Learn_Attractiveness.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Learn Attractiveness.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-0GWFbiBr1D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae40654b-afec-44b2-d24e-283f2f9cbf23"
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "import tensorflow as tf"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8SbuGTtEunA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9ff101d-f08d-4d43-ebd5-84bfb15d015e"
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.applications import ResNet50\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers import Convolution2D, MaxPooling2D\n",
        "from keras.utils import np_utils\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.optimizers import SGD, Adam\n",
        "import matplotlib.pyplot as plt\n",
        "import glob, os\n",
        "import PIL.Image\n",
        "\n",
        "import csv\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqawOWZp_Kce"
      },
      "source": [
        "def plot_loss(history):\n",
        "  plt.plot(history.history['loss'], label='loss')\n",
        "  plt.plot(history.history['val_loss'], label='val_loss')\n",
        "  plt.ylim([0, 2])\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Error')\n",
        "  plt.legend()\n",
        "  plt.grid(True)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSgWDv1JB4YL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49c70bce-e0bf-43a2-dfab-b8e5fc9c0297"
      },
      "source": [
        "import csv\n",
        "\n",
        "images = []\n",
        "elos = []\n",
        "\n",
        "with open('/content/drive/My Drive/Generated Images/image.csv', 'r') as file:\n",
        "    reader = csv.reader(file)\n",
        "    next(reader)\n",
        "    for row in reader:\n",
        "        if row[2] == \"f\":\n",
        "          images.append(row[0])\n",
        "          elos.append(row[1])\n",
        "\n",
        "print(images)\n",
        "print(elos)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['p-1595534853.630988', 'p-1595534961.358644', 'p-1595532164.179338', 'p-1595535196.387671', 'p-1595534323.046717', 'p-1595534437.098345', 'p-1595532069.337625', 'p-1595532028.881686', 'p-1595534825.743948', 'p-1595532902.150686', 'p-1595532973.821134', 'p-1595533584.038633', 'p-1595534749.604625', 'p-1595533757.364826', 'p-1595534556.357363', 'p-1595533405.496014', 'p-1595535170.844591', 'p-1595532812.420884', 'p-1595534183.092287', 'p-1595532318.824006', 'p-1595535058.651056', 'p-1595533904.811982', 'p-1595534807.612129', 'p-1595533883.742407', 'p-1595534946.685618', 'p-1595533589.479844', 'p-1595532469.216357', 'p-1595532390.669085', 'p-1595533640.19628', 'p-1595532341.310596', 'p-1595532226.06622', 'p-1595532173.296463', 'p-1595533999.738313', 'p-1595535138.896913', 'p-1595535311.33148', 'p-1595533509.235699', 'p-1595534060.153684', 'p-1595533565.297115', 'p-1595534291.848506', 'p-1595532447.953139', 'p-1595534674.737492', 'p-1595532697.382799', 'p-1595532590.053157', 'p-1595534707.811787', 'p-1595533147.046232', 'p-1595534380.766159', 'p-1595534264.698589', 'p-1595533530.004471', 'p-1595533101.580117', 'p-1595534254.004144', 'p-1595532638.602817', 'p-1595534708.693201', 'p-1595534329.155763', 'p-1595532241.46315', 'p-1595533356.733875', 'p-1595533434.200661', 'p-1595532644.039591', 'p-1595532727.335176', 'p-1595533265.29361', 'p-1595535235.209692', 'p-1595534169.58469', 'p-1595535229.1214', 'p-1595532319.7043', 'p-1595534970.193924', 'p-1595533076.161677', 'p-1595535015.071008', 'p-1595532708.194539', 'p-1595533117.100722', 'p-1595535121.557754', 'p-1595534590.809679', 'p-1595535293.09968', 'p-1595533410.012429', 'p-1595532709.961235', 'p-1595533821.69404', 'p-1595533072.61813', 'p-1595534873.144219', 'p-1595533190.059251', 'p-1595532142.421085', 'p-1595532266.016483', 'p-1595533890.883521', 'p-1595534507.28211', 'p-1595532031.674628', 'p-1595534087.850875', 'p-1595534670.178731', 'p-1595532980.044623', 'p-1595532756.245338', 'p-1595532135.289056', 'p-1595532030.704946', 'p-1595533374.067466', 'p-1595533174.747973', 'p-1595533580.509933', 'p-1595533279.194631', 'p-1595532859.770212', 'p-1595532847.293088', 'p-1595533346.922396', 'p-1595534316.799826', 'p-1595534740.50982', 'p-1595533175.768241', 'p-1595533034.715737', 'p-1595535272.527576', 'p-1595532100.084224', 'p-1595533296.596376', 'p-1595535082.446008', 'p-1595534114.428084', 'p-1595534368.824425', 'p-1595535134.413309', 'p-1595532593.153055', 'p-1595533444.072', 'p-1595532941.270862', 'p-1595534227.239491', 'p-1595533172.058022', 'p-1595533861.132279', 'p-1595532901.277018', 'p-1595533474.509917', 'p-1595534596.081702', 'p-1595533121.656234', 'p-1595534769.809364', 'p-1595534914.874575', 'p-1595535291.165452', 'p-1595534234.713811', 'p-1595533424.961525', 'p-1595533172.96774', 'p-1595532246.044662', 'p-1595535327.877116', 'p-1595535353.755335', 'p-1595534090.666584', 'p-1595532616.975879', 'p-1595533002.804249', 'p-1595532569.308781', 'p-1595532919.560656', 'p-1595533694.399935', 'p-1595534552.815727', 'p-1595533962.345304', 'p-1595533609.900944', 'p-1595535087.30482', 'p-1595535328.982335', 'p-1595534069.105579', 'p-1595533441.295002', 'p-1595533749.936052', 'p-1595533493.863107', 'p-1595532730.045187', 'p-1595534045.780652', 'p-1595533618.390341', 'p-1595532284.953782', 'p-1595533016.981107', 'p-1595532404.475846', 'p-1595533533.76204', 'p-1595532924.922843', 'p-1595532410.825538', 'p-1595534868.756403', 'p-1595533395.637418', 'p-1595533650.860336', 'p-1595532271.342382', 'p-1595534306.274268', 'p-1595534378.951603', 'p-1595533664.944164', 'p-1595534801.064022', 'p-1595534966.634364', 'p-1595534673.837617', 'p-1595533649.9765', 'p-1595535026.872172', 'p-1595532862.546146', 'p-1595533654.557286', 'p-1595534852.736916', 'p-1595535276.991433', 'p-1595534211.203193', 'p-1595533626.470199', 'p-1595532645.794803', 'p-1595532957.696619', 'p-1595532819.697722', 'p-1595532789.292877', 'p-1595534302.578955', 'p-1595532752.759895', 'p-1595534417.107421', 'p-1595533772.715972', 'p-1595532365.014991', 'p-1595535312.249894', 'p-1595532124.416195', 'p-1595532682.663172', 'p-1595534575.704145', 'p-1595534547.333148', 'p-1595532990.899518', 'p-1595534317.646479', 'p-1595533554.076631', 'p-1595533112.566244', 'p-1595534116.219844', 'p-1595532094.701805', 'p-1595533018.030252', 'p-1595534666.578128', 'p-1595533781.117104', 'p-1595533532.73632', 'p-1595534578.287802', 'p-1595532724.440276', 'p-1595534224.83877', 'p-1595534510.937035', 'p-1595533337.808834', 'p-1595535043.21575', 'p-1595535060.546396', 'p-1595532235.07357', 'p-1595532550.373962', 'p-1595532709.05288', 'p-1595532215.417838', 'p-1595534242.134436', 'p-1595535128.906304', 'p-1595533606.24987', 'p-1595534737.907918', 'p-1595532592.067601', 'p-1595532276.758962', 'p-1595533699.713022', 'p-1595535237.941924', 'p-1595534177.823832', 'p-1595534462.950451', 'p-1595533763.692784', 'p-1595533668.65565', 'p-1595533219.337033', 'p-1595534358.031831', 'p-1595533368.651402', 'p-1595533291.07957', 'p-1595534663.956983', 'p-1595532959.463883', 'p-1595532866.214796', 'p-1595532409.9045', 'p-1595532745.43592', 'p-1595534971.05747', 'p-1595532882.274824', 'p-1595535355.525141', 'p-1595534434.508898', 'p-1595534244.77415', 'p-1595532468.279874', 'p-1595534905.477653', 'p-1595533751.001159', 'p-1595534746.899145', 'p-1595534753.45013', 'p-1595533995.082618', 'p-1595534275.577037', 'p-1595532231.594866', 'p-1595534644.835898', 'p-1595535024.180896', 'p-1595533004.721793', 'p-1595533545.70707', 'p-1595534848.282683', 'p-1595534531.633996', 'p-1595533787.42662', 'p-1595532052.556607', 'p-1595535052.273762', 'p-1595533517.438988', 'p-1595533592.912257', 'p-1595533118.955472', 'p-1595533075.286461', 'p-1595532890.456318', 'p-1595534369.930403', 'p-1595534091.577876', 'p-1595533572.536358', 'p-1595535120.613179', 'p-1595534386.457976', 'p-1595534831.357649', 'p-1595534820.300188', 'p-1595534048.406723', 'p-1595533608.036376', 'p-1595533881.024521', 'p-1595532676.109654', 'p-1595533295.723563', 'p-1595534612.519005', 'p-1595535077.019598', 'p-1595535171.739431', 'p-1595534771.618249', 'p-1595531992.523422', 'p-1595533422.455198', 'p-1595534109.023236', 'p-1595534867.915343', 'p-1595533268.959739', 'p-1595533466.239614', 'p-1595533678.626648', 'p-1595534010.691101', 'p-1595532657.932267', 'p-1595532047.12133', 'p-1595532237.038221', 'p-1595534551.005596', 'p-1595532087.594665', 'p-1595532029.831129', 'p-1595533130.354012', 'p-1595532578.177463', 'p-1595532363.231963', 'p-1595534560.059141', 'p-1595534713.210625', 'p-1595534195.69608', 'p-1595532238.8262', 'p-1595532994.564844', 'p-1595534693.95507', 'p-1595534338.278389', 'p-1595535143.541529', 'p-1595533048.542232', 'p-1595534192.120291', 'p-1595534647.506247', 'p-1595534803.904091', 'p-1595534209.288434', 'p-1595532166.008406', 'p-1595534500.698811', 'p-1595532537.48069', 'p-1595534474.396921', 'p-1595534695.70315', 'p-1595533180.13244', 'p-1595532175.183128', 'p-1595535282.716391', 'p-1595532481.915916', 'p-1595535025.95182', 'p-1595533914.419713', 'p-1595534015.301957', 'p-1595534140.417066', 'p-1595535319.680476', 'p-1595532018.685334', 'p-1595534980.365681', 'p-1595532338.416624', 'p-1595534059.252941', 'p-1595534773.44635', 'p-1595534944.846873', 'p-1595535352.816732', 'p-1595533631.041593', 'p-1595535027.808798', 'p-1595534715.981119', 'p-1595535141.748874', 'p-1595532775.336926', 'p-1595532845.497933', 'p-1595535315.964559', 'p-1595535183.679171', 'p-1595534304.410109', 'p-1595533630.017822', 'p-1595535065.829854', 'p-1595534287.41484', 'p-1595533702.553396', 'p-1595534358.946187', 'p-1595532547.765486', 'p-1595532953.167965', 'p-1595533661.231538', 'p-1595535263.714412', 'p-1595532940.389266', 'p-1595534792.08001', 'p-1595532629.443189', 'p-1595533614.689802', 'p-1595532583.664907', 'p-1595534845.543573', 'p-1595535253.646829', 'p-1595532808.579795', 'p-1595533516.640194', 'p-1595534810.364917', 'p-1595534107.213233', 'p-1595534762.446623', 'p-1595534220.267123', 'p-1595533573.425154', 'p-1595534217.478732', 'p-1595532002.700764', 'p-1595535365.736292', 'p-1595533273.730193', 'p-1595535209.126942', 'p-1595533714.411744', 'p-1595532987.19019', 'p-1595532502.462761', 'p-1595532949.466188', 'p-1595535178.031521', 'p-1595534134.367258', 'p-1595532993.6922', 'p-1595532764.624374', 'p-1595534072.773466', 'p-1595534554.558042', 'p-1595535090.814633', 'p-1595533386.566168', 'p-1595532063.746649', 'p-1595534457.528005', 'p-1595534994.882413', 'p-1595535337.151101', 'p-1595534168.551851', 'p-1595532655.110892', 'p-1595534890.283372', 'p-1595533560.60563', 'p-1595534384.681151', 'p-1595535078.793875', 'p-1595533284.685573', 'p-1595532344.92019', 'p-1595533351.549688', 'p-1595532855.276916', 'p-1595533723.532007', 'p-1595534616.143586', 'p-1595532181.719937', 'p-1595532667.097476', 'p-1595531984.435953', 'p-1595531989.965027', 'p-1595534047.558211', 'p-1595532405.353511', 'p-1595533925.933969', 'p-1595534598.821813', 'p-1595533969.679951', 'p-1595533697.931446', 'p-1595534099.833353', 'p-1595534375.20644', 'p-1595532843.745366', 'p-1595533115.249237', 'p-1595533753.729065', 'p-1595533436.899799', 'p-1595534544.454861', 'p-1595532020.466036', 'p-1595533457.578655', 'p-1595535201.913737', 'p-1595535047.717312', 'p-1595534170.537611', 'p-1595535203.721893', 'p-1595532122.633599', 'p-1595532685.470173', 'p-1595535059.631662', 'p-1595532393.422526', 'p-1595533235.069049', 'p-1595532777.357523', 'p-1595532747.259134', 'p-1595532582.815752', 'p-1595532930.386432', 'p-1595533672.230008', 'p-1595532232.5087', 'p-1595534320.331424', 'p-1595533419.876387', 'p-1595534469.696794', 'p-1595535279.076297', 'p-1595532968.538195', 'p-1595533388.487779', 'p-1595534915.754669', 'p-1595535308.648924', 'p-1595532303.690149', 'p-1595534606.068315', 'p-1595534102.504698', 'p-1595532950.365636', 'p-1595534190.334572', 'p-1595534767.957855', 'p-1595534498.888682', 'p-1595534977.524716', 'p-1595532770.027467', 'p-1595532952.109421', 'p-1595532326.290346', 'p-1595533788.318445', 'p-1595533537.476692', 'p-1595535148.780634', 'p-1595532514.58289', 'p-1595535132.628898', 'p-1595534210.193062', 'p-1595533575.050008', 'p-1595535334.340042', 'p-1595533068.157662', 'p-1595532473.845028', 'p-1595533597.90977', 'p-1595533745.483354', 'p-1595534860.352129', 'p-1595533198.850272', 'p-1595532512.593688', 'p-1595534355.485605', 'p-1595532299.232607', 'p-1595532333.774915', 'p-1595534143.149375', 'p-1595533812.468143', 'p-1595532598.558642', 'p-1595533460.182668', 'p-1595532239.704327', 'p-1595531995.243459', 'p-1595533249.935855', 'p-1595535364.860147', 'p-1595534978.429698', 'p-1595534967.587272', 'p-1595532886.847877', 'p-1595533617.449428', 'p-1595534729.651191', 'p-1595533098.732704', 'p-1595534538.019699', 'p-1595532004.384516', 'p-1595533320.51926', 'p-1595532037.1107', 'p-1595534419.808357', 'p-1595532431.620856', 'p-1595534165.044199', 'p-1595532459.478773', 'p-1595532001.794302', 'p-1595533078.011345', 'p-1595533975.907716', 'p-1595534185.755709', 'p-1595533755.502733', 'p-1595532339.514214', 'p-1595532267.737879', 'p-1595532690.018197', 'p-1595534840.960207', 'p-1595533827.782595', 'p-1595534785.387291', 'p-1595534051.983813', 'p-1595533501.972237', 'p-1595532497.112098', 'p-1595535035.991883', 'p-1595533498.368375', 'p-1595535207.338954', 'p-1595534388.196881', 'p-1595534812.221853', 'p-1595532558.459995', 'p-1595535046.85349', 'p-1595533523.672618', 'p-1595534362.602666', 'p-1595532421.569397', 'p-1595532501.596191', 'p-1595534395.108613', 'p-1595533709.891483', 'p-1595533931.241485', 'p-1595532315.457187', 'p-1595534171.433303', 'p-1595534833.114898', 'p-1595533431.508902', 'p-1595535205.409609', 'p-1595534396.08837', 'p-1595534514.518167', 'p-1595533693.302596', 'p-1595533032.017626', 'p-1595535190.803388', 'p-1595535097.121483', 'p-1595533030.160024', 'p-1595533001.892329', 'p-1595533944.738333', 'p-1595533951.665046', 'p-1595534561.820853', 'p-1595532534.723511', 'p-1595533775.486119', 'p-1595533567.961005', 'p-1595532437.947667', 'p-1595532345.85692', 'p-1595534898.301329', 'p-1595533685.764646', 'p-1595533890.023483', 'p-1595533756.435015', 'p-1595532904.903836', 'p-1595534689.513154', 'p-1595533214.516888', 'p-1595532450.548102', 'p-1595533583.190166', 'p-1595533450.422798', 'p-1595534955.84691', 'p-1595534601.665813', 'p-1595533319.641101', 'p-1595532597.664969', 'p-1595533137.775536', 'p-1595533688.702185', 'p-1595532261.732692', 'p-1595533571.659933', 'p-1595535204.595834', 'p-1595534555.446475', 'p-1595534931.314687', 'p-1595533770.949153', 'p-1595534702.251465', 'p-1595533874.649789', 'p-1595532979.149303', 'p-1595534274.650843', 'p-1595535030.631061', 'p-1595532555.672861', 'p-1595535236.147772', 'p-1595532741.905833', 'p-1595532970.209473', 'p-1595534008.843948', 'p-1595535160.750797', 'p-1595533404.591756', 'p-1595534281.101726', 'p-1595533154.285951', 'p-1595533455.646422', 'p-1595533557.896067', 'p-1595535301.161926', 'p-1595532009.262666', 'p-1595532810.444768', 'p-1595532662.418129', 'p-1595535238.876185', 'p-1595533111.690306', 'p-1595532352.082306', 'p-1595532965.573366', 'p-1595534479.770045', 'p-1595532627.612528', 'p-1595532161.396905', 'p-1595535070.44821', 'p-1595533269.859726', 'p-1595532912.19419', 'p-1595534233.800603', 'p-1595533185.545861', 'p-1595534360.746067', 'p-1595533232.391287', 'p-1595535270.724945', 'p-1595532237.978313', 'p-1595532954.949096', 'p-1595532457.555001', 'p-1595533547.55145', 'p-1595533692.410709', 'p-1595534268.338638', 'p-1595532401.579835', 'p-1595534313.270918', 'p-1595535006.78766', 'p-1595533438.695712', 'p-1595534791.133959', 'p-1595532302.788632', 'p-1595534062.065191', 'p-1595534818.532391', 'p-1595534752.443197', 'p-1595532309.886', 'p-1595535295.811241', 'p-1595535199.081116', 'p-1595532259.862449', 'p-1595534526.32719', 'p-1595532392.441524', 'p-1595534991.180069', 'p-1595534760.621277', 'p-1595535262.003664', 'p-1595532255.2453', 'p-1595534261.09391', 'p-1595534823.082904', 'p-1595532740.009183', 'p-1595534117.114778', 'p-1595532954.109866', 'p-1595534212.098586', 'p-1595532076.553408', 'p-1595533054.023655', 'p-1595532778.27615', 'p-1595534110.76043', 'p-1595533601.584058', 'p-1595532746.305828', 'p-1595533849.224137', 'p-1595534761.543729', 'p-1595533006.444239', 'p-1595532201.060971', 'p-1595532474.793396', 'p-1595534229.07202', 'p-1595535004.155827', 'p-1595532206.312431', 'p-1595532791.204696', 'p-1595535280.008103', 'p-1595534661.984055', 'p-1595533759.291951', 'p-1595531999.988419', 'p-1595534841.843412', 'p-1595533850.1373', 'p-1595533741.936608', 'p-1595534880.332487', 'p-1595534789.323378', 'p-1595533680.426851', 'p-1595535066.707211', 'p-1595532779.167768', 'p-1595532683.575594', 'p-1595533676.749947', 'p-1595532358.513103', 'p-1595532379.653865', 'p-1595532614.237293', 'p-1595535344.527399', 'p-1595533895.34903', 'p-1595533538.362605', 'p-1595534404.47197', 'p-1595535009.460623', 'p-1595533795.429878', 'p-1595534824.837529', 'p-1595534681.18699', 'p-1595532599.49993', 'p-1595533521.050622', 'p-1595535251.858029', 'p-1595532408.078883', 'p-1595534213.872364', 'p-1595533106.00313', 'p-1595533726.261819', 'p-1595533024.94', 'p-1595532455.833245', 'p-1595534245.827669', 'p-1595535290.217878', 'p-1595532376.082439', 'p-1595533623.638302', 'p-1595535025.050284', 'p-1595532668.023213', 'p-1595533621.023944', 'p-1595532294.689097', 'p-1595534985.650839', 'p-1595534486.843984', 'p-1595534517.303493', 'p-1595534837.528032', 'p-1595532079.29565', 'p-1595533285.603583', 'p-1595532420.665772', 'p-1595533032.940668', 'p-1595535168.115741', 'p-1595533660.311435', 'p-1595534683.05464', 'p-1595533401.024934', 'p-1595535293.985372', 'p-1595535068.490473', 'p-1595533567.099994', 'p-1595533982.318168', 'p-1595535095.413537', 'p-1595533408.161507', 'p-1595533884.625437', 'p-1595532158.669336', 'p-1595532980.890427', 'p-1595532575.599951', 'p-1595532285.827992', 'p-1595533491.007498', 'p-1595535001.388372', 'p-1595532025.167713', 'p-1595533770.030982', 'p-1595534999.433257', 'p-1595535340.658416', 'p-1595532308.188587', 'p-1595532603.11126', 'p-1595534372.593471', 'p-1595533256.128095', 'p-1595533984.162796', 'p-1595532203.644488', 'p-1595533435.99503', 'p-1595532757.182483', 'p-1595534528.095854', 'p-1595535118.681243', 'p-1595534903.649274', 'p-1595534582.81899', 'p-1595533299.340437', 'p-1595533041.32588', 'p-1595534381.637084', 'p-1595533270.752266', 'p-1595533899.228648', 'p-1595534949.422843', 'p-1595533622.798208', 'p-1595533332.339237', 'p-1595533237.778615', 'p-1595533307.869315', 'p-1595533766.380394', 'p-1595533741.0088', 'p-1595532625.857851', 'p-1595533641.986848', 'p-1595532417.15006', 'p-1595535018.542629', 'p-1595534279.266627', 'p-1595533376.725362', 'p-1595533942.895523', 'p-1595532818.615641', 'p-1595533390.240895', 'p-1595533059.442698', 'p-1595533804.461129', 'p-1595532881.237137', 'p-1595532370.529636', 'p-1595533143.375949', 'p-1595533900.095116', 'p-1595533216.293017', 'p-1595532519.079397', 'p-1595531982.561587', 'p-1595533864.759516', 'p-1595532342.194199', 'p-1595532793.953753', 'p-1595533230.547271', 'p-1595533746.372188', 'p-1595533907.527481', 'p-1595533148.028385', 'p-1595534082.391969', 'p-1595534089.726491', 'p-1595532572.088431', 'p-1595532563.776387', 'p-1595534628.806037', 'p-1595533482.818689', 'p-1595532357.613443', 'p-1595533396.574577', 'p-1595534682.110648', 'p-1595533220.260838', 'p-1595533957.833386', 'p-1595532877.957155', 'p-1595535109.834062', 'p-1595532689.1322', 'p-1595535176.263137', 'p-1595534830.492233', 'p-1595534421.614025', 'p-1595534148.411041', 'p-1595534115.35203', 'p-1595534679.366266', 'p-1595533508.270373', 'p-1595532895.928188', 'p-1595532639.521686', 'p-1595535347.291535', 'p-1595534239.494974', 'p-1595532240.613322', 'p-1595533684.895511', 'p-1595532359.497149', 'p-1595532887.746446', 'p-1595532284.026944', 'p-1595533009.907279', 'p-1595532216.304349', 'p-1595533811.611222', 'p-1595532742.749479', 'p-1595533619.329601', 'p-1595532093.814866', 'p-1595533875.57607', 'p-1595533374.922949', 'p-1595534412.57365', 'p-1595532611.280906', 'p-1595533923.238525', 'p-1595534251.164025', 'p-1595532642.077787', 'p-1595532268.593811', 'p-1595535122.521694', 'p-1595534652.888745', 'p-1595533418.076295', 'p-1595535265.480261', 'p-1595534172.322827', 'p-1595533754.626796', 'p-1595533415.410623', 'p-1595534687.750833', 'p-1595533911.824425', 'p-1595532794.860003', 'p-1595534759.74706', 'p-1595533631.903351', 'p-1595532218.915875', 'p-1595532107.392744', 'p-1595534057.285604', 'p-1595532010.143854', 'p-1595533288.428578', 'p-1595532039.860428', 'p-1595533478.374332', 'p-1595533777.284604', 'p-1595532613.15091', 'p-1595532974.717232', 'p-1595533662.123518', 'p-1595532032.540372', 'p-1595532844.60933', 'p-1595533526.290034', 'p-1595533275.493532', 'p-1595534298.011499', 'p-1595533477.354529', 'p-1595533918.013484', 'p-1595532562.860918', 'p-1595534150.281968', 'p-1595533150.589652', 'p-1595532984.436825', 'p-1595534399.000853', 'p-1595534389.806642', 'p-1595533472.72378', 'p-1595533975.001767', 'p-1595534623.390931', 'p-1595532663.274208', 'p-1595534344.553322', 'p-1595532336.526833', 'p-1595533196.227396', 'p-1595534165.948483', 'p-1595532815.029149', 'p-1595532274.05612', 'p-1595532282.198901', 'p-1595532515.515117', 'p-1595533257.964569', 'p-1595533989.725316', 'p-1595532058.316971', 'p-1595533157.851026', 'p-1595534173.187899', 'p-1595534475.27888', 'p-1595534131.658763', 'p-1595533331.456756', 'p-1595532082.883604', 'p-1595532605.792912', 'p-1595533683.160668', 'p-1595535114.31814', 'p-1595534497.999526', 'p-1595532264.250755', 'p-1595533921.586888', 'p-1595532680.831138', 'p-1595535016.790185', 'p-1595535246.271972', 'p-1595532615.083275', 'p-1595535349.042103', 'p-1595532946.816564', 'p-1595534454.919028', 'p-1595533885.527186', 'p-1595532324.335166', 'p-1595533458.432725', 'p-1595534727.837061', 'p-1595534950.361077', 'p-1595534280.307513', 'p-1595534736.954658', 'p-1595534256.59444', 'p-1595532494.420508', 'p-1595534229.996142', 'p-1595532328.20471', 'p-1595533086.259113', 'p-1595533070.842417', 'p-1595532446.175391', 'p-1595532492.43071', 'p-1595533208.849801', 'p-1595533613.73929', 'p-1595534906.356762', 'p-1595533443.119344', 'p-1595534253.125257', 'p-1595534426.147287', 'p-1595531994.305848', 'p-1595533739.224874', 'p-1595532065.71851', 'p-1595532897.722853', 'p-1595533097.878512', 'p-1595532826.974105', 'p-1595532432.533224', 'p-1595534605.162382', 'p-1595535326.11678', 'p-1595534098.88243', 'p-1595534162.181945', 'p-1595534295.351233', 'p-1595533625.631776', 'p-1595533344.217932', 'p-1595532256.156906', 'p-1595534305.301068', 'p-1595534558.16136', 'p-1595533217.343692', 'p-1595534553.65237', 'p-1595532483.708336', 'p-1595534972.9069', 'p-1595534672.897155', 'p-1595534497.093735', 'p-1595532838.961597', 'p-1595532967.433352', 'p-1595532471.992062', 'p-1595535367.614932', 'p-1595534787.415023', 'p-1595532837.191597', 'p-1595534651.130843', 'p-1595533510.189004', 'p-1595532283.09821', 'p-1595533378.42386', 'p-1595533069.054718', 'p-1595534388.962214', 'p-1595532057.294269', 'p-1595533021.337784', 'p-1595533536.571702', 'p-1595534041.076324', 'p-1595534408.880336', 'p-1595532169.616991', 'p-1595534570.134262', 'p-1595534187.522178', 'p-1595534520.851971', 'p-1595534630.669463', 'p-1595534516.407421', 'p-1595533164.983429', 'p-1595535094.5293', 'p-1595535299.259881', 'p-1595534910.105432', 'p-1595533291.95682', 'p-1595532934.023132', 'p-1595533837.365785', 'p-1595534650.230867', 'p-1595533595.747245', 'p-1595533138.667162', 'p-1595532140.73764', 'p-1595532045.307971', 'p-1595534471.506245', 'p-1595534911.976385', 'p-1595533160.429435', 'p-1595533771.854778', 'p-1595535123.429945', 'p-1595532451.478503', 'p-1595533182.917782', 'p-1595534033.523285', 'p-1595532003.528859', 'p-1595534835.804117', 'p-1595532260.833002', 'p-1595534943.049004', 'p-1595534671.986909', 'p-1595534593.514686', 'p-1595533110.755715', 'p-1595534882.213088', 'p-1595534756.983599', 'p-1595535031.581649', 'p-1595532630.375148', 'p-1595534778.862276', 'p-1595534491.513503', 'p-1595535307.731455', 'p-1595532654.224352', 'p-1595532493.480693', 'p-1595534659.387337', 'p-1595532989.991673', 'p-1595533485.825238', 'p-1595532658.809641', 'p-1595533563.429774', 'p-1595532526.421337', 'p-1595535274.247907', 'p-1595534962.208656', 'p-1595533096.951007', 'p-1595534788.370397', 'p-1595535249.045733', 'p-1595533090.736263', 'p-1595534735.218796', 'p-1595533901.01361', 'p-1595532622.311367', 'p-1595532996.41623', 'p-1595535005.027834', 'p-1595532278.653493', 'p-1595533206.982826', 'p-1595533164.098039', 'p-1595532666.132689', 'p-1595532758.149874', 'p-1595534927.606227', 'p-1595532992.765409', 'p-1595533684.018972', 'p-1595533496.599128', 'p-1595534648.37946', 'p-1595533276.421632', 'p-1595534494.23119', 'p-1595534496.165629', 'p-1595533188.287079', 'p-1595534000.618822', 'p-1595532924.059297', 'p-1595533257.037383', 'p-1595533687.74722', 'p-1595534548.18824', 'p-1595533809.843', 'p-1595533197.115974', 'p-1595534675.698641', 'p-1595535039.526857', 'p-1595535126.307581', 'p-1595533987.773279', 'p-1595532132.528536', 'p-1595533011.616632', 'p-1595533484.682828', 'p-1595532301.045625', 'p-1595532612.229786', 'p-1595534416.158475', 'p-1595533141.587201', 'p-1595532733.604235', 'p-1595533993.309307', 'p-1595534019.897622', 'p-1595535343.61289', 'p-1595532858.846675', 'p-1595532692.693946', 'p-1595534765.124944', 'p-1595532486.376179', 'p-1595534373.465302', 'p-1595534398.073686', 'p-1595532884.984103', 'p-1595533104.256815', 'p-1595533224.051976', 'p-1595534834.916323', 'p-1595534894.845036', 'p-1595532536.594109', 'p-1595535174.529978', 'p-1595534640.414231', 'p-1595532257.125448', 'p-1595534241.298084', 'p-1595533696.212892', 'p-1595535097.998585', 'p-1595533392.103903', 'p-1595534971.981021', 'p-1595534336.487612', 'p-1595532610.425182', 'p-1595532194.648312', 'p-1595534436.230806', 'p-1595532372.390539', 'p-1595534546.467737', 'p-1595534243.870123', 'p-1595533176.659097', 'p-1595534710.458517', 'p-1595532511.746641', 'p-1595532123.523971', 'p-1595532822.405165', 'p-1595533015.195112', 'p-1595535271.602704', 'p-1595533336.733015', 'p-1595535346.347869', 'p-1595532371.452321', 'p-1595533629.072447', 'p-1595533327.731527', 'p-1595534075.492577', 'p-1595532916.788148', 'p-1595533358.629795', 'p-1595533988.816866', 'p-1595532527.372999', 'p-1595533253.567137', 'p-1595533569.929935', 'p-1595533322.304574', 'p-1595534269.275881', 'p-1595534337.40898', 'p-1595533635.547837', 'p-1595534415.212763', 'p-1595535125.190839', 'p-1595534619.673772', 'p-1595532234.204111', 'p-1595534587.214771', 'p-1595534073.650936', 'p-1595534314.177848', 'p-1595532510.88116', 'p-1595534120.64917', 'p-1595532177.00703', 'p-1595532499.792533', 'p-1595532332.889129', 'p-1595534086.075666', 'p-1595534128.856169', 'p-1595534159.357525', 'p-1595533194.345314', 'p-1595534982.198991', 'p-1595532444.138762', 'p-1595534232.856655', 'p-1595534887.611991', 'p-1595532532.954761', 'p-1595533228.578464', 'p-1595534341.939207', 'p-1595534310.633265', 'p-1595532674.184793', 'p-1595533040.423381', 'p-1595535084.526775', 'p-1595535054.932465', 'p-1595532989.019246', 'p-1595533968.760862', 'p-1595532026.101162', 'p-1595534774.33982', 'p-1595534343.653812', 'p-1595534378.009583', 'p-1595534596.996842', 'p-1595534307.151184', 'p-1595534288.274524', 'p-1595532137.053502', 'p-1595532524.600313', 'p-1595532151.369149', 'p-1595533991.495944', 'p-1595534512.731989', 'p-1595534100.70664', 'p-1595534070.881791', 'p-1595533382.145634', 'p-1595534254.884772', 'p-1595535313.1931', 'p-1595534007.925889', 'p-1595532971.113595', 'p-1595533125.873616', 'p-1595535342.69503', 'p-1595534577.431422', 'p-1595533584.956936', 'p-1595532311.876704', 'p-1595533000.161575', 'p-1595534685.871415', 'p-1595533363.169366', 'p-1595533549.337083', 'p-1595534006.120779', 'p-1595533728.102174', 'p-1595534458.441616', 'p-1595532242.347163', 'p-1595534923.924623', 'p-1595532978.308713', 'p-1595534405.342189', 'p-1595533845.357743', 'p-1595534321.338836', 'p-1595533695.293042', 'p-1595533880.154279', 'p-1595532542.238065', 'p-1595532236.182379', 'p-1595532374.338059', 'p-1595534974.716619', 'p-1595533152.36347', 'p-1595535022.426628', 'p-1595534222.879833', 'p-1595533426.742367', 'p-1595535303.916467', 'p-1595532246.947404', 'p-1595534101.626616', 'p-1595534438.991407', 'p-1595533181.056888', 'p-1595532329.146306', 'p-1595534027.190612', 'p-1595532753.638206', 'p-1595532101.108461', 'p-1595533674.057099', 'p-1595535048.582829', 'p-1595532797.65181', 'p-1595535033.303773', 'p-1595535002.334054', 'p-1595532427.991985', 'p-1595533400.04916', 'p-1595534921.142181', 'p-1595535266.359298', 'p-1595535247.185978', 'p-1595534427.963351', 'p-1595534106.17968', 'p-1595532960.34982', 'p-1595533473.614511', 'p-1595534703.969621', 'p-1595533421.616012', 'p-1595533323.403773', 'p-1595532565.752732', 'p-1595533123.392437', 'p-1595534109.915095', 'p-1595534604.344039', 'p-1595533941.226301', 'p-1595533840.042167', 'p-1595534938.462056', 'p-1595535133.511707', 'p-1595532209.166937', 'p-1595534995.7845', 'p-1595532691.761092', 'p-1595533105.14089', 'p-1595532997.465659', 'p-1595532208.253233', 'p-1595532072.01336', 'p-1595532841.857738', 'p-1595534019.001868', 'p-1595533657.493574', 'p-1595532991.823503', 'p-1595532505.212243', 'p-1595534821.153458', 'p-1595532088.438936', 'p-1595534775.253223', 'p-1595535298.430745', 'p-1595533798.15688', 'p-1595534899.171101', 'p-1595532218.065053', 'p-1595534261.872311', 'p-1595532156.893388', 'p-1595532943.080245', 'p-1595532972.882176', 'p-1595532397.095845', 'p-1595533440.4452', 'p-1595534832.224164', 'p-1595533251.692447', 'p-1595532350.270835', 'p-1595532540.216105', 'p-1595533492.925516', 'p-1595535131.598363', 'p-1595534738.796464', 'p-1595533833.983614', 'p-1595533998.865228', 'p-1595532698.283064', 'p-1595532380.567472', 'p-1595533077.109015', 'p-1595532195.533136', 'p-1595534163.208033', 'p-1595532646.714126', 'p-1595534733.31035', 'p-1595533444.984537', 'p-1595532865.333317', 'p-1595533748.117378', 'p-1595533031.07776', 'p-1595532356.738191', 'p-1595534124.355338', 'p-1595532643.144787', 'p-1595532644.914232', 'p-1595532090.152598', 'p-1595534780.761448', 'p-1595534904.524376', 'p-1595532601.396253', 'p-1595534080.669532', 'p-1595532226.968928', 'p-1595533311.500856', 'p-1595532352.945934', 'p-1595533225.866959', 'p-1595534137.849418', 'p-1595535366.68361', 'p-1595532068.474754', 'p-1595534113.520401', 'p-1595533481.091211', 'p-1595533576.953621', 'p-1595532748.170866', 'p-1595532793.075082', 'p-1595533747.261073', 'p-1595534676.636435', 'p-1595533783.722721', 'p-1595533841.81697', 'p-1595532402.628043', 'p-1595533100.631144', 'p-1595535044.966966', 'p-1595535179.890631', 'p-1595532830.579055', 'p-1595533316.059644', 'p-1595534809.432352', 'p-1595533863.79577', 'p-1595532508.159678', 'p-1595535128.055431', 'p-1595532110.026663', 'p-1595533929.395172', 'p-1595532227.91779', 'p-1595532875.255422', 'p-1595535258.354107', 'p-1595533719.861485', 'p-1595534923.006209', 'p-1595533274.617046', 'p-1595534393.318775', 'p-1595532914.970204', 'p-1595534857.346765', 'p-1595532054.424485', 'p-1595533776.419187', 'p-1595535063.33434', 'p-1595534940.238255', 'p-1595535145.303958', 'p-1595533953.400679', 'p-1595534534.382077', 'p-1595532750.019473', 'p-1595533057.544834', 'p-1595534917.56893', 'p-1595534576.54461', 'p-1595532616.012701', 'p-1595532059.200847', 'p-1595533888.208862', 'p-1595532785.651217', 'p-1595534635.890666', 'p-1595532719.916335', 'p-1595534699.479831', 'p-1595533780.166327', 'p-1595533342.261534', 'p-1595532048.899367', 'p-1595534712.315371', 'p-1595535108.042264', 'p-1595533893.563076', 'p-1595534941.137704', 'p-1595535021.485929', 'p-1595533761.070212', 'p-1595535089.934022', 'p-1595533170.322352', 'p-1595534856.419709', 'p-1595532210.062028', 'p-1595533206.074398', 'p-1595533959.811198', 'p-1595534003.409954', 'p-1595534361.573938', 'p-1595535332.561897', 'p-1595534260.212317', 'p-1595534262.777954', 'p-1595533189.139489', 'p-1595534571.011093', 'p-1595533932.881484', 'p-1595535150.632665', 'p-1595532955.916881', 'p-1595533222.117676', 'p-1595532870.794135', 'p-1595534351.881034', 'p-1595533155.161136', 'p-1595534407.112874', 'p-1595534189.27649', 'p-1595533922.437287', 'p-1595532723.566534', 'p-1595533142.477524', 'p-1595533877.471937', 'p-1595534844.632954', 'p-1595533808.871631', 'p-1595533131.210289', 'p-1595533562.50396', 'p-1595533543.85337', 'p-1595533046.768579', 'p-1595532137.984826', 'p-1595533551.232182', 'p-1595534088.835698', 'p-1595533725.35932', 'p-1595532331.863353', 'p-1595532091.876673', 'p-1595535208.227365', 'p-1595533085.184815', 'p-1595533663.003043', 'p-1595532013.010851', 'p-1595535230.976042', 'p-1595532716.411726', 'p-1595532798.544253', 'p-1595534709.55846', 'p-1595532786.546202', 'p-1595532566.658588', 'p-1595532026.949328', 'p-1595532574.748625', 'p-1595532961.210978', 'p-1595533338.773989', 'p-1595532491.609856', 'p-1595534001.528576', 'p-1595534748.67264', 'p-1595533212.645754', 'p-1595534456.640641', 'p-1595532036.260685', 'p-1595532424.378668', 'p-1595533503.746878', 'p-1595534248.461052', 'p-1595533792.72067', 'p-1595533183.826413', 'p-1595533211.765095', 'p-1595535093.671711', 'p-1595532918.706793', 'p-1595532800.418427', 'p-1595532969.407657', 'p-1595532917.64186', 'p-1595533163.216202', 'p-1595534356.339605', 'p-1595534081.595165', 'p-1595532386.105217', 'p-1595534308.126565', 'p-1595532738.287762', 'p-1595533312.368327', 'p-1595533346.037084', 'p-1595532556.579039', 'p-1595534149.254414', 'p-1595533235.896966', 'p-1595534796.598704', 'p-1595532253.413238', 'p-1595533277.342645', 'p-1595535152.352254', 'p-1595532656.052448', 'p-1595533862.027832', 'p-1595534560.945193', 'p-1595532921.367789', 'p-1595532762.754074', 'p-1595534711.371002', 'p-1595534155.671941', 'p-1595533260.697517', 'p-1595532095.624806', 'p-1595534236.664088', 'p-1595532159.599517', 'p-1595534493.336711', 'p-1595532596.727809', 'p-1595532942.159066', 'p-1595532557.541685', 'p-1595532180.810698', 'p-1595532560.179905', 'p-1595534685.008304', 'p-1595532429.719317', 'p-1595534325.640251', 'p-1595533005.587526', 'p-1595533089.025563', 'p-1595534893.922953', 'p-1595534382.633775', 'p-1595532718.20018', 'p-1595534499.778158', 'p-1595532856.165163', 'p-1595534690.339871', 'p-1595534933.91361', 'p-1595533700.770109', 'p-1595533249.007211', 'p-1595533016.111767', 'p-1595534424.365915', 'p-1595532198.163324', 'p-1595533045.947402', 'p-1595532383.36691', 'p-1595534626.96444', 'p-1595533326.844477', 'p-1595534883.115721', 'p-1595532453.247243', 'p-1595534443.610655', 'p-1595533394.783736', 'p-1595534038.353101', 'p-1595533050.299093', 'p-1595534096.150418', 'p-1595533128.565835', 'p-1595532743.674051', 'p-1595532343.104194', 'p-1595535257.240706', 'p-1595535264.568982', 'p-1595534265.613046', 'p-1595533701.647475', 'p-1595533673.177843', 'p-1595535320.59797', 'p-1595532133.421127', 'p-1595535202.809223', 'p-1595532571.048899', 'p-1595533555.944846', 'p-1595534161.22469', 'p-1595534919.337971', 'p-1595533364.981346', 'p-1595535284.580072', 'p-1595533881.908129', 'p-1595533603.563773', 'p-1595532228.9097', 'p-1595533321.40892', 'p-1595534657.549627', 'p-1595532559.34228', 'p-1595535335.313577', 'p-1595535314.042505', 'p-1595532988.112975', 'p-1595534442.734935', 'p-1595532820.611558', 'p-1595532189.203292', 'p-1595532702.802248', 'p-1595535074.101276', 'p-1595533569.01052', 'p-1595532081.982224', 'p-1595533711.725686', 'p-1595533607.220694', 'p-1595532296.46935', 'p-1595534477.927241', 'p-1595531988.843514', 'p-1595532535.661598', 'p-1595533752.790899', 'p-1595533007.310226', 'p-1595533200.597232', 'p-1595535199.9776', 'p-1595534199.469721', 'p-1595533326.009732', 'p-1595533632.840573', 'p-1595533029.322403', 'p-1595535169.899473', 'p-1595532671.629265', 'p-1595534611.692172', 'p-1595534665.737935', 'p-1595532157.767353', 'p-1595535147.887265', 'p-1595534558.986262', 'p-1595534445.39474', 'p-1595533591.174469', 'p-1595534174.790871', 'p-1595534182.193521', 'p-1595533407.21672', 'p-1595534814.832955', 'p-1595534766.157716', 'p-1595532322.472415', 'p-1595534881.299488', 'p-1595534093.432426', 'p-1595532072.883153', 'p-1595532463.011666', 'p-1595534755.217437', 'p-1595533834.812913', 'p-1595534506.336368', 'p-1595533974.100018', 'p-1595534800.212509', 'p-1595532621.437908', 'p-1595533878.309627', 'p-1595534208.41453', 'p-1595533596.722955', 'p-1595534483.427148', 'p-1595534083.2477', 'p-1595532463.882975', 'p-1595531986.181677', 'p-1595533158.71625', 'p-1595534589.965528', 'p-1595534574.740122', 'p-1595535136.155772', 'p-1595534353.628097', 'p-1595534671.112905', 'p-1595534020.777006', 'p-1595532096.551816', 'p-1595535054.106627', 'p-1595532389.794627', 'p-1595534365.24589', 'p-1595533872.848414', 'p-1595535040.439757', 'p-1595533481.884047', 'p-1595535011.359481', 'p-1595532532.040681', 'p-1595535213.636145', 'p-1595535323.47008', 'p-1595534608.044171', 'p-1595533310.55249', 'p-1595533513.881129', 'p-1595534222.064029', 'p-1595533815.338582', 'p-1595533166.714122', 'p-1595534126.161243', 'p-1595534122.568488', 'p-1595534822.072918', 'p-1595533182.034218', 'p-1595534649.382363', 'p-1595533268.039007', 'p-1595532354.869045', 'p-1595535161.682851', 'p-1595532385.146337', 'p-1595534376.113505', 'p-1595534007.02327', 'p-1595533062.954206', 'p-1595532060.151356', 'p-1595533902.801081', 'p-1595533168.618912', 'p-1595534042.918284', 'p-1595534920.232866', 'p-1595532490.733487', 'p-1595533977.619824', 'p-1595534259.294941', 'p-1595535177.123022', 'p-1595533851.006622', 'p-1595533035.65461', 'p-1595534854.565693', 'p-1595533204.262433', 'p-1595535322.591437', 'p-1595534252.052575', 'p-1595534105.270293', 'p-1595533316.979445', 'p-1595533924.153805', 'p-1595534794.860233', 'p-1595533280.998691', 'p-1595533456.490582', 'p-1595535217.251652', 'p-1595533605.31749', 'p-1595532907.652107', 'p-1595535294.870365', 'p-1595535233.528499', 'p-1595533409.10136', 'p-1595532926.862667', 'p-1595534327.339125', 'p-1595533207.936384', 'p-1595533447.525382', 'p-1595532378.781538', 'p-1595532033.4162', 'p-1595532781.85824', 'p-1595532388.906567', 'p-1595532179.0461', 'p-1595533132.990794', 'p-1595532488.095194', 'p-1595532795.748485', 'p-1595534942.065586', 'p-1595532572.955943', 'p-1595534492.473625', 'p-1595532376.958786', 'p-1595532034.26912', 'p-1595532211.815696', 'p-1595533108.027533', 'p-1595535225.398245', 'p-1595534678.513403', 'p-1595533544.787634', 'p-1595532364.096559', 'p-1595534956.715051', 'p-1595533413.633181', 'p-1595533371.29838', 'p-1595533186.414131', 'p-1595532539.318115', 'p-1595534557.255954', 'p-1595533069.899019', 'p-1595533847.197129', 'p-1595534153.839802', 'p-1595533644.558076', 'p-1595535277.926178', 'p-1595532828.790531', 'p-1595532430.743421', 'p-1595532809.515866', 'p-1595534957.802389', 'p-1595532694.492171', 'p-1595534158.483437', 'p-1595533304.152538', 'p-1595532277.777316', 'p-1595534200.351063', 'p-1595533932.024963', 'p-1595532170.477071', 'p-1595535363.916365', 'p-1595532885.916836', 'p-1595532084.742691', 'p-1595532888.602988', 'p-1595532805.842733', 'p-1595532913.137476', 'p-1595534511.858443', 'p-1595532275.870778', 'p-1595533494.779459', 'p-1595533690.52213', 'p-1595534482.551616', 'p-1595534869.624689', 'p-1595535341.58384', 'p-1595534350.187377', 'p-1595532414.278897', 'p-1595533578.752318', 'p-1595533608.951536', 'p-1595533670.357104', 'p-1595535194.401306', 'p-1595534476.194006']\n",
            "['1033.5448692679195', '958.2346363936579', '988.7423774908417', '1029.6501547145572', '1021.1621617738489', '967.7256240308351', '1011.675588517592', '959.0882680991638', '966.9483888783291', '1029.9736083848713', '1010.9998281990048', '1020.28774368332', '1001.1989707192621', '1030', '898.5579685097055', '999.4489562845667', '1010.5829919115935', '980.0509336697496', '1019.4084791535936', '957.6321351738934', '977.0021611004524', '1010', '968.2358245057513', '1052.6627552465673', '1010.5745278625617', '1019.6864442597501', '980.2577352959851', '1010.28774368332', '958.1913371651858', '1041.4625564249438', '990.0255397538068', '1001.267989188804', '999.9914796885608', '1022.3716540727589', '969.1040831226048', '976.960666222267', '1010.917373145345', '958.8513525423585', '990.329521601363', '1030.9462858473337', '979.7539202413313', '989.413211228163', '971.2324144830977', '1089.2826212871614', '967.6164428017515', '924.3487767072456', '1010.9135549008294', '1029.9999996052125', '1053.5438522690815', '939.5602471548465', '989.4164903926246', '1060.0095045740227', '947.9506501168684', '1001.3141624644355', '969.9912129177628', '1101.977452928326', '989.4249824992444', '1001.169778979822', '1042.112152758397', '990', '978.827609894334', '998.8075068535605', '970.0002079297581', '1050.880767771615', '960', '980.8961085308288', '1020.8784529545576', '1000.6168436962088', '936.0829238354335', '1017.2112301454488', '998.042553868079', '967.334255827645', '1049.385747779393', '1011.8567710222796', '1019.6955350487588', '948.8848328630658', '1006.0414704350185', '1063.2515133266031', '1041.6896558592161', '999.4167349768752', '1030.2122268583933', '1009.9566927458906', '928.2877076197296', '999.7045281892384', '999.4334857193415', '980', '980.0085256857055', '1000.5832654286248', '989.4072348840468', '999.6716708482708', '946.4418648331545', '1041.7908715403769', '956.7724873256185', '1020.2615281453777', '1020.5752484265557', '1087.1159944076478', '1031.7671356514877', '1022.2036436099232', '975.5582091274342', '1009.6586996499788', '1039.0886565693286', '979.4490950014234', '999.71225631668', '1008.7540825996621', '989.1468681882161', '939.1519358025356', '1000.6101458498515', '1021.6948642419954', '948.834214041533', '978.1921783741727', '959.2244841463', '958.8324896345222', '970', '1012.6100612995635', '1010.6004702462075', '1021.1749905149891', '999.3828063021375', '956.4202055804122', '1119.2483863622606', '999.6161366236444', '988.5025099429902', '1009.1017355500632', '945.3144178831418', '990', '1010.5665187332113', '967.9393370326109', '989.9733957413827', '959.783062969929', '1042.609095528381', '1031.4786219987386', '989.9917744013816', '979.71225631668', '1009.4169514085364', '1020.28774368332', '1018.5147220582982', '980.3020885118541', '1041.1903531679163', '967.6027563201121', '1050.8542911369618', '979.1913563948629', '990.0511053270831', '957.6589000681057', '979.0890233570724', '1009.3741911896437', '977.2855146602082', '1009.3374880013057', '1029.2974900071501', '937.7372635969372', '1052.6458310902797', '1001.2270029452461', '1009.4000452246217', '999.6799727595487', '1040', '1039.9941935960733', '1021.1769379286928', '1021.4543299121301', '1010', '1010.6001403724644', '1001.1989747361245', '979.1304725371655', '1030.29779506061', '948.2903586016506', '970.0252528838275', '989.3861149867154', '976.4418977419895', '979.0324588983198', '990.9290726090678', '990.0435117405225', '1000', '1011.4288009028206', '990.3130280305687', '969.6827404554768', '1010.5993756892007', '1000.2960048916211', '969.7122565256715', '979.1205008191298', '990', '1030.8350940663772', '1098.0922964897338', '1059.9881448693613', '1020.2877414093225', '1000.602478380001', '979.7921893105902', '1020.54563423822', '1020', '1030.8695497384208', '989.9915547219659', '1018.3477554439042', '1010.5670662487714', '987.4924186864371', '977.028225663902', '1043.1986773848123', '1012.7771968493745', '1000.3130125641718', '931.5434380883183', '891.7436606586214', '999.9835120484805', '1030.9081142623363', '970.2879861599324', '978.5106757411324', '979.3185054906917', '1011.433522463936', '934.7510945804927', '923.5361352597', '990', '1022.2187629576794', '999.7122486315776', '1010', '970.7286848421925', '1000.626663223783', '989.7475469244788', '1039.6777193318794', '988.7621303333298', '913.4725076573166', '1052.6508937491349', '1041.444864042754', '1019.991480112251', '1019.660558138582', '1030.28774368332', '969.3825361300555', '1022.6203646655481', '1068.1271090548025', '988.3899558182152', '1030.28774368332', '1016.8793445984547', '1030.8710067439094', '1020.28774368332', '1030.5830225516097', '1000.5998840116863', '1019.7210429276794', '957.9622921398474', '969.7471596577836', '1000.600324982707', '1023.1568949148007', '989.1044569044465', '989.1204472058637', '957.9862077818816', '1008.6225658876049', '1000.5832581914245', '1011.1570164342968', '989.9995099075551', '1003.1282019196783', '1052.6144956994065', '935.2445155945574', '1021.4873171726656', '960.3047830147834', '989.1044404202573', '960.0082979338414', '1039.788438598824', '980.9302803415951', '960.2873357625515', '1020.289029241743', '988.8209080475931', '1012.1919026406038', '979.7390761558422', '1065.0264974336285', '1008.5108037598777', '1009.3530884226036', '1042.3621205020304', '990.2795478173636', '946.7449129327258', '999.6231866141202', '1051.2206816443438', '989.4504213058124', '999.7545913401631', '1010.3136945151856', '977.5965412481996', '970.0000072844821', '989.713023562194', '979.7122565316866', '1010.244138709138', '956.1362476313249', '1019.71225631668', '1041.1896691565846', '989.1130491958329', '991.4695081115857', '1000.6381661133295', '1041.4055050280074', '1010.307649428254', '970.0272721500583', '1042.3593498709256', '998.2001360498882', '979.2077589421355', '1052.2948222325895', '1020.5837622393582', '935.2545992588695', '958.5128661126014', '1041.7867807406883', '1052.0605853337938', '989.1304480279687', '1010', '947.6362543338196', '979.4501676901558', '1030.2830214573617', '958.8703587333835', '1020.2870020901187', '1020.5327099632356', '1019.9032185762594', '978.8422574900293', '960', '1011.2805705477423', '989.4507666590638', '939.9192965646098', '999.9978792574998', '1042.0366194469289', '949.7451145998562', '989.3500430943617', '1030.9176324688233', '1010.1622407846022', '1011.5415960532196', '956.1113226760817', '1030.8963060007982', '1000', '1010.5173542164328', '970', '989.0968051145305', '1010.3002361486789', '886.934529273489', '967.8425726021873', '957.9544114394881', '1050.6007052107448', '945.2711474943372', '967.7587158904167', '1000.5750120575766', '969.7503649439727', '958.5795934878206', '1010.0619738599154', '1000.5830126045831', '999.408516761447', '1031.816175132748', '1010.2877579826823', '999.71225631668', '991.1809696563141', '1020.8852345821003', '999.6350983717509', '979.4243452144395', '1008.8374538666679', '1022.6658533432674', '968.5144981526973', '1064.726460441991', '990.3129340459833', '1000.321779683334', '1010', '1027.8225920373582', '1020.87757597114', '1053.7951782157247', '1051.8509180583128', '978.8350687622824', '1040.2615430588896', '1035.5249131233545', '1030.3030290600107', '938.2612083203812', '1040.5832652307301', '1069.7398280670716', '976.9595442017062', '945.6156775274652', '980', '1009.8275662235086', '1045.439836760092', '1040.5653357074636', '998.1934614507229', '1063.27888755251', '979.7122422958727', '1000.2962501263421', '977.7807078874212', '966.6017179835229', '1020.6096593436831', '970.0335503327433', '1000.8931700090625', '959.149682024223', '957.7130054515668', '1042.111027590947', '970.3550894405282', '1009.7124792672989', '1052.6861690381672', '980.2870076220199', '1050.5819784326507', '1052.995760626598', '1000.6099985336668', '1033.6552928322951', '986.5399022711654', '1010', '1001.2761236421828', '959.7185539256174', '1021.1802666751789', '988.2711127619115', '978.5319054502381', '1050.906774114591', '1000', '978.5578513022259', '1020.0437205869122', '976.683203735144', '1001.8178750313689', '979.434313204286', '1043.1573289884527', '989.988898638348', '1009.9914797000919', '1020.0013120433481', '1009.1204340371587', '1050.25514779065', '1052.4536880832075', '1009.3205382096355', '989.4001718554924', '986.3789498451633', '1087.3709535440867', '987.9126831320522', '1009.7121285182483', '1002.7860115338111', '1011.1442018532551', '1020.0188888307434', '1022.0595017005521', '968.8434229043897', '1020.5999943917634', '1002.2577013339337', '1021.4769416541137', '1009.399466373904', '1029.3930142139075', '1010.0090236192685', '1010.017757659532', '1032.7766631176457', '990.0082536932093', '1021.1960450886464', '1009.6784353068871', '970.6253680224977', '989.7390562996751', '1034.1826784620375', '957.5813549632672', '1030.5583631270476', '1021.4950236357308', '1009.7037364226265', '987.8365082699604', '1009.3572096072103', '1009.9737573893327', '1040.602864062015', '898.9397517670329', '1010.0000080587391', '1029.7107638093817', '1001.6775805545633', '1020.8661249444399', '1032.4612385757305', '978.5122573215118', '1021.2159211096451', '1030.5291748120794', '968.2595200872184', '1009.7205110324339', '1021.5985895821759', '1009.7127533720926', '979.1292145587366', '987.8980937878399', '1021.1615138386006', '1030.28774368332', '1021.1823120496604', '979.130574400814', '998.5385000194109', '1021.7714957218159', '1009.3568464997533', '980.0516139293064', '1125.4075135527016', '990.0002517681976', '990', '990', '936.0963209499819', '990.8923815421437', '1020.6094107194385', '978.7727593340842', '1008.8093880589763', '1012.0510593172465', '1019.1861970963001', '1020.5479569958266', '1053.4763495374877', '1010.28774368332', '970.0333139673318', '1020.5810405890209', '1000.0267317659535', '1012.1527670839478', '969.424995531601', '1020.8879966676947', '1020.2926888944215', '1010.0023100209278', '1032.0838848739952', '1032.295177855266', '958.5910190275464', '1021.5273333842136', '1050.28774368332', '977.3627661687009', '1019.9995119052252', '1020.3044610184664', '1039.9573398827035', '955.8016254942984', '990', '1020.0249531917285', '1000.6613150168143', '969.4167345713752', '1019.4072953924735', '980.8861656489013', '1001.1544050144541', '958.848240573423', '1044.185047599997', '980.000000005452', '1062.6409727635378', '1020.2877436833201', '989.9912657847448', '1008.7767612100768', '989.3920460959464', '1009.9580634311396', '960', '1020.28774368332', '979.4332372075894', '956.0781903898023', '980.8793297567029', '1022.7507103554744', '1030.2877501538726', '1022.1282485495391', '999.1220019977391', '946.410121352279', '1030.5832658703691', '1041.767070046092', '1000', '936.2752026899253', '911.4455888034926', '1036.6045776980204', '988.4459014651637', '999.4162302993639', '1009.0887408882082', '1048.404573084823', '980.233900282234', '989.7215455277217', '990.6499809604044', '1030', '980.5915213359965', '1000.304430944722', '1010.5582157309731', '970.3213808856718', '1044.2603870398118', '1013.5112250746938', '988.1842779392025', '980.929099695542', '967.5844095837302', '999.9914796999236', '1019.9492064129674', '1040.2877436833198', '1024.2003540293317', '1022.704625020897', '1040.2714651681652', '1031.1409514184877', '1011.7833211870278', '956.0886552059306', '1031.8366206738572', '1000.5832654286248', '969.6954538546129', '977.3212411161437', '1040.2121030671124', '1010', '998.7460617206682', '1029.4084514780081', '979.679231127952', '1022.6171568370728', '919.0979139418774', '1012.4554683785617', '1001.7716479989019', '987.8827493079585', '1009.6780924608263', '968.2829825768823', '1020', '1019.400468395586', '970.3414458966998', '917.0536927206356', '979.3983118882529', '1010.7612743234303', '936.4523945669465', '1009.71225631668', '1041.1621945181948', '1002.361087789106', '979.9917180729458', '980.5832654286248', '1011.7486421575122', '990.28774368332', '1010.026489303845', '988.7774844775429', '978.2023891904948', '990.0189973515087', '987.8770901107418', '1023.6049039456541', '1022.6972472923686', '932.2869887344018', '1018.8763705686804', '999.4084789716434', '1019.9747258740864', '979.71225631668', '1010.5753186016026', '980.0090034877477', '959.1289465860096', '990.0002392843368', '1001.5114328117511', '1062.9400685815576', '1001.7959413518561', '915.0214957487985', '980.5993325661668', '989.7037856852852', '988.8170270276482', '1010.892143234287', '1000.3826673528223', '1001.7716639812884', '980.6092204631057', '958.8341325448613', '1020', '999.4167345713752', '1031.165327864207', '969.4164980560128', '1030.8691447031051', '1033.7136837920118', '958.8281936045145', '990.3294362389087', '1010', '999.71225631668', '1021.4511496353111', '937.618346282402', '1052.964074813624', '982.9434352156234', '968.7984931573076', '1020.8964041667592', '1020.6010095079661', '1037.5034386572117', '1020.591010900119', '1001.8401862497223', '1002.7078977896017', '959.4569477480483', '1010.0335546383897', '990', '959.1298446854856', '990.5665187332113', '1041.1327215405038', '957.3338930711869', '949.4164969918219', '979.9914790836974', '1042.9488243927822', '990.3289737638976', '967.0489790846347', '1030', '980.2967736327548', '1010.2962328823265', '979.6956839232824', '922.4477648445236', '1041.7260352401022', '859.0759675144182', '970', '999.6787061798556', '1022.2010856048657', '1000.6089901637894', '1030.871512174452', '999.0697671213157', '980.28774368332', '1019.0266721657946', '1010.0612566680371', '980.2666162528295', '1051.4132160984523', '990.589739962619', '1010.8531910140554', '1022.1069437388142', '978.8427065782593', '1010.28774368332', '1000.28774368332', '1032.038528877316', '990', '1041.5255639638776', '1020.840528584725', '1012.1346189370832', '1041.1462326752458', '980.0080258103377', '957.6995994301074', '1010.3027507040648', '871.870733022001', '967.9107439287396', '1020.2787648152232', '1010.8943207827913', '1046.4606020107299', '1020.2607123752457', '1030.8707627783142', '968.8368099617564', '998.7355669898998', '1032.0773183322542', '1043.2911726655502', '1043.271314910574', '1010.5407241619314', '980.6530678696129', '1063.533986077851', '1030.7419675444582', '1057.0327742414133', '1011.1993829697919', '1014.7397224897976', '1030.8418840437396', '1000.2877443451993', '1039.712501341745', '1009.3883386111801', '957.6258567442022', '967.6441720402418', '1010.5657561047327', '1011.1713596510797', '989.9902121892302', '1020', '989.7037513511192', '979.1135192294906', '1009.0966609537041', '1008.1410642245047', '1087.0733070793651', '960.5684793909014', '999.703981056752', '979.71225631668', '1020.8695497384208', '1032.7238540173548', '1009.9737215243388', '1000.6097204856472', '1039.6695501346446', '967.6988314193883', '1030.8958774056873', '1000.2877507415374', '1029.6954384188555', '1010.5975721467265', '1000.28774368332', '979.4288928009029', '1012.037368494277', '999.136986131225', '969.4757832640842', '1041.4446530661303', '978.8212219767066', '990.28774368332', '1019.9148473383026', '1010.9041911896954', '969.3990409005416', '1031.5090096023478', '990', '1054.4109143946503', '1010.6355269435217', '1011.048020719254', '1033.3386711356484', '1021.187274473592', '989.4085602983588', '1032.1836414390086', '978.1865208081348', '1021.7180121977264', '1022.7025622603818', '1021.5558026313568', '1039.9832034704966', '1056.6471705355955', '1000.0007249944856', '1053.5077475973935', '968.5917870928627', '1000.2957596199012', '1042.9450995436093', '1010', '958.2338301171693', '1009.6316726399987', '989.9917180774239', '1012.5110331323297', '990.0001187714229', '979.6869933346368', '1010.2957698185834', '1020', '1020.583028690785', '1018.7963956587517', '1009.9289537824351', '999.4344482788464', '948.5149906817978', '1020.287736462766', '999.0808728363963', '998.6841199926326', '999.828267150173', '999.4167330719067', '1030', '1050.8695077342743', '971.8240209192842', '1008.8047807152466', '1042.313153686475', '968.2167050767362', '1000', '990.9116636775817', '1070.4653360522298', '980.28774368332', '1043.8533081938021', '1039.6687474674923', '989.7202934580044', '989.1374484440108', '1010.2794684162', '999.6864599461328', '989.7039814439618', '1010.9236046160001', '1042.262736403063', '956.1958387506108', '1052.6453170979507', '990.8790526496442', '1030.2354620282554', '1031.5260448384286', '988.8032232910087', '1030.5405252503076', '945.2464259007289', '1040.2522918528696', '990', '1030.0350133641523', '990.9379765953879', '1041.204092874302', '969.9764244687431', '978.8038843947188', '998.5778080308617', '1011.1796184516608', '998.9241768825431', '969.1387043395757', '968.5549370133568', '979.4346515508641', '1020.1809266545316', '988.7718467093681', '1030.5983960660315', '1023.3795884161573', '981.2474386061072', '1010', '925.7751866111793', '1010.2562027992096', '999.4164887113761', '969.43349448177', '1030.575024802388', '1010.3164187550511', '988.2090999754423', '958.7960000160713', '979.0049654926344', '1029.6447648961962', '1022.440340493204', '979.2091693316531', '967.9376690500662', '1022.0345334278128', '989.7042087195972', '1010.2002840883665', '1076.066009615454', '1031.041863991519', '1000.6187237316468', '1024.3957969639894', '1030', '1019.416958065902', '977.5251183784982', '957.9454157283733', '877.6693197164243', '992.4298217976991', '1011.8101139214453', '1020', '1000.6236171396237', '1000.0182568440426', '937.8809732165378', '1065.2477104009513', '1029.3920612547254', '990.5899405427353', '989.6957291550204', '979.71225631668', '999.71225631668', '990.5834961537805', '970.0363140905881', '1050.5477848150872', '1031.8548539467863', '1000.8779922485903', '999.4183117646228', '968.817003512382', '1011.5428431806531', '970.6083350335679', '1022.4148429367253', '1019.9918012817565', '989.158515526231', '1010.6095158813248', '1050.8779973718417', '1089.4408615295943', '1031.8094943080316', '949.1308058844048', '1010.8329244813611', '1010.9045686531376', '1021.8445782772834', '1021.5019495103054', '1000', '959.3577818303675', '998.0806769455725', '1020.8855722479404', '988.5385294357403', '1030', '1029.6145863340507', '968.2582517582999', '989.3951091294018', '1030.5830214276143', '989.4167345713752', '964.2660812850545', '1000.8861614819676', '1042.672469267444', '1039.9229804126999', '992.8764479705554', '934.438324180371', '970.603269496694', '966.394403984873', '1041.6980303603996', '979.71225631668', '1010.5798265422594', '1024.2716208295574', '989.9922353180264', '1030.5915536694154', '969.2695213089852', '1047.1951452507626', '944.2533543045754', '1039.942601111699', '925.5278476872727', '1010.864838988727', '990.0503504646827', '990.903483651675', '990.3736655051472', '1052.354602005175', '1030', '1022.3318534749726', '1010.0198824360784', '986.1960538651322', '999.9664076539476', '979.6871756312097', '1010.8957183783348', '1030.8689724054059', '1019.3533971890079', '958.8499037511718', '969.1316679490735', '968.5290942636607', '1032.6155938639697', '1081.9761410136296', '1000.28774368332', '999.9999997908694', '1021.5196291746113', '983.8352667481785', '999.71225631668', '1020', '1021.7993816691393', '966.7624828617229', '1055.9695436887898', '957.6983461204446', '970.3568511581333', '946.7983747533699', '999.6811096080513', '946.4395860982751', '1001.5229630630635', '988.2018069794699', '1010', '1018.94123411616', '989.1710646623849', '1013.3705447902187', '938.2038775721932', '1001.2380474818581', '1008.8476818599612', '969.4169510166914', '999.0968746312712', '980.236943389329', '1032.0471396680337', '979.9680331712733', '1009.9917179128818', '960.9562839469886', '949.71225631668', '1000.3316780620953', '1021.7737925667544', '988.6076932045634', '1040.9528963074379', '1010.0084989813711', '1009.713993592285', '1010.0027960680524', '980.0170333715116', '979.7653199167156', '980.0341635175271', '1031.8048284813165', '987.8310799490637', '1000.5750244240111', '1000.3551357083413', '1039.7127241287442', '969.71225631668', '1000.270724899351', '991.5227685374872', '979.0622180849792', '1015.5484637145802', '1011.2219230901146', '959.729018084202', '1018.7699640662879', '1019.3997556954839', '990.28774368332', '969.4499689484732', '1010.8913828262132', '1012.531663318189', '1030.8955099654647', '959.4164975956188', '1000', '967.6111800206845', '989.0971815864866', '990', '967.6581640030518', '935.8822416770972', '980.0248440268878', '1001.4842493164348', '1052.3363853741478', '1019.6525901655693', '989.7465653802647', '1000.6188415901254', '999.3957322746137', '883.493899327807', '1030.8955611563665', '996.5510389115127', '969.9999997850178', '987.8143608340121', '990', '1030.287764242169', '990.2957733281058', '1033.8999343024004', '1009.7122563341807', '967.9359280158832', '1010.28774368332', '969.71225631668', '924.1047527861087', '977.2995369240037', '990.287750544252', '1020.2614537305498', '1024.880927010014', '999.4070231381686', '1053.4691853936763', '1009.3919546855835', '958.7724102056087', '989.4247167236829', '969.4250113195689', '1020.3129975442911', '1021.4970363944523', '1040.0003316718064', '970.6655467203403', '1021.4860852419467', '970', '1000.3047552423747', '1000', '1022.8364391944698', '1041.1549728611233', '1084.7218551523024', '1009.0866987423481', '1010.5641757082741', '988.8431591333094', '1021.3786588205636', '980.2930056172834', '1020.5907653200719', '957.2692018646791', '1009.7039528257671', '999.9991982811524', '1021.7923618672161', '1023.2654191050215', '1022.5281752060534', '999.7389238093027', '980', '968.8460713664687', '1000.6625599198737', '969.363823574942', '979.1374687813324', '1020.6294017023401', '990.3035201631241', '991.8449048430357', '990.3302425136708', '999.0613507344267', '1020.5320882583812', '988.203886931817', '969.8074000779299', '1009.1204629452325', '990.3810105927739', '1032.0701546352836', '967.6762425404594', '989.9912463842095', '1018.785700603863', '996.871183601924', '1011.857913448282', '1042.0941736799923', '980.5921863068539', '1030.3166255681865', '1019.0540825245387', '1039.7049229140073', '959.4512148569341', '990.2872325732243', '1041.1965555778843', '982.8402032877767', '1032.146594046325', '1012.0881703375428', '999.3409937589669', '1000.0084909391512', '980.2699692695525', '980', '989.1207433329831', '980.8577150109877', '980.7232914420193', '980.3122945942204', '1029.6064494545062', '989.1051459296027', '1035.0553523492842', '999.6957104720267', '1023.9432367892636', '1032.0946251856233', '979.9746906592109', '980.5832505025675', '1009.7117734752256', '989.0609165343715', '1010', '988.233710419296', '998.5209107204662', '971.4711930420135', '990.3140896616189', '1020.0082821187425', '1086.561684804513', '979.1110563573163', '1010.6078629484963', '980', '1040.560390310937', '999.0400548146283', '979.3565821510925', '988.801904938676', '1009.0418213043682', '1031.4547617298538', '959.3053859639226', '958.1928295097789', '1000.2652349044898', '1043.3231726315366', '1010.8859255746197', '980.2944594600971', '1043.2611520711014', '1031.3979129271465', '1030', '1053.5457145968128', '989.164184094221', '1029.705004360077', '844.9077557341711', '990', '979.7039952842143', '1008.7696101156625', '979.974943196107', '990.634369579093', '998.816200163328', '1040.57525602316', '1042.0056283476465', '1031.4723827434784', '1000.2704599181976', '1019.6780442478768', '948.1748746025661', '988.1730559007249', '1011.1963608180268', '978.2182165982576', '1008.8121995677833', '929.9682635547667', '1053.5408025305671', '977.4739985866007', '978.2500016887218', '1018.6872783980089', '1039.4084745604619', '1022.9179832258417', '929.7276357815949', '1088.7410720651385', '1030.5787548595265', '1000.4619589910395', '1020.28774368332', '1030.9961674351373', '992.1531744891831', '1010.008482769242', '1019.3935785633073', '960.6186857918668', '1030.8697863085742', '990', '967.0362470926447', '1030.0084354053095', '977.7522231071848', '967.8805394300284', '1000', '1051.4558605145671', '1054.8663346405738', '980.6132080518287', '1020.5754997204804', '988.1450727268976', '980.0461030055366', '1000.0082819270542', '1010.1779509806028', '1030', '1022.0688515626957', '1030.2794684162', '1010.7781835967106', '947.0316047588175', '967.5922152076887', '1031.8187035209235', '1009.3479125368127', '1019.7207642613072', '1020.2784534033228', '1009.1305123096771', '1000.2604610607522', '1000.0077781783338', '966.0218468775621', '979.416720877018', '976.3566866985656', '979.1393988698759', '966.1701520129643', '978.5291871985927', '980.0080441281456', '950.4989198613982', '1030.008520299908', '954.2682015045146', '1010.5675207994116', '1036.3763876708122', '967.2889543734834', '979.1287395569143', '1012.1097493825782', '948.6162659956191', '989.707022136447', '990', '991.2416283954152', '958.2410482354821', '1030', '1000.8792906106281', '999.9739796767417', '1009.6952231714547', '970.3952995677158', '1000.2792371004916', '949.7110114214357', '988.1685893390307', '1051.7658550388164', '969.459618940351', '967.0137124035605', '1040', '991.9241711579045', '1030.6207373506059', '1019.7789066237561', '1000.3605847979231', '1041.4457229648185', '967.5362800791626', '1000.2959879946255', '1000.28774368332', '968.8037377058502', '988.8234780976106', '1010.8532701430063', '1011.5097557280375', '970.0002379982644', '960.2875557955704', '990.000244607846', '1010', '1010.912181797845', '989.6692508214325', '990.0082616569412', '1010.0080317460142', '1022.3435010540281', '1001.1880149254417', '1010.9253921731383', '1000.28774368332', '1031.127370878286', '1008.7837822513801', '1030.583291750912', '1019.9393823871102', '1033.4577342410923', '1020.3134239518329', '999.4087454643641', '1063.1758054527256', '988.4078532076915', '1002.6110622989513', '958.8245264530422', '999.3730125986534', '1042.8905222619837', '1000.3359712672762', '1022.8236510554783', '988.8204052924622', '979.6655143723224', '1040.262705696951', '969.4252265100515', '1074.4740315881988', '982.1495166474622', '988.2262554977134', '1019.3825295153238', '999.71225631668', '999.399108769632', '1010', '969.4274229991356', '1021.8842510679971', '958.5375166675061', '1020.2632163237091', '957.6316275992298', '956.4290617871325', '1018.3694273779994', '1020.2797616369583', '972.9910534895353', '1055.4460670551557', '989.4334744175621', '958.8118160742363', '1030.600232241816', '1042.0744151623387', '1053.2155646056851', '998.4805720311864', '999.6701134846803', '968.7475571893622', '989.2767897815347', '956.336361696612', '1010.8980213438974', '1020.8878738326899', '989.1138855826898', '1009.696576173902', '1010.0000141111158', '955.8171377483951', '1000.0082819270542', '1009.13774108962', '999.7209863982324', '969.4167345713752', '1041.187848614385', '969.71225631668', '1009.9917180847394', '957.666823250458', '1020.5832722636654', '950', '1020', '1010.4123386613453', '1072.8757169989958', '958.8003171463374', '990.0172717019195', '999.1087200535458', '958.1701359481514', '970', '998.5202779633433', '990.0348146681615', '1010.2877436833201', '979.1294809177155', '999.131197434675', '1030.8855953697773', '1010', '998.4714492310954', '1010.557971865927', '1010.6441016229276', '1019.9742435759711', '1010.8627539146783', '1041.212499705762', '992.3776217665618', '969.407990240193', '1029.9999929359608', '1001.5040095410022', '1020.2874845833704', '1021.5040266061184', '1021.4792540062194', '1001.6729053282717', '999.983450057244', '1020', '928.7571760134308', '916.1369287249516', '956.7694004061929', '978.7597581327648', '948.90796413625', '989.9667548877337', '989.4172381449448', '1020.261224780549', '990', '979.0948285685081', '978.4875733911474', '990', '990', '1011.2310183141642', '1001.478401107137', '991.1200793695888', '979.7131042423769', '1011.4615304986712', '989.3917829520526', '1020', '1010', '985.0086929746315', '999.6872163334752', '999.9915017322227', '1062.3229902073172', '979.416734613326', '978.1460285595667', '980.000000191626', '1010.961378759951', '1010.6000180707455', '980.6120985013449', '1012.1056521168259', '970.4741830723306', '1020.5832725020872', '1039.991466662706', '1030.5718214767053', '1032.6508107238244', '969.415963685582', '990', '947.1147586146653', '1009.7066845035896', '1000', '1010.9377555691133', '1031.9213368936846', '968.4961908656593', '1076.3621301390497', '1010.3210297629396', '990.5832654286248', '991.5355308619311', '1087.1666958073233', '1048.7819258742668', '1000', '1021.1714493073832', '1042.0997753064607', '988.092745530006', '990.929001608575', '1000', '999.3248451918432', '1004.302166621429', '993.350783184586', '1000.8089167644431', '1041.190292625409', '1009.0428258616894', '1089.3065393337192', '959.1223648998892', '1019.6866970527593', '1001.1986071299588', '1041.5791026221646', '968.7060118316266', '1010.5832654286248', '1010.5745085671258', '969.1221793213427', '968.5378926644964', '1000.0813293990428', '999.6796273643653', '1009.6554480464542', '980', '998.4004332999086', '1009.7037596996634', '1050.2887575974257', '959.7020729187228', '1009.4167345713752', '1019.4167345713752', '999.0625613287109', '970', '969.4162300840887', '1078.9139150659546', '1021.807032321327', '1001.2151463831088', '979.9826930398872', '968.5334583353757', '1009.695733007567', '988.7943493688717', '989.71225631668', '980', '1021.3730520959609', '1019.1137919152865', '1022.1358302587244', '991.0854792605362', '977.6095762581754', '957.3648904681688', '1050.1977783607886', '979.080041689893', '986.8879088971267', '980', '966.3022105794153', '958.8331540506633', '1041.1619394768743', '1010.3045309661343', '1013.996628172889', '968.1561434303219', '1030', '990.0406929235228', '1030.5835097755157', '969.983001554949', '991.0280023964183', '1000.2790345968832', '1000.5832654167789', '988.7776028560435', '1022.0402883451646', '1010.28774368332', '969.7655638915587', '1023.12443072378', '989.3785709081002', '977.1786112654344', '1009.3922533542358', '990.287736899535', '1011.1907407318736', '989.1222469863451', '1022.6279520964135', '988.8230046305458', '943.7003965873703', '988.8418068086305', '999.1516971775566', '1000.9195977069015', '1019.965534208211', '998.4875733911474', '1000.3300421066248', '1018.3952915936356', '1010.28774368332', '958.1801104376608', '966.4224935997781', '1076.540694584866', '1000.877759859854', '1002.4447040168399', '1020.8521112512437', '999.4082581309876', '1010.6003876671491', '990.5947765220317', '1010.8939048581789', '989.1124689302143', '1000.8860604474887', '960.0169707783612', '981.0511762897737', '1001.58148042647', '986.7918545468513', '1021.7630152575816', '1022.7655630553268', '1042.4185126675293', '1000.6527859854206', '1000.5832654286248', '990', '999.1137919152865', '1043.8762293272323', '1019.9917176490087', '1030.5466006870158', '990.0165433071138', '1030.5750112778455', '1054.688284993015', '978.5329585361607', '989.1113538207875', '944.887098310143', '999.6956984940695', '948.2629377194303', '1010.5837840019731', '979.111707009275', '979.408259868318', '979.3642408375629', '1010.0002599538764', '1030', '1000.5837690937566', '1008.1163667544363', '998.2073989986935', '947.6403818995772', '969.4167093805298', '911.7427519380618', '1062.9805279374664', '987.5559800433883', '1042.3166456269719', '1020.1181570042822', '1040.2407509698417', '1013.7414039936084', '966.4492215834067', '946.4341049458886', '1075.6067671033288', '998.1567219711249', '1099.5488975582653', '964.514024774133', '989.9749795766004', '934.3552697865862', '936.1560930391632', '1032.9488615123823', '987.9358478388513', '968.5134631896792', '976.0023284566081', '1031.7505873067448', '868.9882986870647', '1000.8858665188328', '990.03418202334', '1031.1959414557762', '1000', '1009.4703548156751', '957.3379979981376', '947.3804445416974', '979.7205390071733', '978.7880665928163', '958.1086451309351', '1019.931605383957', '1000.5647624421597', '1010.3082002153099', '1000.2957472228354', '1020.2248011998336', '1012.078528157896', '966.717103150357', '968.5564202394478', '989.7207631639504', '968.5378499765045', '980.3209195411604', '968.5633684750835', '999.4415303406855', '1019.4514938246926', '1000', '989.1384339007958', '1138.4328305937825', '1023.2646223370353', '979.0453556291374', '989.7209723679545', '959.8332083138896', '960.0950869452573', '960.3207641624667', '989.1303359034706', '1000.2603042808764', '989.4249889246694', '998.0781444596942', '968.8458504500975', '978.4858190761527', '999.71225631668', '969.1566599271687', '990.0340336793196', '1018.0917624398287', '971.823508158536', '969.4252111511111', '1009.9832317362617', '898.8416106381251', '969.4417356538978', '1008.7566057769881', '1000', '967.8966689217966', '977.0376655390722', '934.5709387545194', '1021.4461338158972', '978.1486602245831', '989.4052021849228', '978.8538348832876', '999.9832253726028', '959.1294888654191', '978.829091592749', '969.4249886765871', '990.8490372830029', '933.3199706299191', '1009.0596402126404', '969.147316942602', '1027.8520625895108', '939.1193412294365', '970', '979.7213077628005', '1010.8641739908912', '1010.5897906514235', '969.9999447159454', '904.71811570516', '1011.0632629771414', '979.6870223037689', '967.8623571577199', '957.6661702187039', '978.2376147112278', '958.5464616672481', '1009.71225631668', '1020.2875056343946', '990.0422264190578', '1000', '978.8201503042723', '998.7185658758734', '988.4709849818778', '1000.3436371506259', '1000.5832584070754', '1009.711789832366', '911.3071263535053', '1010.9499317380989', '989.3986936120506', '1000.5910473004451']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KaDJ7YrUEJI-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "145ae7ee-eef6-4c9c-b4e2-66e590b9be65"
      },
      "source": [
        "print(len(elos))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1632\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DfKfeoVEjby"
      },
      "source": [
        "path = '/content/drive/My Drive/Generated Images/vecs/'\n",
        "valid_images = \".npy\"\n",
        "vectors = []\n",
        "for file in tqdm(images):\n",
        "    vec = np.load(path + file + \".npy\")\n",
        "    vectors.append(vec)\n",
        "\n",
        "vectors = np.array(vectors)#.reshape(-1,128,128,3)\n",
        "elos = np.array(elos).astype(np.float)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Tjivo2d6r-V"
      },
      "source": [
        "vectors = np.array(vectors)#.reshape(-1,128,128,3)\n",
        "elos = np.array(elos).astype(np.float)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLxXPw7-MowU"
      },
      "source": [
        "np.save('/content/drive/My Drive/Generated Images/vecs_combined.npy', vectors)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TYLTczk38A3"
      },
      "source": [
        "vectors = np.load('/content/drive/My Drive/Generated Images/vecs_combined.npy')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6YmnNgqEM5Kz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9da2b64f-682f-4e50-c81f-64cab366fdf2"
      },
      "source": [
        "vectors = vectors.reshape(-1, 512)\n",
        "print(vectors.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1632, 512)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JuBdzxQzNI35"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=20)\n",
        "vecsPCA = pca.fit_transform(vectors)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Keg6hXNZLWUU"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(elos.reshape(-1,1))\n",
        "elos_scaled = scaler.transform(elos.reshape(-1,1))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "2iL_aPiEXr8g",
        "outputId": "a573128b-320c-4d14-b510-e6e2d54b487a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hist(elos_scaled, bins = 20)\n",
        "plt.show()\n",
        "\n",
        "print(np.mean(elos_scaled))"
      ],
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "-2.1769078914218755e-17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XF_XYtZs4MuR"
      },
      "source": [
        "(trainX, testX, trainY, testY) = train_test_split(vectors, elos_scaled, test_size=0.2, random_state=42)"
      ],
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqgJaxvi6PyO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50cb1a3d-c487-4f9c-c2a7-a3000e714556"
      },
      "source": [
        "print(trainX)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 1.23684549  0.42353694 -1.01001605 ...  1.48244017  1.06113806\n",
            "  -0.44588541]\n",
            " [ 0.15713157 -0.69370653  1.82941307 ... -2.22805009  1.30176343\n",
            "   0.57557569]\n",
            " [ 0.41101982  0.82926288  0.2773068  ... -1.18451994  0.32692662\n",
            "   1.05128393]\n",
            " ...\n",
            " [-1.02875648 -1.72820494 -0.50480645 ...  2.69430414  0.66855121\n",
            "   0.1587326 ]\n",
            " [-0.87250512 -0.45720249  0.68693799 ...  0.07700557 -0.00627578\n",
            "  -0.08065343]\n",
            " [-0.39623639 -0.23915602 -0.44787301 ...  1.32078959  0.04956764\n",
            "  -2.02120947]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQTRaDDC4fvO"
      },
      "source": [
        "model = Sequential()\n",
        "#model.add(keras.layers.Conv2D(4, [3,3], activation='relu', padding='same'))\n",
        "#model.add(keras.layers.Conv2D(8, [3,3], activation='relu', padding='same'))\n",
        "#model.add(keras.layers.Conv2D(16, [3,3], activation='relu', padding='same'))\n",
        "\n",
        "model.add(keras.layers.Dense(256, activation='relu', kernel_regularizer=\"l2\", activity_regularizer=\"l2\", bias_regularizer=\"l2\"))\n",
        "model.add(keras.layers.BatchNormalization())\n",
        "model.add(keras.layers.Dropout(0.5))\n",
        "\n",
        "model.add(keras.layers.Dense(128, activation='relu', kernel_regularizer=\"l2\"))\n",
        "model.add(keras.layers.BatchNormalization())\n",
        "model.add(keras.layers.Dropout(0.5))\n",
        "\n",
        "model.add(keras.layers.Dense(16, activation='relu', kernel_regularizer=\"l2\", activity_regularizer=\"l2\", bias_regularizer=\"l1\"))\n",
        "model.add(keras.layers.BatchNormalization())\n",
        "model.add(keras.layers.Dropout(0.2))\n",
        "\n",
        "model.add(keras.layers.Dense(8, activation='relu', kernel_regularizer=\"l2\", activity_regularizer=\"l2\"))\n",
        "model.add(keras.layers.BatchNormalization())\n",
        "model.add(keras.layers.Dropout(0.1))\n",
        "\n",
        "#model.add(keras.layers.Dense(16, activation='relu', kernel_regularizer=\"l2\"))\n",
        "\n",
        "#model.add(keras.layers.Activation('relu'))\n",
        "#model.add(keras.layers.Flatten())\n",
        "#model.add(keras.layers.Dense(8, activation='relu', kernel_regularizer=\"l2\"))\n",
        "model.add(keras.layers.Dense(1))"
      ],
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8aijQ9Q4pRh"
      },
      "source": [
        "opt = keras.optimizers.Adam(0.0001)\n",
        "model.compile(loss=\"mean_absolute_error\", optimizer=opt, metrics=['mse'])"
      ],
      "execution_count": 240,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mL_fFnhP5SUL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a176620-552a-4408-cd56-bfc830741a89"
      },
      "source": [
        "H = model.fit(x=trainX, y=trainY, validation_data=(testX, testY), epochs=500, batch_size=128)\n",
        "#train_mse = model.evaluate(trainX, trainY, verbose=0)\n",
        "#test_mse = model.evaluate(testX, testY, verbose=0)"
      ],
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 1305 samples, validate on 327 samples\n",
            "Epoch 1/500\n",
            "1305/1305 [==============================] - 2s 2ms/step - loss: 0.7751 - mse: 1.0175 - val_loss: 0.7518 - val_mse: 0.9298\n",
            "Epoch 2/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7744 - mse: 1.0176 - val_loss: 0.7504 - val_mse: 0.9298\n",
            "Epoch 3/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7738 - mse: 1.0176 - val_loss: 0.7495 - val_mse: 0.9298\n",
            "Epoch 4/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7736 - mse: 1.0179 - val_loss: 0.7489 - val_mse: 0.9299\n",
            "Epoch 5/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7732 - mse: 1.0177 - val_loss: 0.7484 - val_mse: 0.9299\n",
            "Epoch 6/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7729 - mse: 1.0177 - val_loss: 0.7481 - val_mse: 0.9299\n",
            "Epoch 7/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7727 - mse: 1.0177 - val_loss: 0.7478 - val_mse: 0.9299\n",
            "Epoch 8/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7724 - mse: 1.0177 - val_loss: 0.7475 - val_mse: 0.9299\n",
            "Epoch 9/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7723 - mse: 1.0177 - val_loss: 0.7473 - val_mse: 0.9299\n",
            "Epoch 10/500\n",
            "1305/1305 [==============================] - 0s 125us/step - loss: 0.7722 - mse: 1.0178 - val_loss: 0.7471 - val_mse: 0.9299\n",
            "Epoch 11/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7720 - mse: 1.0178 - val_loss: 0.7469 - val_mse: 0.9299\n",
            "Epoch 12/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7718 - mse: 1.0178 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 13/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7717 - mse: 1.0177 - val_loss: 0.7466 - val_mse: 0.9298\n",
            "Epoch 14/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7716 - mse: 1.0177 - val_loss: 0.7465 - val_mse: 0.9298\n",
            "Epoch 15/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7715 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 16/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7713 - mse: 1.0177 - val_loss: 0.7462 - val_mse: 0.9298\n",
            "Epoch 17/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7712 - mse: 1.0176 - val_loss: 0.7461 - val_mse: 0.9298\n",
            "Epoch 18/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7712 - mse: 1.0178 - val_loss: 0.7461 - val_mse: 0.9298\n",
            "Epoch 19/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7711 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 20/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7709 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 21/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7709 - mse: 1.0178 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 22/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9298\n",
            "Epoch 23/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7707 - mse: 1.0176 - val_loss: 0.7457 - val_mse: 0.9298\n",
            "Epoch 24/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7707 - mse: 1.0179 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 25/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 26/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 27/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 28/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7703 - mse: 1.0176 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 29/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 30/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7703 - mse: 1.0176 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 31/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 32/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7702 - mse: 1.0178 - val_loss: 0.7452 - val_mse: 0.9299\n",
            "Epoch 33/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7703 - mse: 1.0179 - val_loss: 0.7452 - val_mse: 0.9299\n",
            "Epoch 34/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7702 - mse: 1.0178 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 35/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 36/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7701 - mse: 1.0178 - val_loss: 0.7451 - val_mse: 0.9299\n",
            "Epoch 37/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9299\n",
            "Epoch 38/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9299\n",
            "Epoch 39/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7701 - mse: 1.0178 - val_loss: 0.7451 - val_mse: 0.9299\n",
            "Epoch 40/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9299\n",
            "Epoch 41/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9299\n",
            "Epoch 42/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7699 - mse: 1.0176 - val_loss: 0.7450 - val_mse: 0.9299\n",
            "Epoch 43/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9299\n",
            "Epoch 44/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 45/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 46/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7699 - mse: 1.0179 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 47/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 48/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 49/500\n",
            "1305/1305 [==============================] - 0s 92us/step - loss: 0.7699 - mse: 1.0178 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 50/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7698 - mse: 1.0175 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 51/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7698 - mse: 1.0176 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 52/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 53/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 54/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7698 - mse: 1.0176 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 55/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7698 - mse: 1.0179 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 56/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 57/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7699 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 58/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 59/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7699 - mse: 1.0179 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 60/500\n",
            "1305/1305 [==============================] - 0s 91us/step - loss: 0.7698 - mse: 1.0176 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 61/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 62/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 63/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 64/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 65/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 66/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 67/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 68/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 69/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 70/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 71/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 72/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 73/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 74/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 75/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 76/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 77/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 78/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 79/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7698 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 80/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 81/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 82/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7696 - mse: 1.0175 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 83/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 84/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0175 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 85/500\n",
            "1305/1305 [==============================] - 0s 130us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 86/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 87/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 88/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7698 - mse: 1.0179 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 89/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 90/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 91/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 92/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 93/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 94/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 95/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 96/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 97/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 98/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 99/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 100/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 101/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 102/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7696 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 103/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 104/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 105/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 106/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 107/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 108/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7698 - mse: 1.0179 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 109/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 110/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 111/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 112/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 113/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 114/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 115/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 116/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 117/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 118/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 119/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 120/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 121/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 122/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 123/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 124/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 125/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 126/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 127/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 128/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 129/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 130/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 131/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 132/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 133/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 134/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 135/500\n",
            "1305/1305 [==============================] - 0s 92us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 136/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 137/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 138/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 139/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 140/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 141/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 142/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 143/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 144/500\n",
            "1305/1305 [==============================] - 0s 121us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 145/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 146/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 147/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 148/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 149/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 150/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 151/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 152/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 153/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 154/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 155/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 156/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 157/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 158/500\n",
            "1305/1305 [==============================] - 0s 92us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 159/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 160/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 161/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 162/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 163/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 164/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 165/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 166/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 167/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 168/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 169/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 170/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 171/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 172/500\n",
            "1305/1305 [==============================] - 0s 93us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7446 - val_mse: 0.9298\n",
            "Epoch 173/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0176 - val_loss: 0.7446 - val_mse: 0.9298\n",
            "Epoch 174/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 175/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 176/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 177/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 178/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 179/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 180/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 181/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 182/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 183/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 184/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 185/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 186/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 187/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7699 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 188/500\n",
            "1305/1305 [==============================] - 0s 95us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 189/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 190/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 191/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7700 - mse: 1.0178 - val_loss: 0.7450 - val_mse: 0.9299\n",
            "Epoch 192/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 193/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 194/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 195/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 196/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 197/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 198/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 199/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 200/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 201/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 202/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 203/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 204/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 205/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 206/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 207/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 208/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7700 - mse: 1.0178 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 209/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7699 - mse: 1.0178 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 210/500\n",
            "1305/1305 [==============================] - 0s 125us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 211/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 212/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 213/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 214/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 215/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 216/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7699 - mse: 1.0176 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 217/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 218/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 219/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 220/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 221/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7703 - mse: 1.0178 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 222/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9298\n",
            "Epoch 223/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9299\n",
            "Epoch 224/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9299\n",
            "Epoch 225/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9299\n",
            "Epoch 226/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9299\n",
            "Epoch 227/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7453 - val_mse: 0.9299\n",
            "Epoch 228/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7453 - val_mse: 0.9298\n",
            "Epoch 229/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 230/500\n",
            "1305/1305 [==============================] - 0s 94us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 231/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 232/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 233/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 234/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7703 - mse: 1.0176 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 235/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 236/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 237/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 238/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7462 - val_mse: 0.9299\n",
            "Epoch 239/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9299\n",
            "Epoch 240/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7466 - val_mse: 0.9299\n",
            "Epoch 241/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7465 - val_mse: 0.9298\n",
            "Epoch 242/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 243/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 244/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7465 - val_mse: 0.9298\n",
            "Epoch 245/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7465 - val_mse: 0.9298\n",
            "Epoch 246/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7706 - mse: 1.0176 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 247/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7467 - val_mse: 0.9298\n",
            "Epoch 248/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7466 - val_mse: 0.9298\n",
            "Epoch 249/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7705 - mse: 1.0176 - val_loss: 0.7467 - val_mse: 0.9298\n",
            "Epoch 250/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 251/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7712 - mse: 1.0177 - val_loss: 0.7476 - val_mse: 0.9298\n",
            "Epoch 252/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7713 - mse: 1.0177 - val_loss: 0.7481 - val_mse: 0.9298\n",
            "Epoch 253/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7715 - mse: 1.0177 - val_loss: 0.7481 - val_mse: 0.9298\n",
            "Epoch 254/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7713 - mse: 1.0177 - val_loss: 0.7478 - val_mse: 0.9298\n",
            "Epoch 255/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7477 - val_mse: 0.9298\n",
            "Epoch 256/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7710 - mse: 1.0178 - val_loss: 0.7476 - val_mse: 0.9298\n",
            "Epoch 257/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7477 - val_mse: 0.9298\n",
            "Epoch 258/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7709 - mse: 1.0177 - val_loss: 0.7477 - val_mse: 0.9299\n",
            "Epoch 259/500\n",
            "1305/1305 [==============================] - 0s 97us/step - loss: 0.7712 - mse: 1.0177 - val_loss: 0.7477 - val_mse: 0.9298\n",
            "Epoch 260/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7711 - mse: 1.0177 - val_loss: 0.7474 - val_mse: 0.9298\n",
            "Epoch 261/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7475 - val_mse: 0.9298\n",
            "Epoch 262/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7709 - mse: 1.0176 - val_loss: 0.7473 - val_mse: 0.9298\n",
            "Epoch 263/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 264/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 265/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 266/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 267/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7467 - val_mse: 0.9298\n",
            "Epoch 268/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 269/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7471 - val_mse: 0.9298\n",
            "Epoch 270/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7709 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 271/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7707 - mse: 1.0176 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 272/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7709 - mse: 1.0177 - val_loss: 0.7472 - val_mse: 0.9298\n",
            "Epoch 273/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7471 - val_mse: 0.9298\n",
            "Epoch 274/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 275/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 276/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7473 - val_mse: 0.9298\n",
            "Epoch 277/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7712 - mse: 1.0177 - val_loss: 0.7475 - val_mse: 0.9298\n",
            "Epoch 278/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7712 - mse: 1.0177 - val_loss: 0.7476 - val_mse: 0.9298\n",
            "Epoch 279/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7471 - val_mse: 0.9298\n",
            "Epoch 280/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 281/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 282/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7706 - mse: 1.0176 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 283/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 284/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7711 - mse: 1.0177 - val_loss: 0.7471 - val_mse: 0.9298\n",
            "Epoch 285/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 286/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 287/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7708 - mse: 1.0176 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 288/500\n",
            "1305/1305 [==============================] - 0s 130us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 289/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 290/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 291/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 292/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 293/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9298\n",
            "Epoch 294/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7708 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 295/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7466 - val_mse: 0.9298\n",
            "Epoch 296/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 297/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7710 - mse: 1.0177 - val_loss: 0.7469 - val_mse: 0.9299\n",
            "Epoch 298/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7709 - mse: 1.0177 - val_loss: 0.7472 - val_mse: 0.9298\n",
            "Epoch 299/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7711 - mse: 1.0177 - val_loss: 0.7473 - val_mse: 0.9298\n",
            "Epoch 300/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7709 - mse: 1.0177 - val_loss: 0.7471 - val_mse: 0.9298\n",
            "Epoch 301/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7706 - mse: 1.0177 - val_loss: 0.7470 - val_mse: 0.9298\n",
            "Epoch 302/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7468 - val_mse: 0.9298\n",
            "Epoch 303/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7705 - mse: 1.0176 - val_loss: 0.7466 - val_mse: 0.9298\n",
            "Epoch 304/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7465 - val_mse: 0.9298\n",
            "Epoch 305/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9299\n",
            "Epoch 306/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7462 - val_mse: 0.9299\n",
            "Epoch 307/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9299\n",
            "Epoch 308/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9299\n",
            "Epoch 309/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7464 - val_mse: 0.9299\n",
            "Epoch 310/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9299\n",
            "Epoch 311/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7705 - mse: 1.0178 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 312/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7462 - val_mse: 0.9298\n",
            "Epoch 313/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 314/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7707 - mse: 1.0177 - val_loss: 0.7463 - val_mse: 0.9298\n",
            "Epoch 315/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7707 - mse: 1.0176 - val_loss: 0.7462 - val_mse: 0.9298\n",
            "Epoch 316/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 317/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7461 - val_mse: 0.9298\n",
            "Epoch 318/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 319/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9299\n",
            "Epoch 320/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7461 - val_mse: 0.9298\n",
            "Epoch 321/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 322/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 323/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 324/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 325/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 326/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9298\n",
            "Epoch 327/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 328/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 329/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7705 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9298\n",
            "Epoch 330/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 331/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 332/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7460 - val_mse: 0.9299\n",
            "Epoch 333/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7704 - mse: 1.0177 - val_loss: 0.7459 - val_mse: 0.9298\n",
            "Epoch 334/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 335/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7702 - mse: 1.0178 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 336/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 337/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 338/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7701 - mse: 1.0176 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 339/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 340/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 341/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9299\n",
            "Epoch 342/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 343/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9299\n",
            "Epoch 344/500\n",
            "1305/1305 [==============================] - 0s 118us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9299\n",
            "Epoch 345/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 346/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 347/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 348/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 349/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 350/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 351/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 352/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 353/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9298\n",
            "Epoch 354/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7703 - mse: 1.0177 - val_loss: 0.7458 - val_mse: 0.9298\n",
            "Epoch 355/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7457 - val_mse: 0.9298\n",
            "Epoch 356/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7456 - val_mse: 0.9298\n",
            "Epoch 357/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 358/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 359/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 360/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 361/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 362/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7702 - mse: 1.0177 - val_loss: 0.7455 - val_mse: 0.9298\n",
            "Epoch 363/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 364/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7454 - val_mse: 0.9298\n",
            "Epoch 365/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 366/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 367/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 368/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 369/500\n",
            "1305/1305 [==============================] - 0s 98us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 370/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 371/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 372/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 373/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 374/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 375/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 376/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 377/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 378/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7701 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 379/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 380/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 381/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 382/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 383/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 384/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 385/500\n",
            "1305/1305 [==============================] - 0s 122us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 386/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 387/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7700 - mse: 1.0177 - val_loss: 0.7452 - val_mse: 0.9298\n",
            "Epoch 388/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7451 - val_mse: 0.9298\n",
            "Epoch 389/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7699 - mse: 1.0178 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 390/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 391/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 392/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 393/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 394/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 395/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 396/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 397/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7698 - mse: 1.0178 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 398/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 399/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 400/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 401/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 402/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 403/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 404/500\n",
            "1305/1305 [==============================] - 0s 122us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 405/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 406/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 407/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 408/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 409/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7698 - mse: 1.0176 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 410/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 411/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 412/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 413/500\n",
            "1305/1305 [==============================] - 0s 99us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 414/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 415/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 416/500\n",
            "1305/1305 [==============================] - 0s 127us/step - loss: 0.7699 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 417/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7450 - val_mse: 0.9298\n",
            "Epoch 418/500\n",
            "1305/1305 [==============================] - 0s 125us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 419/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 420/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 421/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 422/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 423/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 424/500\n",
            "1305/1305 [==============================] - 0s 121us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 425/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 426/500\n",
            "1305/1305 [==============================] - 0s 110us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 427/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 428/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 429/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 430/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 431/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 432/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9298\n",
            "Epoch 433/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 434/500\n",
            "1305/1305 [==============================] - 0s 118us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 435/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 436/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7449 - val_mse: 0.9299\n",
            "Epoch 437/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7698 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 438/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 439/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 440/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 441/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 442/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9298\n",
            "Epoch 443/500\n",
            "1305/1305 [==============================] - 0s 126us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 444/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 445/500\n",
            "1305/1305 [==============================] - 0s 124us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 446/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 447/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 448/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 449/500\n",
            "1305/1305 [==============================] - 0s 102us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 450/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 451/500\n",
            "1305/1305 [==============================] - 0s 124us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 452/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 453/500\n",
            "1305/1305 [==============================] - 0s 105us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 454/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 455/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 456/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 457/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 458/500\n",
            "1305/1305 [==============================] - 0s 122us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 459/500\n",
            "1305/1305 [==============================] - 0s 122us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 460/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 461/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 462/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 463/500\n",
            "1305/1305 [==============================] - 0s 96us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 464/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 465/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 466/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 467/500\n",
            "1305/1305 [==============================] - 0s 111us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 468/500\n",
            "1305/1305 [==============================] - 0s 112us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 469/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 470/500\n",
            "1305/1305 [==============================] - 0s 103us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 471/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 472/500\n",
            "1305/1305 [==============================] - 0s 120us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 473/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 474/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 475/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 476/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 477/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 478/500\n",
            "1305/1305 [==============================] - 0s 100us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 479/500\n",
            "1305/1305 [==============================] - 0s 123us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 480/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 481/500\n",
            "1305/1305 [==============================] - 0s 119us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 482/500\n",
            "1305/1305 [==============================] - 0s 106us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 483/500\n",
            "1305/1305 [==============================] - 0s 114us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 484/500\n",
            "1305/1305 [==============================] - 0s 113us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 485/500\n",
            "1305/1305 [==============================] - 0s 123us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 486/500\n",
            "1305/1305 [==============================] - 0s 123us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 487/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 488/500\n",
            "1305/1305 [==============================] - 0s 101us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 489/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 490/500\n",
            "1305/1305 [==============================] - 0s 116us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 491/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 492/500\n",
            "1305/1305 [==============================] - 0s 115us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 493/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7448 - val_mse: 0.9299\n",
            "Epoch 494/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 495/500\n",
            "1305/1305 [==============================] - 0s 108us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9299\n",
            "Epoch 496/500\n",
            "1305/1305 [==============================] - 0s 107us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 497/500\n",
            "1305/1305 [==============================] - 0s 104us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 498/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 499/500\n",
            "1305/1305 [==============================] - 0s 117us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n",
            "Epoch 500/500\n",
            "1305/1305 [==============================] - 0s 109us/step - loss: 0.7697 - mse: 1.0177 - val_loss: 0.7447 - val_mse: 0.9298\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNbVoeIp7fbj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55f3c765-ca84-4bb6-9617-2d91f2d5bc8e"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_29\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_100 (Dense)            (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "batch_normalization_50 (Batc (None, 256)               1024      \n",
            "_________________________________________________________________\n",
            "dropout_50 (Dropout)         (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_101 (Dense)            (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "batch_normalization_51 (Batc (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_51 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_102 (Dense)            (None, 16)                2064      \n",
            "_________________________________________________________________\n",
            "batch_normalization_52 (Batc (None, 16)                64        \n",
            "_________________________________________________________________\n",
            "dropout_52 (Dropout)         (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_103 (Dense)            (None, 8)                 136       \n",
            "_________________________________________________________________\n",
            "batch_normalization_53 (Batc (None, 8)                 32        \n",
            "_________________________________________________________________\n",
            "dropout_53 (Dropout)         (None, 8)                 0         \n",
            "_________________________________________________________________\n",
            "dense_104 (Dense)            (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 168,065\n",
            "Trainable params: 167,249\n",
            "Non-trainable params: 816\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSlTvt31_Sxy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "8c3f13d5-b7ec-46f9-9b7a-1a79b623f298"
      },
      "source": [
        "plot_loss(H)"
      ],
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3uXm7ew6P1R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fcd4e32-ba45-4ec1-890c-a20f3b99be2e"
      },
      "source": [
        "from sklearn.externals.joblib import dump, load\n",
        "\n",
        "model.save('/content/drive/My Drive/RetouchML/models/normalised.keras')\n",
        "dump(scaler, '/content/drive/My Drive/RetouchML/models/label_scaler.bin', compress=True)"
      ],
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/My Drive/RetouchML/models/label_scaler.bin']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    }
  ]
}