build_model_torbeam.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Format largely inspired by David Wang's code for creating surrogate model for toray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Package imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n",
    "import sklearn.metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from tqdm import tqdm\n",
    "import time\n",
    "import math\n",
    "import random\n",
    "import os\n",
    "\n",
    "from sklearn.model_selection import ParameterSampler\n",
    "from sklearn.model_selection import ParameterGrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = h5py.File('training_inputs.h5', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<KeysViewHDF5 ['33384', '33385', '33386', '33388', '33389', '33390', '33391', '33394', '33395', '33396', '33397', '33398', '33431', '33432', '33433', '33434', '33435', '33436', '33437', '33438', '33439', '33440', '33441', '33443', '33446', '33472', '33474', '33475', '33476', '33477', '33482', '33483', '33485', '33487', '33489', '33496', '33497', '33498', '33500', '33503', '33505', '33509', '33516', '33527', '33528', '33531', '33532', '33533', '33542', '33543', '33544', '33545', '33546', '33547', '33548', '33549', '33550', '33552', '33553', '33554', '33555', '33556', '33557', '33558', '33562', '33563', '33564', '33565', '33576', '33590', '33591', '33592', '33596', '33642', '33644', '33648', '33649', '33650', '33651', '33652', '33653', '33655', '33656', '33657', '33661', '33662', '33663', '33664', '33665', '33666', '33667', '33668', '33669', '33672', '33673', '33674', '33675', '33676', '33678', '33679', '33680', '33681', '33688', '33689', '33690', '33691', '33692', '33693', '33694', '33695', '33696', '33697', '33698', '33699', '33700', '33701', '33702', '33703', '33704', '33705', '33706', '33707', '33708', '33709', '33711', '33712', '33713', '33714', '33716', '33718', '33719', '33720', '33721', '33722', '33723', '33724', '33725', '33726', '33727', '33735', '33750', '33754', '33755', '33756', '33757', '33758', '33759', '33760', '33763', '33764', '33765', '33766', '33767', '33768', '33769', '33770', '33771', '33772', '33773', '33774', '33783', '33787', '33789', '33795', '33796', '33797', '33798', '33799', '33800', '33801', '33802', '33803', '33804', '33805', '33806', '33807', '33808', '33809', '33810', '33811', '33812', '33814', '33815', '33816', '33817', '33818', '33819', '33820', '33822', '33824', '33831', '33832', '33835', '33836', '33837', '33838', '33839', '33840', '33842', '33843', '33844', '33845', '33847', '33848', '33849', '33850', '33851', '33852', '33853', '33854', '33855', '33862', '33863', '33864', '33865', '33867', '33868', '33869', '33870', '33878', '33879', '33880', '33881', '33882', '33883', '33884', '33885', '33887', '33888', '33889', '33890', '33891', '33892', '33893', '33894', '33895', '33896', '33897', '33898', '33899', '33900', '33901', '33904', '33905', '33906', '33907', '33913', '33914', '33916', '33917', '33918', '33926', '33928', '33929', '33930', '33931', '33933', '33935', '33936', '33938', '33939', '33940', '33941', '33943', '33952', '33953', '33954', '33955', '33959', '33960', '33962', '33963', '33966', '33968', '33969', '33970', '33971', '33972', '33973', '33974', '33975', '33978', '33979', '33980', '33981', '33982', '33983', '33985', '33987', '33989', '33990', '33999', '34000', '34001', '34002', '34004', '34005', '34006', '34010', '34058', '34061', '34064', '34065', '34066', '34067', '34068', '34069', '34071', '34072', '34073', '34076', '34077', '34078', '34079', '34080', '34081', '34083', '34084', '34085', '34094', '34095', '34119', '34129', '34130', '34131', '34132', '34135', '34136', '34141', '34142', '34143', '34144', '34155', '34157', '34158', '34159', '34160', '34163', '34170', '34217', '34299', '34300', '34301', '34302', '34303', '34305', '34309', '34310', '34311', '34312', '34313', '34315', '34316', '34317', '34327', '34329', '34330', '34331', '34334', '34350', '34353', '34354', '34355', '34356', '34357', '34358', '34359', '34360', '34364', '34365', '34367', '34368', '34369', '34371', '34373', '34374']>\n"
     ]
    }
   ],
   "source": [
    "print(np.array(file.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<KeysViewHDF5 ['1.653', '1.7550000000000001', '1.857', '1.95', '2.052', '2.154', '2.2560000000000002', '2.35', '2.452', '2.5540000000000003', '2.656', '2.75', '2.851', '2.953', '3.055', '3.157', '3.251', '3.353', '3.455', '3.557', '3.6510000000000002', '3.753', '3.855', '3.9570000000000003', '4.05', '4.152', '4.2540000000000004', '4.356', '4.449', '4.5520000000000005', '4.6530000000000005', '4.756', '4.857', '4.9510000000000005', '5.053', '5.155', '5.257']>\n"
     ]
    }
   ],
   "source": [
    "print(np.array(file['34374'].keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<KeysViewHDF5 ['Br', 'Bt', 'Bt_center', 'Bz', 'Ip', 'PSIRZ_norm', 'R', 'R0', 'R_in', 'R_mid', 'R_out', 'Te_pos', 'Te_val', 'Z', 'Z0', 'aminor', 'bcentr_2', 'betan', 'betan_2', 'betap', 'elong', 'elongm', 'gapbot', 'gapin', 'gapout', 'gaptop', 'ipmhd', 'li', 'ltri', 'ne_pos', 'ne_val', 'rcencm', 'rcntr', 'rho_pol', 'rho_tor', 'rmidin', 'rmidout', 'rseps', 'utri', 'volume', 'wmhd', 'zcntr', 'zm', 'zseps']>\n"
     ]
    }
   ],
   "source": [
    "h5_param_keys = np.array(file['33384']['1.756'].keys())\n",
    "print(h5_param_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.06265033 -0.07479593 -0.08284204 ...  0.00099237  0.00151645\n",
      "   0.0019647 ]\n",
      " [-0.06706729 -0.07755297 -0.08311465 ...  0.00264143  0.00320266\n",
      "   0.00358989]\n",
      " [-0.07960579 -0.08335995 -0.08286235 ...  0.00654404  0.0068644\n",
      "   0.00703625]\n",
      " ...\n",
      " [ 0.06529678  0.06918781  0.06889316 ...  0.00108553  0.00173815\n",
      "   0.0025635 ]\n",
      " [ 0.04985824  0.06046062  0.06627885 ...  0.00443735  0.00488813\n",
      "   0.00555654]\n",
      " [ 0.04359592  0.05585859  0.06425403 ...  0.00574076  0.00627059\n",
      "   0.00690308]]\n",
      "(33, 33)\n"
     ]
    }
   ],
   "source": [
    "Br_test = np.array(file['33384']['1.756']['Br'])\n",
    "print(Br_test)\n",
    "print(Br_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start with combining data for just EC2 and X mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('torbeam_df/34374_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>rho_pol</th>\n",
       "      <th>psiN</th>\n",
       "      <th>rho_tor</th>\n",
       "      <th>R</th>\n",
       "      <th>Z</th>\n",
       "      <th>total_CD</th>\n",
       "      <th>absorbed_power_frac</th>\n",
       "      <th>CD_eta</th>\n",
       "      <th>pol_ang</th>\n",
       "      <th>tor_ang</th>\n",
       "      <th>mode</th>\n",
       "      <th>time</th>\n",
       "      <th>shot</th>\n",
       "      <th>gyro</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.141199</td>\n",
       "      <td>0.019937</td>\n",
       "      <td>0.081435</td>\n",
       "      <td>1.838247</td>\n",
       "      <td>0.079480</td>\n",
       "      <td>2090.118754</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004180</td>\n",
       "      <td>0.398481</td>\n",
       "      <td>-0.012812</td>\n",
       "      <td>-1</td>\n",
       "      <td>51456.0</td>\n",
       "      <td>34374</td>\n",
       "      <td>EC2</td>\n",
       "      <td>1.050000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.088642</td>\n",
       "      <td>0.007857</td>\n",
       "      <td>0.032094</td>\n",
       "      <td>1.837975</td>\n",
       "      <td>0.042554</td>\n",
       "      <td>2131.277443</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>0.437755</td>\n",
       "      <td>-0.012812</td>\n",
       "      <td>-1</td>\n",
       "      <td>51456.0</td>\n",
       "      <td>34374</td>\n",
       "      <td>EC2</td>\n",
       "      <td>1.050000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.350386</td>\n",
       "      <td>0.122770</td>\n",
       "      <td>0.257235</td>\n",
       "      <td>1.843898</td>\n",
       "      <td>0.207335</td>\n",
       "      <td>1739.355528</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003479</td>\n",
       "      <td>0.245794</td>\n",
       "      <td>-0.012812</td>\n",
       "      <td>-1</td>\n",
       "      <td>51456.0</td>\n",
       "      <td>34374</td>\n",
       "      <td>EC2</td>\n",
       "      <td>1.050000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.152156</td>\n",
       "      <td>0.023151</td>\n",
       "      <td>0.094564</td>\n",
       "      <td>1.838519</td>\n",
       "      <td>0.086481</td>\n",
       "      <td>2078.370303</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004157</td>\n",
       "      <td>0.390873</td>\n",
       "      <td>-0.012812</td>\n",
       "      <td>-1</td>\n",
       "      <td>51456.0</td>\n",
       "      <td>34374</td>\n",
       "      <td>EC2</td>\n",
       "      <td>1.050000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.173423</td>\n",
       "      <td>0.030076</td>\n",
       "      <td>0.122847</td>\n",
       "      <td>1.839623</td>\n",
       "      <td>0.100265</td>\n",
       "      <td>2052.766204</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004106</td>\n",
       "      <td>0.375912</td>\n",
       "      <td>-0.012812</td>\n",
       "      <td>-1</td>\n",
       "      <td>51456.0</td>\n",
       "      <td>34374</td>\n",
       "      <td>EC2</td>\n",
       "      <td>1.050000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4194</th>\n",
       "      <td>0</td>\n",
       "      <td>0.125348</td>\n",
       "      <td>0.015712</td>\n",
       "      <td>0.066065</td>\n",
       "      <td>1.838648</td>\n",
       "      <td>0.035207</td>\n",
       "      <td>8374.255653</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016749</td>\n",
       "      <td>0.451161</td>\n",
       "      <td>-0.068635</td>\n",
       "      <td>-1</td>\n",
       "      <td>9553.0</td>\n",
       "      <td>34374</td>\n",
       "      <td>EC2</td>\n",
       "      <td>1.050000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4195</th>\n",
       "      <td>0</td>\n",
       "      <td>0.464913</td>\n",
       "      <td>0.216144</td>\n",
       "      <td>0.357314</td>\n",
       "      <td>1.852197</td>\n",
       "      <td>0.253245</td>\n",
       "      <td>5865.450889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011731</td>\n",
       "      <td>0.195330</td>\n",
       "      <td>-0.068635</td>\n",
       "      <td>-1</td>\n",
       "      <td>9553.0</td>\n",
       "      <td>34374</td>\n",
       "      <td>EC2</td>\n",
       "      <td>1.050000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4196</th>\n",
       "      <td>0</td>\n",
       "      <td>0.447072</td>\n",
       "      <td>0.199873</td>\n",
       "      <td>0.342385</td>\n",
       "      <td>1.850677</td>\n",
       "      <td>0.242146</td>\n",
       "      <td>5947.700041</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011895</td>\n",
       "      <td>0.209919</td>\n",
       "      <td>-0.068635</td>\n",
       "      <td>-1</td>\n",
       "      <td>9553.0</td>\n",
       "      <td>34374</td>\n",
       "      <td>EC2</td>\n",
       "      <td>1.050000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4197</th>\n",
       "      <td>0</td>\n",
       "      <td>0.267040</td>\n",
       "      <td>0.071310</td>\n",
       "      <td>0.199094</td>\n",
       "      <td>1.843918</td>\n",
       "      <td>0.128824</td>\n",
       "      <td>7009.466950</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014019</td>\n",
       "      <td>0.352110</td>\n",
       "      <td>-0.068635</td>\n",
       "      <td>-1</td>\n",
       "      <td>9553.0</td>\n",
       "      <td>34374</td>\n",
       "      <td>EC2</td>\n",
       "      <td>1.050000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4198</th>\n",
       "      <td>0</td>\n",
       "      <td>0.516015</td>\n",
       "      <td>0.266271</td>\n",
       "      <td>0.400488</td>\n",
       "      <td>1.855036</td>\n",
       "      <td>0.285237</td>\n",
       "      <td>5608.505245</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011217</td>\n",
       "      <td>0.151213</td>\n",
       "      <td>-0.068635</td>\n",
       "      <td>-1</td>\n",
       "      <td>9553.0</td>\n",
       "      <td>34374</td>\n",
       "      <td>EC2</td>\n",
       "      <td>1.050000e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>741 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0   rho_pol      psiN   rho_tor         R         Z  \\\n",
       "0              0  0.141199  0.019937  0.081435  1.838247  0.079480   \n",
       "1              0  0.088642  0.007857  0.032094  1.837975  0.042554   \n",
       "2              0  0.350386  0.122770  0.257235  1.843898  0.207335   \n",
       "3              0  0.152156  0.023151  0.094564  1.838519  0.086481   \n",
       "4              0  0.173423  0.030076  0.122847  1.839623  0.100265   \n",
       "...          ...       ...       ...       ...       ...       ...   \n",
       "4194           0  0.125348  0.015712  0.066065  1.838648  0.035207   \n",
       "4195           0  0.464913  0.216144  0.357314  1.852197  0.253245   \n",
       "4196           0  0.447072  0.199873  0.342385  1.850677  0.242146   \n",
       "4197           0  0.267040  0.071310  0.199094  1.843918  0.128824   \n",
       "4198           0  0.516015  0.266271  0.400488  1.855036  0.285237   \n",
       "\n",
       "         total_CD  absorbed_power_frac    CD_eta   pol_ang   tor_ang  mode  \\\n",
       "0     2090.118754                  1.0  0.004180  0.398481 -0.012812    -1   \n",
       "1     2131.277443                  1.0  0.004263  0.437755 -0.012812    -1   \n",
       "2     1739.355528                  1.0  0.003479  0.245794 -0.012812    -1   \n",
       "3     2078.370303                  1.0  0.004157  0.390873 -0.012812    -1   \n",
       "4     2052.766204                  1.0  0.004106  0.375912 -0.012812    -1   \n",
       "...           ...                  ...       ...       ...       ...   ...   \n",
       "4194  8374.255653                  1.0  0.016749  0.451161 -0.068635    -1   \n",
       "4195  5865.450889                  1.0  0.011731  0.195330 -0.068635    -1   \n",
       "4196  5947.700041                  1.0  0.011895  0.209919 -0.068635    -1   \n",
       "4197  7009.466950                  1.0  0.014019  0.352110 -0.068635    -1   \n",
       "4198  5608.505245                  1.0  0.011217  0.151213 -0.068635    -1   \n",
       "\n",
       "         time   shot gyro          freq  \n",
       "0     51456.0  34374  EC2  1.050000e+11  \n",
       "1     51456.0  34374  EC2  1.050000e+11  \n",
       "2     51456.0  34374  EC2  1.050000e+11  \n",
       "3     51456.0  34374  EC2  1.050000e+11  \n",
       "4     51456.0  34374  EC2  1.050000e+11  \n",
       "...       ...    ...  ...           ...  \n",
       "4194   9553.0  34374  EC2  1.050000e+11  \n",
       "4195   9553.0  34374  EC2  1.050000e+11  \n",
       "4196   9553.0  34374  EC2  1.050000e+11  \n",
       "4197   9553.0  34374  EC2  1.050000e+11  \n",
       "4198   9553.0  34374  EC2  1.050000e+11  \n",
       "\n",
       "[741 rows x 16 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df[df['gyro'] == 'EC2']\n",
    "temp[temp['mode'] == -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<KeysViewHDF5 ['1.6560000000000001', '1.7550000000000001', '1.855', '1.955', '10.057', '10.156', '10.255', '10.355', '10.455', '10.555', '10.654', '10.753', '10.852', '10.951', '11.05', '11.158', '11.257', '11.356', '11.456', '11.555', '11.654', '11.753', '11.852', '11.951', '12.05', '12.149000000000001', '12.258000000000001', '12.358', '12.458', '12.558', '12.657', '12.756', '12.855', '12.955', '13.054', '13.153', '13.252', '13.351', '13.450000000000001', '13.558', '13.657', '13.756', '13.855', '13.954', '14.053', '14.152000000000001', '14.252', '14.351', '14.450000000000001', '14.55', '14.658', '14.757', '14.856', '14.955', '15.054', '15.154', '15.253', '15.353', '15.453000000000001', '15.552', '15.651', '15.751', '15.851', '15.951', '16.051000000000002', '16.15', '16.25', '16.35', '16.458000000000002', '16.558', '16.649', '16.75', '16.85', '16.95', '17.05', '17.150000000000002', '17.25', '17.35', '17.45', '17.55', '17.650000000000002', '17.75', '17.85', '17.95', '18.058', '18.157', '18.257', '18.356', '18.456', '18.555', '18.655', '18.754', '18.854', '18.954', '19.054000000000002', '19.153', '19.253', '19.353', '19.453', '19.553', '19.653', '19.753', '19.853', '19.952', '2.055', '2.154', '2.253', '2.352', '2.452', '2.551', '2.652', '2.7520000000000002', '2.851', '2.95', '20.052', '20.152', '20.252', '20.353', '20.453', '20.554000000000002', '20.654', '20.754', '20.854', '20.955000000000002', '21.055', '21.156', '21.256', '21.357', '21.458000000000002', '21.549', '21.650000000000002', '21.75', '21.85', '21.951', '22.051000000000002', '22.152', '22.252', '22.352', '22.452', '22.552', '22.652', '22.752', '22.852', '22.952', '23.052', '23.152', '23.252', '23.351', '23.452', '23.552', '23.651', '23.751', '23.851', '23.951', '24.052', '24.152', '24.252', '24.352', '24.451', '24.552', '24.651', '24.75', '24.85', '24.95', '25.049', '25.158', '25.258', '25.35', '25.45', '25.558', '25.658', '25.758', '25.857', '25.957', '26.057000000000002', '26.157', '26.257', '26.357', '26.458000000000002', '26.558', '26.658', '26.759', '26.85', '26.95', '27.051000000000002', '27.150000000000002', '27.251', '27.351', '27.451', '27.552', '27.652', '27.754', '27.854', '27.955000000000002', '28.056', '28.156000000000002', '28.257', '28.358', '28.458000000000002', '28.55', '28.651', '28.753', '28.854', '28.956', '29.058', '29.150000000000002', '29.251', '29.353', '29.454', '29.555', '29.657', '29.757', '29.85', '29.951', '3.0580000000000003', '3.158', '3.257', '3.356', '3.455', '3.5540000000000003', '3.653', '3.7520000000000002', '3.851', '3.95', '30.053', '30.155', '30.256', '30.358', '30.451', '30.552', '30.654', '30.755', '30.857', '30.958000000000002', '31.051000000000002', '31.152', '31.253', '31.355', '31.457', '31.559', '31.651', '31.752', '31.853', '31.954', '32.055', '32.156', '32.257', '32.349000000000004', '32.451', '32.553', '32.654', '32.756', '32.856', '32.957', '33.058', '33.15', '33.251', '33.352000000000004', '33.453', '33.554', '33.654', '33.756', '33.857', '33.958', '34.058', '34.15', '34.251', '34.351', '34.451', '34.552', '34.652', '34.753', '34.854', '34.955', '35.055', '35.157000000000004', '35.257', '35.358000000000004', '35.45', '35.551', '35.651', '35.752', '35.853', '35.953', '36.055', '36.155', '36.257', '36.349000000000004', '36.451', '36.552', '36.653', '36.754', '36.854', '36.956', '37.057', '37.158', '37.25', '37.352000000000004', '37.453', '37.554', '37.655', '37.757', '37.858000000000004', '37.95', '38.051', '38.152', '38.254', '38.355000000000004', '38.457', '38.558', '38.651', '38.752', '38.854', '38.956', '39.057', '39.159', '39.251', '39.352000000000004', '39.454', '39.555', '39.657000000000004', '39.758', '39.851', '39.952', '4.058', '4.157', '4.256', '4.355', '4.454', '4.553', '4.652', '4.751', '4.8500000000000005', '4.949', '40.054', '40.155', '40.257', '40.358000000000004', '40.45', '40.551', '40.652', '40.753', '40.854', '40.955', '41.057', '41.158', '41.25', '41.351', '41.453', '41.555', '41.656', '41.757', '41.858000000000004', '41.951', '42.052', '42.153', '42.254', '42.355000000000004', '42.457', '42.558', '42.65', '42.751', '42.853', '42.953', '43.055', '43.156', '43.256', '43.357', '43.458', '43.551', '43.652', '43.754', '43.855000000000004', '43.957', '44.057', '44.159', '44.251', '44.352000000000004', '44.453', '44.554', '44.655', '44.756', '44.858000000000004', '44.95', '45.051', '45.152', '45.254', '45.355000000000004', '45.456', '45.558', '45.659', '45.751', '45.852000000000004', '45.954', '46.055', '46.157000000000004', '46.257', '46.358000000000004', '46.45', '46.550000000000004', '46.651', '46.752', '46.853', '46.953', '47.054', '47.155', '47.256', '47.357', '47.458', '47.558', '47.658', '47.758', '47.858000000000004', '47.958', '48.058', '48.158', '48.258', '48.358000000000004', '48.45', '48.550000000000004', '48.651', '48.751', '48.851', '48.951', '49.051', '49.151', '49.25', '49.35', '49.45', '49.550000000000004', '49.65', '49.75', '49.858000000000004', '49.957', '5.058', '5.157', '5.256', '5.355', '5.454', '5.553', '5.652', '5.751', '5.8500000000000005', '5.958', '50.056000000000004', '50.156', '50.255', '50.354', '50.453', '50.552', '50.651', '50.751', '50.851', '50.951', '51.050000000000004', '51.158', '51.257', '51.357', '51.456', '51.555', '51.654', '51.754', '51.853', '51.952', '52.051', '52.15', '52.258', '52.358000000000004', '52.457', '52.556000000000004', '52.655', '52.756', '52.855000000000004', '52.954', '53.053000000000004', '53.152', '53.252', '53.353', '53.453', '53.553000000000004', '53.653', '53.753', '53.852000000000004', '53.952', '54.053000000000004', '54.152', '54.252', '54.352000000000004', '54.452', '54.552', '54.652', '6.057', '6.156', '6.255', '6.354', '6.453', '6.5520000000000005', '6.651', '6.75', '6.8580000000000005', '6.957', '7.056', '7.155', '7.2540000000000004', '7.353', '7.453', '7.5520000000000005', '7.651', '7.75', '7.857', '7.957', '8.056000000000001', '8.156', '8.255', '8.354000000000001', '8.453', '8.552', '8.651', '8.75', '8.85', '8.958', '9.057', '9.156', '9.255', '9.354000000000001', '9.454', '9.553', '9.652000000000001', '9.751', '9.85', '9.958']>\n"
     ]
    }
   ],
   "source": [
    "print(np.array(file['34374'].keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "531"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(file['34374'].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       51456.0\n",
       "1       51456.0\n",
       "2       51456.0\n",
       "3       51456.0\n",
       "4       51456.0\n",
       "         ...   \n",
       "5923     9553.0\n",
       "5924     9553.0\n",
       "5925     9553.0\n",
       "5926     9553.0\n",
       "5927     9553.0\n",
       "Name: time, Length: 5928, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2352.,  9553., 51456.])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df = pd.read_csv('TORBEAM_all_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>rho_pol</th>\n",
       "      <th>R</th>\n",
       "      <th>Z</th>\n",
       "      <th>CD_eta</th>\n",
       "      <th>pol_ang</th>\n",
       "      <th>tor_ang</th>\n",
       "      <th>gyro</th>\n",
       "      <th>mode</th>\n",
       "      <th>time</th>\n",
       "      <th>...</th>\n",
       "      <th>elong</th>\n",
       "      <th>gapin</th>\n",
       "      <th>gapout</th>\n",
       "      <th>gaptop</th>\n",
       "      <th>gapbot</th>\n",
       "      <th>li</th>\n",
       "      <th>ltri</th>\n",
       "      <th>utri</th>\n",
       "      <th>volume</th>\n",
       "      <th>wmhd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.386395</td>\n",
       "      <td>1.732197</td>\n",
       "      <td>0.176420</td>\n",
       "      <td>0.023184</td>\n",
       "      <td>0.275472</td>\n",
       "      <td>-0.143863</td>\n",
       "      <td>EC2</td>\n",
       "      <td>-1</td>\n",
       "      <td>6494.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.973659</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>7.949647</td>\n",
       "      <td>45.040767</td>\n",
       "      <td>37.499530</td>\n",
       "      <td>1.060367</td>\n",
       "      <td>0.522887</td>\n",
       "      <td>0.369570</td>\n",
       "      <td>13386183.6</td>\n",
       "      <td>257656.819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.267569</td>\n",
       "      <td>1.726815</td>\n",
       "      <td>-0.047142</td>\n",
       "      <td>0.025859</td>\n",
       "      <td>0.442036</td>\n",
       "      <td>-0.143863</td>\n",
       "      <td>EC2</td>\n",
       "      <td>-1</td>\n",
       "      <td>6494.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.973659</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>7.949647</td>\n",
       "      <td>45.040767</td>\n",
       "      <td>37.499530</td>\n",
       "      <td>1.060367</td>\n",
       "      <td>0.522887</td>\n",
       "      <td>0.369570</td>\n",
       "      <td>13386183.6</td>\n",
       "      <td>257656.819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.417614</td>\n",
       "      <td>1.735665</td>\n",
       "      <td>0.207672</td>\n",
       "      <td>0.022597</td>\n",
       "      <td>0.244748</td>\n",
       "      <td>-0.143863</td>\n",
       "      <td>EC2</td>\n",
       "      <td>-1</td>\n",
       "      <td>6494.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.973659</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>7.949647</td>\n",
       "      <td>45.040767</td>\n",
       "      <td>37.499530</td>\n",
       "      <td>1.060367</td>\n",
       "      <td>0.522887</td>\n",
       "      <td>0.369570</td>\n",
       "      <td>13386183.6</td>\n",
       "      <td>257656.819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.292321</td>\n",
       "      <td>1.727766</td>\n",
       "      <td>-0.111077</td>\n",
       "      <td>0.024718</td>\n",
       "      <td>0.476505</td>\n",
       "      <td>-0.143863</td>\n",
       "      <td>EC2</td>\n",
       "      <td>-1</td>\n",
       "      <td>6494.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.973659</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>7.949647</td>\n",
       "      <td>45.040767</td>\n",
       "      <td>37.499530</td>\n",
       "      <td>1.060367</td>\n",
       "      <td>0.522887</td>\n",
       "      <td>0.369570</td>\n",
       "      <td>13386183.6</td>\n",
       "      <td>257656.819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.489211</td>\n",
       "      <td>1.736356</td>\n",
       "      <td>0.267942</td>\n",
       "      <td>0.021069</td>\n",
       "      <td>0.180526</td>\n",
       "      <td>-0.143863</td>\n",
       "      <td>EC2</td>\n",
       "      <td>-1</td>\n",
       "      <td>6494.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.973659</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>7.949647</td>\n",
       "      <td>45.040767</td>\n",
       "      <td>37.499530</td>\n",
       "      <td>1.060367</td>\n",
       "      <td>0.522887</td>\n",
       "      <td>0.369570</td>\n",
       "      <td>13386183.6</td>\n",
       "      <td>257656.819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927376</th>\n",
       "      <td>927376</td>\n",
       "      <td>0.317231</td>\n",
       "      <td>1.707376</td>\n",
       "      <td>-0.073220</td>\n",
       "      <td>0.018352</td>\n",
       "      <td>0.454654</td>\n",
       "      <td>-0.088325</td>\n",
       "      <td>EC2</td>\n",
       "      <td>-1</td>\n",
       "      <td>9227.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.922403</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>8.688286</td>\n",
       "      <td>49.019197</td>\n",
       "      <td>39.357015</td>\n",
       "      <td>0.993877</td>\n",
       "      <td>0.570798</td>\n",
       "      <td>0.362073</td>\n",
       "      <td>13126724.9</td>\n",
       "      <td>280962.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927377</th>\n",
       "      <td>927377</td>\n",
       "      <td>0.316127</td>\n",
       "      <td>1.709019</td>\n",
       "      <td>0.049801</td>\n",
       "      <td>0.019155</td>\n",
       "      <td>0.372828</td>\n",
       "      <td>-0.088325</td>\n",
       "      <td>EC2</td>\n",
       "      <td>-1</td>\n",
       "      <td>9227.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.922403</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>8.688286</td>\n",
       "      <td>49.019197</td>\n",
       "      <td>39.357015</td>\n",
       "      <td>0.993877</td>\n",
       "      <td>0.570798</td>\n",
       "      <td>0.362073</td>\n",
       "      <td>13126724.9</td>\n",
       "      <td>280962.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927378</th>\n",
       "      <td>927378</td>\n",
       "      <td>0.350750</td>\n",
       "      <td>1.709540</td>\n",
       "      <td>-0.145107</td>\n",
       "      <td>0.016497</td>\n",
       "      <td>0.495757</td>\n",
       "      <td>-0.088325</td>\n",
       "      <td>EC2</td>\n",
       "      <td>-1</td>\n",
       "      <td>9227.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.922403</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>8.688286</td>\n",
       "      <td>49.019197</td>\n",
       "      <td>39.357015</td>\n",
       "      <td>0.993877</td>\n",
       "      <td>0.570798</td>\n",
       "      <td>0.362073</td>\n",
       "      <td>13126724.9</td>\n",
       "      <td>280962.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927379</th>\n",
       "      <td>927379</td>\n",
       "      <td>0.492018</td>\n",
       "      <td>1.718064</td>\n",
       "      <td>0.276742</td>\n",
       "      <td>0.015733</td>\n",
       "      <td>0.164368</td>\n",
       "      <td>-0.088325</td>\n",
       "      <td>EC2</td>\n",
       "      <td>-1</td>\n",
       "      <td>9227.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.922403</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>8.688286</td>\n",
       "      <td>49.019197</td>\n",
       "      <td>39.357015</td>\n",
       "      <td>0.993877</td>\n",
       "      <td>0.570798</td>\n",
       "      <td>0.362073</td>\n",
       "      <td>13126724.9</td>\n",
       "      <td>280962.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927380</th>\n",
       "      <td>927380</td>\n",
       "      <td>0.513872</td>\n",
       "      <td>1.720049</td>\n",
       "      <td>0.297619</td>\n",
       "      <td>0.015284</td>\n",
       "      <td>0.141724</td>\n",
       "      <td>-0.088325</td>\n",
       "      <td>EC2</td>\n",
       "      <td>-1</td>\n",
       "      <td>9227.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.922403</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>8.688286</td>\n",
       "      <td>49.019197</td>\n",
       "      <td>39.357015</td>\n",
       "      <td>0.993877</td>\n",
       "      <td>0.570798</td>\n",
       "      <td>0.362073</td>\n",
       "      <td>13126724.9</td>\n",
       "      <td>280962.370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155032 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0   rho_pol         R         Z    CD_eta   pol_ang  \\\n",
       "0                0  0.386395  1.732197  0.176420  0.023184  0.275472   \n",
       "1                1  0.267569  1.726815 -0.047142  0.025859  0.442036   \n",
       "2                2  0.417614  1.735665  0.207672  0.022597  0.244748   \n",
       "3                3  0.292321  1.727766 -0.111077  0.024718  0.476505   \n",
       "4                4  0.489211  1.736356  0.267942  0.021069  0.180526   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "927376      927376  0.317231  1.707376 -0.073220  0.018352  0.454654   \n",
       "927377      927377  0.316127  1.709019  0.049801  0.019155  0.372828   \n",
       "927378      927378  0.350750  1.709540 -0.145107  0.016497  0.495757   \n",
       "927379      927379  0.492018  1.718064  0.276742  0.015733  0.164368   \n",
       "927380      927380  0.513872  1.720049  0.297619  0.015284  0.141724   \n",
       "\n",
       "         tor_ang gyro  mode    time  ...     elong         gapin    gapout  \\\n",
       "0      -0.143863  EC2    -1  6494.0  ...  1.973659  1.000000e+10  7.949647   \n",
       "1      -0.143863  EC2    -1  6494.0  ...  1.973659  1.000000e+10  7.949647   \n",
       "2      -0.143863  EC2    -1  6494.0  ...  1.973659  1.000000e+10  7.949647   \n",
       "3      -0.143863  EC2    -1  6494.0  ...  1.973659  1.000000e+10  7.949647   \n",
       "4      -0.143863  EC2    -1  6494.0  ...  1.973659  1.000000e+10  7.949647   \n",
       "...          ...  ...   ...     ...  ...       ...           ...       ...   \n",
       "927376 -0.088325  EC2    -1  9227.0  ...  1.922403  1.000000e+10  8.688286   \n",
       "927377 -0.088325  EC2    -1  9227.0  ...  1.922403  1.000000e+10  8.688286   \n",
       "927378 -0.088325  EC2    -1  9227.0  ...  1.922403  1.000000e+10  8.688286   \n",
       "927379 -0.088325  EC2    -1  9227.0  ...  1.922403  1.000000e+10  8.688286   \n",
       "927380 -0.088325  EC2    -1  9227.0  ...  1.922403  1.000000e+10  8.688286   \n",
       "\n",
       "           gaptop     gapbot        li      ltri      utri      volume  \\\n",
       "0       45.040767  37.499530  1.060367  0.522887  0.369570  13386183.6   \n",
       "1       45.040767  37.499530  1.060367  0.522887  0.369570  13386183.6   \n",
       "2       45.040767  37.499530  1.060367  0.522887  0.369570  13386183.6   \n",
       "3       45.040767  37.499530  1.060367  0.522887  0.369570  13386183.6   \n",
       "4       45.040767  37.499530  1.060367  0.522887  0.369570  13386183.6   \n",
       "...           ...        ...       ...       ...       ...         ...   \n",
       "927376  49.019197  39.357015  0.993877  0.570798  0.362073  13126724.9   \n",
       "927377  49.019197  39.357015  0.993877  0.570798  0.362073  13126724.9   \n",
       "927378  49.019197  39.357015  0.993877  0.570798  0.362073  13126724.9   \n",
       "927379  49.019197  39.357015  0.993877  0.570798  0.362073  13126724.9   \n",
       "927380  49.019197  39.357015  0.993877  0.570798  0.362073  13126724.9   \n",
       "\n",
       "              wmhd  \n",
       "0       257656.819  \n",
       "1       257656.819  \n",
       "2       257656.819  \n",
       "3       257656.819  \n",
       "4       257656.819  \n",
       "...            ...  \n",
       "927376  280962.370  \n",
       "927377  280962.370  \n",
       "927378  280962.370  \n",
       "927379  280962.370  \n",
       "927380  280962.370  \n",
       "\n",
       "[155032 rows x 51 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = full_df[full_df['gyro'] == 'EC2']\n",
    "temp = temp[temp['mode'] == -1]\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'rho_pol', 'R', 'Z', 'CD_eta', 'pol_ang', 'tor_ang',\n",
      "       'gyro', 'mode', 'time', 'shot', 'freq', 'Bt_center', 'ne_val_0',\n",
      "       'ne_val_1', 'ne_val_2', 'ne_val_3', 'ne_val_4', 'ne_val_5', 'ne_val_6',\n",
      "       'ne_val_7', 'ne_val_8', 'ne_val_9', 'ne_val_10', 'ne_val_11',\n",
      "       'ne_val_12', 'ne_val_13', 'ne_val_14', 'ne_val_15', 'ne_val_16',\n",
      "       'ne_val_17', 'ne_val_18', 'ne_val_19', 'ne_val_20', 'Ip', 'R0', 'R_in',\n",
      "       'R_out', 'Z0', 'aminor', 'betan', 'elong', 'gapin', 'gapout', 'gaptop',\n",
      "       'gapbot', 'li', 'ltri', 'utri', 'volume', 'wmhd'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(full_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data(data):\n",
    "    # split chronologically before shuffling\n",
    "\n",
    "    data = data.sort_values(by='shot', ascending=False)\n",
    "    \n",
    "    # Decide how large to make validation and test sets\n",
    "    total_count = data.shape[0]\n",
    "    n_val = math.floor(total_count * 0.10)\n",
    "    n_test = math.floor(total_count * 0.10)\n",
    "    print(n_val)\n",
    "    \n",
    "    # separate data into test, validation, and training sets\n",
    "    data_test = data.iloc[:n_test]\n",
    "    data_val = data.iloc[n_test:n_test+n_val]\n",
    "    data_train = data.iloc[n_test+n_val:]\n",
    "    \n",
    "    training_size = len(data_train)\n",
    "    \n",
    "    print(\"Training Set Size:\", len(data_train))\n",
    "    print(\"Validation Set Size:\", len(data_val))\n",
    "    print(\"Testing Set Size:\", len(data_test))\n",
    "    print(\"Total:\", len(data_train)+len(data_test)+len(data_val))\n",
    "\n",
    "    return data_train, data_val, data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jj5684/.conda/envs/torch-env/lib/python3.12/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 4203896809455616.0000 - mae: 6573777.5000 - val_loss: 0.7854 - val_mae: 0.5494\n",
      "Epoch 2/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7842 - mae: 0.5492 - val_loss: 0.7845 - val_mae: 0.5492\n",
      "Epoch 3/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7833 - mae: 0.5487 - val_loss: 0.7836 - val_mae: 0.5490\n",
      "Epoch 4/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7824 - mae: 0.5485 - val_loss: 0.7827 - val_mae: 0.5487\n",
      "Epoch 5/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7809 - mae: 0.5478 - val_loss: 0.7818 - val_mae: 0.5485\n",
      "Epoch 6/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7812 - mae: 0.5485 - val_loss: 0.7808 - val_mae: 0.5482\n",
      "Epoch 7/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7802 - mae: 0.5480 - val_loss: 0.7799 - val_mae: 0.5480\n",
      "Epoch 8/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7785 - mae: 0.5476 - val_loss: 0.7789 - val_mae: 0.5477\n",
      "Epoch 9/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7775 - mae: 0.5472 - val_loss: 0.7780 - val_mae: 0.5475\n",
      "Epoch 10/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7760 - mae: 0.5465 - val_loss: 0.7770 - val_mae: 0.5472\n",
      "Epoch 11/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7761 - mae: 0.5471 - val_loss: 0.7760 - val_mae: 0.5469\n",
      "Epoch 12/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7744 - mae: 0.5462 - val_loss: 0.7750 - val_mae: 0.5466\n",
      "Epoch 13/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7742 - mae: 0.5465 - val_loss: 0.7740 - val_mae: 0.5463\n",
      "Epoch 14/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7727 - mae: 0.5460 - val_loss: 0.7730 - val_mae: 0.5460\n",
      "Epoch 15/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7711 - mae: 0.5452 - val_loss: 0.7720 - val_mae: 0.5457\n",
      "Epoch 16/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7712 - mae: 0.5454 - val_loss: 0.7710 - val_mae: 0.5454\n",
      "Epoch 17/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7690 - mae: 0.5447 - val_loss: 0.7699 - val_mae: 0.5451\n",
      "Epoch 18/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7689 - mae: 0.5451 - val_loss: 0.7689 - val_mae: 0.5448\n",
      "Epoch 19/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7679 - mae: 0.5445 - val_loss: 0.7678 - val_mae: 0.5445\n",
      "Epoch 20/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7669 - mae: 0.5441 - val_loss: 0.7668 - val_mae: 0.5441\n",
      "Epoch 21/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7660 - mae: 0.5439 - val_loss: 0.7657 - val_mae: 0.5438\n",
      "Epoch 22/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7644 - mae: 0.5432 - val_loss: 0.7647 - val_mae: 0.5435\n",
      "Epoch 23/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7634 - mae: 0.5429 - val_loss: 0.7636 - val_mae: 0.5431\n",
      "Epoch 24/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7624 - mae: 0.5428 - val_loss: 0.7625 - val_mae: 0.5428\n",
      "Epoch 25/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7614 - mae: 0.5425 - val_loss: 0.7614 - val_mae: 0.5424\n",
      "Epoch 26/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7609 - mae: 0.5423 - val_loss: 0.7603 - val_mae: 0.5421\n",
      "Epoch 27/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7592 - mae: 0.5418 - val_loss: 0.7590 - val_mae: 0.5417\n",
      "Epoch 28/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7577 - mae: 0.5414 - val_loss: 0.7578 - val_mae: 0.5413\n",
      "Epoch 29/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7568 - mae: 0.5411 - val_loss: 0.7566 - val_mae: 0.5409\n",
      "Epoch 30/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7553 - mae: 0.5404 - val_loss: 0.7553 - val_mae: 0.5405\n",
      "Epoch 31/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7537 - mae: 0.5396 - val_loss: 0.7541 - val_mae: 0.5401\n",
      "Epoch 32/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7526 - mae: 0.5394 - val_loss: 0.7529 - val_mae: 0.5396\n",
      "Epoch 33/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7522 - mae: 0.5396 - val_loss: 0.7516 - val_mae: 0.5392\n",
      "Epoch 34/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7504 - mae: 0.5386 - val_loss: 0.7504 - val_mae: 0.5389\n",
      "Epoch 35/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7492 - mae: 0.5385 - val_loss: 0.7491 - val_mae: 0.5385\n",
      "Epoch 36/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7477 - mae: 0.5381 - val_loss: 0.7478 - val_mae: 0.5380\n",
      "Epoch 37/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7466 - mae: 0.5375 - val_loss: 0.7464 - val_mae: 0.5376\n",
      "Epoch 38/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7457 - mae: 0.5373 - val_loss: 0.7450 - val_mae: 0.5372\n",
      "Epoch 39/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7437 - mae: 0.5366 - val_loss: 0.7436 - val_mae: 0.5368\n",
      "Epoch 40/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7425 - mae: 0.5365 - val_loss: 0.7422 - val_mae: 0.5363\n",
      "Epoch 41/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7419 - mae: 0.5364 - val_loss: 0.7409 - val_mae: 0.5359\n",
      "Epoch 42/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7399 - mae: 0.5355 - val_loss: 0.7395 - val_mae: 0.5354\n",
      "Epoch 43/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7395 - mae: 0.5357 - val_loss: 0.7380 - val_mae: 0.5350\n",
      "Epoch 44/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7366 - mae: 0.5346 - val_loss: 0.7366 - val_mae: 0.5345\n",
      "Epoch 45/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7353 - mae: 0.5340 - val_loss: 0.7352 - val_mae: 0.5340\n",
      "Epoch 46/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7340 - mae: 0.5334 - val_loss: 0.7337 - val_mae: 0.5335\n",
      "Epoch 47/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7323 - mae: 0.5329 - val_loss: 0.7322 - val_mae: 0.5331\n",
      "Epoch 48/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7315 - mae: 0.5328 - val_loss: 0.7307 - val_mae: 0.5326\n",
      "Epoch 49/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7297 - mae: 0.5322 - val_loss: 0.7292 - val_mae: 0.5320\n",
      "Epoch 50/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7282 - mae: 0.5318 - val_loss: 0.7276 - val_mae: 0.5315\n",
      "Epoch 51/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7262 - mae: 0.5310 - val_loss: 0.7260 - val_mae: 0.5310\n",
      "Epoch 52/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7250 - mae: 0.5307 - val_loss: 0.7244 - val_mae: 0.5304\n",
      "Epoch 53/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7227 - mae: 0.5297 - val_loss: 0.7227 - val_mae: 0.5299\n",
      "Epoch 54/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7210 - mae: 0.5291 - val_loss: 0.7210 - val_mae: 0.5293\n",
      "Epoch 55/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7199 - mae: 0.5291 - val_loss: 0.7193 - val_mae: 0.5287\n",
      "Epoch 56/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7180 - mae: 0.5285 - val_loss: 0.7175 - val_mae: 0.5281\n",
      "Epoch 57/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7161 - mae: 0.5275 - val_loss: 0.7155 - val_mae: 0.5274\n",
      "Epoch 58/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7143 - mae: 0.5269 - val_loss: 0.7134 - val_mae: 0.5267\n",
      "Epoch 59/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7122 - mae: 0.5262 - val_loss: 0.7112 - val_mae: 0.5259\n",
      "Epoch 60/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7096 - mae: 0.5253 - val_loss: 0.7089 - val_mae: 0.5251\n",
      "Epoch 61/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7076 - mae: 0.5244 - val_loss: 0.7065 - val_mae: 0.5242\n",
      "Epoch 62/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7044 - mae: 0.5232 - val_loss: 0.7039 - val_mae: 0.5233\n",
      "Epoch 63/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7024 - mae: 0.5228 - val_loss: 0.7012 - val_mae: 0.5223\n",
      "Epoch 64/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6995 - mae: 0.5217 - val_loss: 0.6982 - val_mae: 0.5213\n",
      "Epoch 65/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6967 - mae: 0.5207 - val_loss: 0.6950 - val_mae: 0.5201\n",
      "Epoch 66/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6928 - mae: 0.5192 - val_loss: 0.6917 - val_mae: 0.5189\n",
      "Epoch 67/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6905 - mae: 0.5187 - val_loss: 0.6881 - val_mae: 0.5176\n",
      "Epoch 68/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6862 - mae: 0.5168 - val_loss: 0.6843 - val_mae: 0.5163\n",
      "Epoch 69/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6821 - mae: 0.5153 - val_loss: 0.6803 - val_mae: 0.5148\n",
      "Epoch 70/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6784 - mae: 0.5143 - val_loss: 0.6761 - val_mae: 0.5133\n",
      "Epoch 71/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6746 - mae: 0.5127 - val_loss: 0.6717 - val_mae: 0.5117\n",
      "Epoch 72/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6703 - mae: 0.5111 - val_loss: 0.6672 - val_mae: 0.5100\n",
      "Epoch 73/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6656 - mae: 0.5094 - val_loss: 0.6626 - val_mae: 0.5083\n",
      "Epoch 74/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6606 - mae: 0.5076 - val_loss: 0.6578 - val_mae: 0.5066\n",
      "Epoch 75/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6560 - mae: 0.5060 - val_loss: 0.6531 - val_mae: 0.5048\n",
      "Epoch 76/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6510 - mae: 0.5040 - val_loss: 0.6482 - val_mae: 0.5030\n",
      "Epoch 77/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6465 - mae: 0.5023 - val_loss: 0.6432 - val_mae: 0.5011\n",
      "Epoch 78/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6411 - mae: 0.5002 - val_loss: 0.6383 - val_mae: 0.4993\n",
      "Epoch 79/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6363 - mae: 0.4985 - val_loss: 0.6331 - val_mae: 0.4974\n",
      "Epoch 80/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6302 - mae: 0.4961 - val_loss: 0.6280 - val_mae: 0.4954\n",
      "Epoch 81/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6259 - mae: 0.4948 - val_loss: 0.6229 - val_mae: 0.4935\n",
      "Epoch 82/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6208 - mae: 0.4927 - val_loss: 0.6177 - val_mae: 0.4915\n",
      "Epoch 83/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6157 - mae: 0.4909 - val_loss: 0.6125 - val_mae: 0.4895\n",
      "Epoch 84/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6102 - mae: 0.4888 - val_loss: 0.6073 - val_mae: 0.4875\n",
      "Epoch 85/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6055 - mae: 0.4869 - val_loss: 0.6020 - val_mae: 0.4855\n",
      "Epoch 86/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6002 - mae: 0.4848 - val_loss: 0.5968 - val_mae: 0.4835\n",
      "Epoch 87/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5947 - mae: 0.4828 - val_loss: 0.5915 - val_mae: 0.4814\n",
      "Epoch 88/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5894 - mae: 0.4806 - val_loss: 0.5861 - val_mae: 0.4793\n",
      "Epoch 89/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5839 - mae: 0.4784 - val_loss: 0.5808 - val_mae: 0.4773\n",
      "Epoch 90/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5785 - mae: 0.4764 - val_loss: 0.5754 - val_mae: 0.4752\n",
      "Epoch 91/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5738 - mae: 0.4747 - val_loss: 0.5700 - val_mae: 0.4731\n",
      "Epoch 92/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5682 - mae: 0.4724 - val_loss: 0.5645 - val_mae: 0.4709\n",
      "Epoch 93/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5627 - mae: 0.4702 - val_loss: 0.5591 - val_mae: 0.4688\n",
      "Epoch 94/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5573 - mae: 0.4682 - val_loss: 0.5536 - val_mae: 0.4666\n",
      "Epoch 95/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5518 - mae: 0.4659 - val_loss: 0.5481 - val_mae: 0.4644\n",
      "Epoch 96/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5461 - mae: 0.4637 - val_loss: 0.5426 - val_mae: 0.4623\n",
      "Epoch 97/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5407 - mae: 0.4615 - val_loss: 0.5370 - val_mae: 0.4601\n",
      "Epoch 98/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5351 - mae: 0.4594 - val_loss: 0.5315 - val_mae: 0.4578\n",
      "Epoch 99/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5296 - mae: 0.4572 - val_loss: 0.5259 - val_mae: 0.4556\n",
      "Epoch 100/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5240 - mae: 0.4550 - val_loss: 0.5203 - val_mae: 0.4533\n",
      "Epoch 101/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5183 - mae: 0.4526 - val_loss: 0.5146 - val_mae: 0.4511\n",
      "Epoch 102/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5124 - mae: 0.4503 - val_loss: 0.5090 - val_mae: 0.4487\n",
      "Epoch 103/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5071 - mae: 0.4482 - val_loss: 0.5033 - val_mae: 0.4464\n",
      "Epoch 104/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5011 - mae: 0.4457 - val_loss: 0.4976 - val_mae: 0.4441\n",
      "Epoch 105/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4954 - mae: 0.4433 - val_loss: 0.4919 - val_mae: 0.4417\n",
      "Epoch 106/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4903 - mae: 0.4410 - val_loss: 0.4862 - val_mae: 0.4393\n",
      "Epoch 107/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4843 - mae: 0.4387 - val_loss: 0.4804 - val_mae: 0.4369\n",
      "Epoch 108/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4786 - mae: 0.4363 - val_loss: 0.4747 - val_mae: 0.4344\n",
      "Epoch 109/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4729 - mae: 0.4336 - val_loss: 0.4689 - val_mae: 0.4320\n",
      "Epoch 110/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4668 - mae: 0.4312 - val_loss: 0.4631 - val_mae: 0.4295\n",
      "Epoch 111/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4613 - mae: 0.4288 - val_loss: 0.4573 - val_mae: 0.4269\n",
      "Epoch 112/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4553 - mae: 0.4261 - val_loss: 0.4515 - val_mae: 0.4244\n",
      "Epoch 113/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4496 - mae: 0.4237 - val_loss: 0.4457 - val_mae: 0.4218\n",
      "Epoch 114/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4437 - mae: 0.4210 - val_loss: 0.4399 - val_mae: 0.4192\n",
      "Epoch 115/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4381 - mae: 0.4184 - val_loss: 0.4340 - val_mae: 0.4166\n",
      "Epoch 116/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4322 - mae: 0.4160 - val_loss: 0.4282 - val_mae: 0.4140\n",
      "Epoch 117/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4264 - mae: 0.4134 - val_loss: 0.4223 - val_mae: 0.4113\n",
      "Epoch 118/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4204 - mae: 0.4106 - val_loss: 0.4165 - val_mae: 0.4086\n",
      "Epoch 119/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4144 - mae: 0.4079 - val_loss: 0.4106 - val_mae: 0.4059\n",
      "Epoch 120/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4086 - mae: 0.4050 - val_loss: 0.4048 - val_mae: 0.4032\n",
      "Epoch 121/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4026 - mae: 0.4023 - val_loss: 0.3989 - val_mae: 0.4004\n",
      "Epoch 122/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3969 - mae: 0.3998 - val_loss: 0.3930 - val_mae: 0.3976\n",
      "Epoch 123/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3914 - mae: 0.3970 - val_loss: 0.3872 - val_mae: 0.3948\n",
      "Epoch 124/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3855 - mae: 0.3941 - val_loss: 0.3813 - val_mae: 0.3920\n",
      "Epoch 125/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3795 - mae: 0.3912 - val_loss: 0.3755 - val_mae: 0.3891\n",
      "Epoch 126/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3738 - mae: 0.3884 - val_loss: 0.3696 - val_mae: 0.3863\n",
      "Epoch 127/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3678 - mae: 0.3855 - val_loss: 0.3638 - val_mae: 0.3834\n",
      "Epoch 128/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3621 - mae: 0.3828 - val_loss: 0.3579 - val_mae: 0.3804\n",
      "Epoch 129/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3562 - mae: 0.3796 - val_loss: 0.3521 - val_mae: 0.3775\n",
      "Epoch 130/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3503 - mae: 0.3767 - val_loss: 0.3463 - val_mae: 0.3745\n",
      "Epoch 131/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3445 - mae: 0.3737 - val_loss: 0.3405 - val_mae: 0.3715\n",
      "Epoch 132/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3387 - mae: 0.3709 - val_loss: 0.3347 - val_mae: 0.3685\n",
      "Epoch 133/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3331 - mae: 0.3678 - val_loss: 0.3289 - val_mae: 0.3655\n",
      "Epoch 134/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3272 - mae: 0.3647 - val_loss: 0.3232 - val_mae: 0.3624\n",
      "Epoch 135/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3211 - mae: 0.3614 - val_loss: 0.3174 - val_mae: 0.3594\n",
      "Epoch 136/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3157 - mae: 0.3586 - val_loss: 0.3117 - val_mae: 0.3563\n",
      "Epoch 137/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3102 - mae: 0.3556 - val_loss: 0.3060 - val_mae: 0.3531\n",
      "Epoch 138/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3044 - mae: 0.3525 - val_loss: 0.3003 - val_mae: 0.3500\n",
      "Epoch 139/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2990 - mae: 0.3495 - val_loss: 0.2947 - val_mae: 0.3469\n",
      "Epoch 140/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2932 - mae: 0.3464 - val_loss: 0.2891 - val_mae: 0.3437\n",
      "Epoch 141/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2874 - mae: 0.3429 - val_loss: 0.2835 - val_mae: 0.3405\n",
      "Epoch 142/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2822 - mae: 0.3401 - val_loss: 0.2779 - val_mae: 0.3373\n",
      "Epoch 143/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2764 - mae: 0.3368 - val_loss: 0.2724 - val_mae: 0.3341\n",
      "Epoch 144/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2708 - mae: 0.3333 - val_loss: 0.2669 - val_mae: 0.3308\n",
      "Epoch 145/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2655 - mae: 0.3304 - val_loss: 0.2614 - val_mae: 0.3276\n",
      "Epoch 146/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2599 - mae: 0.3270 - val_loss: 0.2559 - val_mae: 0.3243\n",
      "Epoch 147/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2544 - mae: 0.3236 - val_loss: 0.2505 - val_mae: 0.3210\n",
      "Epoch 148/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2490 - mae: 0.3203 - val_loss: 0.2452 - val_mae: 0.3177\n",
      "Epoch 149/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2434 - mae: 0.3167 - val_loss: 0.2398 - val_mae: 0.3144\n",
      "Epoch 150/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2386 - mae: 0.3139 - val_loss: 0.2345 - val_mae: 0.3110\n",
      "Epoch 151/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2330 - mae: 0.3101 - val_loss: 0.2293 - val_mae: 0.3077\n",
      "Epoch 152/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2274 - mae: 0.3067 - val_loss: 0.2240 - val_mae: 0.3042\n",
      "Epoch 153/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2223 - mae: 0.3034 - val_loss: 0.2187 - val_mae: 0.3008\n",
      "Epoch 154/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2174 - mae: 0.3000 - val_loss: 0.2135 - val_mae: 0.2973\n",
      "Epoch 155/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2123 - mae: 0.2967 - val_loss: 0.2083 - val_mae: 0.2938\n",
      "Epoch 156/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2067 - mae: 0.2930 - val_loss: 0.2032 - val_mae: 0.2904\n",
      "Epoch 157/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2021 - mae: 0.2898 - val_loss: 0.1981 - val_mae: 0.2868\n",
      "Epoch 158/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1971 - mae: 0.2863 - val_loss: 0.1931 - val_mae: 0.2833\n",
      "Epoch 159/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1923 - mae: 0.2830 - val_loss: 0.1881 - val_mae: 0.2798\n",
      "Epoch 160/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1869 - mae: 0.2791 - val_loss: 0.1832 - val_mae: 0.2762\n",
      "Epoch 161/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1821 - mae: 0.2756 - val_loss: 0.1783 - val_mae: 0.2726\n",
      "Epoch 162/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1771 - mae: 0.2720 - val_loss: 0.1734 - val_mae: 0.2690\n",
      "Epoch 163/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1723 - mae: 0.2683 - val_loss: 0.1686 - val_mae: 0.2654\n",
      "Epoch 164/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1675 - mae: 0.2646 - val_loss: 0.1639 - val_mae: 0.2617\n",
      "Epoch 165/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1631 - mae: 0.2613 - val_loss: 0.1591 - val_mae: 0.2580\n",
      "Epoch 166/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1579 - mae: 0.2572 - val_loss: 0.1545 - val_mae: 0.2543\n",
      "Epoch 167/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1536 - mae: 0.2537 - val_loss: 0.1498 - val_mae: 0.2506\n",
      "Epoch 168/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1487 - mae: 0.2498 - val_loss: 0.1453 - val_mae: 0.2468\n",
      "Epoch 169/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1446 - mae: 0.2464 - val_loss: 0.1408 - val_mae: 0.2431\n",
      "Epoch 170/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1401 - mae: 0.2426 - val_loss: 0.1364 - val_mae: 0.2393\n",
      "Epoch 171/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1355 - mae: 0.2385 - val_loss: 0.1321 - val_mae: 0.2355\n",
      "Epoch 172/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1304 - mae: 0.2342 - val_loss: 0.1279 - val_mae: 0.2316\n",
      "Epoch 173/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1274 - mae: 0.2311 - val_loss: 0.1237 - val_mae: 0.2278\n",
      "Epoch 174/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1235 - mae: 0.2275 - val_loss: 0.1197 - val_mae: 0.2240\n",
      "Epoch 175/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1195 - mae: 0.2235 - val_loss: 0.1158 - val_mae: 0.2201\n",
      "Epoch 176/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1145 - mae: 0.2188 - val_loss: 0.1120 - val_mae: 0.2162\n",
      "Epoch 177/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1106 - mae: 0.2149 - val_loss: 0.1084 - val_mae: 0.2123\n",
      "Epoch 178/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1079 - mae: 0.2117 - val_loss: 0.1048 - val_mae: 0.2084\n",
      "Epoch 179/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1040 - mae: 0.2075 - val_loss: 0.1014 - val_mae: 0.2044\n",
      "Epoch 180/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1011 - mae: 0.2039 - val_loss: 0.0981 - val_mae: 0.2005\n",
      "Epoch 181/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0981 - mae: 0.2001 - val_loss: 0.0950 - val_mae: 0.1965\n",
      "Epoch 182/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0944 - mae: 0.1956 - val_loss: 0.0920 - val_mae: 0.1926\n",
      "Epoch 183/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0918 - mae: 0.1918 - val_loss: 0.0891 - val_mae: 0.1886\n",
      "Epoch 184/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0891 - mae: 0.1880 - val_loss: 0.0864 - val_mae: 0.1846\n",
      "Epoch 185/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0862 - mae: 0.1840 - val_loss: 0.0839 - val_mae: 0.1806\n",
      "Epoch 186/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0838 - mae: 0.1799 - val_loss: 0.0815 - val_mae: 0.1767\n",
      "Epoch 187/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0814 - mae: 0.1761 - val_loss: 0.0793 - val_mae: 0.1727\n",
      "Epoch 188/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0792 - mae: 0.1721 - val_loss: 0.0772 - val_mae: 0.1688\n",
      "Epoch 189/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0768 - mae: 0.1678 - val_loss: 0.0753 - val_mae: 0.1648\n",
      "Epoch 190/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0754 - mae: 0.1642 - val_loss: 0.0735 - val_mae: 0.1609\n",
      "Epoch 191/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0736 - mae: 0.1602 - val_loss: 0.0719 - val_mae: 0.1570\n",
      "Epoch 192/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0716 - mae: 0.1563 - val_loss: 0.0705 - val_mae: 0.1532\n",
      "Epoch 193/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0713 - mae: 0.1532 - val_loss: 0.0692 - val_mae: 0.1494\n",
      "Epoch 194/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0707 - mae: 0.1498 - val_loss: 0.0681 - val_mae: 0.1458\n",
      "Epoch 195/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0686 - mae: 0.1455 - val_loss: 0.0671 - val_mae: 0.1423\n",
      "Epoch 196/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0672 - mae: 0.1415 - val_loss: 0.0663 - val_mae: 0.1389\n",
      "Epoch 197/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0671 - mae: 0.1387 - val_loss: 0.0657 - val_mae: 0.1357\n",
      "Epoch 198/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0664 - mae: 0.1354 - val_loss: 0.0651 - val_mae: 0.1327\n",
      "Epoch 199/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1324 - val_loss: 0.0648 - val_mae: 0.1299\n",
      "Epoch 200/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1294 - val_loss: 0.0645 - val_mae: 0.1274\n",
      "Epoch 201/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0645 - mae: 0.1269 - val_loss: 0.0643 - val_mae: 0.1253\n",
      "Epoch 202/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1250 - val_loss: 0.0642 - val_mae: 0.1235\n",
      "Epoch 203/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0653 - mae: 0.1239 - val_loss: 0.0641 - val_mae: 0.1221\n",
      "Epoch 204/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1228 - val_loss: 0.0640 - val_mae: 0.1211\n",
      "Epoch 205/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0642 - mae: 0.1210 - val_loss: 0.0640 - val_mae: 0.1204\n",
      "Epoch 206/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1200\n",
      "Epoch 207/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1198\n",
      "Epoch 208/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 209/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 210/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 211/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 212/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0639 - mae: 0.1194 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 213/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 214/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 215/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 216/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 217/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0643 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 218/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 219/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0660 - mae: 0.1209 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 220/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 221/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0641 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 222/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0653 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 223/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0653 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 224/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0638 - mae: 0.1194 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 225/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 226/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0660 - mae: 0.1209 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 227/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0642 - mae: 0.1195 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 228/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0659 - mae: 0.1208 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 229/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 230/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 231/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 232/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1206 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 233/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 234/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 235/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0641 - mae: 0.1195 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 236/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 237/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 238/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 239/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 240/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0645 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 241/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0645 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 242/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 243/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0633 - mae: 0.1190 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 244/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0661 - mae: 0.1211 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 245/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0658 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 246/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0657 - mae: 0.1206 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 247/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0661 - mae: 0.1209 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 248/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 249/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 250/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 251/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0662 - mae: 0.1210 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 252/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0660 - mae: 0.1209 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 253/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0660 - mae: 0.1208 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 254/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 255/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 256/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0643 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 257/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 258/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 259/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 260/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0663 - mae: 0.1212 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 261/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0645 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 262/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 263/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 264/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 265/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 266/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0665 - mae: 0.1213 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 267/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 268/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 269/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 270/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 271/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0662 - mae: 0.1210 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 272/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 273/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 274/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0635 - mae: 0.1192 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 275/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 276/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0644 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 277/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0644 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 278/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0644 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 279/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 280/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 281/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 282/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0642 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 283/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 284/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0643 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 285/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0643 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 286/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 287/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 288/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1206 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 289/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 290/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1194 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 291/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1195 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 292/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0630 - mae: 0.1189 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 293/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0642 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 294/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0637 - mae: 0.1193 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 295/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 296/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 297/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0637 - mae: 0.1193 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 298/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 299/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 300/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 301/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 302/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0639 - mae: 0.1194 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 303/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 304/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0658 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 305/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 306/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0661 - mae: 0.1209 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 307/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0644 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 308/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0642 - mae: 0.1194 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 309/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 310/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 311/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 312/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 313/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 314/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 315/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 316/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 317/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0660 - mae: 0.1209 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 318/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 319/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0636 - mae: 0.1191 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 320/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1195 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 321/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 322/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0653 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 323/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0658 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 324/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0653 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 325/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 326/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0641 - mae: 0.1195 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 327/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0645 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 328/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0644 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 329/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 330/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0638 - mae: 0.1194 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 331/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0643 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 332/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 333/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 334/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 335/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 336/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 337/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 338/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 339/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0639 - mae: 0.1195 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 340/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0645 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 341/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 342/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 343/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0643 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 344/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0643 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 345/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1195 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 346/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 347/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1206 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 348/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0641 - mae: 0.1195 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 349/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 350/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0638 - mae: 0.1193 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 351/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0636 - mae: 0.1193 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 352/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 353/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 354/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0669 - mae: 0.1214 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 355/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0641 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 356/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 357/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0661 - mae: 0.1208 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 358/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0642 - mae: 0.1195 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 359/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0645 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 360/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1194 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 361/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 362/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 363/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 364/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0636 - mae: 0.1193 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 365/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 366/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 367/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0642 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 368/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 369/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0645 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 370/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 371/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 372/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 373/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 374/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 375/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1206 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 376/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0658 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 377/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 378/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 379/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 380/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0661 - mae: 0.1210 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 381/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 382/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0642 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 383/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 384/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 385/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0642 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 386/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0637 - mae: 0.1193 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 387/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 388/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 389/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 390/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0644 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 391/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1206 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 392/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 393/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 394/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 395/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1195 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 396/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 397/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0643 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 398/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0659 - mae: 0.1209 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 399/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0641 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 400/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 401/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1206 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 402/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 403/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 404/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 405/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 406/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 407/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 408/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0657 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 409/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 410/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0643 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 411/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1206 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 412/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0645 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 413/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1208 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 414/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 415/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 416/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0637 - mae: 0.1193 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 417/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 418/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0645 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 419/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 420/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0658 - mae: 0.1208 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 421/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0645 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 422/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0637 - mae: 0.1192 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 423/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 424/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0642 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 425/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 426/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 427/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0644 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 428/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0642 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 429/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0662 - mae: 0.1210 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 430/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 431/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 432/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 433/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1206 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 434/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0641 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 435/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 436/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0641 - mae: 0.1194 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 437/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 438/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1206 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 439/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 440/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 441/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 442/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 443/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0659 - mae: 0.1208 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 444/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 445/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0643 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 446/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 447/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0658 - mae: 0.1208 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 448/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 449/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0660 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 450/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 451/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 452/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1200 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 453/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0657 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 454/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0638 - mae: 0.1193 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 455/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0641 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 456/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 457/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 458/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0658 - mae: 0.1208 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 459/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 460/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 461/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 462/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0636 - mae: 0.1192 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 463/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0648 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 464/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0660 - mae: 0.1209 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 465/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0653 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 466/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0658 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 467/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0654 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 468/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 469/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 470/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 471/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1206 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 472/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0643 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 473/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 474/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0653 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 475/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0656 - mae: 0.1206 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 476/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0644 - mae: 0.1199 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 477/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 478/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0658 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 479/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 480/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 481/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0657 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 482/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 483/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0646 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 484/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 485/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 486/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0652 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 487/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0643 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 488/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1195 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 489/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0642 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Epoch 490/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0649 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 491/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0655 - mae: 0.1205 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 492/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0658 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 493/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0653 - mae: 0.1204 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 494/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1203 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 495/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0651 - mae: 0.1202 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 496/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0644 - mae: 0.1197 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 497/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0660 - mae: 0.1207 - val_loss: 0.0640 - val_mae: 0.1195\n",
      "Epoch 498/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0650 - mae: 0.1201 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 499/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0647 - mae: 0.1198 - val_loss: 0.0640 - val_mae: 0.1196\n",
      "Epoch 500/500\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0640 - mae: 0.1196 - val_loss: 0.0640 - val_mae: 0.1197\n",
      "Test Loss: 0.0658, Test MAE: 0.1203\n",
      "\u001b[1m485/485\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 597us/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "# Define your input and output columns\n",
    "input_columns = [\n",
    "    'pol_ang', 'tor_ang', 'Bt_center', 'Ip', 'R0', 'R_in', 'R_out', 'Z0', 'aminor', 'betan', \n",
    "    'elong', 'gapin', 'gapout', 'gaptop', 'gapbot', 'li', 'ltri', 'utri', 'volume', 'wmhd', \n",
    "    'ne_val_0', 'ne_val_1', 'ne_val_2', 'ne_val_3', 'ne_val_4', 'ne_val_5', 'ne_val_6', \n",
    "    'ne_val_7', 'ne_val_8', 'ne_val_9', 'ne_val_10', 'ne_val_11', 'ne_val_12', 'ne_val_13', \n",
    "    'ne_val_14', 'ne_val_15', 'ne_val_16', 'ne_val_17', 'ne_val_18', 'ne_val_19', 'ne_val_20'\n",
    "]\n",
    "\n",
    "output_columns = ['rho_pol', 'R', 'Z', 'CD_eta']\n",
    "\n",
    "# Load the data\n",
    "df_initial = pd.read_csv(\"TORBEAM_all_df.csv\")  # replace with your CSV file path\n",
    "\n",
    "# Narrow down to a specific gyro and mode\n",
    "temp = df_initial[df_initial['gyro'] == 'EC2']\n",
    "temp = temp[temp['mode'] == -1]\n",
    "df = temp\n",
    "\n",
    "# Extract input features and output targets\n",
    "X = df[input_columns].values\n",
    "y = df[output_columns].values\n",
    "\n",
    "# Optional: Perform a train-validation-test split\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_val, X_test, y_val, y_test   = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Optional: Normalize features (recommended for neural networks)\n",
    "# scaler_X = StandardScaler()\n",
    "# X_train = scaler_X.fit_transform(X_train)\n",
    "# X_val = scaler_X.transform(X_val)\n",
    "# X_test = scaler_X.transform(X_test)\n",
    "\n",
    "# If desired, you can also scale the outputs, especially if their ranges differ significantly.\n",
    "# scaler_y = StandardScaler()\n",
    "# y_train = scaler_y.fit_transform(y_train)\n",
    "# y_val = scaler_y.transform(y_val)\n",
    "# y_test = scaler_y.transform(y_test)\n",
    "\n",
    "# Build a simple feed-forward neural network\n",
    "model = Sequential()\n",
    "model.add(Dense(128, activation='relu', input_shape=(X_train.shape[1],)))\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(len(output_columns), activation='linear'))  # linear output for regression\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer=Adam(learning_rate=1e-6), loss='mse', metrics=['mae'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(\n",
    "    X_train, y_train,\n",
    "    validation_data=(X_val, y_val),\n",
    "    epochs=500,\n",
    "    batch_size=256,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "# Evaluate the model on test data\n",
    "test_loss, test_mae = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(f\"Test Loss: {test_loss:.4f}, Test MAE: {test_mae:.4f}\")\n",
    "\n",
    "# If you scaled the outputs, don't forget to invert the scaling of predictions for interpretation:\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot training & validation loss over epochs\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(history.history['loss'], label='Train Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Model Loss Over Epochs')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# If you also have mae or another metric:\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(history.history['mae'], label='Train MAE')\n",
    "plt.plot(history.history['val_mae'], label='Validation MAE')\n",
    "plt.title('Model MAE Over Epochs')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('MAE')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(12, 10))\n",
    "\n",
    "output_names = ['rho_pol', 'R', 'Z', 'CD_eta']\n",
    "for i, ax in enumerate(axs.flat):\n",
    "    ax.scatter(y_test[:, i], y_pred[:, i], alpha=0.5)\n",
    "    ax.set_title(f'{output_names[i]}: True vs. Predicted')\n",
    "    ax.set_xlabel('True Values')\n",
    "    ax.set_ylabel('Predicted Values')\n",
    "    # Optionally draw a perfect-fit line\n",
    "    min_val = min(y_test[:, i].min(), y_pred[:, i].min())\n",
    "    max_val = max(y_test[:, i].max(), y_pred[:, i].max())\n",
    "    ax.plot([min_val, max_val], [min_val, max_val], 'r--')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00219641, 0.10711956, 0.2608335 , 1.6698617 ], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': [1.0003407001495361,\n",
       "  1.0000495910644531,\n",
       "  1.0000451803207397,\n",
       "  1.0000356435775757,\n",
       "  1.0000568628311157,\n",
       "  1.000068187713623,\n",
       "  1.0000556707382202,\n",
       "  1.0000444650650024,\n",
       "  1.0000495910644531,\n",
       "  1.0000452995300293,\n",
       "  1.0000559091567993,\n",
       "  1.0000306367874146,\n",
       "  1.0000832080841064,\n",
       "  1.0000602006912231,\n",
       "  1.0000537633895874,\n",
       "  1.000056266784668,\n",
       "  1.0000524520874023,\n",
       "  1.0000663995742798,\n",
       "  1.000056505203247,\n",
       "  1.0000468492507935,\n",
       "  1.0000566244125366,\n",
       "  1.0000258684158325,\n",
       "  1.0000340938568115,\n",
       "  1.0000536441802979,\n",
       "  1.0000488758087158,\n",
       "  1.0000497102737427,\n",
       "  1.0000494718551636,\n",
       "  1.0000393390655518,\n",
       "  1.0000468492507935,\n",
       "  1.0000604391098022,\n",
       "  1.0000439882278442,\n",
       "  1.0000406503677368,\n",
       "  1.000060796737671,\n",
       "  1.000056266784668,\n",
       "  1.0000513792037964,\n",
       "  1.0000150203704834,\n",
       "  1.000044822692871,\n",
       "  1.0000455379486084,\n",
       "  1.0000559091567993,\n",
       "  1.0000479221343994,\n",
       "  1.0000531673431396,\n",
       "  1.0000503063201904,\n",
       "  1.0000604391098022,\n",
       "  1.0000466108322144,\n",
       "  1.0000355243682861,\n",
       "  1.0000542402267456,\n",
       "  1.0000512599945068,\n",
       "  1.0000648498535156,\n",
       "  1.0000572204589844,\n",
       "  1.0000492334365845,\n",
       "  1.0000613927841187,\n",
       "  1.000050663948059,\n",
       "  1.0000470876693726,\n",
       "  1.0000622272491455,\n",
       "  1.000046730041504,\n",
       "  1.0000511407852173,\n",
       "  1.000065565109253,\n",
       "  1.0000429153442383,\n",
       "  1.0000419616699219,\n",
       "  1.0000643730163574,\n",
       "  1.000063419342041,\n",
       "  1.0000618696212769,\n",
       "  1.0000500679016113,\n",
       "  1.000058650970459,\n",
       "  1.0000526905059814,\n",
       "  1.0000444650650024,\n",
       "  1.0000592470169067,\n",
       "  1.0000594854354858,\n",
       "  1.0000306367874146,\n",
       "  1.000059723854065,\n",
       "  1.000053882598877,\n",
       "  1.0000584125518799,\n",
       "  1.0000423192977905,\n",
       "  1.000042200088501,\n",
       "  1.0000405311584473,\n",
       "  1.0000636577606201,\n",
       "  1.0000454187393188,\n",
       "  1.0000439882278442,\n",
       "  1.0000566244125366,\n",
       "  1.0000478029251099,\n",
       "  1.0000516176223755,\n",
       "  1.0000457763671875,\n",
       "  1.000065565109253,\n",
       "  1.0000629425048828,\n",
       "  1.0000494718551636,\n",
       "  1.0000391006469727,\n",
       "  1.0000474452972412,\n",
       "  1.0000594854354858,\n",
       "  1.000065565109253,\n",
       "  1.0000510215759277,\n",
       "  1.0000474452972412,\n",
       "  1.0000505447387695,\n",
       "  1.0000660419464111,\n",
       "  1.000035285949707,\n",
       "  1.0000629425048828,\n",
       "  1.0000425577163696,\n",
       "  1.000057339668274,\n",
       "  1.0000662803649902,\n",
       "  1.0000503063201904,\n",
       "  1.0000483989715576,\n",
       "  1.0000540018081665,\n",
       "  1.000065803527832,\n",
       "  1.0000622272491455,\n",
       "  1.0000522136688232,\n",
       "  1.0000511407852173,\n",
       "  1.000029444694519,\n",
       "  1.0000643730163574,\n",
       "  1.0000455379486084,\n",
       "  1.0000487565994263,\n",
       "  1.0000401735305786,\n",
       "  1.0000660419464111,\n",
       "  1.0000557899475098,\n",
       "  1.0000540018081665,\n",
       "  1.000056266784668,\n",
       "  1.000050663948059,\n",
       "  1.0000345706939697,\n",
       "  1.0000673532485962,\n",
       "  1.0000532865524292,\n",
       "  1.0000393390655518,\n",
       "  1.00006103515625,\n",
       "  1.0000494718551636,\n",
       "  1.0000635385513306,\n",
       "  1.000058650970459,\n",
       "  1.000067114830017,\n",
       "  1.0000489950180054,\n",
       "  1.0000604391098022,\n",
       "  1.0000550746917725,\n",
       "  1.0000441074371338,\n",
       "  1.000071406364441,\n",
       "  1.000064730644226,\n",
       "  1.0000590085983276,\n",
       "  1.000050663948059,\n",
       "  1.0000625848770142,\n",
       "  1.0000613927841187,\n",
       "  1.0000449419021606,\n",
       "  1.0000386238098145,\n",
       "  1.00005042552948,\n",
       "  1.0000516176223755,\n",
       "  1.0000473260879517,\n",
       "  1.0000462532043457,\n",
       "  1.000042200088501,\n",
       "  1.0000393390655518,\n",
       "  1.000046968460083,\n",
       "  1.0000532865524292,\n",
       "  1.000038981437683,\n",
       "  1.000062346458435,\n",
       "  1.000043272972107,\n",
       "  1.0000159740447998,\n",
       "  1.0000499486923218,\n",
       "  1.0000485181808472,\n",
       "  1.0000560283660889,\n",
       "  1.0000450611114502,\n",
       "  1.0000393390655518,\n",
       "  1.0000460147857666,\n",
       "  1.0000461339950562,\n",
       "  1.0000627040863037,\n",
       "  1.0000615119934082,\n",
       "  1.0000574588775635,\n",
       "  1.0000638961791992,\n",
       "  1.000059962272644,\n",
       "  1.0000486373901367,\n",
       "  1.0000617504119873,\n",
       "  1.0000417232513428,\n",
       "  1.0000417232513428,\n",
       "  1.0000507831573486,\n",
       "  1.000059962272644,\n",
       "  1.000044345855713,\n",
       "  1.0000512599945068,\n",
       "  1.000051736831665,\n",
       "  1.0000364780426025,\n",
       "  1.000051736831665,\n",
       "  1.000054955482483,\n",
       "  1.0000441074371338,\n",
       "  1.0000438690185547,\n",
       "  1.0000596046447754,\n",
       "  1.0000447034835815,\n",
       "  1.0000523328781128,\n",
       "  1.0000369548797607,\n",
       "  1.0000452995300293,\n",
       "  1.0000555515289307,\n",
       "  1.000053882598877,\n",
       "  1.0000619888305664,\n",
       "  1.0000417232513428,\n",
       "  1.0000566244125366,\n",
       "  1.0000505447387695,\n",
       "  1.0000475645065308,\n",
       "  1.0000537633895874,\n",
       "  1.0000348091125488,\n",
       "  1.0000554323196411,\n",
       "  1.000054955482483,\n",
       "  1.0000396966934204,\n",
       "  1.000054121017456,\n",
       "  1.0000611543655396,\n",
       "  1.0000547170639038,\n",
       "  1.0000483989715576,\n",
       "  1.0000208616256714,\n",
       "  1.0000512599945068,\n",
       "  1.0000474452972412,\n",
       "  1.0000673532485962,\n",
       "  1.0000511407852173,\n",
       "  1.0000556707382202,\n",
       "  1.0000430345535278,\n",
       "  1.0000245571136475,\n",
       "  1.0000487565994263,\n",
       "  1.0000553131103516,\n",
       "  1.0000509023666382,\n",
       "  1.0000661611557007,\n",
       "  1.0000659227371216,\n",
       "  1.0000479221343994,\n",
       "  1.000044345855713,\n",
       "  1.0000373125076294,\n",
       "  1.0000441074371338,\n",
       "  1.0000489950180054,\n",
       "  1.0000524520874023,\n",
       "  1.000049352645874,\n",
       "  1.000057339668274,\n",
       "  1.000056505203247,\n",
       "  1.0000405311584473,\n",
       "  1.000053882598877,\n",
       "  1.000038981437683,\n",
       "  1.0000669956207275,\n",
       "  1.000061273574829,\n",
       "  1.000043511390686,\n",
       "  1.0000438690185547,\n",
       "  1.0000687837600708,\n",
       "  1.0000526905059814,\n",
       "  1.0000460147857666,\n",
       "  1.0000689029693604,\n",
       "  1.0000519752502441,\n",
       "  1.0000535249710083,\n",
       "  1.000051736831665,\n",
       "  1.0000451803207397,\n",
       "  1.000056266784668,\n",
       "  1.0000507831573486,\n",
       "  1.0000684261322021,\n",
       "  1.0000581741333008,\n",
       "  1.0000548362731934,\n",
       "  1.0000555515289307,\n",
       "  1.0000616312026978,\n",
       "  1.0000650882720947,\n",
       "  1.0000406503677368,\n",
       "  1.000065565109253,\n",
       "  1.0000336170196533,\n",
       "  1.000062346458435,\n",
       "  1.00006902217865,\n",
       "  1.0000109672546387,\n",
       "  1.0000592470169067,\n",
       "  1.0000566244125366,\n",
       "  1.0000425577163696,\n",
       "  1.0000745058059692,\n",
       "  1.0000569820404053,\n",
       "  1.0000425577163696,\n",
       "  1.000046968460083,\n",
       "  1.0000452995300293,\n",
       "  1.0000393390655518,\n",
       "  1.0000463724136353,\n",
       "  1.0000585317611694,\n",
       "  1.0000742673873901,\n",
       "  1.0000463724136353,\n",
       "  1.0000452995300293,\n",
       "  1.0000455379486084,\n",
       "  1.000049352645874,\n",
       "  1.000058650970459,\n",
       "  1.0000401735305786,\n",
       "  1.0000615119934082,\n",
       "  1.0000585317611694,\n",
       "  1.000037431716919,\n",
       "  1.0000559091567993,\n",
       "  1.0000578165054321,\n",
       "  1.0000641345977783,\n",
       "  1.0000405311584473,\n",
       "  1.0000635385513306,\n",
       "  1.0000585317611694,\n",
       "  1.0000513792037964,\n",
       "  1.0000501871109009,\n",
       "  1.0000633001327515,\n",
       "  1.000047206878662,\n",
       "  1.000059723854065,\n",
       "  1.0000556707382202,\n",
       "  1.0000545978546143,\n",
       "  1.0000191926956177,\n",
       "  1.0000537633895874,\n",
       "  1.000048279762268,\n",
       "  1.000059962272644,\n",
       "  1.0000547170639038,\n",
       "  1.000038504600525,\n",
       "  1.0000461339950562,\n",
       "  1.000036358833313,\n",
       "  1.0000630617141724,\n",
       "  1.0000604391098022,\n",
       "  1.0000313520431519,\n",
       "  1.0000489950180054,\n",
       "  1.0000618696212769,\n",
       "  1.000040888786316,\n",
       "  1.000045895576477,\n",
       "  1.0000582933425903,\n",
       "  1.0000617504119873,\n",
       "  1.000045657157898,\n",
       "  1.0000492334365845,\n",
       "  1.0000652074813843,\n",
       "  1.0000616312026978,\n",
       "  1.000057339668274,\n",
       "  1.0000478029251099,\n",
       "  1.0000617504119873,\n",
       "  1.0000567436218262,\n",
       "  1.0000455379486084,\n",
       "  1.000054955482483,\n",
       "  1.000051736831665,\n",
       "  1.0000543594360352,\n",
       "  1.0000470876693726,\n",
       "  1.0000320672988892,\n",
       "  1.00003981590271,\n",
       "  1.0000648498535156,\n",
       "  1.0000669956207275,\n",
       "  1.0000627040863037,\n",
       "  1.0000464916229248,\n",
       "  1.0000433921813965,\n",
       "  1.0000464916229248,\n",
       "  1.0000759363174438,\n",
       "  1.0000474452972412,\n",
       "  1.0000499486923218,\n",
       "  1.0000430345535278,\n",
       "  1.0000617504119873,\n",
       "  1.0000237226486206,\n",
       "  1.0000464916229248,\n",
       "  1.000046968460083,\n",
       "  1.000073790550232,\n",
       "  1.00005042552948,\n",
       "  1.000058889389038,\n",
       "  1.0000449419021606,\n",
       "  1.0000717639923096,\n",
       "  1.0000518560409546,\n",
       "  1.0000581741333008,\n",
       "  1.0000327825546265,\n",
       "  1.0000653266906738,\n",
       "  1.0000388622283936,\n",
       "  1.0000553131103516,\n",
       "  1.000064492225647,\n",
       "  1.0000309944152832,\n",
       "  1.00006902217865,\n",
       "  1.0000656843185425,\n",
       "  1.0000545978546143,\n",
       "  1.0000375509262085,\n",
       "  1.0000580549240112,\n",
       "  1.0000479221343994,\n",
       "  1.0000420808792114,\n",
       "  1.0000319480895996,\n",
       "  1.0000524520874023,\n",
       "  1.0000585317611694,\n",
       "  1.0000437498092651,\n",
       "  1.0000497102737427,\n",
       "  1.000063419342041,\n",
       "  1.0000383853912354,\n",
       "  1.000032901763916,\n",
       "  1.0000537633895874,\n",
       "  1.0000475645065308,\n",
       "  1.00006902217865,\n",
       "  1.0000572204589844,\n",
       "  1.000041127204895,\n",
       "  1.000065803527832,\n",
       "  1.0000473260879517,\n",
       "  1.0000594854354858,\n",
       "  1.0000475645065308,\n",
       "  1.0000566244125366,\n",
       "  1.000076413154602,\n",
       "  1.0000780820846558,\n",
       "  1.0000426769256592,\n",
       "  1.000034213066101,\n",
       "  1.0000449419021606,\n",
       "  1.0000568628311157,\n",
       "  1.0000402927398682,\n",
       "  1.0000585317611694,\n",
       "  1.000059962272644,\n",
       "  1.0000702142715454,\n",
       "  1.000058889389038,\n",
       "  1.0000571012496948,\n",
       "  1.0000433921813965,\n",
       "  1.0000543594360352,\n",
       "  1.0000451803207397,\n",
       "  1.0000661611557007,\n",
       "  1.0000404119491577,\n",
       "  1.0000412464141846,\n",
       "  1.0000669956207275,\n",
       "  1.0000333786010742,\n",
       "  1.0000481605529785,\n",
       "  1.000045895576477,\n",
       "  1.0000509023666382,\n",
       "  1.0000402927398682,\n",
       "  1.0000594854354858,\n",
       "  1.000032901763916,\n",
       "  1.0000338554382324,\n",
       "  1.0000699758529663,\n",
       "  1.0000498294830322,\n",
       "  1.0000666379928589,\n",
       "  1.000022053718567,\n",
       "  1.0000394582748413,\n",
       "  1.0000425577163696,\n",
       "  1.0000463724136353,\n",
       "  1.0000516176223755,\n",
       "  1.0000625848770142,\n",
       "  1.0000619888305664,\n",
       "  1.0000663995742798,\n",
       "  1.0000587701797485,\n",
       "  1.0000395774841309,\n",
       "  1.0000689029693604,\n",
       "  1.0000370740890503,\n",
       "  0.9999930262565613,\n",
       "  1.000064730644226,\n",
       "  1.0000594854354858,\n",
       "  1.000030517578125,\n",
       "  1.0000494718551636,\n",
       "  1.0000474452972412,\n",
       "  1.0000457763671875,\n",
       "  1.0000311136245728,\n",
       "  1.000065565109253,\n",
       "  1.0000412464141846,\n",
       "  1.0000580549240112,\n",
       "  1.0000474452972412,\n",
       "  1.0000455379486084,\n",
       "  1.0000512599945068,\n",
       "  1.0000526905059814,\n",
       "  1.000045657157898,\n",
       "  1.0000470876693726,\n",
       "  1.0000402927398682,\n",
       "  1.0000756978988647,\n",
       "  1.0000660419464111,\n",
       "  1.0000457763671875,\n",
       "  1.0000394582748413,\n",
       "  1.0000447034835815,\n",
       "  1.0000592470169067,\n",
       "  1.0000495910644531,\n",
       "  1.0000494718551636,\n",
       "  1.0000505447387695,\n",
       "  1.0000585317611694,\n",
       "  1.0000652074813843,\n",
       "  1.0000464916229248,\n",
       "  1.0000556707382202,\n",
       "  1.0000509023666382,\n",
       "  1.0000602006912231,\n",
       "  1.000059962272644,\n",
       "  1.000040888786316,\n",
       "  1.0000582933425903,\n",
       "  1.0000622272491455,\n",
       "  1.000030517578125,\n",
       "  1.0000616312026978,\n",
       "  1.0000499486923218,\n",
       "  1.0000598430633545,\n",
       "  1.0000334978103638,\n",
       "  1.0000637769699097,\n",
       "  1.000061273574829,\n",
       "  1.0000475645065308,\n",
       "  1.0000603199005127,\n",
       "  1.0000516176223755,\n",
       "  1.0000524520874023,\n",
       "  1.0000475645065308,\n",
       "  1.0000537633895874,\n",
       "  1.0000585317611694,\n",
       "  1.0000460147857666,\n",
       "  1.0000618696212769,\n",
       "  1.000051736831665,\n",
       "  1.0000417232513428,\n",
       "  1.0000534057617188,\n",
       "  1.0000383853912354,\n",
       "  1.0000499486923218,\n",
       "  1.0000778436660767,\n",
       "  1.0000743865966797,\n",
       "  1.000052809715271,\n",
       "  1.0000522136688232,\n",
       "  1.0000423192977905,\n",
       "  1.0000392198562622,\n",
       "  1.000051498413086,\n",
       "  1.000058650970459,\n",
       "  1.0000554323196411,\n",
       "  1.0000407695770264,\n",
       "  1.0000516176223755,\n",
       "  1.000040888786316,\n",
       "  1.0000555515289307,\n",
       "  1.0000686645507812,\n",
       "  1.0000507831573486,\n",
       "  1.0000550746917725,\n",
       "  1.0000497102737427,\n",
       "  1.0000700950622559,\n",
       "  1.0000685453414917,\n",
       "  1.0000354051589966,\n",
       "  1.0000561475753784,\n",
       "  1.000053882598877,\n",
       "  1.0000522136688232,\n",
       "  1.0000582933425903,\n",
       "  1.0000746250152588,\n",
       "  1.0000503063201904,\n",
       "  1.0000413656234741,\n",
       "  1.0000596046447754,\n",
       "  1.0000537633895874,\n",
       "  1.0000313520431519,\n",
       "  1.0000567436218262,\n",
       "  1.0000464916229248,\n",
       "  1.0000282526016235,\n",
       "  1.0000598430633545,\n",
       "  1.0000102519989014,\n",
       "  1.0000494718551636],\n",
       " 'mae': [0.6464635133743286,\n",
       "  0.6472421288490295,\n",
       "  0.6467057466506958,\n",
       "  0.6460741758346558,\n",
       "  0.647011399269104,\n",
       "  0.6469951868057251,\n",
       "  0.6465514898300171,\n",
       "  0.6464351415634155,\n",
       "  0.646920919418335,\n",
       "  0.6465069651603699,\n",
       "  0.6470727324485779,\n",
       "  0.6469234824180603,\n",
       "  0.6463446617126465,\n",
       "  0.6467770934104919,\n",
       "  0.6465448141098022,\n",
       "  0.6466823816299438,\n",
       "  0.6465794444084167,\n",
       "  0.6470028758049011,\n",
       "  0.6463057398796082,\n",
       "  0.6468387246131897,\n",
       "  0.6468899250030518,\n",
       "  0.6466376781463623,\n",
       "  0.6468369960784912,\n",
       "  0.6465909481048584,\n",
       "  0.6464923620223999,\n",
       "  0.6466421484947205,\n",
       "  0.6468479037284851,\n",
       "  0.6469679474830627,\n",
       "  0.6465482115745544,\n",
       "  0.6466591358184814,\n",
       "  0.6468630433082581,\n",
       "  0.6467701196670532,\n",
       "  0.6464433073997498,\n",
       "  0.6466763615608215,\n",
       "  0.6466153264045715,\n",
       "  0.646957278251648,\n",
       "  0.646479606628418,\n",
       "  0.6467350125312805,\n",
       "  0.6468225717544556,\n",
       "  0.64673912525177,\n",
       "  0.6465620994567871,\n",
       "  0.6469900012016296,\n",
       "  0.6466121077537537,\n",
       "  0.6467686891555786,\n",
       "  0.646252453327179,\n",
       "  0.6470727920532227,\n",
       "  0.6468000411987305,\n",
       "  0.6465912461280823,\n",
       "  0.6466307044029236,\n",
       "  0.6465875506401062,\n",
       "  0.6467183828353882,\n",
       "  0.6470462679862976,\n",
       "  0.6462178230285645,\n",
       "  0.6471813917160034,\n",
       "  0.6464495062828064,\n",
       "  0.646918773651123,\n",
       "  0.6464985013008118,\n",
       "  0.64702308177948,\n",
       "  0.6465632319450378,\n",
       "  0.6465038061141968,\n",
       "  0.646744430065155,\n",
       "  0.6465775966644287,\n",
       "  0.6469869613647461,\n",
       "  0.6465082168579102,\n",
       "  0.6469280123710632,\n",
       "  0.6465820074081421,\n",
       "  0.6465253829956055,\n",
       "  0.6467478275299072,\n",
       "  0.6467097997665405,\n",
       "  0.646783709526062,\n",
       "  0.6469366550445557,\n",
       "  0.6466856598854065,\n",
       "  0.6464802622795105,\n",
       "  0.6466040015220642,\n",
       "  0.646592915058136,\n",
       "  0.646902859210968,\n",
       "  0.64677894115448,\n",
       "  0.6468084454536438,\n",
       "  0.6462928056716919,\n",
       "  0.64691561460495,\n",
       "  0.6466279029846191,\n",
       "  0.6469900012016296,\n",
       "  0.6464848518371582,\n",
       "  0.6468839645385742,\n",
       "  0.6467135548591614,\n",
       "  0.6465192437171936,\n",
       "  0.6465379595756531,\n",
       "  0.6467469930648804,\n",
       "  0.6467834711074829,\n",
       "  0.6467463374137878,\n",
       "  0.6467815637588501,\n",
       "  0.6469086408615112,\n",
       "  0.6461942791938782,\n",
       "  0.6472973227500916,\n",
       "  0.6461378335952759,\n",
       "  0.6470116972923279,\n",
       "  0.6464498043060303,\n",
       "  0.6469135284423828,\n",
       "  0.6465299725532532,\n",
       "  0.6468020081520081,\n",
       "  0.6464289426803589,\n",
       "  0.6468636989593506,\n",
       "  0.6469185948371887,\n",
       "  0.6468404531478882,\n",
       "  0.6465082168579102,\n",
       "  0.6467574238777161,\n",
       "  0.6466203927993774,\n",
       "  0.6467611193656921,\n",
       "  0.6467211246490479,\n",
       "  0.6467146277427673,\n",
       "  0.6465203762054443,\n",
       "  0.6469065546989441,\n",
       "  0.64655601978302,\n",
       "  0.6467339992523193,\n",
       "  0.6467835307121277,\n",
       "  0.6465832591056824,\n",
       "  0.6467836499214172,\n",
       "  0.6469970345497131,\n",
       "  0.6467313170433044,\n",
       "  0.646363377571106,\n",
       "  0.6468490958213806,\n",
       "  0.6466909646987915,\n",
       "  0.6467760801315308,\n",
       "  0.6465303897857666,\n",
       "  0.6466216444969177,\n",
       "  0.646864652633667,\n",
       "  0.6466280817985535,\n",
       "  0.6466190218925476,\n",
       "  0.6467921137809753,\n",
       "  0.6466017365455627,\n",
       "  0.6467415690422058,\n",
       "  0.6466537117958069,\n",
       "  0.6468960642814636,\n",
       "  0.6465160250663757,\n",
       "  0.6469893455505371,\n",
       "  0.6466994881629944,\n",
       "  0.6465297341346741,\n",
       "  0.6465798020362854,\n",
       "  0.6469586491584778,\n",
       "  0.6467805504798889,\n",
       "  0.6463910937309265,\n",
       "  0.6467325091362,\n",
       "  0.6468880772590637,\n",
       "  0.6465839743614197,\n",
       "  0.6465203762054443,\n",
       "  0.6470526456832886,\n",
       "  0.6464060544967651,\n",
       "  0.6469836235046387,\n",
       "  0.6465053558349609,\n",
       "  0.6465767621994019,\n",
       "  0.6468691229820251,\n",
       "  0.646619975566864,\n",
       "  0.6465181708335876,\n",
       "  0.647149920463562,\n",
       "  0.6464322209358215,\n",
       "  0.6466690897941589,\n",
       "  0.6470732092857361,\n",
       "  0.6466273069381714,\n",
       "  0.6463400721549988,\n",
       "  0.6468242406845093,\n",
       "  0.6466839909553528,\n",
       "  0.6467565298080444,\n",
       "  0.6467432975769043,\n",
       "  0.6465197801589966,\n",
       "  0.6469905972480774,\n",
       "  0.6464716196060181,\n",
       "  0.6467661261558533,\n",
       "  0.6468372941017151,\n",
       "  0.6465364098548889,\n",
       "  0.646892786026001,\n",
       "  0.6463733315467834,\n",
       "  0.6469916701316833,\n",
       "  0.6465413570404053,\n",
       "  0.647028386592865,\n",
       "  0.6463543772697449,\n",
       "  0.6466459631919861,\n",
       "  0.6469023823738098,\n",
       "  0.6464755535125732,\n",
       "  0.6466494798660278,\n",
       "  0.6469600200653076,\n",
       "  0.6468919515609741,\n",
       "  0.6466721296310425,\n",
       "  0.6465247273445129,\n",
       "  0.6466824412345886,\n",
       "  0.6467361450195312,\n",
       "  0.6466939449310303,\n",
       "  0.6466718912124634,\n",
       "  0.6469870805740356,\n",
       "  0.6465191841125488,\n",
       "  0.6466034650802612,\n",
       "  0.6470738649368286,\n",
       "  0.6465182900428772,\n",
       "  0.6466647386550903,\n",
       "  0.6466971635818481,\n",
       "  0.6467071771621704,\n",
       "  0.6465907692909241,\n",
       "  0.6466633081436157,\n",
       "  0.64678555727005,\n",
       "  0.646769642829895,\n",
       "  0.6466856598854065,\n",
       "  0.6465414762496948,\n",
       "  0.6468631029129028,\n",
       "  0.6463950276374817,\n",
       "  0.6468119025230408,\n",
       "  0.6468985676765442,\n",
       "  0.6467925906181335,\n",
       "  0.6467266082763672,\n",
       "  0.6465765237808228,\n",
       "  0.6464620232582092,\n",
       "  0.6469069123268127,\n",
       "  0.6463255286216736,\n",
       "  0.6472894549369812,\n",
       "  0.6465684175491333,\n",
       "  0.6467012166976929,\n",
       "  0.6464523077011108,\n",
       "  0.6471004486083984,\n",
       "  0.646298348903656,\n",
       "  0.647025465965271,\n",
       "  0.6464409828186035,\n",
       "  0.6465989947319031,\n",
       "  0.6470087170600891,\n",
       "  0.646506130695343,\n",
       "  0.6466016173362732,\n",
       "  0.6471481919288635,\n",
       "  0.6463437676429749,\n",
       "  0.6468808650970459,\n",
       "  0.646522045135498,\n",
       "  0.6468313932418823,\n",
       "  0.6467776298522949,\n",
       "  0.646565854549408,\n",
       "  0.6466304063796997,\n",
       "  0.6465725898742676,\n",
       "  0.6469769477844238,\n",
       "  0.646652102470398,\n",
       "  0.6464396715164185,\n",
       "  0.6468943953514099,\n",
       "  0.6469160914421082,\n",
       "  0.6466222405433655,\n",
       "  0.6466781497001648,\n",
       "  0.6465132832527161,\n",
       "  0.6471813917160034,\n",
       "  0.6463902592658997,\n",
       "  0.6463492512702942,\n",
       "  0.6471461653709412,\n",
       "  0.6467688679695129,\n",
       "  0.6466197967529297,\n",
       "  0.6468522548675537,\n",
       "  0.6465597152709961,\n",
       "  0.6465150117874146,\n",
       "  0.6467342376708984,\n",
       "  0.646935224533081,\n",
       "  0.6464669704437256,\n",
       "  0.6465721130371094,\n",
       "  0.6470652222633362,\n",
       "  0.6466010212898254,\n",
       "  0.6464483737945557,\n",
       "  0.6467987298965454,\n",
       "  0.6466138958930969,\n",
       "  0.6470034122467041,\n",
       "  0.6469322443008423,\n",
       "  0.6463690400123596,\n",
       "  0.6467469930648804,\n",
       "  0.6467898488044739,\n",
       "  0.6466374397277832,\n",
       "  0.6467990279197693,\n",
       "  0.64657062292099,\n",
       "  0.6468830704689026,\n",
       "  0.6464169025421143,\n",
       "  0.6470116376876831,\n",
       "  0.64649498462677,\n",
       "  0.6466023921966553,\n",
       "  0.6466519236564636,\n",
       "  0.6469364762306213,\n",
       "  0.6465843915939331,\n",
       "  0.6466104388237,\n",
       "  0.646828830242157,\n",
       "  0.6464771032333374,\n",
       "  0.6469466686248779,\n",
       "  0.6467306017875671,\n",
       "  0.6467382907867432,\n",
       "  0.6468513607978821,\n",
       "  0.6462612152099609,\n",
       "  0.6470242142677307,\n",
       "  0.6464366316795349,\n",
       "  0.646736741065979,\n",
       "  0.6467204689979553,\n",
       "  0.6467299461364746,\n",
       "  0.6465176343917847,\n",
       "  0.6470117568969727,\n",
       "  0.6465118527412415,\n",
       "  0.6469559669494629,\n",
       "  0.6466881632804871,\n",
       "  0.6467107534408569,\n",
       "  0.6466618180274963,\n",
       "  0.6466400027275085,\n",
       "  0.6467466950416565,\n",
       "  0.6466252207756042,\n",
       "  0.6467281579971313,\n",
       "  0.6468220353126526,\n",
       "  0.6463869214057922,\n",
       "  0.6470215320587158,\n",
       "  0.6467733383178711,\n",
       "  0.6464009881019592,\n",
       "  0.6467902660369873,\n",
       "  0.6468374729156494,\n",
       "  0.6465075016021729,\n",
       "  0.6467537879943848,\n",
       "  0.6468237638473511,\n",
       "  0.6467137336730957,\n",
       "  0.6463484168052673,\n",
       "  0.6469509601593018,\n",
       "  0.6466847062110901,\n",
       "  0.6467082500457764,\n",
       "  0.646831214427948,\n",
       "  0.6466790437698364,\n",
       "  0.6465253233909607,\n",
       "  0.6467851996421814,\n",
       "  0.6466178894042969,\n",
       "  0.646674633026123,\n",
       "  0.6470609903335571,\n",
       "  0.6465853452682495,\n",
       "  0.6465914845466614,\n",
       "  0.6466536521911621,\n",
       "  0.6466801166534424,\n",
       "  0.6470003128051758,\n",
       "  0.6466455459594727,\n",
       "  0.6463629007339478,\n",
       "  0.6469299793243408,\n",
       "  0.6466789245605469,\n",
       "  0.6469319462776184,\n",
       "  0.6463401317596436,\n",
       "  0.6467437148094177,\n",
       "  0.6468695402145386,\n",
       "  0.646822988986969,\n",
       "  0.6464637517929077,\n",
       "  0.6468130946159363,\n",
       "  0.6465924382209778,\n",
       "  0.6468287706375122,\n",
       "  0.646255373954773,\n",
       "  0.6469768285751343,\n",
       "  0.6468318104743958,\n",
       "  0.6465407013893127,\n",
       "  0.6469739079475403,\n",
       "  0.6466233134269714,\n",
       "  0.6464923620223999,\n",
       "  0.6470654010772705,\n",
       "  0.6465948820114136,\n",
       "  0.6464187502861023,\n",
       "  0.6466650366783142,\n",
       "  0.6469671130180359,\n",
       "  0.6466602087020874,\n",
       "  0.6466796398162842,\n",
       "  0.6466419696807861,\n",
       "  0.6466643214225769,\n",
       "  0.646547794342041,\n",
       "  0.6470029354095459,\n",
       "  0.6466125249862671,\n",
       "  0.6468411087989807,\n",
       "  0.6464183926582336,\n",
       "  0.6467907428741455,\n",
       "  0.6467021107673645,\n",
       "  0.6466934084892273,\n",
       "  0.6465407609939575,\n",
       "  0.6468969583511353,\n",
       "  0.6466703414916992,\n",
       "  0.6467245817184448,\n",
       "  0.6470922827720642,\n",
       "  0.6462862491607666,\n",
       "  0.6469175219535828,\n",
       "  0.6465806365013123,\n",
       "  0.6467452645301819,\n",
       "  0.6465425491333008,\n",
       "  0.6469744443893433,\n",
       "  0.6464359164237976,\n",
       "  0.6465719938278198,\n",
       "  0.6467639207839966,\n",
       "  0.6468760371208191,\n",
       "  0.6466001272201538,\n",
       "  0.6465916633605957,\n",
       "  0.6468350887298584,\n",
       "  0.6468265056610107,\n",
       "  0.6467158794403076,\n",
       "  0.646570086479187,\n",
       "  0.6469155550003052,\n",
       "  0.6466115713119507,\n",
       "  0.646632730960846,\n",
       "  0.6466224789619446,\n",
       "  0.6468533277511597,\n",
       "  0.6466652750968933,\n",
       "  0.646751880645752,\n",
       "  0.6465732455253601,\n",
       "  0.6465622186660767,\n",
       "  0.6469135284423828,\n",
       "  0.6468414664268494,\n",
       "  0.6464560031890869,\n",
       "  0.6467593908309937,\n",
       "  0.6465734839439392,\n",
       "  0.646819531917572,\n",
       "  0.6464730501174927,\n",
       "  0.6467402577400208,\n",
       "  0.6469780802726746,\n",
       "  0.6465034484863281,\n",
       "  0.6468212008476257,\n",
       "  0.6471313834190369,\n",
       "  0.6461637020111084,\n",
       "  0.6468935608863831,\n",
       "  0.6465405821800232,\n",
       "  0.646888792514801,\n",
       "  0.6467675566673279,\n",
       "  0.6468030214309692,\n",
       "  0.6461477875709534,\n",
       "  0.6469975709915161,\n",
       "  0.6466476917266846,\n",
       "  0.6469538807868958,\n",
       "  0.6464309096336365,\n",
       "  0.6464899778366089,\n",
       "  0.6469364166259766,\n",
       "  0.6466166377067566,\n",
       "  0.6465815901756287,\n",
       "  0.6471030116081238,\n",
       "  0.6465395092964172,\n",
       "  0.6466451287269592,\n",
       "  0.6468608379364014,\n",
       "  0.6466135382652283,\n",
       "  0.6465969085693359,\n",
       "  0.6467630863189697,\n",
       "  0.6466136574745178,\n",
       "  0.6468394994735718,\n",
       "  0.6464614868164062,\n",
       "  0.6468555927276611,\n",
       "  0.6468775868415833,\n",
       "  0.6468274593353271,\n",
       "  0.6465490460395813,\n",
       "  0.6468256711959839,\n",
       "  0.6463709473609924,\n",
       "  0.6467679142951965,\n",
       "  0.6467000246047974,\n",
       "  0.6467275023460388,\n",
       "  0.6466902494430542,\n",
       "  0.6469207406044006,\n",
       "  0.646484375,\n",
       "  0.6467663049697876,\n",
       "  0.6467477679252625,\n",
       "  0.6466580629348755,\n",
       "  0.6466884613037109,\n",
       "  0.6467897891998291,\n",
       "  0.6464778184890747,\n",
       "  0.6469672918319702,\n",
       "  0.6465960741043091,\n",
       "  0.6469395160675049,\n",
       "  0.6462540030479431,\n",
       "  0.6469454765319824,\n",
       "  0.6465027332305908,\n",
       "  0.6471254825592041,\n",
       "  0.6467649340629578,\n",
       "  0.6464629769325256,\n",
       "  0.6466024518013,\n",
       "  0.646545946598053,\n",
       "  0.6469584703445435,\n",
       "  0.6467584371566772,\n",
       "  0.6468121409416199,\n",
       "  0.6463115811347961,\n",
       "  0.646958589553833,\n",
       "  0.6466264724731445,\n",
       "  0.6465869545936584,\n",
       "  0.6467925906181335,\n",
       "  0.6468744874000549,\n",
       "  0.6464143395423889,\n",
       "  0.6468545198440552,\n",
       "  0.6465485692024231,\n",
       "  0.6470069885253906,\n",
       "  0.6465327739715576,\n",
       "  0.6467543840408325,\n",
       "  0.6468786001205444,\n",
       "  0.6463337540626526,\n",
       "  0.6467740535736084,\n",
       "  0.6469027996063232,\n",
       "  0.6466671228408813,\n",
       "  0.6466771364212036,\n",
       "  0.6465982794761658,\n",
       "  0.6469067931175232,\n",
       "  0.6465092897415161,\n",
       "  0.6468051671981812,\n",
       "  0.6466923952102661,\n",
       "  0.6465033292770386,\n",
       "  0.647036612033844,\n",
       "  0.6464161276817322,\n",
       "  0.6469916105270386,\n",
       "  0.6466700434684753,\n",
       "  0.6467170715332031,\n",
       "  0.6466663479804993,\n",
       "  0.6464620232582092,\n",
       "  0.6469817757606506,\n",
       "  0.646722674369812,\n",
       "  0.6463978290557861,\n",
       "  0.6471269130706787,\n",
       "  0.6465110182762146,\n",
       "  0.6465778350830078,\n",
       "  0.6468391418457031,\n",
       "  0.6466317772865295],\n",
       " 'val_loss': [0.9911229610443115,\n",
       "  0.9911189079284668,\n",
       "  0.9911623001098633,\n",
       "  0.9912311434745789,\n",
       "  0.9911264181137085,\n",
       "  0.9910801649093628,\n",
       "  0.9910802245140076,\n",
       "  0.9911972284317017,\n",
       "  0.9910394549369812,\n",
       "  0.9910698533058167,\n",
       "  0.9911220669746399,\n",
       "  0.9912213087081909,\n",
       "  0.9911301732063293,\n",
       "  0.991070032119751,\n",
       "  0.9911549091339111,\n",
       "  0.9912047982215881,\n",
       "  0.9911133050918579,\n",
       "  0.9910455942153931,\n",
       "  0.9912994503974915,\n",
       "  0.991115152835846,\n",
       "  0.9911779761314392,\n",
       "  0.9911112785339355,\n",
       "  0.9911134839057922,\n",
       "  0.9910669922828674,\n",
       "  0.9912928938865662,\n",
       "  0.9911752939224243,\n",
       "  0.9911097288131714,\n",
       "  0.9911237359046936,\n",
       "  0.9912241101264954,\n",
       "  0.9910619854927063,\n",
       "  0.9911535382270813,\n",
       "  0.9911127686500549,\n",
       "  0.9910945892333984,\n",
       "  0.9911725521087646,\n",
       "  0.9911278486251831,\n",
       "  0.9910401701927185,\n",
       "  0.9912775754928589,\n",
       "  0.9911013841629028,\n",
       "  0.9910977482795715,\n",
       "  0.9910909533500671,\n",
       "  0.9910834431648254,\n",
       "  0.9911434054374695,\n",
       "  0.9910999536514282,\n",
       "  0.9911920428276062,\n",
       "  0.9911549091339111,\n",
       "  0.99118971824646,\n",
       "  0.991155207157135,\n",
       "  0.9912108778953552,\n",
       "  0.9911225438117981,\n",
       "  0.9911015629768372,\n",
       "  0.991155207157135,\n",
       "  0.991125226020813,\n",
       "  0.9912227988243103,\n",
       "  0.9911442399024963,\n",
       "  0.9911078810691833,\n",
       "  0.9910681843757629,\n",
       "  0.991055428981781,\n",
       "  0.991097092628479,\n",
       "  0.9911323189735413,\n",
       "  0.9910990595817566,\n",
       "  0.9911282062530518,\n",
       "  0.9911378622055054,\n",
       "  0.9911951422691345,\n",
       "  0.9911590218544006,\n",
       "  0.9910598993301392,\n",
       "  0.9910643696784973,\n",
       "  0.9911440014839172,\n",
       "  0.9911390542984009,\n",
       "  0.9911097884178162,\n",
       "  0.9910613298416138,\n",
       "  0.9910863637924194,\n",
       "  0.9910742044448853,\n",
       "  0.9910534620285034,\n",
       "  0.9911827445030212,\n",
       "  0.9911308884620667,\n",
       "  0.9910843968391418,\n",
       "  0.9910615682601929,\n",
       "  0.9910464286804199,\n",
       "  0.9910978078842163,\n",
       "  0.9911091923713684,\n",
       "  0.9911343455314636,\n",
       "  0.9911097884178162,\n",
       "  0.9910823702812195,\n",
       "  0.9911046028137207,\n",
       "  0.9911400675773621,\n",
       "  0.9911727905273438,\n",
       "  0.9912129640579224,\n",
       "  0.991159200668335,\n",
       "  0.9911690950393677,\n",
       "  0.9911190867424011,\n",
       "  0.9912728667259216,\n",
       "  0.99114590883255,\n",
       "  0.9911078810691833,\n",
       "  0.9911338090896606,\n",
       "  0.991159975528717,\n",
       "  0.9910867810249329,\n",
       "  0.991155207157135,\n",
       "  0.9911369681358337,\n",
       "  0.9911299347877502,\n",
       "  0.9911521673202515,\n",
       "  0.9912709593772888,\n",
       "  0.9911659359931946,\n",
       "  0.9910719990730286,\n",
       "  0.9911289215087891,\n",
       "  0.9910563826560974,\n",
       "  0.9910774827003479,\n",
       "  0.9910958409309387,\n",
       "  0.9911275506019592,\n",
       "  0.9911138415336609,\n",
       "  0.9911344647407532,\n",
       "  0.9911810755729675,\n",
       "  0.991105854511261,\n",
       "  0.9911185503005981,\n",
       "  0.9910655617713928,\n",
       "  0.9911397099494934,\n",
       "  0.9910649061203003,\n",
       "  0.9911131262779236,\n",
       "  0.991095244884491,\n",
       "  0.9911097884178162,\n",
       "  0.991090714931488,\n",
       "  0.991068422794342,\n",
       "  0.9910577535629272,\n",
       "  0.9910944700241089,\n",
       "  0.991153359413147,\n",
       "  0.9910677075386047,\n",
       "  0.9911283254623413,\n",
       "  0.991055965423584,\n",
       "  0.9910932779312134,\n",
       "  0.991125226020813,\n",
       "  0.9910994172096252,\n",
       "  0.9910780191421509,\n",
       "  0.9910801649093628,\n",
       "  0.9910702705383301,\n",
       "  0.9911118149757385,\n",
       "  0.9910958409309387,\n",
       "  0.9910638332366943,\n",
       "  0.9910821914672852,\n",
       "  0.991070032119751,\n",
       "  0.9911616444587708,\n",
       "  0.9910549521446228,\n",
       "  0.9911004304885864,\n",
       "  0.9912271499633789,\n",
       "  0.991087794303894,\n",
       "  0.9910338521003723,\n",
       "  0.9910950064659119,\n",
       "  0.9910737872123718,\n",
       "  0.9911479949951172,\n",
       "  0.9910818934440613,\n",
       "  0.9911343455314636,\n",
       "  0.9910745024681091,\n",
       "  0.9910867214202881,\n",
       "  0.991144597530365,\n",
       "  0.9911984801292419,\n",
       "  0.9910441637039185,\n",
       "  0.9911081790924072,\n",
       "  0.9911118149757385,\n",
       "  0.991123378276825,\n",
       "  0.9911276698112488,\n",
       "  0.991188645362854,\n",
       "  0.9911605715751648,\n",
       "  0.9911516904830933,\n",
       "  0.9912169575691223,\n",
       "  0.9910612106323242,\n",
       "  0.9912089705467224,\n",
       "  0.9910816550254822,\n",
       "  0.9911283254623413,\n",
       "  0.9912737607955933,\n",
       "  0.9910895228385925,\n",
       "  0.9910619258880615,\n",
       "  0.9910814166069031,\n",
       "  0.9911102652549744,\n",
       "  0.9911875128746033,\n",
       "  0.9910743832588196,\n",
       "  0.9911003112792969,\n",
       "  0.9910664558410645,\n",
       "  0.9911566376686096,\n",
       "  0.9911518692970276,\n",
       "  0.9911757707595825,\n",
       "  0.9912503361701965,\n",
       "  0.9911194443702698,\n",
       "  0.9910687208175659,\n",
       "  0.9910534620285034,\n",
       "  0.9910855889320374,\n",
       "  0.9910804033279419,\n",
       "  0.9912093877792358,\n",
       "  0.9911414980888367,\n",
       "  0.9910552501678467,\n",
       "  0.991081714630127,\n",
       "  0.9910412430763245,\n",
       "  0.9911006093025208,\n",
       "  0.9913037419319153,\n",
       "  0.9911213517189026,\n",
       "  0.9910348057746887,\n",
       "  0.9911189079284668,\n",
       "  0.9911775588989258,\n",
       "  0.99112868309021,\n",
       "  0.9911163449287415,\n",
       "  0.991031289100647,\n",
       "  0.9911153316497803,\n",
       "  0.9911744594573975,\n",
       "  0.9910997152328491,\n",
       "  0.991083025932312,\n",
       "  0.9913651943206787,\n",
       "  0.9910655617713928,\n",
       "  0.9912564754486084,\n",
       "  0.991068959236145,\n",
       "  0.9910290837287903,\n",
       "  0.9910565614700317,\n",
       "  0.9911554455757141,\n",
       "  0.9910560250282288,\n",
       "  0.991307258605957,\n",
       "  0.991128146648407,\n",
       "  0.9911879301071167,\n",
       "  0.9910874962806702,\n",
       "  0.9910944104194641,\n",
       "  0.9910841584205627,\n",
       "  0.9911144971847534,\n",
       "  0.9911900162696838,\n",
       "  0.991122305393219,\n",
       "  0.9911593198776245,\n",
       "  0.9911184310913086,\n",
       "  0.9911092519760132,\n",
       "  0.991133987903595,\n",
       "  0.9912399053573608,\n",
       "  0.9910323619842529,\n",
       "  0.9910528063774109,\n",
       "  0.9911295771598816,\n",
       "  0.9910508394241333,\n",
       "  0.9910947680473328,\n",
       "  0.9910839796066284,\n",
       "  0.9911153316497803,\n",
       "  0.9911084771156311,\n",
       "  0.9911210536956787,\n",
       "  0.9910352826118469,\n",
       "  0.9911441206932068,\n",
       "  0.9911477565765381,\n",
       "  0.9911026358604431,\n",
       "  0.9911004304885864,\n",
       "  0.9911080598831177,\n",
       "  0.991101086139679,\n",
       "  0.9911162257194519,\n",
       "  0.9911153316497803,\n",
       "  0.9912629723548889,\n",
       "  0.9910803437232971,\n",
       "  0.9910203814506531,\n",
       "  0.9910414814949036,\n",
       "  0.9910553693771362,\n",
       "  0.991084098815918,\n",
       "  0.9912948608398438,\n",
       "  0.9912081360816956,\n",
       "  0.9910887479782104,\n",
       "  0.9911668300628662,\n",
       "  0.9911456108093262,\n",
       "  0.9910578727722168,\n",
       "  0.9910493493080139,\n",
       "  0.9911468625068665,\n",
       "  0.9911790490150452,\n",
       "  0.9911354184150696,\n",
       "  0.991101086139679,\n",
       "  0.9910925030708313,\n",
       "  0.9910738468170166,\n",
       "  0.9911941885948181,\n",
       "  0.9911148548126221,\n",
       "  0.991117000579834,\n",
       "  0.9910798072814941,\n",
       "  0.9910849332809448,\n",
       "  0.991093635559082,\n",
       "  0.9911382794380188,\n",
       "  0.9910948276519775,\n",
       "  0.9910600781440735,\n",
       "  0.9912421107292175,\n",
       "  0.991115391254425,\n",
       "  0.9910945892333984,\n",
       "  0.9910667538642883,\n",
       "  0.9911121129989624,\n",
       "  0.9910456538200378,\n",
       "  0.9911136627197266,\n",
       "  0.9911667108535767,\n",
       "  0.9910897016525269,\n",
       "  0.9911633133888245,\n",
       "  0.9912049174308777,\n",
       "  0.991115391254425,\n",
       "  0.9911324977874756,\n",
       "  0.9911582469940186,\n",
       "  0.9911956787109375,\n",
       "  0.9910577535629272,\n",
       "  0.9912016987800598,\n",
       "  0.9912415742874146,\n",
       "  0.9910690188407898,\n",
       "  0.9910930395126343,\n",
       "  0.9912787079811096,\n",
       "  0.9910421967506409,\n",
       "  0.9911467432975769,\n",
       "  0.9911273717880249,\n",
       "  0.9910726547241211,\n",
       "  0.9910473227500916,\n",
       "  0.9911454916000366,\n",
       "  0.9910977482795715,\n",
       "  0.9911637902259827,\n",
       "  0.9911433458328247,\n",
       "  0.9910762906074524,\n",
       "  0.9910584092140198,\n",
       "  0.9913203120231628,\n",
       "  0.991066038608551,\n",
       "  0.9910428524017334,\n",
       "  0.9910721778869629,\n",
       "  0.9912029504776001,\n",
       "  0.9910680055618286,\n",
       "  0.9911122918128967,\n",
       "  0.9911701083183289,\n",
       "  0.9911687970161438,\n",
       "  0.9911292195320129,\n",
       "  0.9911648035049438,\n",
       "  0.9910793304443359,\n",
       "  0.9910800457000732,\n",
       "  0.9912602305412292,\n",
       "  0.9911280870437622,\n",
       "  0.9912972450256348,\n",
       "  0.9911194443702698,\n",
       "  0.9910893440246582,\n",
       "  0.9910974502563477,\n",
       "  0.9911091923713684,\n",
       "  0.9910874962806702,\n",
       "  0.9911637902259827,\n",
       "  0.9911205172538757,\n",
       "  0.9911754131317139,\n",
       "  0.991119921207428,\n",
       "  0.9910309314727783,\n",
       "  0.9910598397254944,\n",
       "  0.991073727607727,\n",
       "  0.9910634756088257,\n",
       "  0.9912012815475464,\n",
       "  0.9910659790039062,\n",
       "  0.9911443591117859,\n",
       "  0.9911602139472961,\n",
       "  0.9912155866622925,\n",
       "  0.9911162853240967,\n",
       "  0.9910703301429749,\n",
       "  0.9912599325180054,\n",
       "  0.9910504817962646,\n",
       "  0.9910427331924438,\n",
       "  0.9912005662918091,\n",
       "  0.9911178350448608,\n",
       "  0.9910488128662109,\n",
       "  0.9910661578178406,\n",
       "  0.9910591840744019,\n",
       "  0.991144597530365,\n",
       "  0.9911653995513916,\n",
       "  0.9910950660705566,\n",
       "  0.9910699725151062,\n",
       "  0.9910562634468079,\n",
       "  0.9910787343978882,\n",
       "  0.9910498261451721,\n",
       "  0.9912070035934448,\n",
       "  0.9911552667617798,\n",
       "  0.9910690188407898,\n",
       "  0.9911186695098877,\n",
       "  0.9910609126091003,\n",
       "  0.9911453723907471,\n",
       "  0.9911043047904968,\n",
       "  0.9910847544670105,\n",
       "  0.9910401105880737,\n",
       "  0.9910943508148193,\n",
       "  0.9910855293273926,\n",
       "  0.9910804033279419,\n",
       "  0.9911116361618042,\n",
       "  0.9911341667175293,\n",
       "  0.9911230802536011,\n",
       "  0.991077184677124,\n",
       "  0.9910441637039185,\n",
       "  0.9911142587661743,\n",
       "  0.9910548329353333,\n",
       "  0.9911159873008728,\n",
       "  0.9911478757858276,\n",
       "  0.9911084771156311,\n",
       "  0.9911158084869385,\n",
       "  0.9910865426063538,\n",
       "  0.9911465048789978,\n",
       "  0.9911388754844666,\n",
       "  0.9910920858383179,\n",
       "  0.9910847544670105,\n",
       "  0.9910904169082642,\n",
       "  0.9911085963249207,\n",
       "  0.9910774230957031,\n",
       "  0.9911258816719055,\n",
       "  0.9911466240882874,\n",
       "  0.9910913109779358,\n",
       "  0.9911677837371826,\n",
       "  0.9910439848899841,\n",
       "  0.9910567998886108,\n",
       "  0.9911790490150452,\n",
       "  0.9911248683929443,\n",
       "  0.9910266399383545,\n",
       "  0.9910615086555481,\n",
       "  0.9911161065101624,\n",
       "  0.9910755753517151,\n",
       "  0.9910504221916199,\n",
       "  0.9910737872123718,\n",
       "  0.9911420345306396,\n",
       "  0.9911251068115234,\n",
       "  0.9910996556282043,\n",
       "  0.9911280870437622,\n",
       "  0.991288423538208,\n",
       "  0.9912089109420776,\n",
       "  0.9911417961120605,\n",
       "  0.9910815954208374,\n",
       "  0.9910368919372559,\n",
       "  0.9910491108894348,\n",
       "  0.9910619258880615,\n",
       "  0.9911379218101501,\n",
       "  0.9912216067314148,\n",
       "  0.9910679459571838,\n",
       "  0.991036057472229,\n",
       "  0.9910843968391418,\n",
       "  0.9910417795181274,\n",
       "  0.9911375045776367,\n",
       "  0.991178035736084,\n",
       "  0.9911176562309265,\n",
       "  0.9911687970161438,\n",
       "  0.9910759329795837,\n",
       "  0.9911817908287048,\n",
       "  0.9911653399467468,\n",
       "  0.9910305142402649,\n",
       "  0.9911190271377563,\n",
       "  0.9910967946052551,\n",
       "  0.9910896420478821,\n",
       "  0.9911432266235352,\n",
       "  0.9912422299385071,\n",
       "  0.9912704825401306,\n",
       "  0.9910645484924316,\n",
       "  0.991095781326294,\n",
       "  0.9912229776382446,\n",
       "  0.9911624193191528,\n",
       "  0.9911174774169922,\n",
       "  0.9911432266235352,\n",
       "  0.9911774396896362,\n",
       "  0.99114990234375,\n",
       "  0.9911026954650879,\n",
       "  0.9911535978317261,\n",
       "  0.9910795092582703,\n",
       "  0.9911385774612427,\n",
       "  0.9911196827888489,\n",
       "  0.9910435080528259,\n",
       "  0.991174042224884,\n",
       "  0.9911797046661377,\n",
       "  0.9910520315170288,\n",
       "  0.9911502003669739,\n",
       "  0.9912020564079285,\n",
       "  0.9910675287246704,\n",
       "  0.991071343421936,\n",
       "  0.9911497235298157,\n",
       "  0.9911258220672607,\n",
       "  0.9913412928581238,\n",
       "  0.9910620450973511,\n",
       "  0.9910898208618164,\n",
       "  0.991084098815918,\n",
       "  0.9910945892333984,\n",
       "  0.9910854697227478,\n",
       "  0.9910523295402527,\n",
       "  0.9911425709724426,\n",
       "  0.9910426139831543,\n",
       "  0.9912165999412537,\n",
       "  0.9910594820976257,\n",
       "  0.9911460280418396,\n",
       "  0.9910617470741272,\n",
       "  0.991116464138031,\n",
       "  0.9910707473754883,\n",
       "  0.9911116361618042,\n",
       "  0.9910794496536255,\n",
       "  0.991097092628479,\n",
       "  0.991132915019989,\n",
       "  0.991075336933136,\n",
       "  0.9910956025123596,\n",
       "  0.9910500645637512,\n",
       "  0.9911337494850159,\n",
       "  0.991088330745697,\n",
       "  0.9911481142044067,\n",
       "  0.9910932779312134,\n",
       "  0.991058349609375,\n",
       "  0.9910557866096497,\n",
       "  0.9911439418792725,\n",
       "  0.9910538196563721,\n",
       "  0.9910523295402527,\n",
       "  0.9910553693771362,\n",
       "  0.9911165833473206,\n",
       "  0.9910772442817688,\n",
       "  0.9911487102508545,\n",
       "  0.9911026358604431,\n",
       "  0.9911187887191772,\n",
       "  0.9911131262779236,\n",
       "  0.9911336898803711,\n",
       "  0.9912132024765015,\n",
       "  0.9910335540771484,\n",
       "  0.9910275340080261,\n",
       "  0.9910696744918823,\n",
       "  0.9911319017410278,\n",
       "  0.9911291003227234,\n",
       "  0.9910874366760254,\n",
       "  0.9911404252052307,\n",
       "  0.9911121129989624],\n",
       " 'val_mae': [0.6482454538345337,\n",
       "  0.6424840688705444,\n",
       "  0.6434569954872131,\n",
       "  0.6496367454528809,\n",
       "  0.6478398442268372,\n",
       "  0.6447865962982178,\n",
       "  0.6447172164916992,\n",
       "  0.6480942368507385,\n",
       "  0.6461578607559204,\n",
       "  0.6474988460540771,\n",
       "  0.6448402404785156,\n",
       "  0.6425777077674866,\n",
       "  0.6460960507392883,\n",
       "  0.6449026465415955,\n",
       "  0.6467640995979309,\n",
       "  0.6463614702224731,\n",
       "  0.6478039622306824,\n",
       "  0.644269585609436,\n",
       "  0.6487739682197571,\n",
       "  0.6473216414451599,\n",
       "  0.6458894610404968,\n",
       "  0.6460678577423096,\n",
       "  0.644629716873169,\n",
       "  0.6460028290748596,\n",
       "  0.6491201519966125,\n",
       "  0.6488128304481506,\n",
       "  0.6469494700431824,\n",
       "  0.6451472043991089,\n",
       "  0.6458857655525208,\n",
       "  0.6464828848838806,\n",
       "  0.6457062363624573,\n",
       "  0.6439759135246277,\n",
       "  0.6471312642097473,\n",
       "  0.6464577317237854,\n",
       "  0.6479571461677551,\n",
       "  0.6446775197982788,\n",
       "  0.6471507549285889,\n",
       "  0.6466078162193298,\n",
       "  0.6463069319725037,\n",
       "  0.6461855173110962,\n",
       "  0.6476652026176453,\n",
       "  0.6451805233955383,\n",
       "  0.6452133059501648,\n",
       "  0.6444451212882996,\n",
       "  0.648583710193634,\n",
       "  0.6455101370811462,\n",
       "  0.6428154110908508,\n",
       "  0.6460304260253906,\n",
       "  0.6462154388427734,\n",
       "  0.6476303935050964,\n",
       "  0.6477500200271606,\n",
       "  0.6429250836372375,\n",
       "  0.6494426131248474,\n",
       "  0.643358051776886,\n",
       "  0.6467863917350769,\n",
       "  0.6442206501960754,\n",
       "  0.6466304063796997,\n",
       "  0.6423665881156921,\n",
       "  0.644260048866272,\n",
       "  0.6462687253952026,\n",
       "  0.646602988243103,\n",
       "  0.6467471718788147,\n",
       "  0.6452562808990479,\n",
       "  0.6468894481658936,\n",
       "  0.6437630653381348,\n",
       "  0.6459577679634094,\n",
       "  0.6476180553436279,\n",
       "  0.6467284560203552,\n",
       "  0.6466826796531677,\n",
       "  0.6459023952484131,\n",
       "  0.6443639993667603,\n",
       "  0.6440393328666687,\n",
       "  0.6463339924812317,\n",
       "  0.6470499038696289,\n",
       "  0.6475419998168945,\n",
       "  0.6467248797416687,\n",
       "  0.6452627182006836,\n",
       "  0.6440882086753845,\n",
       "  0.6479434370994568,\n",
       "  0.6464276313781738,\n",
       "  0.6470955014228821,\n",
       "  0.6445066332817078,\n",
       "  0.6459599137306213,\n",
       "  0.6446340084075928,\n",
       "  0.6447960734367371,\n",
       "  0.6470127701759338,\n",
       "  0.6486325860023499,\n",
       "  0.647571861743927,\n",
       "  0.6455005407333374,\n",
       "  0.646162748336792,\n",
       "  0.6457556486129761,\n",
       "  0.6443696618080139,\n",
       "  0.6475017070770264,\n",
       "  0.6426580548286438,\n",
       "  0.6487175226211548,\n",
       "  0.6454955339431763,\n",
       "  0.6480342745780945,\n",
       "  0.6466661691665649,\n",
       "  0.6475740075111389,\n",
       "  0.6459625959396362,\n",
       "  0.6493018865585327,\n",
       "  0.6471401453018188,\n",
       "  0.645792543888092,\n",
       "  0.6445235013961792,\n",
       "  0.6465508341789246,\n",
       "  0.6457908749580383,\n",
       "  0.6468750238418579,\n",
       "  0.6453020572662354,\n",
       "  0.645979106426239,\n",
       "  0.6456007957458496,\n",
       "  0.6481186151504517,\n",
       "  0.6454948782920837,\n",
       "  0.6463914513587952,\n",
       "  0.6467499136924744,\n",
       "  0.6458531618118286,\n",
       "  0.6471510529518127,\n",
       "  0.646310031414032,\n",
       "  0.6432151198387146,\n",
       "  0.6430565714836121,\n",
       "  0.6466413736343384,\n",
       "  0.6443915963172913,\n",
       "  0.6449649333953857,\n",
       "  0.6445173025131226,\n",
       "  0.6463274359703064,\n",
       "  0.6473177671432495,\n",
       "  0.645402193069458,\n",
       "  0.6451976895332336,\n",
       "  0.6470738649368286,\n",
       "  0.6470335721969604,\n",
       "  0.6467920541763306,\n",
       "  0.6470757126808167,\n",
       "  0.6473321914672852,\n",
       "  0.6454296112060547,\n",
       "  0.6452251672744751,\n",
       "  0.6439325213432312,\n",
       "  0.643751859664917,\n",
       "  0.6464265584945679,\n",
       "  0.6469611525535583,\n",
       "  0.6445537209510803,\n",
       "  0.643638551235199,\n",
       "  0.6467787623405457,\n",
       "  0.6458607316017151,\n",
       "  0.6451815962791443,\n",
       "  0.646177351474762,\n",
       "  0.6477403044700623,\n",
       "  0.6446154117584229,\n",
       "  0.6478351950645447,\n",
       "  0.6440691351890564,\n",
       "  0.6468146443367004,\n",
       "  0.6472690105438232,\n",
       "  0.6461172103881836,\n",
       "  0.646263062953949,\n",
       "  0.6487715840339661,\n",
       "  0.6444746255874634,\n",
       "  0.6456151604652405,\n",
       "  0.6464424729347229,\n",
       "  0.643136739730835,\n",
       "  0.6443007588386536,\n",
       "  0.6472802758216858,\n",
       "  0.6471366286277771,\n",
       "  0.6466219425201416,\n",
       "  0.64622962474823,\n",
       "  0.6456839442253113,\n",
       "  0.6488120555877686,\n",
       "  0.644207775592804,\n",
       "  0.6464479565620422,\n",
       "  0.6469353437423706,\n",
       "  0.6450181603431702,\n",
       "  0.6473342180252075,\n",
       "  0.6445779800415039,\n",
       "  0.6473264098167419,\n",
       "  0.6441904306411743,\n",
       "  0.6474639177322388,\n",
       "  0.643368661403656,\n",
       "  0.6472765803337097,\n",
       "  0.6473566889762878,\n",
       "  0.6460821032524109,\n",
       "  0.6480936408042908,\n",
       "  0.6493918895721436,\n",
       "  0.6463261246681213,\n",
       "  0.6447119116783142,\n",
       "  0.6447502970695496,\n",
       "  0.6463879942893982,\n",
       "  0.6463669538497925,\n",
       "  0.6470588445663452,\n",
       "  0.6469739079475403,\n",
       "  0.6471081972122192,\n",
       "  0.6440377235412598,\n",
       "  0.6460347771644592,\n",
       "  0.6467515826225281,\n",
       "  0.6424406170845032,\n",
       "  0.6436881422996521,\n",
       "  0.645258367061615,\n",
       "  0.6449947357177734,\n",
       "  0.6459537744522095,\n",
       "  0.6461547613143921,\n",
       "  0.6467540860176086,\n",
       "  0.6452729105949402,\n",
       "  0.6455722451210022,\n",
       "  0.6454749703407288,\n",
       "  0.6461514830589294,\n",
       "  0.6452496647834778,\n",
       "  0.6496024131774902,\n",
       "  0.6474369168281555,\n",
       "  0.6467167735099792,\n",
       "  0.6454506516456604,\n",
       "  0.6444365978240967,\n",
       "  0.6457992196083069,\n",
       "  0.6472955942153931,\n",
       "  0.6461853384971619,\n",
       "  0.6500542759895325,\n",
       "  0.6437990665435791,\n",
       "  0.6455535292625427,\n",
       "  0.645557701587677,\n",
       "  0.6484370231628418,\n",
       "  0.6434857249259949,\n",
       "  0.6474044322967529,\n",
       "  0.6437410116195679,\n",
       "  0.6472467184066772,\n",
       "  0.6476697325706482,\n",
       "  0.6443037390708923,\n",
       "  0.6465727090835571,\n",
       "  0.6482958197593689,\n",
       "  0.6438949108123779,\n",
       "  0.6464187502861023,\n",
       "  0.6449123620986938,\n",
       "  0.6472137570381165,\n",
       "  0.6463078856468201,\n",
       "  0.6441270709037781,\n",
       "  0.6463181972503662,\n",
       "  0.6474919319152832,\n",
       "  0.6485395431518555,\n",
       "  0.6461944580078125,\n",
       "  0.6459759473800659,\n",
       "  0.6489648818969727,\n",
       "  0.6460918188095093,\n",
       "  0.6441934108734131,\n",
       "  0.6456270217895508,\n",
       "  0.6448482871055603,\n",
       "  0.6479905843734741,\n",
       "  0.6430968642234802,\n",
       "  0.6459653377532959,\n",
       "  0.6503483653068542,\n",
       "  0.6455587148666382,\n",
       "  0.6450556516647339,\n",
       "  0.6457202434539795,\n",
       "  0.645355224609375,\n",
       "  0.6456469893455505,\n",
       "  0.6473011374473572,\n",
       "  0.6472517251968384,\n",
       "  0.6453150510787964,\n",
       "  0.6476081013679504,\n",
       "  0.6480956077575684,\n",
       "  0.6447286009788513,\n",
       "  0.6455941200256348,\n",
       "  0.6484928131103516,\n",
       "  0.6478154063224792,\n",
       "  0.6478037238121033,\n",
       "  0.6447803378105164,\n",
       "  0.6429396867752075,\n",
       "  0.6462711691856384,\n",
       "  0.6459347605705261,\n",
       "  0.6451056003570557,\n",
       "  0.6462144255638123,\n",
       "  0.6461565494537354,\n",
       "  0.6455015540122986,\n",
       "  0.643844723701477,\n",
       "  0.6477126479148865,\n",
       "  0.6441093683242798,\n",
       "  0.6453895568847656,\n",
       "  0.6480526924133301,\n",
       "  0.6472756266593933,\n",
       "  0.6452935934066772,\n",
       "  0.6458969712257385,\n",
       "  0.6462046504020691,\n",
       "  0.6456299424171448,\n",
       "  0.6477130055427551,\n",
       "  0.6449069976806641,\n",
       "  0.6446200609207153,\n",
       "  0.6452333927154541,\n",
       "  0.6425858736038208,\n",
       "  0.648013710975647,\n",
       "  0.6442533731460571,\n",
       "  0.647574782371521,\n",
       "  0.647680938243866,\n",
       "  0.6475492119789124,\n",
       "  0.6466173529624939,\n",
       "  0.6491231322288513,\n",
       "  0.6459066271781921,\n",
       "  0.6477271318435669,\n",
       "  0.645894467830658,\n",
       "  0.6451089978218079,\n",
       "  0.6450255513191223,\n",
       "  0.6457628011703491,\n",
       "  0.6448493003845215,\n",
       "  0.6454039216041565,\n",
       "  0.6467130184173584,\n",
       "  0.6464127898216248,\n",
       "  0.6443841457366943,\n",
       "  0.6475416421890259,\n",
       "  0.6442448496818542,\n",
       "  0.6431947946548462,\n",
       "  0.6479787230491638,\n",
       "  0.6466028690338135,\n",
       "  0.6454982757568359,\n",
       "  0.6467063426971436,\n",
       "  0.6468243598937988,\n",
       "  0.6458361744880676,\n",
       "  0.6452569365501404,\n",
       "  0.6488078236579895,\n",
       "  0.6463759541511536,\n",
       "  0.6467409133911133,\n",
       "  0.646358072757721,\n",
       "  0.6445494294166565,\n",
       "  0.6450402736663818,\n",
       "  0.6466974020004272,\n",
       "  0.646354615688324,\n",
       "  0.6478545069694519,\n",
       "  0.6468169689178467,\n",
       "  0.6444361209869385,\n",
       "  0.6447925567626953,\n",
       "  0.6471647024154663,\n",
       "  0.6467602252960205,\n",
       "  0.6470628380775452,\n",
       "  0.6441147327423096,\n",
       "  0.644925594329834,\n",
       "  0.6483091711997986,\n",
       "  0.6451404690742493,\n",
       "  0.6460649371147156,\n",
       "  0.6442028880119324,\n",
       "  0.6472601890563965,\n",
       "  0.6463344693183899,\n",
       "  0.644533097743988,\n",
       "  0.6441443562507629,\n",
       "  0.6464962363243103,\n",
       "  0.6456350088119507,\n",
       "  0.6463227868080139,\n",
       "  0.6446547508239746,\n",
       "  0.6489647030830383,\n",
       "  0.6459203362464905,\n",
       "  0.6457232236862183,\n",
       "  0.646671712398529,\n",
       "  0.6444318294525146,\n",
       "  0.6457528471946716,\n",
       "  0.6474099159240723,\n",
       "  0.6437340974807739,\n",
       "  0.6447740793228149,\n",
       "  0.6470629572868347,\n",
       "  0.6467347741127014,\n",
       "  0.6449198126792908,\n",
       "  0.6450991034507751,\n",
       "  0.6460540294647217,\n",
       "  0.6458764672279358,\n",
       "  0.6468973159790039,\n",
       "  0.6469062566757202,\n",
       "  0.6444734930992126,\n",
       "  0.6457861661911011,\n",
       "  0.6454589366912842,\n",
       "  0.6476144194602966,\n",
       "  0.6463379263877869,\n",
       "  0.645454466342926,\n",
       "  0.6456044912338257,\n",
       "  0.6480698585510254,\n",
       "  0.6462035179138184,\n",
       "  0.6460124850273132,\n",
       "  0.6460644006729126,\n",
       "  0.6433348655700684,\n",
       "  0.6475505232810974,\n",
       "  0.644609808921814,\n",
       "  0.645494282245636,\n",
       "  0.6453230977058411,\n",
       "  0.6461910605430603,\n",
       "  0.6436820030212402,\n",
       "  0.6464539766311646,\n",
       "  0.647393524646759,\n",
       "  0.6472041606903076,\n",
       "  0.6455554366111755,\n",
       "  0.6467880606651306,\n",
       "  0.6471443772315979,\n",
       "  0.6461049318313599,\n",
       "  0.6447348594665527,\n",
       "  0.6445167660713196,\n",
       "  0.6461814045906067,\n",
       "  0.6441052556037903,\n",
       "  0.6447111964225769,\n",
       "  0.6454066634178162,\n",
       "  0.6463691592216492,\n",
       "  0.6441968679428101,\n",
       "  0.6444864869117737,\n",
       "  0.6447258591651917,\n",
       "  0.6452471017837524,\n",
       "  0.6475231051445007,\n",
       "  0.6451123952865601,\n",
       "  0.6436446905136108,\n",
       "  0.6466258764266968,\n",
       "  0.6452383399009705,\n",
       "  0.6471861004829407,\n",
       "  0.6459343433380127,\n",
       "  0.6475870013237,\n",
       "  0.6477848291397095,\n",
       "  0.6449592709541321,\n",
       "  0.6460356712341309,\n",
       "  0.6447409987449646,\n",
       "  0.6410555839538574,\n",
       "  0.6471043229103088,\n",
       "  0.6455209851264954,\n",
       "  0.6465833783149719,\n",
       "  0.6442036032676697,\n",
       "  0.6440854668617249,\n",
       "  0.6424234509468079,\n",
       "  0.6491223573684692,\n",
       "  0.6458168625831604,\n",
       "  0.6467414498329163,\n",
       "  0.6438602805137634,\n",
       "  0.6463173627853394,\n",
       "  0.6486197710037231,\n",
       "  0.6458361148834229,\n",
       "  0.6469240188598633,\n",
       "  0.6486936807632446,\n",
       "  0.6449905633926392,\n",
       "  0.6467470526695251,\n",
       "  0.646557092666626,\n",
       "  0.6455126404762268,\n",
       "  0.6464194655418396,\n",
       "  0.6467241048812866,\n",
       "  0.6465263962745667,\n",
       "  0.6475281119346619,\n",
       "  0.6456579566001892,\n",
       "  0.6484744548797607,\n",
       "  0.6470785140991211,\n",
       "  0.644717812538147,\n",
       "  0.6440115571022034,\n",
       "  0.6455036997795105,\n",
       "  0.6435487866401672,\n",
       "  0.647087574005127,\n",
       "  0.6465400457382202,\n",
       "  0.6468771696090698,\n",
       "  0.647101104259491,\n",
       "  0.6466362476348877,\n",
       "  0.6437972187995911,\n",
       "  0.6468000411987305,\n",
       "  0.6453207731246948,\n",
       "  0.645866334438324,\n",
       "  0.6457560658454895,\n",
       "  0.6463161110877991,\n",
       "  0.645344614982605,\n",
       "  0.6483542919158936,\n",
       "  0.6451563239097595,\n",
       "  0.6465994715690613,\n",
       "  0.6443084478378296,\n",
       "  0.6485958099365234,\n",
       "  0.6466003656387329,\n",
       "  0.647889256477356,\n",
       "  0.6443337798118591,\n",
       "  0.6429207921028137,\n",
       "  0.6452839970588684,\n",
       "  0.6470301747322083,\n",
       "  0.6471659541130066,\n",
       "  0.6452271938323975,\n",
       "  0.6461624503135681,\n",
       "  0.643753170967102,\n",
       "  0.6474056243896484,\n",
       "  0.6460586190223694,\n",
       "  0.6458783149719238,\n",
       "  0.6471737027168274,\n",
       "  0.6466824412345886,\n",
       "  0.6442009806632996,\n",
       "  0.6477827429771423,\n",
       "  0.6467019319534302,\n",
       "  0.6477248072624207,\n",
       "  0.6459630727767944,\n",
       "  0.6467891931533813,\n",
       "  0.6460311412811279,\n",
       "  0.6445629000663757,\n",
       "  0.6488982439041138,\n",
       "  0.6469643712043762,\n",
       "  0.6450231075286865,\n",
       "  0.6460210084915161,\n",
       "  0.6451494693756104,\n",
       "  0.646316409111023,\n",
       "  0.6457783579826355,\n",
       "  0.6460784077644348,\n",
       "  0.6456156373023987,\n",
       "  0.6464467644691467,\n",
       "  0.6473833918571472,\n",
       "  0.644716739654541,\n",
       "  0.6467512249946594,\n",
       "  0.6443033814430237,\n",
       "  0.646006166934967,\n",
       "  0.6451358199119568,\n",
       "  0.6453407406806946,\n",
       "  0.6470014452934265,\n",
       "  0.6447980403900146,\n",
       "  0.6444129347801208,\n",
       "  0.6477721333503723,\n",
       "  0.6436175107955933,\n",
       "  0.6445358395576477,\n",
       "  0.646515429019928,\n",
       "  0.6446914672851562,\n",
       "  0.6447349786758423]}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}