\n",
            "Epoch 172/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7587 - accuracy: 0.7458 - val_loss: 1.3787 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00172: val_loss did not improve from 1.31546\n",
            "Epoch 173/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7937 - accuracy: 0.7208 - val_loss: 1.4037 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00173: val_loss did not improve from 1.31546\n",
            "Epoch 174/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.8116 - accuracy: 0.7292 - val_loss: 1.4530 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00174: val_loss did not improve from 1.31546\n",
            "Epoch 175/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7713 - accuracy: 0.7208 - val_loss: 1.4864 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00175: val_loss did not improve from 1.31546\n",
            "Epoch 176/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6836 - accuracy: 0.7750 - val_loss: 1.5175 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00176: val_loss did not improve from 1.31546\n",
            "Epoch 177/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6246 - accuracy: 0.7958 - val_loss: 1.5079 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00177: val_loss did not improve from 1.31546\n",
            "Epoch 178/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7211 - accuracy: 0.7667 - val_loss: 1.5054 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00178: val_loss did not improve from 1.31546\n",
            "Epoch 179/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6262 - accuracy: 0.7708 - val_loss: 1.5255 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00179: val_loss did not improve from 1.31546\n",
            "Epoch 180/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6706 - accuracy: 0.7417 - val_loss: 1.5067 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00180: val_loss did not improve from 1.31546\n",
            "Epoch 181/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6893 - accuracy: 0.7583 - val_loss: 1.5079 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00181: val_loss did not improve from 1.31546\n",
            "Epoch 182/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6620 - accuracy: 0.7708 - val_loss: 1.4813 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00182: val_loss did not improve from 1.31546\n",
            "Epoch 183/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6552 - accuracy: 0.7625 - val_loss: 1.4735 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00183: val_loss did not improve from 1.31546\n",
            "Epoch 184/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6514 - accuracy: 0.7833 - val_loss: 1.5325 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00184: val_loss did not improve from 1.31546\n",
            "Epoch 185/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6217 - accuracy: 0.7500 - val_loss: 1.6216 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00185: val_loss did not improve from 1.31546\n",
            "Epoch 186/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6440 - accuracy: 0.8000 - val_loss: 1.6962 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00186: val_loss did not improve from 1.31546\n",
            "Epoch 187/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6588 - accuracy: 0.7917 - val_loss: 1.6629 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00187: val_loss did not improve from 1.31546\n",
            "Epoch 188/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6341 - accuracy: 0.7917 - val_loss: 1.6479 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00188: val_loss did not improve from 1.31546\n",
            "Epoch 189/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6291 - accuracy: 0.7667 - val_loss: 1.6170 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00189: val_loss did not improve from 1.31546\n",
            "Epoch 190/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6305 - accuracy: 0.7875 - val_loss: 1.6415 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00190: val_loss did not improve from 1.31546\n",
            "Epoch 191/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6253 - accuracy: 0.7625 - val_loss: 1.6945 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00191: val_loss did not improve from 1.31546\n",
            "Epoch 192/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7174 - accuracy: 0.7875 - val_loss: 1.6966 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00192: val_loss did not improve from 1.31546\n",
            "Epoch 193/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6677 - accuracy: 0.7667 - val_loss: 1.6415 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00193: val_loss did not improve from 1.31546\n",
            "Epoch 194/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.8261 - accuracy: 0.7333 - val_loss: 1.6369 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00194: val_loss did not improve from 1.31546\n",
            "Epoch 195/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7535 - accuracy: 0.7583 - val_loss: 1.6340 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00195: val_loss did not improve from 1.31546\n",
            "Epoch 196/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7852 - accuracy: 0.7583 - val_loss: 1.6027 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00196: val_loss did not improve from 1.31546\n",
            "Epoch 197/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6196 - accuracy: 0.7542 - val_loss: 1.6063 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00197: val_loss did not improve from 1.31546\n",
            "Epoch 198/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7323 - accuracy: 0.7583 - val_loss: 1.6094 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00198: val_loss did not improve from 1.31546\n",
            "Epoch 199/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7164 - accuracy: 0.7417 - val_loss: 1.5906 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00199: val_loss did not improve from 1.31546\n",
            "Epoch 200/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7014 - accuracy: 0.7625 - val_loss: 1.5902 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00200: val_loss did not improve from 1.31546\n",
            "Epoch 201/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7204 - accuracy: 0.7750 - val_loss: 1.5692 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00201: val_loss did not improve from 1.31546\n",
            "Epoch 202/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6524 - accuracy: 0.7500 - val_loss: 1.5751 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00202: val_loss did not improve from 1.31546\n",
            "Epoch 203/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5822 - accuracy: 0.8042 - val_loss: 1.5982 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00203: val_loss did not improve from 1.31546\n",
            "Epoch 204/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6833 - accuracy: 0.7875 - val_loss: 1.6152 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00204: val_loss did not improve from 1.31546\n",
            "Epoch 205/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6170 - accuracy: 0.8042 - val_loss: 1.6429 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00205: val_loss did not improve from 1.31546\n",
            "Epoch 206/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6476 - accuracy: 0.7833 - val_loss: 1.6447 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00206: val_loss did not improve from 1.31546\n",
            "Epoch 207/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5584 - accuracy: 0.7958 - val_loss: 1.6762 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00207: val_loss did not improve from 1.31546\n",
            "Epoch 208/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7983 - accuracy: 0.7458 - val_loss: 1.6360 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00208: val_loss did not improve from 1.31546\n",
            "Epoch 209/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5891 - accuracy: 0.7917 - val_loss: 1.6312 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00209: val_loss did not improve from 1.31546\n",
            "Epoch 210/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5619 - accuracy: 0.7958 - val_loss: 1.6791 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00210: val_loss did not improve from 1.31546\n",
            "Epoch 211/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7628 - accuracy: 0.7333 - val_loss: 1.7059 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00211: val_loss did not improve from 1.31546\n",
            "Epoch 212/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5638 - accuracy: 0.7958 - val_loss: 1.6917 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00212: val_loss did not improve from 1.31546\n",
            "Epoch 213/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6227 - accuracy: 0.7583 - val_loss: 1.6459 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00213: val_loss did not improve from 1.31546\n",
            "Epoch 214/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6821 - accuracy: 0.7667 - val_loss: 1.6096 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00214: val_loss did not improve from 1.31546\n",
            "Epoch 215/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6313 - accuracy: 0.7750 - val_loss: 1.6676 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00215: val_loss did not improve from 1.31546\n",
            "Epoch 216/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.7097 - accuracy: 0.7708 - val_loss: 1.7761 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00216: val_loss did not improve from 1.31546\n",
            "Epoch 217/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5607 - accuracy: 0.8083 - val_loss: 1.8185 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00217: val_loss did not improve from 1.31546\n",
            "Epoch 218/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6783 - accuracy: 0.7750 - val_loss: 1.7440 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00218: val_loss did not improve from 1.31546\n",
            "Epoch 219/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.7219 - accuracy: 0.7750 - val_loss: 1.7273 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00219: val_loss did not improve from 1.31546\n",
            "Epoch 220/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6023 - accuracy: 0.8000 - val_loss: 1.7639 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00220: val_loss did not improve from 1.31546\n",
            "Epoch 221/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6783 - accuracy: 0.7333 - val_loss: 1.7451 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00221: val_loss did not improve from 1.31546\n",
            "Epoch 222/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5573 - accuracy: 0.8042 - val_loss: 1.6759 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00222: val_loss did not improve from 1.31546\n",
            "Epoch 223/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6595 - accuracy: 0.7792 - val_loss: 1.6817 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00223: val_loss did not improve from 1.31546\n",
            "Epoch 224/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6115 - accuracy: 0.7833 - val_loss: 1.7021 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00224: val_loss did not improve from 1.31546\n",
            "Epoch 225/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6129 - accuracy: 0.7708 - val_loss: 1.7267 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00225: val_loss did not improve from 1.31546\n",
            "Epoch 226/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6282 - accuracy: 0.7667 - val_loss: 1.7396 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00226: val_loss did not improve from 1.31546\n",
            "Epoch 227/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6349 - accuracy: 0.7917 - val_loss: 1.7318 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00227: val_loss did not improve from 1.31546\n",
            "Epoch 228/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5698 - accuracy: 0.8042 - val_loss: 1.6241 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00228: val_loss did not improve from 1.31546\n",
            "Epoch 229/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7162 - accuracy: 0.7708 - val_loss: 1.5533 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00229: val_loss did not improve from 1.31546\n",
            "Epoch 230/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5859 - accuracy: 0.7958 - val_loss: 1.5554 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00230: val_loss did not improve from 1.31546\n",
            "Epoch 231/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7940 - accuracy: 0.7625 - val_loss: 1.5601 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00231: val_loss did not improve from 1.31546\n",
            "Epoch 232/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6228 - accuracy: 0.7708 - val_loss: 1.5744 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00232: val_loss did not improve from 1.31546\n",
            "Epoch 233/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6848 - accuracy: 0.8042 - val_loss: 1.6127 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00233: val_loss did not improve from 1.31546\n",
            "Epoch 234/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5901 - accuracy: 0.7792 - val_loss: 1.6209 - val_accuracy: 0.7333\n",
            "\n",
            "Epoch 00234: val_loss did not improve from 1.31546\n",
            "Epoch 235/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7013 - accuracy: 0.7750 - val_loss: 1.6815 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00235: val_loss did not improve from 1.31546\n",
            "Epoch 236/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6518 - accuracy: 0.7667 - val_loss: 1.7722 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00236: val_loss did not improve from 1.31546\n",
            "Epoch 237/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6272 - accuracy: 0.8000 - val_loss: 1.7540 - val_accuracy: 0.7333\n",
            "\n",
            "Epoch 00237: val_loss did not improve from 1.31546\n",
            "Epoch 238/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5674 - accuracy: 0.8208 - val_loss: 1.7397 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00238: val_loss did not improve from 1.31546\n",
            "Epoch 239/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5329 - accuracy: 0.7958 - val_loss: 1.7547 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00239: val_loss did not improve from 1.31546\n",
            "Epoch 240/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5703 - accuracy: 0.8042 - val_loss: 1.7950 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00240: val_loss did not improve from 1.31546\n",
            "Epoch 241/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6320 - accuracy: 0.7708 - val_loss: 1.8339 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00241: val_loss did not improve from 1.31546\n",
            "Epoch 242/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5368 - accuracy: 0.8042 - val_loss: 1.8705 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00242: val_loss did not improve from 1.31546\n",
            "Epoch 243/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5802 - accuracy: 0.8208 - val_loss: 1.8771 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00243: val_loss did not improve from 1.31546\n",
            "Epoch 244/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7384 - accuracy: 0.7875 - val_loss: 1.8824 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00244: val_loss did not improve from 1.31546\n",
            "Epoch 245/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5637 - accuracy: 0.7833 - val_loss: 1.9170 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00245: val_loss did not improve from 1.31546\n",
            "Epoch 246/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5723 - accuracy: 0.8000 - val_loss: 1.9778 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00246: val_loss did not improve from 1.31546\n",
            "Epoch 247/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5948 - accuracy: 0.7958 - val_loss: 1.9666 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00247: val_loss did not improve from 1.31546\n",
            "Epoch 248/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5899 - accuracy: 0.8000 - val_loss: 1.9374 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00248: val_loss did not improve from 1.31546\n",
            "Epoch 249/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5166 - accuracy: 0.8417 - val_loss: 1.9541 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00249: val_loss did not improve from 1.31546\n",
            "Epoch 250/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5456 - accuracy: 0.7958 - val_loss: 2.0198 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00250: val_loss did not improve from 1.31546\n",
            "Epoch 251/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5706 - accuracy: 0.8042 - val_loss: 2.0183 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00251: val_loss did not improve from 1.31546\n",
            "Epoch 252/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.7254 - accuracy: 0.7583 - val_loss: 2.0626 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00252: val_loss did not improve from 1.31546\n",
            "Epoch 253/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5644 - accuracy: 0.8042 - val_loss: 2.0283 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00253: val_loss did not improve from 1.31546\n",
            "Epoch 254/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5782 - accuracy: 0.7958 - val_loss: 2.0121 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00254: val_loss did not improve from 1.31546\n",
            "Epoch 255/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5601 - accuracy: 0.8042 - val_loss: 2.0182 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00255: val_loss did not improve from 1.31546\n",
            "Epoch 256/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6102 - accuracy: 0.7833 - val_loss: 2.0144 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00256: val_loss did not improve from 1.31546\n",
            "Epoch 257/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5609 - accuracy: 0.8500 - val_loss: 1.8958 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00257: val_loss did not improve from 1.31546\n",
            "Epoch 258/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6828 - accuracy: 0.7833 - val_loss: 1.8092 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00258: val_loss did not improve from 1.31546\n",
            "Epoch 259/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5729 - accuracy: 0.8208 - val_loss: 1.7571 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00259: val_loss did not improve from 1.31546\n",
            "Epoch 260/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5751 - accuracy: 0.8167 - val_loss: 1.7250 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00260: val_loss did not improve from 1.31546\n",
            "Epoch 261/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5460 - accuracy: 0.8417 - val_loss: 1.7848 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00261: val_loss did not improve from 1.31546\n",
            "Epoch 262/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5288 - accuracy: 0.8208 - val_loss: 1.7819 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00262: val_loss did not improve from 1.31546\n",
            "Epoch 263/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6323 - accuracy: 0.7583 - val_loss: 1.7807 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00263: val_loss did not improve from 1.31546\n",
            "Epoch 264/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7418 - accuracy: 0.7542 - val_loss: 1.8403 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00264: val_loss did not improve from 1.31546\n",
            "Epoch 265/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6119 - accuracy: 0.7750 - val_loss: 1.8313 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00265: val_loss did not improve from 1.31546\n",
            "Epoch 266/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6191 - accuracy: 0.7958 - val_loss: 1.8322 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00266: val_loss did not improve from 1.31546\n",
            "Epoch 267/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5054 - accuracy: 0.8125 - val_loss: 1.8348 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00267: val_loss did not improve from 1.31546\n",
            "Epoch 268/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6911 - accuracy: 0.7792 - val_loss: 1.8400 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00268: val_loss did not improve from 1.31546\n",
            "Epoch 269/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5807 - accuracy: 0.7875 - val_loss: 1.8358 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00269: val_loss did not improve from 1.31546\n",
            "Epoch 270/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5870 - accuracy: 0.8417 - val_loss: 1.8882 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00270: val_loss did not improve from 1.31546\n",
            "Epoch 271/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6286 - accuracy: 0.7875 - val_loss: 1.8492 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00271: val_loss did not improve from 1.31546\n",
            "Epoch 272/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5570 - accuracy: 0.8083 - val_loss: 1.8571 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00272: val_loss did not improve from 1.31546\n",
            "Epoch 273/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5793 - accuracy: 0.8167 - val_loss: 1.9360 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00273: val_loss did not improve from 1.31546\n",
            "Epoch 274/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5233 - accuracy: 0.8000 - val_loss: 1.9927 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00274: val_loss did not improve from 1.31546\n",
            "Epoch 275/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4541 - accuracy: 0.8375 - val_loss: 1.9979 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00275: val_loss did not improve from 1.31546\n",
            "Epoch 276/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5547 - accuracy: 0.7958 - val_loss: 1.9555 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00276: val_loss did not improve from 1.31546\n",
            "Epoch 277/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5422 - accuracy: 0.8000 - val_loss: 1.9836 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00277: val_loss did not improve from 1.31546\n",
            "Epoch 278/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6568 - accuracy: 0.7625 - val_loss: 1.9518 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00278: val_loss did not improve from 1.31546\n",
            "Epoch 279/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5457 - accuracy: 0.8083 - val_loss: 1.9353 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00279: val_loss did not improve from 1.31546\n",
            "Epoch 280/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6030 - accuracy: 0.7792 - val_loss: 1.9851 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00280: val_loss did not improve from 1.31546\n",
            "Epoch 281/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5287 - accuracy: 0.8083 - val_loss: 2.0106 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00281: val_loss did not improve from 1.31546\n",
            "Epoch 282/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6111 - accuracy: 0.8208 - val_loss: 1.9872 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00282: val_loss did not improve from 1.31546\n",
            "Epoch 283/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4979 - accuracy: 0.8292 - val_loss: 1.9242 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00283: val_loss did not improve from 1.31546\n",
            "Epoch 284/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7217 - accuracy: 0.7792 - val_loss: 1.8988 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00284: val_loss did not improve from 1.31546\n",
            "Epoch 285/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5217 - accuracy: 0.8208 - val_loss: 1.8915 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00285: val_loss did not improve from 1.31546\n",
            "Epoch 286/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4484 - accuracy: 0.8292 - val_loss: 1.8628 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00286: val_loss did not improve from 1.31546\n",
            "Epoch 287/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6592 - accuracy: 0.7958 - val_loss: 1.8570 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00287: val_loss did not improve from 1.31546\n",
            "Epoch 288/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6166 - accuracy: 0.8375 - val_loss: 1.7913 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00288: val_loss did not improve from 1.31546\n",
            "Epoch 289/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5944 - accuracy: 0.8000 - val_loss: 1.7681 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00289: val_loss did not improve from 1.31546\n",
            "Epoch 290/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5171 - accuracy: 0.8208 - val_loss: 1.7894 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00290: val_loss did not improve from 1.31546\n",
            "Epoch 291/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5058 - accuracy: 0.8000 - val_loss: 1.8050 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00291: val_loss did not improve from 1.31546\n",
            "Epoch 292/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5672 - accuracy: 0.8250 - val_loss: 1.8200 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00292: val_loss did not improve from 1.31546\n",
            "Epoch 293/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6263 - accuracy: 0.7958 - val_loss: 1.7940 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00293: val_loss did not improve from 1.31546\n",
            "Epoch 294/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5661 - accuracy: 0.7875 - val_loss: 1.8066 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00294: val_loss did not improve from 1.31546\n",
            "Epoch 295/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5788 - accuracy: 0.8083 - val_loss: 1.8025 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00295: val_loss did not improve from 1.31546\n",
            "Epoch 296/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5535 - accuracy: 0.7958 - val_loss: 1.7649 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00296: val_loss did not improve from 1.31546\n",
            "Epoch 297/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5433 - accuracy: 0.7750 - val_loss: 1.7911 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00297: val_loss did not improve from 1.31546\n",
            "Epoch 298/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5563 - accuracy: 0.8167 - val_loss: 1.8275 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00298: val_loss did not improve from 1.31546\n",
            "Epoch 299/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5155 - accuracy: 0.8250 - val_loss: 1.8375 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00299: val_loss did not improve from 1.31546\n",
            "Epoch 300/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4986 - accuracy: 0.8167 - val_loss: 1.8522 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00300: val_loss did not improve from 1.31546\n",
            "Epoch 301/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5242 - accuracy: 0.7833 - val_loss: 1.8998 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00301: val_loss did not improve from 1.31546\n",
            "Epoch 302/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5361 - accuracy: 0.8125 - val_loss: 1.9365 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00302: val_loss did not improve from 1.31546\n",
            "Epoch 303/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6185 - accuracy: 0.7792 - val_loss: 1.9493 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00303: val_loss did not improve from 1.31546\n",
            "Epoch 304/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5309 - accuracy: 0.8250 - val_loss: 1.9523 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00304: val_loss did not improve from 1.31546\n",
            "Epoch 305/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4907 - accuracy: 0.8208 - val_loss: 1.9406 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00305: val_loss did not improve from 1.31546\n",
            "Epoch 306/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4764 - accuracy: 0.8417 - val_loss: 1.9303 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00306: val_loss did not improve from 1.31546\n",
            "Epoch 307/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5764 - accuracy: 0.8083 - val_loss: 1.9438 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00307: val_loss did not improve from 1.31546\n",
            "Epoch 308/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.3741 - accuracy: 0.8583 - val_loss: 1.9439 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00308: val_loss did not improve from 1.31546\n",
            "Epoch 309/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5356 - accuracy: 0.8167 - val_loss: 1.9280 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00309: val_loss did not improve from 1.31546\n",
            "Epoch 310/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5499 - accuracy: 0.8167 - val_loss: 1.9368 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00310: val_loss did not improve from 1.31546\n",
            "Epoch 311/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4100 - accuracy: 0.8500 - val_loss: 2.0160 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00311: val_loss did not improve from 1.31546\n",
            "Epoch 312/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4962 - accuracy: 0.8375 - val_loss: 2.0621 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00312: val_loss did not improve from 1.31546\n",
            "Epoch 313/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4980 - accuracy: 0.8167 - val_loss: 2.0890 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00313: val_loss did not improve from 1.31546\n",
            "Epoch 314/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4777 - accuracy: 0.8083 - val_loss: 2.0959 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00314: val_loss did not improve from 1.31546\n",
            "Epoch 315/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3694 - accuracy: 0.8625 - val_loss: 2.1139 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00315: val_loss did not improve from 1.31546\n",
            "Epoch 316/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5334 - accuracy: 0.8292 - val_loss: 2.1240 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00316: val_loss did not improve from 1.31546\n",
            "Epoch 317/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4989 - accuracy: 0.8208 - val_loss: 2.0837 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00317: val_loss did not improve from 1.31546\n",
            "Epoch 318/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5912 - accuracy: 0.8083 - val_loss: 2.0599 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00318: val_loss did not improve from 1.31546\n",
            "Epoch 319/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5942 - accuracy: 0.7833 - val_loss: 2.0224 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00319: val_loss did not improve from 1.31546\n",
            "Epoch 320/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5062 - accuracy: 0.8083 - val_loss: 2.0169 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00320: val_loss did not improve from 1.31546\n",
            "Epoch 321/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4747 - accuracy: 0.8458 - val_loss: 2.0284 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00321: val_loss did not improve from 1.31546\n",
            "Epoch 322/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4649 - accuracy: 0.8375 - val_loss: 2.0260 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00322: val_loss did not improve from 1.31546\n",
            "Epoch 323/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5517 - accuracy: 0.8292 - val_loss: 2.0061 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00323: val_loss did not improve from 1.31546\n",
            "Epoch 324/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4311 - accuracy: 0.8500 - val_loss: 1.9650 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00324: val_loss did not improve from 1.31546\n",
            "Epoch 325/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5132 - accuracy: 0.8250 - val_loss: 2.0361 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00325: val_loss did not improve from 1.31546\n",
            "Epoch 326/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5800 - accuracy: 0.7875 - val_loss: 2.0829 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00326: val_loss did not improve from 1.31546\n",
            "Epoch 327/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4425 - accuracy: 0.8417 - val_loss: 2.1360 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00327: val_loss did not improve from 1.31546\n",
            "Epoch 328/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5666 - accuracy: 0.7917 - val_loss: 2.1625 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00328: val_loss did not improve from 1.31546\n",
            "Epoch 329/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5818 - accuracy: 0.8167 - val_loss: 2.1525 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00329: val_loss did not improve from 1.31546\n",
            "Epoch 330/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4947 - accuracy: 0.8167 - val_loss: 2.1148 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00330: val_loss did not improve from 1.31546\n",
            "Epoch 331/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4813 - accuracy: 0.8500 - val_loss: 2.0434 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00331: val_loss did not improve from 1.31546\n",
            "Epoch 332/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4700 - accuracy: 0.8333 - val_loss: 2.0147 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00332: val_loss did not improve from 1.31546\n",
            "Epoch 333/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4923 - accuracy: 0.8167 - val_loss: 2.0758 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00333: val_loss did not improve from 1.31546\n",
            "Epoch 334/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6280 - accuracy: 0.8042 - val_loss: 2.0614 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00334: val_loss did not improve from 1.31546\n",
            "Epoch 335/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5871 - accuracy: 0.8208 - val_loss: 1.9528 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00335: val_loss did not improve from 1.31546\n",
            "Epoch 336/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4647 - accuracy: 0.8250 - val_loss: 1.9011 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00336: val_loss did not improve from 1.31546\n",
            "Epoch 337/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5001 - accuracy: 0.8167 - val_loss: 1.9272 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00337: val_loss did not improve from 1.31546\n",
            "Epoch 338/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4655 - accuracy: 0.8375 - val_loss: 1.9230 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00338: val_loss did not improve from 1.31546\n",
            "Epoch 339/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6327 - accuracy: 0.7583 - val_loss: 1.9156 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00339: val_loss did not improve from 1.31546\n",
            "Epoch 340/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5235 - accuracy: 0.8250 - val_loss: 1.9661 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00340: val_loss did not improve from 1.31546\n",
            "Epoch 341/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4267 - accuracy: 0.8542 - val_loss: 2.0269 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00341: val_loss did not improve from 1.31546\n",
            "Epoch 342/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4630 - accuracy: 0.8292 - val_loss: 1.9889 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00342: val_loss did not improve from 1.31546\n",
            "Epoch 343/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5739 - accuracy: 0.8167 - val_loss: 2.0429 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00343: val_loss did not improve from 1.31546\n",
            "Epoch 344/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4929 - accuracy: 0.8167 - val_loss: 2.0691 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00344: val_loss did not improve from 1.31546\n",
            "Epoch 345/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4941 - accuracy: 0.8167 - val_loss: 2.0765 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00345: val_loss did not improve from 1.31546\n",
            "Epoch 346/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5579 - accuracy: 0.8042 - val_loss: 2.1038 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00346: val_loss did not improve from 1.31546\n",
            "Epoch 347/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5296 - accuracy: 0.8333 - val_loss: 2.1156 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00347: val_loss did not improve from 1.31546\n",
            "Epoch 348/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4630 - accuracy: 0.8375 - val_loss: 2.1023 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00348: val_loss did not improve from 1.31546\n",
            "Epoch 349/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4406 - accuracy: 0.8375 - val_loss: 2.0899 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00349: val_loss did not improve from 1.31546\n",
            "Epoch 350/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4312 - accuracy: 0.8292 - val_loss: 2.0535 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00350: val_loss did not improve from 1.31546\n",
            "Epoch 351/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5212 - accuracy: 0.8208 - val_loss: 2.0531 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00351: val_loss did not improve from 1.31546\n",
            "Epoch 352/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4758 - accuracy: 0.8417 - val_loss: 2.1015 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00352: val_loss did not improve from 1.31546\n",
            "Epoch 353/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5158 - accuracy: 0.8417 - val_loss: 2.1651 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00353: val_loss did not improve from 1.31546\n",
            "Epoch 354/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4357 - accuracy: 0.8417 - val_loss: 2.1762 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00354: val_loss did not improve from 1.31546\n",
            "Epoch 355/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4332 - accuracy: 0.8292 - val_loss: 2.1689 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00355: val_loss did not improve from 1.31546\n",
            "Epoch 356/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5599 - accuracy: 0.8083 - val_loss: 2.1706 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00356: val_loss did not improve from 1.31546\n",
            "Epoch 357/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4675 - accuracy: 0.8417 - val_loss: 2.0748 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00357: val_loss did not improve from 1.31546\n",
            "Epoch 358/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5081 - accuracy: 0.8458 - val_loss: 2.0797 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00358: val_loss did not improve from 1.31546\n",
            "Epoch 359/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4635 - accuracy: 0.8583 - val_loss: 2.1590 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00359: val_loss did not improve from 1.31546\n",
            "Epoch 360/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4744 - accuracy: 0.8250 - val_loss: 2.2195 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00360: val_loss did not improve from 1.31546\n",
            "Epoch 361/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5935 - accuracy: 0.8292 - val_loss: 2.1848 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00361: val_loss did not improve from 1.31546\n",
            "Epoch 362/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6021 - accuracy: 0.8125 - val_loss: 2.0602 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00362: val_loss did not improve from 1.31546\n",
            "Epoch 363/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5425 - accuracy: 0.7958 - val_loss: 1.9904 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00363: val_loss did not improve from 1.31546\n",
            "Epoch 364/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5258 - accuracy: 0.8458 - val_loss: 1.9808 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00364: val_loss did not improve from 1.31546\n",
            "Epoch 365/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4399 - accuracy: 0.8167 - val_loss: 2.0196 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00365: val_loss did not improve from 1.31546\n",
            "Epoch 366/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6009 - accuracy: 0.7917 - val_loss: 2.1021 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00366: val_loss did not improve from 1.31546\n",
            "Epoch 367/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4432 - accuracy: 0.8458 - val_loss: 2.1741 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00367: val_loss did not improve from 1.31546\n",
            "Epoch 368/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4649 - accuracy: 0.8125 - val_loss: 2.1831 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00368: val_loss did not improve from 1.31546\n",
            "Epoch 369/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5897 - accuracy: 0.8250 - val_loss: 2.1853 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00369: val_loss did not improve from 1.31546\n",
            "Epoch 370/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5892 - accuracy: 0.7875 - val_loss: 2.1683 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00370: val_loss did not improve from 1.31546\n",
            "Epoch 371/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4774 - accuracy: 0.8125 - val_loss: 2.1944 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00371: val_loss did not improve from 1.31546\n",
            "Epoch 372/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5193 - accuracy: 0.8083 - val_loss: 2.1992 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00372: val_loss did not improve from 1.31546\n",
            "Epoch 373/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5427 - accuracy: 0.7833 - val_loss: 2.2165 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00373: val_loss did not improve from 1.31546\n",
            "Epoch 374/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4806 - accuracy: 0.8250 - val_loss: 2.2243 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00374: val_loss did not improve from 1.31546\n",
            "Epoch 375/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4454 - accuracy: 0.8417 - val_loss: 2.2859 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00375: val_loss did not improve from 1.31546\n",
            "Epoch 376/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4183 - accuracy: 0.8458 - val_loss: 2.3263 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00376: val_loss did not improve from 1.31546\n",
            "Epoch 377/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5176 - accuracy: 0.8208 - val_loss: 2.3162 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00377: val_loss did not improve from 1.31546\n",
            "Epoch 378/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4697 - accuracy: 0.8458 - val_loss: 2.3891 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00378: val_loss did not improve from 1.31546\n",
            "Epoch 379/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5089 - accuracy: 0.8250 - val_loss: 2.4330 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00379: val_loss did not improve from 1.31546\n",
            "Epoch 380/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5179 - accuracy: 0.8250 - val_loss: 2.4687 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00380: val_loss did not improve from 1.31546\n",
            "Epoch 381/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4595 - accuracy: 0.8417 - val_loss: 2.4690 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00381: val_loss did not improve from 1.31546\n",
            "Epoch 382/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5691 - accuracy: 0.8250 - val_loss: 2.4314 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00382: val_loss did not improve from 1.31546\n",
            "Epoch 383/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3624 - accuracy: 0.8708 - val_loss: 2.4762 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00383: val_loss did not improve from 1.31546\n",
            "Epoch 384/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5623 - accuracy: 0.8333 - val_loss: 2.5240 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00384: val_loss did not improve from 1.31546\n",
            "Epoch 385/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5495 - accuracy: 0.8167 - val_loss: 2.4655 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00385: val_loss did not improve from 1.31546\n",
            "Epoch 386/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4223 - accuracy: 0.8708 - val_loss: 2.4419 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00386: val_loss did not improve from 1.31546\n",
            "Epoch 387/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5445 - accuracy: 0.8167 - val_loss: 2.4786 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00387: val_loss did not improve from 1.31546\n",
            "Epoch 388/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4640 - accuracy: 0.8125 - val_loss: 2.4355 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00388: val_loss did not improve from 1.31546\n",
            "Epoch 389/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4301 - accuracy: 0.8417 - val_loss: 2.3487 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00389: val_loss did not improve from 1.31546\n",
            "Epoch 390/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4198 - accuracy: 0.8625 - val_loss: 2.3059 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00390: val_loss did not improve from 1.31546\n",
            "Epoch 391/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5385 - accuracy: 0.8208 - val_loss: 2.2738 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00391: val_loss did not improve from 1.31546\n",
            "Epoch 392/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3631 - accuracy: 0.8583 - val_loss: 2.2761 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00392: val_loss did not improve from 1.31546\n",
            "Epoch 393/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.5796 - accuracy: 0.8167 - val_loss: 2.3428 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00393: val_loss did not improve from 1.31546\n",
            "Epoch 394/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4276 - accuracy: 0.8792 - val_loss: 2.3815 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00394: val_loss did not improve from 1.31546\n",
            "Epoch 395/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5288 - accuracy: 0.8167 - val_loss: 2.3525 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00395: val_loss did not improve from 1.31546\n",
            "Epoch 396/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5851 - accuracy: 0.8333 - val_loss: 2.3982 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00396: val_loss did not improve from 1.31546\n",
            "Epoch 397/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3953 - accuracy: 0.8667 - val_loss: 2.4392 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00397: val_loss did not improve from 1.31546\n",
            "Epoch 398/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4723 - accuracy: 0.8417 - val_loss: 2.4063 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00398: val_loss did not improve from 1.31546\n",
            "Epoch 399/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4297 - accuracy: 0.8417 - val_loss: 2.3797 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00399: val_loss did not improve from 1.31546\n",
            "Epoch 400/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5007 - accuracy: 0.8500 - val_loss: 2.3725 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00400: val_loss did not improve from 1.31546\n",
            "Epoch 401/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6710 - accuracy: 0.8042 - val_loss: 2.2820 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00401: val_loss did not improve from 1.31546\n",
            "Epoch 402/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4546 - accuracy: 0.8708 - val_loss: 2.2320 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00402: val_loss did not improve from 1.31546\n",
            "Epoch 403/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4266 - accuracy: 0.8542 - val_loss: 2.2368 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00403: val_loss did not improve from 1.31546\n",
            "Epoch 404/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.6011 - accuracy: 0.8042 - val_loss: 2.2332 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00404: val_loss did not improve from 1.31546\n",
            "Epoch 405/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.6322 - accuracy: 0.7875 - val_loss: 2.2444 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00405: val_loss did not improve from 1.31546\n",
            "Epoch 406/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4777 - accuracy: 0.8375 - val_loss: 2.2606 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00406: val_loss did not improve from 1.31546\n",
            "Epoch 407/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3896 - accuracy: 0.8583 - val_loss: 2.2991 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00407: val_loss did not improve from 1.31546\n",
            "Epoch 408/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5958 - accuracy: 0.8000 - val_loss: 2.3149 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00408: val_loss did not improve from 1.31546\n",
            "Epoch 409/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4892 - accuracy: 0.8250 - val_loss: 2.3142 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00409: val_loss did not improve from 1.31546\n",
            "Epoch 410/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4561 - accuracy: 0.8417 - val_loss: 2.2918 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00410: val_loss did not improve from 1.31546\n",
            "Epoch 411/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4727 - accuracy: 0.8375 - val_loss: 2.3133 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00411: val_loss did not improve from 1.31546\n",
            "Epoch 412/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4161 - accuracy: 0.8500 - val_loss: 2.2903 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00412: val_loss did not improve from 1.31546\n",
            "Epoch 413/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4294 - accuracy: 0.8583 - val_loss: 2.2560 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00413: val_loss did not improve from 1.31546\n",
            "Epoch 414/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4770 - accuracy: 0.8167 - val_loss: 2.2451 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00414: val_loss did not improve from 1.31546\n",
            "Epoch 415/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4352 - accuracy: 0.8542 - val_loss: 2.2402 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00415: val_loss did not improve from 1.31546\n",
            "Epoch 416/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5040 - accuracy: 0.8292 - val_loss: 2.2411 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00416: val_loss did not improve from 1.31546\n",
            "Epoch 417/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3816 - accuracy: 0.8667 - val_loss: 2.2954 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00417: val_loss did not improve from 1.31546\n",
            "Epoch 418/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3664 - accuracy: 0.8750 - val_loss: 2.3060 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00418: val_loss did not improve from 1.31546\n",
            "Epoch 419/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4901 - accuracy: 0.8250 - val_loss: 2.2998 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00419: val_loss did not improve from 1.31546\n",
            "Epoch 420/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4282 - accuracy: 0.8375 - val_loss: 2.2712 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00420: val_loss did not improve from 1.31546\n",
            "Epoch 421/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4230 - accuracy: 0.8375 - val_loss: 2.2601 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00421: val_loss did not improve from 1.31546\n",
            "Epoch 422/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4266 - accuracy: 0.8458 - val_loss: 2.2694 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00422: val_loss did not improve from 1.31546\n",
            "Epoch 423/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4731 - accuracy: 0.8250 - val_loss: 2.2860 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00423: val_loss did not improve from 1.31546\n",
            "Epoch 424/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5092 - accuracy: 0.8250 - val_loss: 2.2667 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00424: val_loss did not improve from 1.31546\n",
            "Epoch 425/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5025 - accuracy: 0.8083 - val_loss: 2.2930 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00425: val_loss did not improve from 1.31546\n",
            "Epoch 426/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4809 - accuracy: 0.8417 - val_loss: 2.3705 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00426: val_loss did not improve from 1.31546\n",
            "Epoch 427/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4608 - accuracy: 0.8167 - val_loss: 2.5024 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00427: val_loss did not improve from 1.31546\n",
            "Epoch 428/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4661 - accuracy: 0.8458 - val_loss: 2.7031 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00428: val_loss did not improve from 1.31546\n",
            "Epoch 429/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4064 - accuracy: 0.8458 - val_loss: 2.5769 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00429: val_loss did not improve from 1.31546\n",
            "Epoch 430/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4546 - accuracy: 0.8333 - val_loss: 2.4908 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00430: val_loss did not improve from 1.31546\n",
            "Epoch 431/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4812 - accuracy: 0.8375 - val_loss: 2.4678 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00431: val_loss did not improve from 1.31546\n",
            "Epoch 432/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3584 - accuracy: 0.8667 - val_loss: 2.4638 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00432: val_loss did not improve from 1.31546\n",
            "Epoch 433/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5571 - accuracy: 0.8167 - val_loss: 2.4904 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00433: val_loss did not improve from 1.31546\n",
            "Epoch 434/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4513 - accuracy: 0.8458 - val_loss: 2.4676 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00434: val_loss did not improve from 1.31546\n",
            "Epoch 435/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3646 - accuracy: 0.8542 - val_loss: 2.4805 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00435: val_loss did not improve from 1.31546\n",
            "Epoch 436/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4921 - accuracy: 0.8125 - val_loss: 2.4592 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00436: val_loss did not improve from 1.31546\n",
            "Epoch 437/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3537 - accuracy: 0.8917 - val_loss: 2.4399 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00437: val_loss did not improve from 1.31546\n",
            "Epoch 438/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5235 - accuracy: 0.8208 - val_loss: 2.4062 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00438: val_loss did not improve from 1.31546\n",
            "Epoch 439/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3730 - accuracy: 0.8750 - val_loss: 2.4431 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00439: val_loss did not improve from 1.31546\n",
            "Epoch 440/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4259 - accuracy: 0.8750 - val_loss: 2.4406 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00440: val_loss did not improve from 1.31546\n",
            "Epoch 441/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4097 - accuracy: 0.8708 - val_loss: 2.4604 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00441: val_loss did not improve from 1.31546\n",
            "Epoch 442/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4198 - accuracy: 0.8375 - val_loss: 2.4776 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00442: val_loss did not improve from 1.31546\n",
            "Epoch 443/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4098 - accuracy: 0.8458 - val_loss: 2.5031 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00443: val_loss did not improve from 1.31546\n",
            "Epoch 444/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4102 - accuracy: 0.8583 - val_loss: 2.5689 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00444: val_loss did not improve from 1.31546\n",
            "Epoch 445/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5560 - accuracy: 0.8250 - val_loss: 2.6274 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00445: val_loss did not improve from 1.31546\n",
            "Epoch 446/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3524 - accuracy: 0.8500 - val_loss: 2.6169 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00446: val_loss did not improve from 1.31546\n",
            "Epoch 447/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3678 - accuracy: 0.8708 - val_loss: 2.5466 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00447: val_loss did not improve from 1.31546\n",
            "Epoch 448/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3893 - accuracy: 0.8458 - val_loss: 2.4939 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00448: val_loss did not improve from 1.31546\n",
            "Epoch 449/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4215 - accuracy: 0.8417 - val_loss: 2.4884 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00449: val_loss did not improve from 1.31546\n",
            "Epoch 450/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4286 - accuracy: 0.8708 - val_loss: 2.5149 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00450: val_loss did not improve from 1.31546\n",
            "Epoch 451/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4899 - accuracy: 0.8417 - val_loss: 2.5179 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00451: val_loss did not improve from 1.31546\n",
            "Epoch 452/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4700 - accuracy: 0.8250 - val_loss: 2.5530 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00452: val_loss did not improve from 1.31546\n",
            "Epoch 453/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4393 - accuracy: 0.8083 - val_loss: 2.5821 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00453: val_loss did not improve from 1.31546\n",
            "Epoch 454/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4714 - accuracy: 0.8250 - val_loss: 2.5787 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00454: val_loss did not improve from 1.31546\n",
            "Epoch 455/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5016 - accuracy: 0.8125 - val_loss: 2.5224 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00455: val_loss did not improve from 1.31546\n",
            "Epoch 456/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5409 - accuracy: 0.8042 - val_loss: 2.4920 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00456: val_loss did not improve from 1.31546\n",
            "Epoch 457/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4381 - accuracy: 0.8292 - val_loss: 2.4845 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00457: val_loss did not improve from 1.31546\n",
            "Epoch 458/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4784 - accuracy: 0.8458 - val_loss: 2.4360 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00458: val_loss did not improve from 1.31546\n",
            "Epoch 459/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3973 - accuracy: 0.8750 - val_loss: 2.4539 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00459: val_loss did not improve from 1.31546\n",
            "Epoch 460/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4021 - accuracy: 0.8500 - val_loss: 2.4729 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00460: val_loss did not improve from 1.31546\n",
            "Epoch 461/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5635 - accuracy: 0.7917 - val_loss: 2.4687 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00461: val_loss did not improve from 1.31546\n",
            "Epoch 462/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5116 - accuracy: 0.8167 - val_loss: 2.4505 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00462: val_loss did not improve from 1.31546\n",
            "Epoch 463/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4406 - accuracy: 0.8417 - val_loss: 2.4765 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00463: val_loss did not improve from 1.31546\n",
            "Epoch 464/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4319 - accuracy: 0.8417 - val_loss: 2.4822 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00464: val_loss did not improve from 1.31546\n",
            "Epoch 465/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6328 - accuracy: 0.8000 - val_loss: 2.4454 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00465: val_loss did not improve from 1.31546\n",
            "Epoch 466/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3808 - accuracy: 0.8667 - val_loss: 2.4198 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00466: val_loss did not improve from 1.31546\n",
            "Epoch 467/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3995 - accuracy: 0.8625 - val_loss: 2.4261 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00467: val_loss did not improve from 1.31546\n",
            "Epoch 468/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5496 - accuracy: 0.8417 - val_loss: 2.4625 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00468: val_loss did not improve from 1.31546\n",
            "Epoch 469/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5248 - accuracy: 0.8458 - val_loss: 2.5124 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00469: val_loss did not improve from 1.31546\n",
            "Epoch 470/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4363 - accuracy: 0.8167 - val_loss: 2.5623 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00470: val_loss did not improve from 1.31546\n",
            "Epoch 471/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5484 - accuracy: 0.8375 - val_loss: 2.4973 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00471: val_loss did not improve from 1.31546\n",
            "Epoch 472/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4528 - accuracy: 0.8292 - val_loss: 2.5185 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00472: val_loss did not improve from 1.31546\n",
            "Epoch 473/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3952 - accuracy: 0.8500 - val_loss: 2.5721 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00473: val_loss did not improve from 1.31546\n",
            "Epoch 474/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3842 - accuracy: 0.8583 - val_loss: 2.6157 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00474: val_loss did not improve from 1.31546\n",
            "Epoch 475/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3490 - accuracy: 0.8750 - val_loss: 2.6163 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00475: val_loss did not improve from 1.31546\n",
            "Epoch 476/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3931 - accuracy: 0.8417 - val_loss: 2.6048 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00476: val_loss did not improve from 1.31546\n",
            "Epoch 477/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5181 - accuracy: 0.7958 - val_loss: 2.5863 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00477: val_loss did not improve from 1.31546\n",
            "Epoch 478/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4497 - accuracy: 0.8292 - val_loss: 2.5289 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00478: val_loss did not improve from 1.31546\n",
            "Epoch 479/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4815 - accuracy: 0.8250 - val_loss: 2.5740 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00479: val_loss did not improve from 1.31546\n",
            "Epoch 480/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5095 - accuracy: 0.8417 - val_loss: 2.5725 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00480: val_loss did not improve from 1.31546\n",
            "Epoch 481/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4698 - accuracy: 0.8375 - val_loss: 2.6228 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00481: val_loss did not improve from 1.31546\n",
            "Epoch 482/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4619 - accuracy: 0.8333 - val_loss: 2.5343 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00482: val_loss did not improve from 1.31546\n",
            "Epoch 483/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.5166 - accuracy: 0.8292 - val_loss: 2.5640 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00483: val_loss did not improve from 1.31546\n",
            "Epoch 484/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4480 - accuracy: 0.8250 - val_loss: 2.5729 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00484: val_loss did not improve from 1.31546\n",
            "Epoch 485/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4596 - accuracy: 0.8375 - val_loss: 2.5194 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00485: val_loss did not improve from 1.31546\n",
            "Epoch 486/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4350 - accuracy: 0.8458 - val_loss: 2.4934 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00486: val_loss did not improve from 1.31546\n",
            "Epoch 487/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4013 - accuracy: 0.8542 - val_loss: 2.4453 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00487: val_loss did not improve from 1.31546\n",
            "Epoch 488/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4203 - accuracy: 0.8500 - val_loss: 2.4068 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00488: val_loss did not improve from 1.31546\n",
            "Epoch 489/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4175 - accuracy: 0.8542 - val_loss: 2.3988 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00489: val_loss did not improve from 1.31546\n",
            "Epoch 490/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4301 - accuracy: 0.8542 - val_loss: 2.4345 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00490: val_loss did not improve from 1.31546\n",
            "Epoch 491/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4345 - accuracy: 0.8375 - val_loss: 2.4732 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00491: val_loss did not improve from 1.31546\n",
            "Epoch 492/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4549 - accuracy: 0.8417 - val_loss: 2.4579 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00492: val_loss did not improve from 1.31546\n",
            "Epoch 493/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4789 - accuracy: 0.8292 - val_loss: 2.4360 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00493: val_loss did not improve from 1.31546\n",
            "Epoch 494/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4659 - accuracy: 0.8417 - val_loss: 2.3940 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00494: val_loss did not improve from 1.31546\n",
            "Epoch 495/1000\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.4041 - accuracy: 0.8667 - val_loss: 2.4142 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00495: val_loss did not improve from 1.31546\n",
            "Epoch 496/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3878 - accuracy: 0.8458 - val_loss: 2.4019 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00496: val_loss did not improve from 1.31546\n",
            "Epoch 497/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3885 - accuracy: 0.8625 - val_loss: 2.4305 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00497: val_loss did not improve from 1.31546\n",
            "Epoch 498/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4433 - accuracy: 0.8417 - val_loss: 2.4057 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00498: val_loss did not improve from 1.31546\n",
            "Epoch 499/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4320 - accuracy: 0.8375 - val_loss: 2.3995 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00499: val_loss did not improve from 1.31546\n",
            "Epoch 500/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3926 - accuracy: 0.8667 - val_loss: 2.4484 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00500: val_loss did not improve from 1.31546\n",
            "Epoch 501/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4498 - accuracy: 0.8417 - val_loss: 2.4794 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00501: val_loss did not improve from 1.31546\n",
            "Epoch 502/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5615 - accuracy: 0.8333 - val_loss: 2.5863 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00502: val_loss did not improve from 1.31546\n",
            "Epoch 503/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4273 - accuracy: 0.8500 - val_loss: 2.7120 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00503: val_loss did not improve from 1.31546\n",
            "Epoch 504/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.8333 - val_loss: 2.7126 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00504: val_loss did not improve from 1.31546\n",
            "Epoch 505/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5640 - accuracy: 0.8125 - val_loss: 2.6961 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00505: val_loss did not improve from 1.31546\n",
            "Epoch 506/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4635 - accuracy: 0.8458 - val_loss: 2.7342 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00506: val_loss did not improve from 1.31546\n",
            "Epoch 507/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4994 - accuracy: 0.8042 - val_loss: 2.7448 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00507: val_loss did not improve from 1.31546\n",
            "Epoch 508/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3942 - accuracy: 0.8292 - val_loss: 2.7231 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00508: val_loss did not improve from 1.31546\n",
            "Epoch 509/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4353 - accuracy: 0.8375 - val_loss: 2.7072 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00509: val_loss did not improve from 1.31546\n",
            "Epoch 510/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3945 - accuracy: 0.8625 - val_loss: 2.6997 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00510: val_loss did not improve from 1.31546\n",
            "Epoch 511/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4410 - accuracy: 0.8375 - val_loss: 2.6877 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00511: val_loss did not improve from 1.31546\n",
            "Epoch 512/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4471 - accuracy: 0.8583 - val_loss: 2.7068 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00512: val_loss did not improve from 1.31546\n",
            "Epoch 513/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4744 - accuracy: 0.8208 - val_loss: 2.8054 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00513: val_loss did not improve from 1.31546\n",
            "Epoch 514/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4272 - accuracy: 0.8750 - val_loss: 2.8621 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00514: val_loss did not improve from 1.31546\n",
            "Epoch 515/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5028 - accuracy: 0.8333 - val_loss: 2.9303 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00515: val_loss did not improve from 1.31546\n",
            "Epoch 516/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4316 - accuracy: 0.8250 - val_loss: 2.8671 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00516: val_loss did not improve from 1.31546\n",
            "Epoch 517/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4015 - accuracy: 0.8625 - val_loss: 2.8330 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00517: val_loss did not improve from 1.31546\n",
            "Epoch 518/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4958 - accuracy: 0.8167 - val_loss: 2.8244 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00518: val_loss did not improve from 1.31546\n",
            "Epoch 519/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4525 - accuracy: 0.8333 - val_loss: 2.8055 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00519: val_loss did not improve from 1.31546\n",
            "Epoch 520/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3558 - accuracy: 0.8833 - val_loss: 2.7622 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00520: val_loss did not improve from 1.31546\n",
            "Epoch 521/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4753 - accuracy: 0.8250 - val_loss: 2.7735 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00521: val_loss did not improve from 1.31546\n",
            "Epoch 522/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4400 - accuracy: 0.8417 - val_loss: 2.7978 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00522: val_loss did not improve from 1.31546\n",
            "Epoch 523/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4035 - accuracy: 0.8542 - val_loss: 2.8123 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00523: val_loss did not improve from 1.31546\n",
            "Epoch 524/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4067 - accuracy: 0.8583 - val_loss: 2.8055 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00524: val_loss did not improve from 1.31546\n",
            "Epoch 525/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3885 - accuracy: 0.8458 - val_loss: 2.7503 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00525: val_loss did not improve from 1.31546\n",
            "Epoch 526/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3964 - accuracy: 0.8625 - val_loss: 2.7455 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00526: val_loss did not improve from 1.31546\n",
            "Epoch 527/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3724 - accuracy: 0.8625 - val_loss: 2.8147 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00527: val_loss did not improve from 1.31546\n",
            "Epoch 528/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3818 - accuracy: 0.8917 - val_loss: 2.8789 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00528: val_loss did not improve from 1.31546\n",
            "Epoch 529/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3910 - accuracy: 0.8500 - val_loss: 2.8897 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00529: val_loss did not improve from 1.31546\n",
            "Epoch 530/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3189 - accuracy: 0.8875 - val_loss: 2.8953 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00530: val_loss did not improve from 1.31546\n",
            "Epoch 531/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3801 - accuracy: 0.8750 - val_loss: 2.8673 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00531: val_loss did not improve from 1.31546\n",
            "Epoch 532/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5500 - accuracy: 0.8542 - val_loss: 2.7003 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00532: val_loss did not improve from 1.31546\n",
            "Epoch 533/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4225 - accuracy: 0.8208 - val_loss: 2.6458 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00533: val_loss did not improve from 1.31546\n",
            "Epoch 534/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5153 - accuracy: 0.8375 - val_loss: 2.6262 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00534: val_loss did not improve from 1.31546\n",
            "Epoch 535/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3858 - accuracy: 0.8667 - val_loss: 2.5920 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00535: val_loss did not improve from 1.31546\n",
            "Epoch 536/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4272 - accuracy: 0.8417 - val_loss: 2.5479 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00536: val_loss did not improve from 1.31546\n",
            "Epoch 537/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3753 - accuracy: 0.8708 - val_loss: 2.5711 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00537: val_loss did not improve from 1.31546\n",
            "Epoch 538/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4359 - accuracy: 0.8542 - val_loss: 2.5921 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00538: val_loss did not improve from 1.31546\n",
            "Epoch 539/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3799 - accuracy: 0.8750 - val_loss: 2.5829 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00539: val_loss did not improve from 1.31546\n",
            "Epoch 540/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3998 - accuracy: 0.8417 - val_loss: 2.5732 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00540: val_loss did not improve from 1.31546\n",
            "Epoch 541/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3798 - accuracy: 0.8375 - val_loss: 2.5875 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00541: val_loss did not improve from 1.31546\n",
            "Epoch 542/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4261 - accuracy: 0.8333 - val_loss: 2.5785 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00542: val_loss did not improve from 1.31546\n",
            "Epoch 543/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3929 - accuracy: 0.8500 - val_loss: 2.5901 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00543: val_loss did not improve from 1.31546\n",
            "Epoch 544/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5554 - accuracy: 0.8667 - val_loss: 2.6198 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00544: val_loss did not improve from 1.31546\n",
            "Epoch 545/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3984 - accuracy: 0.8750 - val_loss: 2.6782 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00545: val_loss did not improve from 1.31546\n",
            "Epoch 546/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4446 - accuracy: 0.8333 - val_loss: 2.7081 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00546: val_loss did not improve from 1.31546\n",
            "Epoch 547/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2948 - accuracy: 0.8917 - val_loss: 2.7552 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00547: val_loss did not improve from 1.31546\n",
            "Epoch 548/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3502 - accuracy: 0.8792 - val_loss: 2.7579 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00548: val_loss did not improve from 1.31546\n",
            "Epoch 549/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3446 - accuracy: 0.8792 - val_loss: 2.7327 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00549: val_loss did not improve from 1.31546\n",
            "Epoch 550/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3379 - accuracy: 0.8500 - val_loss: 2.7319 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00550: val_loss did not improve from 1.31546\n",
            "Epoch 551/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4063 - accuracy: 0.8542 - val_loss: 2.7258 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00551: val_loss did not improve from 1.31546\n",
            "Epoch 552/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3977 - accuracy: 0.8792 - val_loss: 2.6939 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00552: val_loss did not improve from 1.31546\n",
            "Epoch 553/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3532 - accuracy: 0.8875 - val_loss: 2.7412 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00553: val_loss did not improve from 1.31546\n",
            "Epoch 554/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3858 - accuracy: 0.8625 - val_loss: 2.6488 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00554: val_loss did not improve from 1.31546\n",
            "Epoch 555/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3082 - accuracy: 0.9042 - val_loss: 2.6099 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00555: val_loss did not improve from 1.31546\n",
            "Epoch 556/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3839 - accuracy: 0.8500 - val_loss: 2.6360 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00556: val_loss did not improve from 1.31546\n",
            "Epoch 557/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4051 - accuracy: 0.8542 - val_loss: 2.6603 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00557: val_loss did not improve from 1.31546\n",
            "Epoch 558/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3956 - accuracy: 0.8958 - val_loss: 2.6475 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00558: val_loss did not improve from 1.31546\n",
            "Epoch 559/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3999 - accuracy: 0.8833 - val_loss: 2.6151 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00559: val_loss did not improve from 1.31546\n",
            "Epoch 560/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3599 - accuracy: 0.8667 - val_loss: 2.6075 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00560: val_loss did not improve from 1.31546\n",
            "Epoch 561/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3487 - accuracy: 0.8542 - val_loss: 2.6721 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00561: val_loss did not improve from 1.31546\n",
            "Epoch 562/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4432 - accuracy: 0.8667 - val_loss: 2.6853 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00562: val_loss did not improve from 1.31546\n",
            "Epoch 563/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.2921 - accuracy: 0.8625 - val_loss: 2.6445 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00563: val_loss did not improve from 1.31546\n",
            "Epoch 564/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4573 - accuracy: 0.8750 - val_loss: 2.5750 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00564: val_loss did not improve from 1.31546\n",
            "Epoch 565/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.2952 - accuracy: 0.9042 - val_loss: 2.5218 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00565: val_loss did not improve from 1.31546\n",
            "Epoch 566/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3353 - accuracy: 0.8917 - val_loss: 2.5835 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00566: val_loss did not improve from 1.31546\n",
            "Epoch 567/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4294 - accuracy: 0.8583 - val_loss: 2.5219 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00567: val_loss did not improve from 1.31546\n",
            "Epoch 568/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4784 - accuracy: 0.8375 - val_loss: 2.4614 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00568: val_loss did not improve from 1.31546\n",
            "Epoch 569/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3688 - accuracy: 0.8667 - val_loss: 2.4415 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00569: val_loss did not improve from 1.31546\n",
            "Epoch 570/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4069 - accuracy: 0.8542 - val_loss: 2.4865 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00570: val_loss did not improve from 1.31546\n",
            "Epoch 571/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4419 - accuracy: 0.8333 - val_loss: 2.5804 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00571: val_loss did not improve from 1.31546\n",
            "Epoch 572/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5115 - accuracy: 0.8542 - val_loss: 2.5660 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00572: val_loss did not improve from 1.31546\n",
            "Epoch 573/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3580 - accuracy: 0.8792 - val_loss: 2.5920 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00573: val_loss did not improve from 1.31546\n",
            "Epoch 574/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3811 - accuracy: 0.8583 - val_loss: 2.5936 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00574: val_loss did not improve from 1.31546\n",
            "Epoch 575/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3523 - accuracy: 0.8917 - val_loss: 2.5836 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00575: val_loss did not improve from 1.31546\n",
            "Epoch 576/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4099 - accuracy: 0.8667 - val_loss: 2.6674 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00576: val_loss did not improve from 1.31546\n",
            "Epoch 577/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3084 - accuracy: 0.8750 - val_loss: 2.6849 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00577: val_loss did not improve from 1.31546\n",
            "Epoch 578/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3641 - accuracy: 0.8625 - val_loss: 2.6786 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00578: val_loss did not improve from 1.31546\n",
            "Epoch 579/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3399 - accuracy: 0.8667 - val_loss: 2.7128 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00579: val_loss did not improve from 1.31546\n",
            "Epoch 580/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3955 - accuracy: 0.8583 - val_loss: 2.7856 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00580: val_loss did not improve from 1.31546\n",
            "Epoch 581/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4810 - accuracy: 0.8375 - val_loss: 2.7495 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00581: val_loss did not improve from 1.31546\n",
            "Epoch 582/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3485 - accuracy: 0.8667 - val_loss: 2.6791 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00582: val_loss did not improve from 1.31546\n",
            "Epoch 583/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.2859 - accuracy: 0.8875 - val_loss: 2.6403 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00583: val_loss did not improve from 1.31546\n",
            "Epoch 584/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3371 - accuracy: 0.8500 - val_loss: 2.6404 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00584: val_loss did not improve from 1.31546\n",
            "Epoch 585/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3868 - accuracy: 0.8625 - val_loss: 2.7127 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00585: val_loss did not improve from 1.31546\n",
            "Epoch 586/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3525 - accuracy: 0.8833 - val_loss: 2.7236 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00586: val_loss did not improve from 1.31546\n",
            "Epoch 587/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3990 - accuracy: 0.8375 - val_loss: 2.7366 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00587: val_loss did not improve from 1.31546\n",
            "Epoch 588/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4871 - accuracy: 0.8458 - val_loss: 2.7899 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00588: val_loss did not improve from 1.31546\n",
            "Epoch 589/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2615 - accuracy: 0.9208 - val_loss: 2.7685 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00589: val_loss did not improve from 1.31546\n",
            "Epoch 590/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3208 - accuracy: 0.8792 - val_loss: 2.8182 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00590: val_loss did not improve from 1.31546\n",
            "Epoch 591/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4055 - accuracy: 0.8458 - val_loss: 2.8707 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00591: val_loss did not improve from 1.31546\n",
            "Epoch 592/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4003 - accuracy: 0.8667 - val_loss: 2.9218 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00592: val_loss did not improve from 1.31546\n",
            "Epoch 593/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4760 - accuracy: 0.8250 - val_loss: 2.9607 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00593: val_loss did not improve from 1.31546\n",
            "Epoch 594/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3312 - accuracy: 0.8750 - val_loss: 2.9836 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00594: val_loss did not improve from 1.31546\n",
            "Epoch 595/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3476 - accuracy: 0.8708 - val_loss: 3.0348 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00595: val_loss did not improve from 1.31546\n",
            "Epoch 596/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4268 - accuracy: 0.8417 - val_loss: 2.9095 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00596: val_loss did not improve from 1.31546\n",
            "Epoch 597/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3843 - accuracy: 0.8750 - val_loss: 2.8626 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00597: val_loss did not improve from 1.31546\n",
            "Epoch 598/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3758 - accuracy: 0.8750 - val_loss: 2.8642 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00598: val_loss did not improve from 1.31546\n",
            "Epoch 599/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2923 - accuracy: 0.9042 - val_loss: 2.9285 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00599: val_loss did not improve from 1.31546\n",
            "Epoch 600/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3949 - accuracy: 0.8667 - val_loss: 2.9769 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00600: val_loss did not improve from 1.31546\n",
            "Epoch 601/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3971 - accuracy: 0.9000 - val_loss: 2.8822 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00601: val_loss did not improve from 1.31546\n",
            "Epoch 602/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3142 - accuracy: 0.8833 - val_loss: 2.9728 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00602: val_loss did not improve from 1.31546\n",
            "Epoch 603/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4239 - accuracy: 0.8542 - val_loss: 3.1341 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00603: val_loss did not improve from 1.31546\n",
            "Epoch 604/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4109 - accuracy: 0.8583 - val_loss: 2.9946 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00604: val_loss did not improve from 1.31546\n",
            "Epoch 605/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3296 - accuracy: 0.8917 - val_loss: 3.0236 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00605: val_loss did not improve from 1.31546\n",
            "Epoch 606/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3533 - accuracy: 0.8375 - val_loss: 2.9816 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00606: val_loss did not improve from 1.31546\n",
            "Epoch 607/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3290 - accuracy: 0.8792 - val_loss: 2.9271 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00607: val_loss did not improve from 1.31546\n",
            "Epoch 608/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4378 - accuracy: 0.8583 - val_loss: 2.9253 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00608: val_loss did not improve from 1.31546\n",
            "Epoch 609/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3418 - accuracy: 0.8792 - val_loss: 2.9505 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00609: val_loss did not improve from 1.31546\n",
            "Epoch 610/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.5014 - accuracy: 0.8583 - val_loss: 2.8910 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00610: val_loss did not improve from 1.31546\n",
            "Epoch 611/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4744 - accuracy: 0.8583 - val_loss: 2.8817 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00611: val_loss did not improve from 1.31546\n",
            "Epoch 612/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3174 - accuracy: 0.8792 - val_loss: 2.8864 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00612: val_loss did not improve from 1.31546\n",
            "Epoch 613/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4063 - accuracy: 0.8667 - val_loss: 2.8699 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00613: val_loss did not improve from 1.31546\n",
            "Epoch 614/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3978 - accuracy: 0.8667 - val_loss: 2.9255 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00614: val_loss did not improve from 1.31546\n",
            "Epoch 615/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4585 - accuracy: 0.8375 - val_loss: 2.8950 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00615: val_loss did not improve from 1.31546\n",
            "Epoch 616/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3568 - accuracy: 0.8625 - val_loss: 2.9702 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00616: val_loss did not improve from 1.31546\n",
            "Epoch 617/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4126 - accuracy: 0.8583 - val_loss: 2.9415 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00617: val_loss did not improve from 1.31546\n",
            "Epoch 618/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5378 - accuracy: 0.8208 - val_loss: 2.9408 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00618: val_loss did not improve from 1.31546\n",
            "Epoch 619/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4204 - accuracy: 0.8667 - val_loss: 2.8283 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00619: val_loss did not improve from 1.31546\n",
            "Epoch 620/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4374 - accuracy: 0.8542 - val_loss: 2.6541 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00620: val_loss did not improve from 1.31546\n",
            "Epoch 621/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3887 - accuracy: 0.8792 - val_loss: 2.6290 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00621: val_loss did not improve from 1.31546\n",
            "Epoch 622/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3652 - accuracy: 0.8708 - val_loss: 2.8022 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00622: val_loss did not improve from 1.31546\n",
            "Epoch 623/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4457 - accuracy: 0.8500 - val_loss: 3.0115 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00623: val_loss did not improve from 1.31546\n",
            "Epoch 624/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4066 - accuracy: 0.8750 - val_loss: 2.7723 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00624: val_loss did not improve from 1.31546\n",
            "Epoch 625/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5492 - accuracy: 0.8375 - val_loss: 2.7770 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00625: val_loss did not improve from 1.31546\n",
            "Epoch 626/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3950 - accuracy: 0.8667 - val_loss: 2.8041 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00626: val_loss did not improve from 1.31546\n",
            "Epoch 627/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4042 - accuracy: 0.8625 - val_loss: 2.7880 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00627: val_loss did not improve from 1.31546\n",
            "Epoch 628/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4257 - accuracy: 0.8375 - val_loss: 2.8330 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00628: val_loss did not improve from 1.31546\n",
            "Epoch 629/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5096 - accuracy: 0.8417 - val_loss: 2.7613 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00629: val_loss did not improve from 1.31546\n",
            "Epoch 630/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3758 - accuracy: 0.8583 - val_loss: 2.7577 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00630: val_loss did not improve from 1.31546\n",
            "Epoch 631/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3794 - accuracy: 0.8667 - val_loss: 2.7239 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00631: val_loss did not improve from 1.31546\n",
            "Epoch 632/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4109 - accuracy: 0.8458 - val_loss: 2.7616 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00632: val_loss did not improve from 1.31546\n",
            "Epoch 633/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3181 - accuracy: 0.8792 - val_loss: 2.8221 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00633: val_loss did not improve from 1.31546\n",
            "Epoch 634/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3727 - accuracy: 0.8542 - val_loss: 2.8466 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00634: val_loss did not improve from 1.31546\n",
            "Epoch 635/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4122 - accuracy: 0.8708 - val_loss: 2.8598 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00635: val_loss did not improve from 1.31546\n",
            "Epoch 636/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4225 - accuracy: 0.8417 - val_loss: 2.8573 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00636: val_loss did not improve from 1.31546\n",
            "Epoch 637/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4038 - accuracy: 0.8542 - val_loss: 2.8279 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00637: val_loss did not improve from 1.31546\n",
            "Epoch 638/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3869 - accuracy: 0.8458 - val_loss: 2.7480 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00638: val_loss did not improve from 1.31546\n",
            "Epoch 639/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3426 - accuracy: 0.8958 - val_loss: 2.6763 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00639: val_loss did not improve from 1.31546\n",
            "Epoch 640/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3982 - accuracy: 0.8792 - val_loss: 2.6426 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00640: val_loss did not improve from 1.31546\n",
            "Epoch 641/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4287 - accuracy: 0.8500 - val_loss: 2.6529 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00641: val_loss did not improve from 1.31546\n",
            "Epoch 642/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3748 - accuracy: 0.8625 - val_loss: 2.6655 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00642: val_loss did not improve from 1.31546\n",
            "Epoch 643/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3657 - accuracy: 0.8875 - val_loss: 2.6728 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00643: val_loss did not improve from 1.31546\n",
            "Epoch 644/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4081 - accuracy: 0.8500 - val_loss: 2.6182 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00644: val_loss did not improve from 1.31546\n",
            "Epoch 645/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4498 - accuracy: 0.8458 - val_loss: 2.5793 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00645: val_loss did not improve from 1.31546\n",
            "Epoch 646/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4098 - accuracy: 0.8500 - val_loss: 2.5916 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00646: val_loss did not improve from 1.31546\n",
            "Epoch 647/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3360 - accuracy: 0.8458 - val_loss: 2.6337 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00647: val_loss did not improve from 1.31546\n",
            "Epoch 648/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3969 - accuracy: 0.8583 - val_loss: 2.6222 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00648: val_loss did not improve from 1.31546\n",
            "Epoch 649/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4507 - accuracy: 0.8333 - val_loss: 2.5858 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00649: val_loss did not improve from 1.31546\n",
            "Epoch 650/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4480 - accuracy: 0.8458 - val_loss: 2.5355 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00650: val_loss did not improve from 1.31546\n",
            "Epoch 651/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.5329 - accuracy: 0.8250 - val_loss: 2.4600 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00651: val_loss did not improve from 1.31546\n",
            "Epoch 652/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4164 - accuracy: 0.8708 - val_loss: 2.4309 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00652: val_loss did not improve from 1.31546\n",
            "Epoch 653/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3450 - accuracy: 0.8750 - val_loss: 2.4280 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00653: val_loss did not improve from 1.31546\n",
            "Epoch 654/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3935 - accuracy: 0.8583 - val_loss: 2.4451 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00654: val_loss did not improve from 1.31546\n",
            "Epoch 655/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4500 - accuracy: 0.8542 - val_loss: 2.4322 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00655: val_loss did not improve from 1.31546\n",
            "Epoch 656/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4273 - accuracy: 0.8542 - val_loss: 2.4346 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00656: val_loss did not improve from 1.31546\n",
            "Epoch 657/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4603 - accuracy: 0.8208 - val_loss: 2.4852 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00657: val_loss did not improve from 1.31546\n",
            "Epoch 658/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3335 - accuracy: 0.8833 - val_loss: 2.5196 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00658: val_loss did not improve from 1.31546\n",
            "Epoch 659/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4630 - accuracy: 0.8125 - val_loss: 2.4760 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00659: val_loss did not improve from 1.31546\n",
            "Epoch 660/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.5155 - accuracy: 0.8458 - val_loss: 2.5146 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00660: val_loss did not improve from 1.31546\n",
            "Epoch 661/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4420 - accuracy: 0.8625 - val_loss: 2.4804 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00661: val_loss did not improve from 1.31546\n",
            "Epoch 662/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3237 - accuracy: 0.9000 - val_loss: 2.4133 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00662: val_loss did not improve from 1.31546\n",
            "Epoch 663/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3731 - accuracy: 0.8542 - val_loss: 2.4241 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00663: val_loss did not improve from 1.31546\n",
            "Epoch 664/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3529 - accuracy: 0.9000 - val_loss: 2.5078 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00664: val_loss did not improve from 1.31546\n",
            "Epoch 665/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3702 - accuracy: 0.8583 - val_loss: 2.5436 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00665: val_loss did not improve from 1.31546\n",
            "Epoch 666/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3638 - accuracy: 0.8750 - val_loss: 2.5103 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00666: val_loss did not improve from 1.31546\n",
            "Epoch 667/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4771 - accuracy: 0.8333 - val_loss: 2.5076 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00667: val_loss did not improve from 1.31546\n",
            "Epoch 668/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3639 - accuracy: 0.8667 - val_loss: 2.5329 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00668: val_loss did not improve from 1.31546\n",
            "Epoch 669/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4175 - accuracy: 0.8333 - val_loss: 2.5584 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00669: val_loss did not improve from 1.31546\n",
            "Epoch 670/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4570 - accuracy: 0.8542 - val_loss: 2.5598 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00670: val_loss did not improve from 1.31546\n",
            "Epoch 671/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3247 - accuracy: 0.8833 - val_loss: 2.5634 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00671: val_loss did not improve from 1.31546\n",
            "Epoch 672/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3672 - accuracy: 0.8583 - val_loss: 2.5537 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00672: val_loss did not improve from 1.31546\n",
            "Epoch 673/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3038 - accuracy: 0.8792 - val_loss: 2.5685 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00673: val_loss did not improve from 1.31546\n",
            "Epoch 674/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3160 - accuracy: 0.8958 - val_loss: 2.5720 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00674: val_loss did not improve from 1.31546\n",
            "Epoch 675/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3438 - accuracy: 0.8917 - val_loss: 2.5532 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00675: val_loss did not improve from 1.31546\n",
            "Epoch 676/1000\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3801 - accuracy: 0.8375 - val_loss: 2.5599 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00676: val_loss did not improve from 1.31546\n",
            "Epoch 677/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3779 - accuracy: 0.8667 - val_loss: 2.5700 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00677: val_loss did not improve from 1.31546\n",
            "Epoch 678/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3369 - accuracy: 0.8833 - val_loss: 2.5708 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00678: val_loss did not improve from 1.31546\n",
            "Epoch 679/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3236 - accuracy: 0.8875 - val_loss: 2.5762 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00679: val_loss did not improve from 1.31546\n",
            "Epoch 680/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3894 - accuracy: 0.8792 - val_loss: 2.5711 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00680: val_loss did not improve from 1.31546\n",
            "Epoch 681/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4905 - accuracy: 0.8417 - val_loss: 2.5365 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00681: val_loss did not improve from 1.31546\n",
            "Epoch 682/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3835 - accuracy: 0.8583 - val_loss: 2.6161 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00682: val_loss did not improve from 1.31546\n",
            "Epoch 683/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3738 - accuracy: 0.8667 - val_loss: 2.6534 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00683: val_loss did not improve from 1.31546\n",
            "Epoch 684/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3785 - accuracy: 0.8292 - val_loss: 2.6782 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00684: val_loss did not improve from 1.31546\n",
            "Epoch 685/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4779 - accuracy: 0.8583 - val_loss: 2.5183 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00685: val_loss did not improve from 1.31546\n",
            "Epoch 686/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3652 - accuracy: 0.8417 - val_loss: 2.4722 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00686: val_loss did not improve from 1.31546\n",
            "Epoch 687/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4734 - accuracy: 0.8542 - val_loss: 2.4821 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00687: val_loss did not improve from 1.31546\n",
            "Epoch 688/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4801 - accuracy: 0.8500 - val_loss: 2.4620 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00688: val_loss did not improve from 1.31546\n",
            "Epoch 689/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4743 - accuracy: 0.8500 - val_loss: 2.4249 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00689: val_loss did not improve from 1.31546\n",
            "Epoch 690/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4304 - accuracy: 0.8292 - val_loss: 2.4581 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00690: val_loss did not improve from 1.31546\n",
            "Epoch 691/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4118 - accuracy: 0.8542 - val_loss: 2.4920 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00691: val_loss did not improve from 1.31546\n",
            "Epoch 692/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.4048 - accuracy: 0.8708 - val_loss: 2.5376 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00692: val_loss did not improve from 1.31546\n",
            "Epoch 693/1000\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.3890 - accuracy: 0.8750 - val_loss: 2.6193 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00693: val_loss did not improve from 1.31546\n",
            "Epoch 694/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2808 - accuracy: 0.8958 - val_loss: 2.6907 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00694: val_loss did not improve from 1.31546\n",
            "Epoch 695/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4790 - accuracy: 0.8333 - val_loss: 2.6707 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00695: val_loss did not improve from 1.31546\n",
            "Epoch 696/1000\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.4794 - accuracy: 0.8583 - val_loss: 2.6715 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00696: val_loss did not improve from 1.31546\n",
            "Epoch 697/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4903 - accuracy: 0.8542 - val_loss: 2.6375 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00697: val_loss did not improve from 1.31546\n",
            "Epoch 698/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3875 - accuracy: 0.8583 - val_loss: 2.5119 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00698: val_loss did not improve from 1.31546\n",
            "Epoch 699/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3606 - accuracy: 0.8833 - val_loss: 2.4315 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00699: val_loss did not improve from 1.31546\n",
            "Epoch 700/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4407 - accuracy: 0.8667 - val_loss: 2.3478 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00700: val_loss did not improve from 1.31546\n",
            "Epoch 701/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4214 - accuracy: 0.8417 - val_loss: 2.3848 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00701: val_loss did not improve from 1.31546\n",
            "Epoch 702/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3881 - accuracy: 0.8583 - val_loss: 2.3646 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00702: val_loss did not improve from 1.31546\n",
            "Epoch 703/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3151 - accuracy: 0.9042 - val_loss: 2.3343 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00703: val_loss did not improve from 1.31546\n",
            "Epoch 704/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4379 - accuracy: 0.8583 - val_loss: 2.4080 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00704: val_loss did not improve from 1.31546\n",
            "Epoch 705/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3373 - accuracy: 0.8958 - val_loss: 2.4589 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00705: val_loss did not improve from 1.31546\n",
            "Epoch 706/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4680 - accuracy: 0.8625 - val_loss: 2.4026 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00706: val_loss did not improve from 1.31546\n",
            "Epoch 707/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3459 - accuracy: 0.8750 - val_loss: 2.4647 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00707: val_loss did not improve from 1.31546\n",
            "Epoch 708/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3404 - accuracy: 0.8708 - val_loss: 2.5663 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00708: val_loss did not improve from 1.31546\n",
            "Epoch 709/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4443 - accuracy: 0.8667 - val_loss: 2.5664 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00709: val_loss did not improve from 1.31546\n",
            "Epoch 710/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3013 - accuracy: 0.8750 - val_loss: 2.5347 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00710: val_loss did not improve from 1.31546\n",
            "Epoch 711/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4185 - accuracy: 0.8542 - val_loss: 2.6469 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00711: val_loss did not improve from 1.31546\n",
            "Epoch 712/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3412 - accuracy: 0.8417 - val_loss: 2.7085 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00712: val_loss did not improve from 1.31546\n",
            "Epoch 713/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3202 - accuracy: 0.8625 - val_loss: 2.7435 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00713: val_loss did not improve from 1.31546\n",
            "Epoch 714/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3431 - accuracy: 0.8542 - val_loss: 2.7609 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00714: val_loss did not improve from 1.31546\n",
            "Epoch 715/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3455 - accuracy: 0.8917 - val_loss: 2.7376 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00715: val_loss did not improve from 1.31546\n",
            "Epoch 716/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3384 - accuracy: 0.8750 - val_loss: 2.7409 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00716: val_loss did not improve from 1.31546\n",
            "Epoch 717/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3653 - accuracy: 0.8625 - val_loss: 2.7321 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00717: val_loss did not improve from 1.31546\n",
            "Epoch 718/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.2962 - accuracy: 0.8917 - val_loss: 2.7466 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00718: val_loss did not improve from 1.31546\n",
            "Epoch 719/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3873 - accuracy: 0.8875 - val_loss: 2.7017 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00719: val_loss did not improve from 1.31546\n",
            "Epoch 720/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2995 - accuracy: 0.8917 - val_loss: 2.7192 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00720: val_loss did not improve from 1.31546\n",
            "Epoch 721/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3810 - accuracy: 0.8708 - val_loss: 2.7269 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00721: val_loss did not improve from 1.31546\n",
            "Epoch 722/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2544 - accuracy: 0.9000 - val_loss: 2.7692 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00722: val_loss did not improve from 1.31546\n",
            "Epoch 723/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2977 - accuracy: 0.8875 - val_loss: 2.7956 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00723: val_loss did not improve from 1.31546\n",
            "Epoch 724/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4900 - accuracy: 0.8958 - val_loss: 2.8196 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00724: val_loss did not improve from 1.31546\n",
            "Epoch 725/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3246 - accuracy: 0.8708 - val_loss: 2.8665 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00725: val_loss did not improve from 1.31546\n",
            "Epoch 726/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4480 - accuracy: 0.8667 - val_loss: 2.8732 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00726: val_loss did not improve from 1.31546\n",
            "Epoch 727/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3936 - accuracy: 0.8917 - val_loss: 2.8857 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00727: val_loss did not improve from 1.31546\n",
            "Epoch 728/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2994 - accuracy: 0.8875 - val_loss: 2.8960 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00728: val_loss did not improve from 1.31546\n",
            "Epoch 729/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3668 - accuracy: 0.8792 - val_loss: 2.8876 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00729: val_loss did not improve from 1.31546\n",
            "Epoch 730/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4088 - accuracy: 0.8542 - val_loss: 2.8377 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00730: val_loss did not improve from 1.31546\n",
            "Epoch 731/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2880 - accuracy: 0.8833 - val_loss: 2.8106 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00731: val_loss did not improve from 1.31546\n",
            "Epoch 732/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4192 - accuracy: 0.8667 - val_loss: 2.7943 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00732: val_loss did not improve from 1.31546\n",
            "Epoch 733/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.2785 - accuracy: 0.9042 - val_loss: 2.8310 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00733: val_loss did not improve from 1.31546\n",
            "Epoch 734/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3455 - accuracy: 0.8875 - val_loss: 2.8187 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00734: val_loss did not improve from 1.31546\n",
            "Epoch 735/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4415 - accuracy: 0.8208 - val_loss: 2.8376 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00735: val_loss did not improve from 1.31546\n",
            "Epoch 736/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3820 - accuracy: 0.8917 - val_loss: 2.8520 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00736: val_loss did not improve from 1.31546\n",
            "Epoch 737/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.2966 - accuracy: 0.9125 - val_loss: 2.9955 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00737: val_loss did not improve from 1.31546\n",
            "Epoch 738/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4348 - accuracy: 0.8750 - val_loss: 3.0197 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00738: val_loss did not improve from 1.31546\n",
            "Epoch 739/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3784 - accuracy: 0.8625 - val_loss: 3.0310 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00739: val_loss did not improve from 1.31546\n",
            "Epoch 740/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4988 - accuracy: 0.8625 - val_loss: 2.9715 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00740: val_loss did not improve from 1.31546\n",
            "Epoch 741/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4176 - accuracy: 0.8292 - val_loss: 2.9236 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00741: val_loss did not improve from 1.31546\n",
            "Epoch 742/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2877 - accuracy: 0.8917 - val_loss: 2.9831 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00742: val_loss did not improve from 1.31546\n",
            "Epoch 743/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3400 - accuracy: 0.8833 - val_loss: 3.0453 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00743: val_loss did not improve from 1.31546\n",
            "Epoch 744/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3823 - accuracy: 0.8750 - val_loss: 3.0816 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00744: val_loss did not improve from 1.31546\n",
            "Epoch 745/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3902 - accuracy: 0.8708 - val_loss: 3.1064 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00745: val_loss did not improve from 1.31546\n",
            "Epoch 746/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.2961 - accuracy: 0.9000 - val_loss: 3.1488 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00746: val_loss did not improve from 1.31546\n",
            "Epoch 747/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3592 - accuracy: 0.8792 - val_loss: 3.1839 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00747: val_loss did not improve from 1.31546\n",
            "Epoch 748/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4658 - accuracy: 0.8458 - val_loss: 3.1072 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00748: val_loss did not improve from 1.31546\n",
            "Epoch 749/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3329 - accuracy: 0.8542 - val_loss: 3.0295 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00749: val_loss did not improve from 1.31546\n",
            "Epoch 750/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3324 - accuracy: 0.8917 - val_loss: 2.9849 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00750: val_loss did not improve from 1.31546\n",
            "Epoch 751/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3077 - accuracy: 0.9000 - val_loss: 2.9740 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00751: val_loss did not improve from 1.31546\n",
            "Epoch 752/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3306 - accuracy: 0.8875 - val_loss: 3.0168 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00752: val_loss did not improve from 1.31546\n",
            "Epoch 753/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.2952 - accuracy: 0.9000 - val_loss: 3.0800 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00753: val_loss did not improve from 1.31546\n",
            "Epoch 754/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4059 - accuracy: 0.8750 - val_loss: 3.1276 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00754: val_loss did not improve from 1.31546\n",
            "Epoch 755/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3346 - accuracy: 0.8833 - val_loss: 3.0692 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00755: val_loss did not improve from 1.31546\n",
            "Epoch 756/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3859 - accuracy: 0.8792 - val_loss: 3.0002 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00756: val_loss did not improve from 1.31546\n",
            "Epoch 757/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3501 - accuracy: 0.8750 - val_loss: 3.0409 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00757: val_loss did not improve from 1.31546\n",
            "Epoch 758/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3434 - accuracy: 0.8917 - val_loss: 3.0879 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00758: val_loss did not improve from 1.31546\n",
            "Epoch 759/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4336 - accuracy: 0.8792 - val_loss: 2.9510 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00759: val_loss did not improve from 1.31546\n",
            "Epoch 760/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3787 - accuracy: 0.8750 - val_loss: 2.9915 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00760: val_loss did not improve from 1.31546\n",
            "Epoch 761/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3387 - accuracy: 0.8708 - val_loss: 3.1030 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00761: val_loss did not improve from 1.31546\n",
            "Epoch 762/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2412 - accuracy: 0.9250 - val_loss: 3.1587 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00762: val_loss did not improve from 1.31546\n",
            "Epoch 763/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3411 - accuracy: 0.8875 - val_loss: 3.1302 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00763: val_loss did not improve from 1.31546\n",
            "Epoch 764/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4019 - accuracy: 0.8750 - val_loss: 3.0845 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00764: val_loss did not improve from 1.31546\n",
            "Epoch 765/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3450 - accuracy: 0.8792 - val_loss: 3.0989 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00765: val_loss did not improve from 1.31546\n",
            "Epoch 766/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3477 - accuracy: 0.8750 - val_loss: 3.1741 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00766: val_loss did not improve from 1.31546\n",
            "Epoch 767/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5809 - accuracy: 0.8292 - val_loss: 3.1322 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00767: val_loss did not improve from 1.31546\n",
            "Epoch 768/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4341 - accuracy: 0.8750 - val_loss: 2.9473 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00768: val_loss did not improve from 1.31546\n",
            "Epoch 769/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3991 - accuracy: 0.8708 - val_loss: 2.8513 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00769: val_loss did not improve from 1.31546\n",
            "Epoch 770/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4618 - accuracy: 0.8542 - val_loss: 2.8508 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00770: val_loss did not improve from 1.31546\n",
            "Epoch 771/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4312 - accuracy: 0.8375 - val_loss: 2.8364 - val_accuracy: 0.6000\n",
            "\n",
            "Epoch 00771: val_loss did not improve from 1.31546\n",
            "Epoch 772/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3943 - accuracy: 0.8625 - val_loss: 2.8531 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00772: val_loss did not improve from 1.31546\n",
            "Epoch 773/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3298 - accuracy: 0.8917 - val_loss: 2.8996 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00773: val_loss did not improve from 1.31546\n",
            "Epoch 774/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4126 - accuracy: 0.8500 - val_loss: 2.9384 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00774: val_loss did not improve from 1.31546\n",
            "Epoch 775/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4189 - accuracy: 0.8583 - val_loss: 2.9669 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00775: val_loss did not improve from 1.31546\n",
            "Epoch 776/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3283 - accuracy: 0.8833 - val_loss: 2.9880 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00776: val_loss did not improve from 1.31546\n",
            "Epoch 777/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3253 - accuracy: 0.8750 - val_loss: 3.0710 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00777: val_loss did not improve from 1.31546\n",
            "Epoch 778/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3432 - accuracy: 0.8750 - val_loss: 3.1483 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00778: val_loss did not improve from 1.31546\n",
            "Epoch 779/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.5035 - accuracy: 0.8583 - val_loss: 2.9724 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00779: val_loss did not improve from 1.31546\n",
            "Epoch 780/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3563 - accuracy: 0.8583 - val_loss: 2.8971 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00780: val_loss did not improve from 1.31546\n",
            "Epoch 781/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3772 - accuracy: 0.8875 - val_loss: 2.9199 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00781: val_loss did not improve from 1.31546\n",
            "Epoch 782/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3571 - accuracy: 0.8708 - val_loss: 2.9183 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00782: val_loss did not improve from 1.31546\n",
            "Epoch 783/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3660 - accuracy: 0.8708 - val_loss: 2.9494 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00783: val_loss did not improve from 1.31546\n",
            "Epoch 784/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3073 - accuracy: 0.8875 - val_loss: 2.9929 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00784: val_loss did not improve from 1.31546\n",
            "Epoch 785/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4409 - accuracy: 0.8708 - val_loss: 3.0231 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00785: val_loss did not improve from 1.31546\n",
            "Epoch 786/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3755 - accuracy: 0.8875 - val_loss: 3.0275 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00786: val_loss did not improve from 1.31546\n",
            "Epoch 787/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3917 - accuracy: 0.8542 - val_loss: 3.0547 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00787: val_loss did not improve from 1.31546\n",
            "Epoch 788/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4544 - accuracy: 0.8333 - val_loss: 3.0524 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00788: val_loss did not improve from 1.31546\n",
            "Epoch 789/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3568 - accuracy: 0.8625 - val_loss: 2.9924 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00789: val_loss did not improve from 1.31546\n",
            "Epoch 790/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3675 - accuracy: 0.8958 - val_loss: 2.9503 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00790: val_loss did not improve from 1.31546\n",
            "Epoch 791/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3723 - accuracy: 0.8750 - val_loss: 2.9195 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00791: val_loss did not improve from 1.31546\n",
            "Epoch 792/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3244 - accuracy: 0.8958 - val_loss: 2.9180 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00792: val_loss did not improve from 1.31546\n",
            "Epoch 793/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3443 - accuracy: 0.8708 - val_loss: 2.9327 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00793: val_loss did not improve from 1.31546\n",
            "Epoch 794/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3197 - accuracy: 0.8750 - val_loss: 2.9038 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00794: val_loss did not improve from 1.31546\n",
            "Epoch 795/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3383 - accuracy: 0.9083 - val_loss: 2.8480 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00795: val_loss did not improve from 1.31546\n",
            "Epoch 796/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3751 - accuracy: 0.8625 - val_loss: 2.8021 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00796: val_loss did not improve from 1.31546\n",
            "Epoch 797/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3872 - accuracy: 0.8750 - val_loss: 2.8317 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00797: val_loss did not improve from 1.31546\n",
            "Epoch 798/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4099 - accuracy: 0.8667 - val_loss: 2.9131 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00798: val_loss did not improve from 1.31546\n",
            "Epoch 799/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4449 - accuracy: 0.8667 - val_loss: 2.7743 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00799: val_loss did not improve from 1.31546\n",
            "Epoch 800/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3795 - accuracy: 0.8875 - val_loss: 2.7773 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00800: val_loss did not improve from 1.31546\n",
            "Epoch 801/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4174 - accuracy: 0.8833 - val_loss: 2.8016 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00801: val_loss did not improve from 1.31546\n",
            "Epoch 802/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2874 - accuracy: 0.9000 - val_loss: 2.7967 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00802: val_loss did not improve from 1.31546\n",
            "Epoch 803/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2872 - accuracy: 0.9083 - val_loss: 2.8260 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00803: val_loss did not improve from 1.31546\n",
            "Epoch 804/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2483 - accuracy: 0.9125 - val_loss: 2.8533 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00804: val_loss did not improve from 1.31546\n",
            "Epoch 805/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3507 - accuracy: 0.8833 - val_loss: 2.8645 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00805: val_loss did not improve from 1.31546\n",
            "Epoch 806/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.2931 - accuracy: 0.8750 - val_loss: 2.8687 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00806: val_loss did not improve from 1.31546\n",
            "Epoch 807/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3269 - accuracy: 0.8875 - val_loss: 2.8677 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00807: val_loss did not improve from 1.31546\n",
            "Epoch 808/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3636 - accuracy: 0.8583 - val_loss: 2.8337 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00808: val_loss did not improve from 1.31546\n",
            "Epoch 809/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3237 - accuracy: 0.8625 - val_loss: 2.9446 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00809: val_loss did not improve from 1.31546\n",
            "Epoch 810/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3084 - accuracy: 0.9000 - val_loss: 2.9968 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00810: val_loss did not improve from 1.31546\n",
            "Epoch 811/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3297 - accuracy: 0.8708 - val_loss: 3.0154 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00811: val_loss did not improve from 1.31546\n",
            "Epoch 812/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3597 - accuracy: 0.8583 - val_loss: 2.9751 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00812: val_loss did not improve from 1.31546\n",
            "Epoch 813/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3703 - accuracy: 0.8917 - val_loss: 2.8600 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00813: val_loss did not improve from 1.31546\n",
            "Epoch 814/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3038 - accuracy: 0.8958 - val_loss: 2.8508 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00814: val_loss did not improve from 1.31546\n",
            "Epoch 815/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4278 - accuracy: 0.8542 - val_loss: 2.7476 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00815: val_loss did not improve from 1.31546\n",
            "Epoch 816/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3672 - accuracy: 0.8708 - val_loss: 2.7679 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00816: val_loss did not improve from 1.31546\n",
            "Epoch 817/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3452 - accuracy: 0.8625 - val_loss: 2.8437 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00817: val_loss did not improve from 1.31546\n",
            "Epoch 818/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3257 - accuracy: 0.9083 - val_loss: 2.8880 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00818: val_loss did not improve from 1.31546\n",
            "Epoch 819/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.4394 - accuracy: 0.8417 - val_loss: 2.9298 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00819: val_loss did not improve from 1.31546\n",
            "Epoch 820/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4277 - accuracy: 0.8500 - val_loss: 2.9819 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00820: val_loss did not improve from 1.31546\n",
            "Epoch 821/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3133 - accuracy: 0.8917 - val_loss: 2.9618 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00821: val_loss did not improve from 1.31546\n",
            "Epoch 822/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3767 - accuracy: 0.8792 - val_loss: 2.9613 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00822: val_loss did not improve from 1.31546\n",
            "Epoch 823/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3079 - accuracy: 0.8958 - val_loss: 2.9848 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00823: val_loss did not improve from 1.31546\n",
            "Epoch 824/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3474 - accuracy: 0.8708 - val_loss: 3.0540 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00824: val_loss did not improve from 1.31546\n",
            "Epoch 825/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3761 - accuracy: 0.8750 - val_loss: 3.1653 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00825: val_loss did not improve from 1.31546\n",
            "Epoch 826/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4309 - accuracy: 0.8625 - val_loss: 3.1117 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00826: val_loss did not improve from 1.31546\n",
            "Epoch 827/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3421 - accuracy: 0.8917 - val_loss: 3.1181 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00827: val_loss did not improve from 1.31546\n",
            "Epoch 828/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3827 - accuracy: 0.8708 - val_loss: 3.0515 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00828: val_loss did not improve from 1.31546\n",
            "Epoch 829/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3759 - accuracy: 0.8833 - val_loss: 2.9972 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00829: val_loss did not improve from 1.31546\n",
            "Epoch 830/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3393 - accuracy: 0.8625 - val_loss: 2.9357 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00830: val_loss did not improve from 1.31546\n",
            "Epoch 831/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3413 - accuracy: 0.8667 - val_loss: 2.9463 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00831: val_loss did not improve from 1.31546\n",
            "Epoch 832/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3043 - accuracy: 0.8958 - val_loss: 2.9726 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00832: val_loss did not improve from 1.31546\n",
            "Epoch 833/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2925 - accuracy: 0.9083 - val_loss: 3.0243 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00833: val_loss did not improve from 1.31546\n",
            "Epoch 834/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4373 - accuracy: 0.8583 - val_loss: 2.9602 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00834: val_loss did not improve from 1.31546\n",
            "Epoch 835/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3012 - accuracy: 0.8875 - val_loss: 2.8769 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00835: val_loss did not improve from 1.31546\n",
            "Epoch 836/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2812 - accuracy: 0.9167 - val_loss: 2.9220 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00836: val_loss did not improve from 1.31546\n",
            "Epoch 837/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3009 - accuracy: 0.9000 - val_loss: 2.9841 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00837: val_loss did not improve from 1.31546\n",
            "Epoch 838/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3034 - accuracy: 0.9042 - val_loss: 2.9576 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00838: val_loss did not improve from 1.31546\n",
            "Epoch 839/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4419 - accuracy: 0.8750 - val_loss: 2.8178 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00839: val_loss did not improve from 1.31546\n",
            "Epoch 840/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4443 - accuracy: 0.8625 - val_loss: 2.7441 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00840: val_loss did not improve from 1.31546\n",
            "Epoch 841/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3255 - accuracy: 0.8917 - val_loss: 2.7837 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00841: val_loss did not improve from 1.31546\n",
            "Epoch 842/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3967 - accuracy: 0.8583 - val_loss: 2.7278 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00842: val_loss did not improve from 1.31546\n",
            "Epoch 843/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3827 - accuracy: 0.8667 - val_loss: 2.6649 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00843: val_loss did not improve from 1.31546\n",
            "Epoch 844/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4326 - accuracy: 0.8708 - val_loss: 2.6639 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00844: val_loss did not improve from 1.31546\n",
            "Epoch 845/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.2754 - accuracy: 0.9083 - val_loss: 2.7310 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00845: val_loss did not improve from 1.31546\n",
            "Epoch 846/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.2453 - accuracy: 0.9125 - val_loss: 2.7928 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00846: val_loss did not improve from 1.31546\n",
            "Epoch 847/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4237 - accuracy: 0.8500 - val_loss: 2.8106 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00847: val_loss did not improve from 1.31546\n",
            "Epoch 848/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4562 - accuracy: 0.8750 - val_loss: 2.8484 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00848: val_loss did not improve from 1.31546\n",
            "Epoch 849/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4447 - accuracy: 0.8542 - val_loss: 2.8446 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00849: val_loss did not improve from 1.31546\n",
            "Epoch 850/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3319 - accuracy: 0.8917 - val_loss: 2.8523 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00850: val_loss did not improve from 1.31546\n",
            "Epoch 851/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3705 - accuracy: 0.8792 - val_loss: 2.8079 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00851: val_loss did not improve from 1.31546\n",
            "Epoch 852/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2313 - accuracy: 0.9208 - val_loss: 2.8066 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00852: val_loss did not improve from 1.31546\n",
            "Epoch 853/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2975 - accuracy: 0.8833 - val_loss: 2.8284 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00853: val_loss did not improve from 1.31546\n",
            "Epoch 854/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3303 - accuracy: 0.8792 - val_loss: 2.8958 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00854: val_loss did not improve from 1.31546\n",
            "Epoch 855/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4982 - accuracy: 0.8583 - val_loss: 2.9522 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00855: val_loss did not improve from 1.31546\n",
            "Epoch 856/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4399 - accuracy: 0.8500 - val_loss: 2.9364 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00856: val_loss did not improve from 1.31546\n",
            "Epoch 857/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2662 - accuracy: 0.9208 - val_loss: 2.9570 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00857: val_loss did not improve from 1.31546\n",
            "Epoch 858/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3723 - accuracy: 0.8708 - val_loss: 3.1411 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00858: val_loss did not improve from 1.31546\n",
            "Epoch 859/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3351 - accuracy: 0.8792 - val_loss: 3.1939 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00859: val_loss did not improve from 1.31546\n",
            "Epoch 860/1000\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.4767 - accuracy: 0.8458 - val_loss: 3.1780 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00860: val_loss did not improve from 1.31546\n",
            "Epoch 861/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3519 - accuracy: 0.8792 - val_loss: 3.1971 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00861: val_loss did not improve from 1.31546\n",
            "Epoch 862/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3430 - accuracy: 0.8750 - val_loss: 3.2457 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00862: val_loss did not improve from 1.31546\n",
            "Epoch 863/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3375 - accuracy: 0.9125 - val_loss: 3.3280 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00863: val_loss did not improve from 1.31546\n",
            "Epoch 864/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3711 - accuracy: 0.8792 - val_loss: 3.3724 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00864: val_loss did not improve from 1.31546\n",
            "Epoch 865/1000\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.3595 - accuracy: 0.8875 - val_loss: 3.3878 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00865: val_loss did not improve from 1.31546\n",
            "Epoch 866/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4278 - accuracy: 0.8708 - val_loss: 3.3369 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00866: val_loss did not improve from 1.31546\n",
            "Epoch 867/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3450 - accuracy: 0.8792 - val_loss: 3.3193 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00867: val_loss did not improve from 1.31546\n",
            "Epoch 868/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3993 - accuracy: 0.8458 - val_loss: 3.1979 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00868: val_loss did not improve from 1.31546\n",
            "Epoch 869/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2950 - accuracy: 0.8708 - val_loss: 3.1011 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00869: val_loss did not improve from 1.31546\n",
            "Epoch 870/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3422 - accuracy: 0.8792 - val_loss: 2.9458 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00870: val_loss did not improve from 1.31546\n",
            "Epoch 871/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3929 - accuracy: 0.8875 - val_loss: 2.8678 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00871: val_loss did not improve from 1.31546\n",
            "Epoch 872/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3687 - accuracy: 0.8792 - val_loss: 2.7688 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00872: val_loss did not improve from 1.31546\n",
            "Epoch 873/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3782 - accuracy: 0.8667 - val_loss: 2.7012 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00873: val_loss did not improve from 1.31546\n",
            "Epoch 874/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3248 - accuracy: 0.8667 - val_loss: 2.6952 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00874: val_loss did not improve from 1.31546\n",
            "Epoch 875/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2848 - accuracy: 0.9042 - val_loss: 2.7357 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00875: val_loss did not improve from 1.31546\n",
            "Epoch 876/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3763 - accuracy: 0.8708 - val_loss: 2.7225 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00876: val_loss did not improve from 1.31546\n",
            "Epoch 877/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.5515 - accuracy: 0.8542 - val_loss: 2.6503 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00877: val_loss did not improve from 1.31546\n",
            "Epoch 878/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3034 - accuracy: 0.9000 - val_loss: 2.6174 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00878: val_loss did not improve from 1.31546\n",
            "Epoch 879/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3871 - accuracy: 0.8750 - val_loss: 2.6009 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00879: val_loss did not improve from 1.31546\n",
            "Epoch 880/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3872 - accuracy: 0.8792 - val_loss: 2.6250 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00880: val_loss did not improve from 1.31546\n",
            "Epoch 881/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4211 - accuracy: 0.8667 - val_loss: 2.6400 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00881: val_loss did not improve from 1.31546\n",
            "Epoch 882/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3491 - accuracy: 0.8458 - val_loss: 2.7347 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00882: val_loss did not improve from 1.31546\n",
            "Epoch 883/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4141 - accuracy: 0.8792 - val_loss: 2.8454 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00883: val_loss did not improve from 1.31546\n",
            "Epoch 884/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2277 - accuracy: 0.9167 - val_loss: 2.8493 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00884: val_loss did not improve from 1.31546\n",
            "Epoch 885/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3289 - accuracy: 0.8625 - val_loss: 2.8345 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00885: val_loss did not improve from 1.31546\n",
            "Epoch 886/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2819 - accuracy: 0.8875 - val_loss: 2.8391 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00886: val_loss did not improve from 1.31546\n",
            "Epoch 887/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3282 - accuracy: 0.8750 - val_loss: 2.8140 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00887: val_loss did not improve from 1.31546\n",
            "Epoch 888/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3404 - accuracy: 0.8833 - val_loss: 2.8353 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00888: val_loss did not improve from 1.31546\n",
            "Epoch 889/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3125 - accuracy: 0.8958 - val_loss: 2.7844 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00889: val_loss did not improve from 1.31546\n",
            "Epoch 890/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.2535 - accuracy: 0.9083 - val_loss: 2.7175 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00890: val_loss did not improve from 1.31546\n",
            "Epoch 891/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3129 - accuracy: 0.8833 - val_loss: 2.6715 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00891: val_loss did not improve from 1.31546\n",
            "Epoch 892/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3842 - accuracy: 0.8792 - val_loss: 2.6958 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00892: val_loss did not improve from 1.31546\n",
            "Epoch 893/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3460 - accuracy: 0.8917 - val_loss: 2.7008 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00893: val_loss did not improve from 1.31546\n",
            "Epoch 894/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4200 - accuracy: 0.8500 - val_loss: 2.6858 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00894: val_loss did not improve from 1.31546\n",
            "Epoch 895/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3256 - accuracy: 0.8833 - val_loss: 2.7136 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00895: val_loss did not improve from 1.31546\n",
            "Epoch 896/1000\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.4834 - accuracy: 0.8583 - val_loss: 2.6844 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00896: val_loss did not improve from 1.31546\n",
            "Epoch 897/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3844 - accuracy: 0.8750 - val_loss: 2.6592 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00897: val_loss did not improve from 1.31546\n",
            "Epoch 898/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3112 - accuracy: 0.8958 - val_loss: 2.6664 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00898: val_loss did not improve from 1.31546\n",
            "Epoch 899/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3756 - accuracy: 0.8833 - val_loss: 2.6801 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00899: val_loss did not improve from 1.31546\n",
            "Epoch 900/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2779 - accuracy: 0.9083 - val_loss: 2.7768 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00900: val_loss did not improve from 1.31546\n",
            "Epoch 901/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3367 - accuracy: 0.8958 - val_loss: 2.8022 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00901: val_loss did not improve from 1.31546\n",
            "Epoch 902/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2994 - accuracy: 0.8708 - val_loss: 2.7389 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00902: val_loss did not improve from 1.31546\n",
            "Epoch 903/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3045 - accuracy: 0.9000 - val_loss: 2.6866 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00903: val_loss did not improve from 1.31546\n",
            "Epoch 904/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3759 - accuracy: 0.8750 - val_loss: 2.7068 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00904: val_loss did not improve from 1.31546\n",
            "Epoch 905/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3329 - accuracy: 0.8875 - val_loss: 2.7925 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00905: val_loss did not improve from 1.31546\n",
            "Epoch 906/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3511 - accuracy: 0.8958 - val_loss: 2.8807 - val_accuracy: 0.6167\n",
            "\n",
            "Epoch 00906: val_loss did not improve from 1.31546\n",
            "Epoch 907/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3528 - accuracy: 0.8542 - val_loss: 2.9153 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00907: val_loss did not improve from 1.31546\n",
            "Epoch 908/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2559 - accuracy: 0.9292 - val_loss: 2.9678 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00908: val_loss did not improve from 1.31546\n",
            "Epoch 909/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3554 - accuracy: 0.8875 - val_loss: 2.9774 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00909: val_loss did not improve from 1.31546\n",
            "Epoch 910/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.4394 - accuracy: 0.8500 - val_loss: 2.9677 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00910: val_loss did not improve from 1.31546\n",
            "Epoch 911/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2842 - accuracy: 0.8958 - val_loss: 2.9342 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00911: val_loss did not improve from 1.31546\n",
            "Epoch 912/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2986 - accuracy: 0.9042 - val_loss: 2.9506 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00912: val_loss did not improve from 1.31546\n",
            "Epoch 913/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.3530 - accuracy: 0.8708 - val_loss: 2.9588 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00913: val_loss did not improve from 1.31546\n",
            "Epoch 914/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3354 - accuracy: 0.8875 - val_loss: 2.9973 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00914: val_loss did not improve from 1.31546\n",
            "Epoch 915/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2451 - accuracy: 0.9000 - val_loss: 3.0161 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00915: val_loss did not improve from 1.31546\n",
            "Epoch 916/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3347 - accuracy: 0.8792 - val_loss: 2.9193 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00916: val_loss did not improve from 1.31546\n",
            "Epoch 917/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4067 - accuracy: 0.8500 - val_loss: 2.8535 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00917: val_loss did not improve from 1.31546\n",
            "Epoch 918/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2259 - accuracy: 0.9000 - val_loss: 2.8417 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00918: val_loss did not improve from 1.31546\n",
            "Epoch 919/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2040 - accuracy: 0.9292 - val_loss: 2.8673 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00919: val_loss did not improve from 1.31546\n",
            "Epoch 920/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3179 - accuracy: 0.8875 - val_loss: 2.8728 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00920: val_loss did not improve from 1.31546\n",
            "Epoch 921/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2911 - accuracy: 0.8958 - val_loss: 2.8485 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00921: val_loss did not improve from 1.31546\n",
            "Epoch 922/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2659 - accuracy: 0.9042 - val_loss: 2.8700 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00922: val_loss did not improve from 1.31546\n",
            "Epoch 923/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3633 - accuracy: 0.8667 - val_loss: 2.8737 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00923: val_loss did not improve from 1.31546\n",
            "Epoch 924/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3199 - accuracy: 0.9042 - val_loss: 2.8501 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00924: val_loss did not improve from 1.31546\n",
            "Epoch 925/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3508 - accuracy: 0.8833 - val_loss: 2.8877 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00925: val_loss did not improve from 1.31546\n",
            "Epoch 926/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2665 - accuracy: 0.9125 - val_loss: 2.9571 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00926: val_loss did not improve from 1.31546\n",
            "Epoch 927/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.2677 - accuracy: 0.9125 - val_loss: 2.9118 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00927: val_loss did not improve from 1.31546\n",
            "Epoch 928/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3283 - accuracy: 0.8833 - val_loss: 2.9223 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00928: val_loss did not improve from 1.31546\n",
            "Epoch 929/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.2710 - accuracy: 0.9125 - val_loss: 2.9614 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00929: val_loss did not improve from 1.31546\n",
            "Epoch 930/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3574 - accuracy: 0.9000 - val_loss: 2.9767 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00930: val_loss did not improve from 1.31546\n",
            "Epoch 931/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2940 - accuracy: 0.9042 - val_loss: 2.9732 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00931: val_loss did not improve from 1.31546\n",
            "Epoch 932/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3266 - accuracy: 0.8833 - val_loss: 2.9458 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00932: val_loss did not improve from 1.31546\n",
            "Epoch 933/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3485 - accuracy: 0.9000 - val_loss: 3.0183 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00933: val_loss did not improve from 1.31546\n",
            "Epoch 934/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2585 - accuracy: 0.9000 - val_loss: 3.0968 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00934: val_loss did not improve from 1.31546\n",
            "Epoch 935/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4853 - accuracy: 0.8375 - val_loss: 3.1004 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00935: val_loss did not improve from 1.31546\n",
            "Epoch 936/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.4864 - accuracy: 0.8458 - val_loss: 3.1048 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00936: val_loss did not improve from 1.31546\n",
            "Epoch 937/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2667 - accuracy: 0.9125 - val_loss: 3.2005 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00937: val_loss did not improve from 1.31546\n",
            "Epoch 938/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3712 - accuracy: 0.8875 - val_loss: 3.2255 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00938: val_loss did not improve from 1.31546\n",
            "Epoch 939/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4573 - accuracy: 0.8333 - val_loss: 3.1457 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00939: val_loss did not improve from 1.31546\n",
            "Epoch 940/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3935 - accuracy: 0.8833 - val_loss: 3.1650 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00940: val_loss did not improve from 1.31546\n",
            "Epoch 941/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2878 - accuracy: 0.8833 - val_loss: 3.1945 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00941: val_loss did not improve from 1.31546\n",
            "Epoch 942/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3362 - accuracy: 0.8875 - val_loss: 3.2429 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00942: val_loss did not improve from 1.31546\n",
            "Epoch 943/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3154 - accuracy: 0.8958 - val_loss: 3.2418 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00943: val_loss did not improve from 1.31546\n",
            "Epoch 944/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3003 - accuracy: 0.8833 - val_loss: 3.1564 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00944: val_loss did not improve from 1.31546\n",
            "Epoch 945/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3853 - accuracy: 0.8750 - val_loss: 3.1500 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00945: val_loss did not improve from 1.31546\n",
            "Epoch 946/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3222 - accuracy: 0.8833 - val_loss: 3.0015 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00946: val_loss did not improve from 1.31546\n",
            "Epoch 947/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2920 - accuracy: 0.9000 - val_loss: 3.0155 - val_accuracy: 0.7000\n",
            "\n",
            "Epoch 00947: val_loss did not improve from 1.31546\n",
            "Epoch 948/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3382 - accuracy: 0.8750 - val_loss: 3.1751 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00948: val_loss did not improve from 1.31546\n",
            "Epoch 949/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3241 - accuracy: 0.8875 - val_loss: 3.2497 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00949: val_loss did not improve from 1.31546\n",
            "Epoch 950/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2925 - accuracy: 0.9000 - val_loss: 3.2523 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00950: val_loss did not improve from 1.31546\n",
            "Epoch 951/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2707 - accuracy: 0.8917 - val_loss: 3.2785 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00951: val_loss did not improve from 1.31546\n",
            "Epoch 952/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2917 - accuracy: 0.8917 - val_loss: 3.2970 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00952: val_loss did not improve from 1.31546\n",
            "Epoch 953/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2486 - accuracy: 0.9042 - val_loss: 3.2899 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00953: val_loss did not improve from 1.31546\n",
            "Epoch 954/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3623 - accuracy: 0.8667 - val_loss: 3.3151 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00954: val_loss did not improve from 1.31546\n",
            "Epoch 955/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3568 - accuracy: 0.8792 - val_loss: 3.3503 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00955: val_loss did not improve from 1.31546\n",
            "Epoch 956/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.2486 - accuracy: 0.9167 - val_loss: 3.4096 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00956: val_loss did not improve from 1.31546\n",
            "Epoch 957/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3674 - accuracy: 0.8958 - val_loss: 3.5089 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00957: val_loss did not improve from 1.31546\n",
            "Epoch 958/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.4023 - accuracy: 0.8917 - val_loss: 3.4894 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00958: val_loss did not improve from 1.31546\n",
            "Epoch 959/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4291 - accuracy: 0.8667 - val_loss: 3.3789 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00959: val_loss did not improve from 1.31546\n",
            "Epoch 960/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2446 - accuracy: 0.9292 - val_loss: 3.3980 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00960: val_loss did not improve from 1.31546\n",
            "Epoch 961/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3762 - accuracy: 0.8792 - val_loss: 3.3650 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00961: val_loss did not improve from 1.31546\n",
            "Epoch 962/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2890 - accuracy: 0.9125 - val_loss: 3.3155 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00962: val_loss did not improve from 1.31546\n",
            "Epoch 963/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2678 - accuracy: 0.9000 - val_loss: 3.2398 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00963: val_loss did not improve from 1.31546\n",
            "Epoch 964/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3160 - accuracy: 0.9000 - val_loss: 3.2371 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00964: val_loss did not improve from 1.31546\n",
            "Epoch 965/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2658 - accuracy: 0.9042 - val_loss: 3.2469 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00965: val_loss did not improve from 1.31546\n",
            "Epoch 966/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3273 - accuracy: 0.8792 - val_loss: 3.2564 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00966: val_loss did not improve from 1.31546\n",
            "Epoch 967/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2857 - accuracy: 0.9125 - val_loss: 3.2533 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00967: val_loss did not improve from 1.31546\n",
            "Epoch 968/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3311 - accuracy: 0.8917 - val_loss: 3.3014 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00968: val_loss did not improve from 1.31546\n",
            "Epoch 969/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4749 - accuracy: 0.8458 - val_loss: 3.2307 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00969: val_loss did not improve from 1.31546\n",
            "Epoch 970/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3598 - accuracy: 0.8708 - val_loss: 3.1802 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00970: val_loss did not improve from 1.31546\n",
            "Epoch 971/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2702 - accuracy: 0.9083 - val_loss: 3.2210 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00971: val_loss did not improve from 1.31546\n",
            "Epoch 972/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3524 - accuracy: 0.9000 - val_loss: 3.1708 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00972: val_loss did not improve from 1.31546\n",
            "Epoch 973/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4142 - accuracy: 0.8708 - val_loss: 3.1086 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00973: val_loss did not improve from 1.31546\n",
            "Epoch 974/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2242 - accuracy: 0.9250 - val_loss: 3.1111 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00974: val_loss did not improve from 1.31546\n",
            "Epoch 975/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4078 - accuracy: 0.8750 - val_loss: 3.1275 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00975: val_loss did not improve from 1.31546\n",
            "Epoch 976/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.2883 - accuracy: 0.9125 - val_loss: 3.1489 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00976: val_loss did not improve from 1.31546\n",
            "Epoch 977/1000\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 0.4177 - accuracy: 0.8875 - val_loss: 3.0492 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00977: val_loss did not improve from 1.31546\n",
            "Epoch 978/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3834 - accuracy: 0.8625 - val_loss: 2.9977 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00978: val_loss did not improve from 1.31546\n",
            "Epoch 979/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3520 - accuracy: 0.8583 - val_loss: 3.0078 - val_accuracy: 0.7167\n",
            "\n",
            "Epoch 00979: val_loss did not improve from 1.31546\n",
            "Epoch 980/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3460 - accuracy: 0.8583 - val_loss: 3.0279 - val_accuracy: 0.6833\n",
            "\n",
            "Epoch 00980: val_loss did not improve from 1.31546\n",
            "Epoch 981/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2980 - accuracy: 0.8708 - val_loss: 3.0575 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00981: val_loss did not improve from 1.31546\n",
            "Epoch 982/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.4226 - accuracy: 0.8667 - val_loss: 3.0462 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00982: val_loss did not improve from 1.31546\n",
            "Epoch 983/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3062 - accuracy: 0.8792 - val_loss: 3.0852 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00983: val_loss did not improve from 1.31546\n",
            "Epoch 984/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3734 - accuracy: 0.9042 - val_loss: 3.1568 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00984: val_loss did not improve from 1.31546\n",
            "Epoch 985/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3130 - accuracy: 0.8792 - val_loss: 3.1795 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00985: val_loss did not improve from 1.31546\n",
            "Epoch 986/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3470 - accuracy: 0.9000 - val_loss: 3.0637 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00986: val_loss did not improve from 1.31546\n",
            "Epoch 987/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2769 - accuracy: 0.8917 - val_loss: 3.0478 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00987: val_loss did not improve from 1.31546\n",
            "Epoch 988/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3297 - accuracy: 0.8875 - val_loss: 3.0461 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00988: val_loss did not improve from 1.31546\n",
            "Epoch 989/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3054 - accuracy: 0.9000 - val_loss: 3.1154 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00989: val_loss did not improve from 1.31546\n",
            "Epoch 990/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3096 - accuracy: 0.8833 - val_loss: 3.1440 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00990: val_loss did not improve from 1.31546\n",
            "Epoch 991/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3123 - accuracy: 0.9000 - val_loss: 3.1288 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00991: val_loss did not improve from 1.31546\n",
            "Epoch 992/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3694 - accuracy: 0.8792 - val_loss: 3.1413 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00992: val_loss did not improve from 1.31546\n",
            "Epoch 993/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.2585 - accuracy: 0.9125 - val_loss: 3.1959 - val_accuracy: 0.6333\n",
            "\n",
            "Epoch 00993: val_loss did not improve from 1.31546\n",
            "Epoch 994/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.3739 - accuracy: 0.8708 - val_loss: 3.2135 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00994: val_loss did not improve from 1.31546\n",
            "Epoch 995/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.4581 - accuracy: 0.8792 - val_loss: 3.0785 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 00995: val_loss did not improve from 1.31546\n",
            "Epoch 996/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2708 - accuracy: 0.8875 - val_loss: 2.9866 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00996: val_loss did not improve from 1.31546\n",
            "Epoch 997/1000\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 0.3294 - accuracy: 0.8958 - val_loss: 3.0031 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00997: val_loss did not improve from 1.31546\n",
            "Epoch 998/1000\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 0.3028 - accuracy: 0.8958 - val_loss: 3.0595 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00998: val_loss did not improve from 1.31546\n",
            "Epoch 999/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2929 - accuracy: 0.8875 - val_loss: 3.0630 - val_accuracy: 0.6667\n",
            "\n",
            "Epoch 00999: val_loss did not improve from 1.31546\n",
            "Epoch 1000/1000\n",
            "8/8 [==============================] - 0s 12ms/step - loss: 0.2864 - accuracy: 0.9000 - val_loss: 3.0878 - val_accuracy: 0.6500\n",
            "\n",
            "Epoch 01000: val_loss did not improve from 1.31546\n",
            "Training completed in time:  0:01:29.497372\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DmcAlct1XBs3"
      },
      "source": [
        "Predicting for the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1LzpD1vW_86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77ba7877-6b84-4bd4-9035-0b7a037afbee"
      },
      "source": [
        "test_accuracy=model.evaluate(X_test,y_test,verbose=0)\n",
        "print(test_accuracy[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6499999761581421\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQfA8fAtXNfX"
      },
      "source": [
        "Predicting for the single audio file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loTlCsCJXRQL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dfa7e30-8d45-417a-dd2d-279e5353fdf2"
      },
      "source": [
        "filename=\"/content/Audio-Classification-master/wavfiles/Acoustic_guitar/160c92d7.wav\"\n",
        "prediction_feature=features_extractor(filename)\n",
        "prediction_feature=prediction_feature.reshape(1,-1)\n",
        "np.argmax(model.predict(prediction_feature), axis=-1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "9t9PtrJ25rYe",
        "outputId": "31eb046c-1211-4737-bc7f-bc0332749353"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-13ef70498eb6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'array' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fNVzPvbRCF9a"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}