{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Pmxv2ioyCRw"
   },
   "source": [
    "##### Copyright 2019 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:05.242995Z",
     "iopub.status.busy": "2024-08-16T02:37:05.242459Z",
     "iopub.status.idle": "2024-08-16T02:37:05.246000Z",
     "shell.execute_reply": "2024-08-16T02:37:05.245457Z"
    },
    "id": "b-2ShX25yNWf"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pa49bUnKyRgF"
   },
   "source": [
    "# Time series forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "11Ilg92myRcw"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/structured_data/time_series\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/structured_data/time_series.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/time_series.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/structured_data/time_series.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GU8C5qm_4vZb"
   },
   "source": [
    "This tutorial is an introduction to time series forecasting using TensorFlow. It builds a few different styles of models including Convolutional and Recurrent Neural Networks (CNNs and RNNs).\n",
    "\n",
    "This is covered in two main parts, with subsections:\n",
    "\n",
    "* Forecast for a single time step:\n",
    "  * A single feature.\n",
    "  * All features.\n",
    "* Forecast multiple steps:\n",
    "  * Single-shot: Make the predictions all at once.\n",
    "  * Autoregressive: Make one prediction at a time and feed the output back to the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XVhK72Pu1cJL"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:05.249735Z",
     "iopub.status.busy": "2024-08-16T02:37:05.249357Z",
     "iopub.status.idle": "2024-08-16T02:37:08.322602Z",
     "shell.execute_reply": "2024-08-16T02:37:08.321779Z"
    },
    "id": "7rZnJaGTWQw0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-16 02:37:06.588180: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-08-16 02:37:06.609911: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-08-16 02:37:06.616329: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import datetime\n",
    "\n",
    "import IPython\n",
    "import IPython.display\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (8, 6)\n",
    "mpl.rcParams['axes.grid'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TokBlnUhWFw9"
   },
   "source": [
    "## The weather dataset\n",
    "\n",
    "This tutorial uses a <a href=\"https://www.bgc-jena.mpg.de/wetter/\" class=\"external\">weather time series dataset</a> recorded by the <a href=\"https://www.bgc-jena.mpg.de\" class=\"external\">Max Planck Institute for Biogeochemistry</a>.\n",
    "\n",
    "This dataset contains 14 different features such as air temperature, atmospheric pressure, and humidity. These were collected every 10 minutes, beginning in 2003. For efficiency, you will use only the data collected between 2009 and 2016. This section of the dataset was prepared by François Chollet for his book <a href=\"https://www.manning.com/books/deep-learning-with-python\" class=\"external\">Deep Learning with Python</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:08.326599Z",
     "iopub.status.busy": "2024-08-16T02:37:08.326150Z",
     "iopub.status.idle": "2024-08-16T02:37:08.897432Z",
     "shell.execute_reply": "2024-08-16T02:37:08.896697Z"
    },
    "id": "xyv_i85IWInT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m       0/13568290\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 0s/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 4202496/13568290\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 0us/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m11476992/13568290\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 0us/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m13568290/13568290\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
     ]
    }
   ],
   "source": [
    "zip_path = tf.keras.utils.get_file(\n",
    "    origin='https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip',\n",
    "    fname='jena_climate_2009_2016.csv.zip',\n",
    "    extract=True)\n",
    "csv_path, _ = os.path.splitext(zip_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R81Wx8WP4c3G"
   },
   "source": [
    "This tutorial will just deal with **hourly predictions**, so start by sub-sampling the data from 10-minute intervals to one-hour intervals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:08.901536Z",
     "iopub.status.busy": "2024-08-16T02:37:08.900847Z",
     "iopub.status.idle": "2024-08-16T02:37:09.741226Z",
     "shell.execute_reply": "2024-08-16T02:37:09.740495Z"
    },
    "id": "TX6uGeeeWIkG"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(csv_path)\n",
    "# Slice [start:stop:step], starting from index 5 take every 6th record.\n",
    "df = df[5::6]\n",
    "\n",
    "date_time = pd.to_datetime(df.pop('Date Time'), format='%d.%m.%Y %H:%M:%S')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VdbOWXiTWM2T"
   },
   "source": [
    "Let's take a glance at the data. Here are the first few rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:09.745548Z",
     "iopub.status.busy": "2024-08-16T02:37:09.744954Z",
     "iopub.status.idle": "2024-08-16T02:37:09.763544Z",
     "shell.execute_reply": "2024-08-16T02:37:09.762973Z"
    },
    "id": "ojHE-iCCWIhz"
   },
   "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>p (mbar)</th>\n",
       "      <th>T (degC)</th>\n",
       "      <th>Tpot (K)</th>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <th>rh (%)</th>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <th>wv (m/s)</th>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <th>wd (deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>996.50</td>\n",
       "      <td>-8.05</td>\n",
       "      <td>265.38</td>\n",
       "      <td>-8.78</td>\n",
       "      <td>94.4</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.96</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1307.86</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.63</td>\n",
       "      <td>192.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>996.62</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>264.54</td>\n",
       "      <td>-9.77</td>\n",
       "      <td>93.2</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.91</td>\n",
       "      <td>1312.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.63</td>\n",
       "      <td>190.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>996.84</td>\n",
       "      <td>-8.81</td>\n",
       "      <td>264.59</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>93.5</td>\n",
       "      <td>3.13</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1312.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.63</td>\n",
       "      <td>167.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>996.99</td>\n",
       "      <td>-9.05</td>\n",
       "      <td>264.34</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>92.6</td>\n",
       "      <td>3.07</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1313.61</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.38</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>997.46</td>\n",
       "      <td>-9.63</td>\n",
       "      <td>263.72</td>\n",
       "      <td>-10.65</td>\n",
       "      <td>92.2</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.71</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2.71</td>\n",
       "      <td>1317.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.88</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  rh (%)  VPmax (mbar)  \\\n",
       "5     996.50     -8.05    265.38        -8.78    94.4          3.33   \n",
       "11    996.62     -8.88    264.54        -9.77    93.2          3.12   \n",
       "17    996.84     -8.81    264.59        -9.66    93.5          3.13   \n",
       "23    996.99     -9.05    264.34       -10.02    92.6          3.07   \n",
       "29    997.46     -9.63    263.72       -10.65    92.2          2.94   \n",
       "\n",
       "    VPact (mbar)  VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  rho (g/m**3)  \\\n",
       "5           3.14          0.19       1.96             3.15       1307.86   \n",
       "11          2.90          0.21       1.81             2.91       1312.25   \n",
       "17          2.93          0.20       1.83             2.94       1312.18   \n",
       "23          2.85          0.23       1.78             2.85       1313.61   \n",
       "29          2.71          0.23       1.69             2.71       1317.19   \n",
       "\n",
       "    wv (m/s)  max. wv (m/s)  wd (deg)  \n",
       "5       0.21           0.63     192.7  \n",
       "11      0.25           0.63     190.3  \n",
       "17      0.18           0.63     167.2  \n",
       "23      0.10           0.38     240.0  \n",
       "29      0.40           0.88     157.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WRzj1inMfgcO"
   },
   "source": [
    "Here is the evolution of a few features over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:09.766568Z",
     "iopub.status.busy": "2024-08-16T02:37:09.766169Z",
     "iopub.status.idle": "2024-08-16T02:37:12.262715Z",
     "shell.execute_reply": "2024-08-16T02:37:12.262026Z"
    },
    "id": "Vg5XIc5tfNlG"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cols = ['T (degC)', 'p (mbar)', 'rho (g/m**3)']\n",
    "plot_features = df[plot_cols]\n",
    "plot_features.index = date_time\n",
    "_ = plot_features.plot(subplots=True)\n",
    "\n",
    "plot_features = df[plot_cols][:480]\n",
    "plot_features.index = date_time[:480]\n",
    "_ = plot_features.plot(subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wXWLG0_WBhZS"
   },
   "source": [
    "### Inspect and cleanup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yhmZXJew6GlS"
   },
   "source": [
    "Next, look at the statistics of the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:12.267172Z",
     "iopub.status.busy": "2024-08-16T02:37:12.266663Z",
     "iopub.status.idle": "2024-08-16T02:37:12.334710Z",
     "shell.execute_reply": "2024-08-16T02:37:12.334089Z"
    },
    "id": "h510pgKVrrai"
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>989.212842</td>\n",
       "      <td>8.358886</td>\n",
       "      <td>913.60</td>\n",
       "      <td>984.20</td>\n",
       "      <td>989.57</td>\n",
       "      <td>994.720</td>\n",
       "      <td>1015.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T (degC)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>9.450482</td>\n",
       "      <td>8.423384</td>\n",
       "      <td>-22.76</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.41</td>\n",
       "      <td>15.480</td>\n",
       "      <td>37.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tpot (K)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>283.493086</td>\n",
       "      <td>8.504424</td>\n",
       "      <td>250.85</td>\n",
       "      <td>277.44</td>\n",
       "      <td>283.46</td>\n",
       "      <td>289.530</td>\n",
       "      <td>311.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>4.956471</td>\n",
       "      <td>6.730081</td>\n",
       "      <td>-24.80</td>\n",
       "      <td>0.24</td>\n",
       "      <td>5.21</td>\n",
       "      <td>10.080</td>\n",
       "      <td>23.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rh (%)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>76.009788</td>\n",
       "      <td>16.474920</td>\n",
       "      <td>13.88</td>\n",
       "      <td>65.21</td>\n",
       "      <td>79.30</td>\n",
       "      <td>89.400</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>13.576576</td>\n",
       "      <td>7.739883</td>\n",
       "      <td>0.97</td>\n",
       "      <td>7.77</td>\n",
       "      <td>11.82</td>\n",
       "      <td>17.610</td>\n",
       "      <td>63.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>9.533968</td>\n",
       "      <td>4.183658</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6.22</td>\n",
       "      <td>8.86</td>\n",
       "      <td>12.360</td>\n",
       "      <td>28.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>4.042536</td>\n",
       "      <td>4.898549</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.19</td>\n",
       "      <td>5.300</td>\n",
       "      <td>46.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>6.022560</td>\n",
       "      <td>2.655812</td>\n",
       "      <td>0.51</td>\n",
       "      <td>3.92</td>\n",
       "      <td>5.59</td>\n",
       "      <td>7.800</td>\n",
       "      <td>18.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>9.640437</td>\n",
       "      <td>4.234862</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6.29</td>\n",
       "      <td>8.96</td>\n",
       "      <td>12.490</td>\n",
       "      <td>28.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>1216.061232</td>\n",
       "      <td>39.974263</td>\n",
       "      <td>1059.45</td>\n",
       "      <td>1187.47</td>\n",
       "      <td>1213.80</td>\n",
       "      <td>1242.765</td>\n",
       "      <td>1393.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wv (m/s)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>1.702567</td>\n",
       "      <td>65.447512</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.860</td>\n",
       "      <td>14.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>2.963041</td>\n",
       "      <td>75.597657</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.98</td>\n",
       "      <td>4.740</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wd (deg)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>174.789095</td>\n",
       "      <td>86.619431</td>\n",
       "      <td>0.00</td>\n",
       "      <td>125.30</td>\n",
       "      <td>198.10</td>\n",
       "      <td>234.000</td>\n",
       "      <td>360.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean        std      min      25%      50%  \\\n",
       "p (mbar)         70091.0   989.212842   8.358886   913.60   984.20   989.57   \n",
       "T (degC)         70091.0     9.450482   8.423384   -22.76     3.35     9.41   \n",
       "Tpot (K)         70091.0   283.493086   8.504424   250.85   277.44   283.46   \n",
       "Tdew (degC)      70091.0     4.956471   6.730081   -24.80     0.24     5.21   \n",
       "rh (%)           70091.0    76.009788  16.474920    13.88    65.21    79.30   \n",
       "VPmax (mbar)     70091.0    13.576576   7.739883     0.97     7.77    11.82   \n",
       "VPact (mbar)     70091.0     9.533968   4.183658     0.81     6.22     8.86   \n",
       "VPdef (mbar)     70091.0     4.042536   4.898549     0.00     0.87     2.19   \n",
       "sh (g/kg)        70091.0     6.022560   2.655812     0.51     3.92     5.59   \n",
       "H2OC (mmol/mol)  70091.0     9.640437   4.234862     0.81     6.29     8.96   \n",
       "rho (g/m**3)     70091.0  1216.061232  39.974263  1059.45  1187.47  1213.80   \n",
       "wv (m/s)         70091.0     1.702567  65.447512 -9999.00     0.99     1.76   \n",
       "max. wv (m/s)    70091.0     2.963041  75.597657 -9999.00     1.76     2.98   \n",
       "wd (deg)         70091.0   174.789095  86.619431     0.00   125.30   198.10   \n",
       "\n",
       "                      75%      max  \n",
       "p (mbar)          994.720  1015.29  \n",
       "T (degC)           15.480    37.28  \n",
       "Tpot (K)          289.530   311.21  \n",
       "Tdew (degC)        10.080    23.06  \n",
       "rh (%)             89.400   100.00  \n",
       "VPmax (mbar)       17.610    63.77  \n",
       "VPact (mbar)       12.360    28.25  \n",
       "VPdef (mbar)        5.300    46.01  \n",
       "sh (g/kg)           7.800    18.07  \n",
       "H2OC (mmol/mol)    12.490    28.74  \n",
       "rho (g/m**3)     1242.765  1393.54  \n",
       "wv (m/s)            2.860    14.01  \n",
       "max. wv (m/s)       4.740    23.50  \n",
       "wd (deg)          234.000   360.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TzOTnWOoWMGK"
   },
   "source": [
    "#### Wind velocity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i47LiW5DCVsP"
   },
   "source": [
    "One thing that should stand out is the `min` value of the wind velocity (`wv (m/s)`) and the maximum value (`max. wv (m/s)`) columns. This `-9999` is likely erroneous.\n",
    "\n",
    "There's a separate wind direction column, so the velocity should be greater than zero (`>=0`). Replace it with zeros:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:12.338550Z",
     "iopub.status.busy": "2024-08-16T02:37:12.337877Z",
     "iopub.status.idle": "2024-08-16T02:37:12.347287Z",
     "shell.execute_reply": "2024-08-16T02:37:12.346714Z"
    },
    "id": "qFOq0_80vF4d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv = df['wv (m/s)']\n",
    "bad_wv = wv == -9999.0\n",
    "wv[bad_wv] = 0.0\n",
    "\n",
    "max_wv = df['max. wv (m/s)']\n",
    "bad_max_wv = max_wv == -9999.0\n",
    "max_wv[bad_max_wv] = 0.0\n",
    "\n",
    "# The above inplace edits are reflected in the DataFrame.\n",
    "df['wv (m/s)'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vtmu2IBPgPG8"
   },
   "source": [
    "### Feature engineering\n",
    "\n",
    "Before diving in to build a model, it's important to understand your data and be sure that you're passing the model appropriately formatted data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FYyEaqiD6j4s"
   },
   "source": [
    "#### Wind\n",
    "The last column of the data, `wd (deg)`—gives the wind direction in units of degrees. Angles do not make good model inputs: 360° and 0° should be close to each other and wrap around smoothly. Direction shouldn't matter if the wind is not blowing.\n",
    "\n",
    "Right now the distribution of wind data looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:12.350845Z",
     "iopub.status.busy": "2024-08-16T02:37:12.350293Z",
     "iopub.status.idle": "2024-08-16T02:37:12.557697Z",
     "shell.execute_reply": "2024-08-16T02:37:12.557043Z"
    },
    "id": "YO7JGTcWQG2z"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Wind Velocity [m/s]')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(df['wd (deg)'], df['wv (m/s)'], bins=(50, 50), vmax=400)\n",
    "plt.colorbar()\n",
    "plt.xlabel('Wind Direction [deg]')\n",
    "plt.ylabel('Wind Velocity [m/s]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yWnf5dwMU1_g"
   },
   "source": [
    "But this will be easier for the model to interpret if you convert the wind direction and velocity columns to a wind **vector**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:12.561308Z",
     "iopub.status.busy": "2024-08-16T02:37:12.560689Z",
     "iopub.status.idle": "2024-08-16T02:37:12.574818Z",
     "shell.execute_reply": "2024-08-16T02:37:12.574242Z"
    },
    "id": "6GmSTHXw6lI1"
   },
   "outputs": [],
   "source": [
    "wv = df.pop('wv (m/s)')\n",
    "max_wv = df.pop('max. wv (m/s)')\n",
    "\n",
    "# Convert to radians.\n",
    "wd_rad = df.pop('wd (deg)')*np.pi / 180\n",
    "\n",
    "# Calculate the wind x and y components.\n",
    "df['Wx'] = wv*np.cos(wd_rad)\n",
    "df['Wy'] = wv*np.sin(wd_rad)\n",
    "\n",
    "# Calculate the max wind x and y components.\n",
    "df['max Wx'] = max_wv*np.cos(wd_rad)\n",
    "df['max Wy'] = max_wv*np.sin(wd_rad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7iI0zDoxWDyB"
   },
   "source": [
    "The distribution of wind vectors is much simpler for the model to correctly interpret:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:12.578732Z",
     "iopub.status.busy": "2024-08-16T02:37:12.578078Z",
     "iopub.status.idle": "2024-08-16T02:37:12.787656Z",
     "shell.execute_reply": "2024-08-16T02:37:12.787029Z"
    },
    "id": "bMgCG5o2SYKD"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-11.305513973134667, 8.24469928549079, -8.27438540335515, 7.7338312955467785)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo8AAAISCAYAAACtaCXmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABZ60lEQVR4nO3deXxU1f3/8fdMVrITyFoCBZRNVkEx1i8QRBbRYqVWLbLJFysGNyxiWgqIVazyrVTLYvtDsN9K7ddWXBBBtqBIQIhSASUKYglLiIokEMg2c39/UAZHCHMCdzKZ5PV8PO5Dc+cz5547N8vhc+/5HIdlWZYAAAAAA85AdwAAAADBg8EjAAAAjDF4BAAAgDEGjwAAADDG4BEAAADGGDwCAADAGINHAAAAGGPwCAAAAGMMHgEAAGCMwSMAAACMMXgEAAAIMk8++aQcDoceeOABz77y8nJlZ2erWbNmiomJ0fDhw3X48GGv9+3bt09Dhw5VVFSUkpOTNXnyZFVXV9fq2AweAQAAgsiWLVv0/PPPq2vXrl77H3zwQb355pt65ZVXtH79eh08eFA333yz53WXy6WhQ4eqsrJSGzdu1IsvvqjFixdr2rRptTq+w7Isy5YzAQAAgF8dP35cl19+uebNm6ff/va36t69u+bMmaOSkhIlJSVpyZIl+ulPfypJ2rVrlzp27Ki8vDxdddVVevvtt3XDDTfo4MGDSklJkSQtWLBAU6ZM0VdffaXw8HCjPoT67ezqIbfbrYMHDyo2NlYOhyPQ3QEAAAYsy9KxY8eUnp4up7Pub5qWl5ersrLSL21blnXWmCQiIkIRERHnjM/OztbQoUM1YMAA/fa3v/Xsz8/PV1VVlQYMGODZ16FDB7Vs2dIzeMzLy1OXLl08A0dJGjRokCZMmKCdO3eqR48eRn1uVIPHgwcPKiMjI9DdAAAAF6CwsFAtWrSo02OWl5erdasYFRW7/NJ+TEyMjh8/7rVv+vTpmjFjxlmxL7/8sj788ENt2bLlrNeKiooUHh6uhIQEr/0pKSkqKiryxHx34Hj69dOvmWpUg8fY2FhJ0jW6XqEKC3BvAACAiWpVaYOWe/6O16XKykoVFbv07/wfKi7W3qxn6TG3WvX8UoWFhYqLi/PsP1fWsbCwUPfff79WrVqlyMhIW/tRW41q8Hg6LRyqMIU6GDwCABAU/jM7I5CPnMXEOhQTa+/x3TrVXlxcnNfg8Vzy8/NVXFysyy+/3LPP5XLp3Xff1R//+EetXLlSlZWVOnr0qFf28fDhw0pNTZUkpaam6oMPPvBq9/Rs7NMxJphtDQAA4IPLcvtlM3Xttddq+/bt2rZtm2fr1auXRowY4fn/sLAwrVmzxvOegoIC7du3T5mZmZKkzMxMbd++XcXFxZ6YVatWKS4uTp06dTLuS6PKPAIAAASj2NhYde7c2WtfdHS0mjVr5tk/btw4TZo0SYmJiYqLi9O9996rzMxMXXXVVZKkgQMHqlOnTho5cqSeeuopFRUVaerUqcrOzq5xgs65MHgEAADwwS1Lbtlb3dDu9p555hk5nU4NHz5cFRUVGjRokObNm+d5PSQkRMuWLdOECROUmZmp6OhojR49WjNnzqzVcRpVncfS0lLFx8ern4bxzCMAAEGi2qpSrl5XSUmJz2cD7XZ67FBU0NIvE2ZS2+8LyHldDDKPAAAAPrjllvkTiuZtBiMmzAAAAMAYmUcAAAAfXJYll81P+tndXl0h8wgAAABjZB4BAAB8CIbZ1nWFwSMAAIAPbllyMXiUxG1rAAAA1AKZRwAAAB+4bX0GmUcAAAAYI/MIAADgA6V6ziDzCAAAAGNkHgEAAHxw/2ezu81gROYRAAAAxsg8AgAA+ODyQ51Hu9urKwweAQAAfHBZpza72wxG3LYGAACAMTKPAAAAPjBh5gwyjwAAADBG5hEAAMAHtxxyyWF7m8GIzCMAAACMkXkEANR/DsNchxWsT5GhvnNbpza72wxGZB4BAABgjMwjAACADy4/PPNod3t1hcEjAACADwwez+C2NQAAAIyReQQAAPDBbTnktmwu1WNze3WFzCMAAACMkXkEAADwgWcezyDzCAAAAGNkHgEAAHxwySmXzTk3l62t1R0GjwAAc4Fa6YWVY4B6g8EjAACAD5YfZltbQTrbmsEjAACAD0yYOYMJMwAAADBG5hEAAMAHl+WUy7J5woxla3N1hswjAAAAjJF5BAAA8MEth9w259zcCs7UI5lHAAAAGCPzCAAA4AOzrc8IqszjgQMHdMcdd6hZs2Zq0qSJunTpoq1btwa6WwDQeFhusy1AHCEhRhuACxc0mcdvv/1WP/rRj5SVlaW3335bSUlJ+vzzz9W0adNAdw0AADRw/pltHZzPPAbN4PF3v/udMjIytGjRIs++1q1bB7BHAACgsTg1Ycbe28x2t1dXgua29RtvvKFevXrplltuUXJysnr06KE///nP531PRUWFSktLvTYAAABcuKAZPH7xxReaP3++Lr30Uq1cuVITJkzQfffdpxdffLHG98yaNUvx8fGeLSMjow57DAAAGgq3nHLZvNld+qeuOCwrOG64h4eHq1evXtq4caNn33333actW7YoLy/vnO+pqKhQRUWF5+vS0lJlZGSon4Yp1BHm9z4DAOqW6WQYy+Xyc09gp2qrSrl6XSUlJYqLi6vTY5eWlio+Pl6v/KuDomLtnWx14phLt3TbFZDzuhhB88xjWlqaOnXq5LWvY8eO+uc//1njeyIiIhQREeHvrgEAgAaOCTNnBE2+9Ec/+pEKCgq89n322Wdq1apVgHoEAADQ+ATN4PHBBx/Upk2b9MQTT2j37t1asmSJ/vSnPyk7OzvQXQMAAA2c+z/PKNq91cb8+fPVtWtXxcXFKS4uTpmZmXr77bc9r/fr108Oh8Nru/vuu73a2Ldvn4YOHaqoqCglJydr8uTJqq6urlU/gua29RVXXKGlS5cqJydHM2fOVOvWrTVnzhyNGDEi0F0DgEbD9mcKHYZ/PA0Lj1tuw9uANh8XqAstWrTQk08+qUsvvVSWZenFF1/UsGHD9NFHH+myyy6TJI0fP14zZ870vCcqKsrz/y6XS0OHDlVqaqo2btyoQ4cOadSoUQoLC9MTTzxh3I+gGTxK0g033KAbbrgh0N0AAACNjMtyyGXZvDzhf9r7finBmuZs3HjjjV5fP/7445o/f742bdrkGTxGRUUpNTX1nMd755139Mknn2j16tVKSUlR9+7d9dhjj2nKlCmaMWOGwsPDjfodNLetAQAAAsXuMj2nN0nKyMjwKi04a9Ys3/1xufTyyy+rrKxMmZmZnv0vvfSSmjdvrs6dOysnJ0cnTpzwvJaXl6cuXbooJSXFs2/QoEEqLS3Vzp07jT+LoMo8AgAANDSFhYVepXrOVylm+/btyszMVHl5uWJiYrR06VJPNZqf//znatWqldLT0/Xxxx9rypQpKigo0KuvvipJKioq8ho4SvJ8XVRUZNxfBo8AAAA+uC2n3DaX6nH/p1TP6QkwJtq3b69t27appKRE//jHPzR69GitX79enTp10l133eWJ69Kli9LS0nTttddqz549atu2rW395rY1AABAkAgPD9cll1yinj17atasWerWrZv+8Ic/nDO2d+/ekqTdu3dLklJTU3X48GGvmNNf1/Sc5LkweAQAAPDBn888Xgy32+21mt53bdu2TdKphVYkKTMzU9u3b1dxcbEnZtWqVYqLiztrIZbz4bY1AABAEMjJydGQIUPUsmVLHTt2TEuWLFFubq5WrlypPXv2aMmSJbr++uvVrFkzffzxx3rwwQfVp08fde3aVZI0cOBAderUSSNHjtRTTz2loqIiTZ06VdnZ2bVakY/BIwAAgA9uyfZSPbWtIlpcXKxRo0bp0KFDio+PV9euXbVy5Updd911Kiws1OrVqzVnzhyVlZUpIyNDw4cP19SpUz3vDwkJ0bJlyzRhwgRlZmYqOjpao0eP9qoLaYLBIwBAjtAwozirusoozmlYL85dWWlre6bFyW0tYk4hcdSRhQsX1vhaRkaG1q9f77ONVq1aafny5RfVDwaPAAAAPlzIcoImbQYjBo8AAAA+uCynXDaX6rG7vboSnL0GAABAQJB5BAAA8MEth9yye8KMve3VFTKPAAAAMEbmEQAAwAeeeTwjOHsNAACAgCDzCAAA4INdywl+v81gFJy9BgAAQECQeQTQ+JisGiIFZuUQ076ZMjwH0xVXHCEhF9ObszgjIm1tTw7LMNDwfJ2+Z8NahovVILi5LYfcdi9PaHN7dYXMIwAAAIyReQQAAPDB7YdnHlmeEAAAoIFyW065bS6tY3d7dSU4ew0AAICAIPMIAADgg0sOuWxeTtDu9uoKmUcAAAAYI/MIAADgA888nhGcvQYAAEBAkHkE0PjYXfzbpLC34TFNilKfas60GLYZu4/rCDXLTVjVVUZxxmwusm5yvqaF000LsdteKN5UIIriBxGX7H9GMVjry5N5BAAAgDEyjwAAAD7wzOMZDB4BAAB8cFlOuWwe7NndXl0Jzl4DAAAgIMg8AgAA+GDJIbfNE2YsioQDAACgoSPzCAAA4APPPJ4RnL0GAABAQJB5BFD/mRZNNi1ybNieaeFss8bCzOJsLtRsXMDasPi3s0mk2YFdhkXRIyKM4qxKs2LiptfMER5udlyDwt62F/+2+fuY4t/2cFsOuS17n1G0u726QuYRAAAAxsg8AgAA+OCSUy6bc252t1dXGDwCAAD4wG3rM4JzyAsAAICAIPMIAADgg1tOuW3OudndXl0Jzl4DAAAgIMg8AgAA+OCyHHLZ/Iyi3e3VFTKPAAAAMEbmEQBqYFr82RlhUDjbsFCzZVpv2rD4t6mQaLNi3XIYZkrCDIuiuw2LiUebtWeVV5jFGV5bR6jv45peC3dVtVGcMYp/1ylmW59B5hEAAADGyDwCAAD4YFlOuS17c26Wze3VleDstaQnn3xSDodDDzzwQKC7AgAAGjiXHH7ZglFQDh63bNmi559/Xl27dg10VwAAABqVoBs8Hj9+XCNGjNCf//xnNW3a9LyxFRUVKi0t9doAAABqy22dmTRj3xbos7owQTd4zM7O1tChQzVgwACfsbNmzVJ8fLxny8jIqIMeAgAANFxBNWHm5Zdf1ocffqgtW7YYxefk5GjSpEmer0tLSxlAAgCAWnP7YcKM3e3VlaAZPBYWFur+++/XqlWrFBlpUFNNUkREhCIiDGuXAQAAwKegGTzm5+eruLhYl19+uWefy+XSu+++qz/+8Y+qqKhQiM1FcwEAACTJLYfcNs+Otru9uhI0g8drr71W27dv99o3duxYdejQQVOmTGHgCFwMh+GtE9MVLUzbCxCH0+wXtuW273OxDJ+MdxjeLbEqq8zaM/3dGGr258ARHWXWXpVZ/2SZfS7u42VGcY5Is8/PYbiyjfvECYPG7P1+N71mpt9Txlixpt6bP3++5s+fry+//FKSdNlll2natGkaMmSIJKm8vFwPPfSQXn75ZVVUVGjQoEGaN2+eUlJSPG3s27dPEyZM0Lp16xQTE6PRo0dr1qxZCjX8HSAF0eAxNjZWnTt39toXHR2tZs2anbUfAADATi7LIZfNywnWtr0WLVroySef1KWXXirLsvTiiy9q2LBh+uijj3TZZZfpwQcf1FtvvaVXXnlF8fHxmjhxom6++Wa9//77p47ncmno0KFKTU3Vxo0bdejQIY0aNUphYWF64oknjPsRNINHAACAQKkPE2ZuvPFGr68ff/xxzZ8/X5s2bVKLFi20cOFCLVmyRP3795ckLVq0SB07dtSmTZt01VVX6Z133tEnn3yi1atXKyUlRd27d9djjz2mKVOmaMaMGQoPDzfqR/2+t+RDbm6u5syZE+huAAAAXLDv16SuqKjw+R6Xy6WXX35ZZWVlyszMVH5+vqqqqrxKGXbo0EEtW7ZUXl6eJCkvL09dunTxuo09aNAglZaWaufOncb9DerBIwAAQF1wy+4C4Wcm4GRkZHjVpZ41a1aN/di+fbtiYmIUERGhu+++W0uXLlWnTp1UVFSk8PBwJSQkeMWnpKSoqKhIklRUVOQ1cDz9+unXTHHbGgAAIIAKCwsVFxfn+fp8ZQbbt2+vbdu2qaSkRP/4xz80evRorV+/vi666cHgEQAAwAfLD6V6rP+0FxcX5zV4PJ/w8HBdcsklkqSePXtqy5Yt+sMf/qBbb71VlZWVOnr0qFf28fDhw0pNTZUkpaam6oMPPvBq7/Dhw57XTHHbGgAAIEi53W5VVFSoZ8+eCgsL05o1azyvFRQUaN++fcrMzJQkZWZmavv27SouLvbErFq1SnFxcerUqZPxMck8AgAA+HD6OUW726yNnJwcDRkyRC1bttSxY8e0ZMkS5ebmauXKlYqPj9e4ceM0adIkJSYmKi4uTvfee68yMzN11VVXSZIGDhyoTp06aeTIkXrqqadUVFSkqVOnKjs7u1Yr8jF4BGDO7uLfhkWJnRFmS5K6KysvpjdnMS3W7K6q9hnjNCyBYcoRHmYU54yNMWuwSROzOINZoJIkw2LdKjdrz5nUzKw9w8LZlknxb5kXbTdi+O1p+n1nOuww/bkwLk7uchkeGXYrLi7WqFGjdOjQIcXHx6tr165auXKlrrvuOknSM888I6fTqeHDh3sVCT8tJCREy5Yt04QJE5SZmano6GiNHj1aM2fOrFU/GDwCAAD4UB/qPC5cuPC8r0dGRmru3LmaO3dujTGtWrXS8uXLa3Xc72PwCAAA4EN9uG1dXzBhBgAAAMbIPAIAAPjg9kOpHrvbqytkHgEAAGCMzCMAAIAPPPN4BplHAAAAGCPzCAAA4AOZxzMYPAINWYCKehsf1zDO7qLEjlCzAtumxw2Jib6Y7nhzmn0mDtMi3KFmv+atGLNC7I5Is2LnlsPsj6LDaVaYWmGGf65Kj5kd187i3y7DnwvTOKfhgMKwILopy+b20HAxeAQAAPCBzOMZDB4BAAB8YPB4BhNmAAAAYIzMIwAAgA+W7C/qHaxPmZJ5BAAAgDEyjwAAAD7wzOMZZB4BAABgjMwjAACAD2Qez2DwCDRkhkW9neFmhZ/dVdVGcQ7TIsc2FzEPiY4yijMuOm4YZtKe07CQuHXipNlBDc9VhufqNiz+bcoVbVaIXY4Yo7CQ41VGcc4Isz9rjq++NYozaivc7FwdpgXbKyuN4twnThjFGTNdBACNHoNHAAAAH8g8nsHgEQAAwAcGj2cwYQYAAADGyDwCAAD4YFkOWTZnCu1ur66QeQQAAIAxMo8AAAA+uOWwfXlCu9urK2QeAQAAYIzMIwAAgA/Mtj6DzCMAAACMkXkEGjBHSIhRnOnKMcbHNVyxxjI8rjMy4mK6cxbTz8UZZbhijcnKHJZl1JYjPs4oThVmq5BYzeKN4lwxZqukVMabxbnNPmK5w8wyL+FNzBqM+MbsuM7kRKM4R6XB92iZ4apAJ8uNwtyGcZbb7HvKeOUY0xWfGulKNMy2PoPMIwAAAIyReQQAAPCBZx7PYPAIAADgA7etz+C2NQAAAIyReQQAAPDB8sNtazKPAAAAaPDIPAIAAPhgybjiVq3aDEZkHgEAAGCMzCMQjAyL+ZoWEXY4zZ67MS2aLadZ/xwRZsW/HeFmhanlMDsPq6rKrL1Qs8LUjlCD84gwLJweHWkUJ7dZoeaT6TFmxzUs1u0KN4s7kWT2PdDkG7PzCKk0i3NHmP1ZswyvbUipQQHwEyfMjmlaTDxQRbgbafFvU2455JDNpXpsbq+ukHkEAACAMTKPAAAAPlDn8QwGjwAAAD64LYccrDAjKYhuW8+aNUtXXHGFYmNjlZycrJtuukkFBQWB7hYAAECjEjSDx/Xr1ys7O1ubNm3SqlWrVFVVpYEDB6qsrCzQXQMAAA2cZflnC0ZBc9t6xYoVXl8vXrxYycnJys/PV58+fQLUKwAAgMYlaAaP31dSUiJJSkxMrDGmoqJCFRUVnq9LS0v93i8AANDwMGHmjKC5bf1dbrdbDzzwgH70ox+pc+fONcbNmjVL8fHxni0jI6MOewkAANDwBGXmMTs7Wzt27NCGDRvOG5eTk6NJkyZ5vi4tLWUAifrNsPi3aTFfZ4RZwWlHdBOzw5ZX+A6S5IyLNYpTmGHxb9Oi3obtOUINf/VVV5vFxfgunm6FmRWldsWaXbOqGLNzOJFqFneyuVkGxG1W61xOs28VVcSZfc+fbGZWUD66yGUU1+SrSqM4R4Xv7wGr0vD70/Dn1nKZnYPx7wvYgszjGUE3eJw4caKWLVumd999Vy1atDhvbEREhCIMV7AAAACAb0EzeLQsS/fee6+WLl2q3NxctW7dOtBdAgAAjQR1Hs8Impx3dna2/vrXv2rJkiWKjY1VUVGRioqKdPKk4VqhAAAAF6g+lOoxqXndr18/ORwOr+3uu+/2itm3b5+GDh2qqKgoJScna/Lkyao2fUxHQZR5nD9/vqRTH8p3LVq0SGPGjKn7DgEAANSh0zWvr7jiClVXV+tXv/qVBg4cqE8++UTR0dGeuPHjx2vmzJmer6OizjyX7XK5NHToUKWmpmrjxo06dOiQRo0apbCwMD3xxBNG/QiawaMVrJU0AQBA0DuVKbR7wkzt4k1rXkdFRSk1NfWcbbzzzjv65JNPtHr1aqWkpKh79+567LHHNGXKFM2YMUPh4b5nxQXNbWsAAICGqLS01Gv7bo3q86mp5vVLL72k5s2bq3PnzsrJydGJEyc8r+Xl5alLly5KSUnx7Bs0aJBKS0u1c+dOo+MGTeYRAAAgUPxZquf7ZQSnT5+uGTNmnPe9NdW8/vnPf65WrVopPT1dH3/8saZMmaKCggK9+uqrkqSioiKvgaMkz9dFRUVG/WbwCAAAEECFhYWKi4vzfG1SZrCmmtd33XWX5/+7dOmitLQ0XXvttdqzZ4/atm1rS38ZPAI1sbNgt83FfJ1NzIp6O0LMClMbHzc5ySwwwqxYtzvarA6r5TT7/KxQs7iQ4+VGcaYFu0OO+77FVJ4WY9SW5TTLbBxvYfYZHzdcF6GyqVkBa1POSrPzqIw3i4s5YHZc0+LfYUVmy9W69x80CDJ7cM1dZT6bFfWP9Z/N7jYlKS4uzmvw6Ettal737t1bkrR79261bdtWqamp+uCDD7xiDh8+LEk1Pif5fTzzCAAAEAQsy9LEiRO1dOlSrV271qjm9bZt2yRJaWlpkqTMzExt375dxcXFnphVq1YpLi5OnTp1MuoHmUcAAAAf6sPyhNnZ2VqyZIlef/11T81rSYqPj1eTJk20Z88eLVmyRNdff72aNWumjz/+WA8++KD69Omjrl27SpIGDhyoTp06aeTIkXrqqadUVFSkqVOnKjs723hVPjKPAAAAvlh+2mph/vz5KikpUb9+/ZSWlubZ/v73v0uSwsPDtXr1ag0cOFAdOnTQQw89pOHDh+vNN9/0tBESEqJly5YpJCREmZmZuuOOOzRq1CivupC+kHkEAAAIAr5qXmdkZGj9+vU+22nVqpWWL19+wf1g8AgAAOCLH25bi7WtAQAA0NCReQQAAPDh1PKE9rcZjMg8AgAAwBiZRwAAAB/qQ6me+oLBI3CRHKG+V/qwe6UXh2EtLke42SokCjX7VWDFmK244jKMO5lqFlcZZ3aTxBVu9ovYWR1lFBd9yGxFkIpmvq9HeaK93wPliWZxFS2qzAJPGt6IamK2Eo3ptYj4xux7z/RahB49aRRnFRX7DpIkh+/zcFf6XmHIL0xWtwL8gMEjAACAL5bD/tnRZB4BAAAaJibMnMGEGQAAABgzyjx+/PHHtW64U6dOCjV8jgoAAKBeu4DlBI3aDEJGo7vu3bvL4XD4XBbnNKfTqc8++0xt2rS5qM4BAACgfjFODW7evFlJSUk+4yzLUufOnS+qUwAAAPUJpXrOMBo89u3bV5dccokSEhKMGu3Tp4+aNGlyMf0CAABAPWQ0eFy3bl2tGl2+fPkFdQYAAKDeCtJnFO120TNaXC6Xtm/frlatWqlp06Z29AmNhcNwsn+gCuEaHtdy+Y4xLRLuCDP7kXREhBvFKcysSLjVNNYozhVtVpz8RLpZ8e+qGLPvgePpZrd23L2OGcUNv2SbUdw7BzoYxZVu9f1IjymX4aWNvuyIUVy3hG+N4koqzK7ZtyfN7ipV5JtVMY/Zb/bXOHpvqVGc9h0yCnMdLzOKM/rZpVg3Gplal+p54IEHtHDhQkmnBo59+/bV5ZdfroyMDOXm5trdPwAAgIA7/cyj3VswqvXg8R//+Ie6desmSXrzzTe1d+9e7dq1Sw8++KB+/etf295BAACAgLP8tAWhWg8ev/76a6Wmpko69WzjLbfconbt2unOO+/U9u3bbe8gAAAA6o9aDx5TUlL0ySefyOVyacWKFbruuuskSSdOnFCI4XNdAAAAwcXhpy341HrCzNixY/Wzn/1MaWlpcjgcGjBggKRTdSA7dDB7uBwAAADBqdaDxxkzZqhz584qLCzULbfcooiIU7MvQ0JC9Mgjj9jeQQAAgIBjeUIP48HjqFGjNGzYMA0aNEg//elPz3p99OjRtnYMAAAA9Y/xM4+XXHKJnnjiCSUlJWnIkCGaP3++Dhw44M++AQAA1A/MtvYwzjxOmzZN06ZN0/79+/XGG2/otdde04MPPqjLLrtMw4YN049//GN1797dj11FgxOowrqmxckNOSN9F862qqqN2nIYtCVJCjX70TUu/h1rViC6zLD4d1mq2WfsMmtOJzpUGsU92mWFUdzI2K+N4h5L3mEUpx5mYXaad7SFUdye8mSjuJNNzArKryjsbBSXVmD28x2/86hRnA4cNgqzyivM2jNkVVfZ2h7QENT6r2iLFi10zz33aOXKlfrqq680ZcoUFRQUqH///mrVqpUmTpyonTt3+qOvAAAAgWE5/LMFoYtKwcTGxupnP/uZXnrpJX311Vd64YUXFBISory8PLv6BwAAEHCW5Z8tGF302tanhYSE6Nprr9W1115rV5MAAACoZ2o9ePzmm280bdo0rVu3TsXFxXK7vZ9rOXLkiG2dAwAAqBco1eNR68HjyJEjtXv3bo0bN04pKSlyOILzfj0AAABqr9aDx/fee08bNmxQt27d/NEfAACA+scfE1way4SZDh066OTJk/7oCwAAAOq5Wg8e582bp1//+tdav369vvnmG5WWlnptAAAADY3D8s8WjGp92zohIUGlpaXq37+/137LsuRwOORyuWzrHBo402LdpsXEbS7+7QgJMQt0+/7pd8ZEmx0zItzsmDFRRmHVCU2M4iqamhWIPtrO7DMub2b2GzG0ZZlR3O+6vWEUd0tMiVFcQ3Bd9C6juPxjrYzi1n3awSiu2ftm36Px/zIrxK6DhsW/q8yKdbsrys2Oa/PvC6AxqfXgccSIEQoLC9OSJUuYMAMAABoHZlt71HrwuGPHDn300Udq3769P/oDAABQ/zBhxqPWeftevXqpsLDQH30BAABAPVfrzOO9996r+++/X5MnT1aXLl0UFub9rFTXrl1t6xwAAEC9wG1rj1oPHm+99VZJ0p133unZ53A4mDADAADQCNR68Lh3715/9AMAAKD+IvPoUevBY6tWZmUf/GXu3Ll6+umnVVRUpG7duum5557TlVdeGdA+AQAANBZGE2beeOMNVRnW2JKk5cuX+2UVmr///e+aNGmSpk+frg8//FDdunXToEGDVFxcbPuxAAAAPCw/bUHIKPP4k5/8REVFRUpKSjJq9LbbbtO2bdvUpk2bi+rc9/3+97/X+PHjNXbsWEnSggUL9NZbb+mFF17QI488YuuxUAdMi3/bzLT4t7NJpH0Hra42i4syK+pthZmdg8Nl9pvJafiocqRh3eeyNmbnO7S1WaHrxlT8O7+y0ijuV1/cahT3+b5Uo7iED8yKfzf9zCwx4Cj+xijObXi+lmGcsQD9/gEaAqPBo2VZGjNmjCIiIowaLS83rPBfC5WVlcrPz1dOTo5nn9Pp1IABA5SXl3fO91RUVKiiosLzNcsnAgCAC0KdRw+jwePo0aNr1eiIESMUFxd3QR2qyddffy2Xy6WUlBSv/SkpKdq169zZi1mzZunRRx+1tR8AAACNmdHgcdGiRf7uh1/k5ORo0qRJnq9LS0uVkZERwB4BAIBg5LBObXa3GYxqPds6UJo3b66QkBAdPnzYa//hw4eVmnruZ3oiIiKMb7UDAADUiFI9HrVenjBQwsPD1bNnT61Zs8azz+12a82aNcrMzAxgzwAAABqPoBk8StKkSZP05z//WS+++KI+/fRTTZgwQWVlZZ7Z1wAAAA3VrFmzdMUVVyg2NlbJycm66aabVFBQ4BVTXl6u7OxsNWvWTDExMRo+fPhZd2337dunoUOHKioqSsnJyZo8ebKqTauCKMgGj7feeqtmz56tadOmqXv37tq2bZtWrFhx1iQaAACAhmb9+vXKzs7Wpk2btGrVKlVVVWngwIEqKyvzxDz44IN688039corr2j9+vU6ePCgbr75Zs/rLpdLQ4cOVWVlpTZu3KgXX3xRixcv1rRp04z7YfzM47x583TPPfcYN+wvEydO1MSJEwPdDQAA0Ig45IcJM7WMX7FihdfXixcvVnJysvLz89WnTx+VlJRo4cKFWrJkifr37y/p1KTnjh07atOmTbrqqqv0zjvv6JNPPtHq1auVkpKi7t2767HHHtOUKVM0Y8YMhYf7rvlqnHmcOnWqBg0apIMHD9byVAEAAFCT0tJSr+27NarPp6Tk1AIKiYmJkqT8/HxVVVVpwIABnpgOHTqoZcuWnprYeXl56tKli9dd20GDBqm0tFQ7d+40Oq5x5nHHjh0aP368OnfurGeffVZ33HGH6VuBc3OY/dvF4TT7t5nlNvsnoXF7LrNlVxxhYb6DDNuSZXgO1YarYxh+JpbhPyMrDcu3OqrNPuPt36aZNZhuFlaf7ak+bhR3+8uTjeIizBZwUeoBs++B2C/KfAdJCj1odmDL8I+fu9wsjhVhEHB+LBL+/TKC06dP14wZM877VrfbrQceeEA/+tGP1LlzZ0lSUVGRwsPDlZCQ4BWbkpKioqIiT8y5amaffs2E8eAxPT1db731lhYvXqz77rtPS5cu1a9//WuFhno30bVrV9MmAQAAGr3CwkKvxVVMygxmZ2drx44d2rBhgz+7dk61rvM4ZswYtWjRQoMHD9Zrr70my7LkcDg8/3WZZlgAAACChR/rPMbFxdVqZb6JEydq2bJlevfdd9WiRQvP/tTUVFVWVuro0aNe2cfv1sROTU3VBx984NXe6dnYNdXN/r5az7b+/e9/r2HDhumOO+7QZ599pr179+qLL77w/BcAAKDBsfy01aYLlqWJEydq6dKlWrt2rVq3bu31es+ePRUWFuZVE7ugoED79u3z1MTOzMzU9u3bVVxc7IlZtWqV4uLi1KlTJ6N+GGcev/jiC40ePVqff/65lixZomHDhpm+FQAAABcpOztbS5Ys0euvv67Y2FjPM4rx8fFq0qSJ4uPjNW7cOE2aNEmJiYmKi4vTvffeq8zMTF111VWSpIEDB6pTp04aOXKknnrqKRUVFWnq1KnKzs42XpXPePDYtWtXDR48WEuXLlXz5s0v4JQBAACCU31Y23r+/PmSpH79+nntX7RokcaMGSNJeuaZZ+R0OjV8+HBVVFRo0KBBmjdvnic2JCREy5Yt04QJE5SZmano6GiNHj1aM2fONO6H8eBxwYIFzLAGAAAIEMugIkdkZKTmzp2ruXPn1hjTqlUrLV++/IL7YTx4ZOAIAAAaLT9OmAk2QbU8IQAAAAKr1qV6ANsYFv21DKs/OUINinXLvPi3cWFvA854wxIMTrN/z1khZoVqQ05UmrXnMHtIusnXZv9Mrow3/NXS1iysIRi6eYJRnGnx7+jDhsW/954wijMt/u3++ohRnFxmP9/mRfvNDgv4DZlHDzKPAAAAMEbmEQAAwIf6MNu6vjAaPN58883GDb766qsX3BkAAIB6yY9rWwcbo9vW8fHxni0uLk5r1qzR1q1bPa/n5+drzZo1io+P91tHAQAAEHhGmcdFixZ5/n/KlCn62c9+pgULFigkJESS5HK5dM8999RqXUYAAICgwYQZj1pPmHnhhRf0y1/+0jNwlE5VK580aZJeeOEFWzsHAACA+qXWg8fq6mrt2rXrrP27du2S221WmgEAACCYnJ4wY/cWjGo923rs2LEaN26c9uzZoyuvvFKStHnzZj355JMaO3as7R0EAABA/VHrwePs2bOVmpqq//mf/9GhQ4ckSWlpaZo8ebIeeugh2zuIBsxhc5lRw6Ljpsd1fOfRjPM357s9d0mpUVvO5olmx6yoNoqrTo4yinOHmX0mlbFmMwMdhpei8OumRnG3fDHAKO6VNqvNDhwAltvss4soMWsv+lCVUVzIsXKzBk8axhmsrStJ7kqzAvXGP7dAoPHMo0etB49Op1MPP/ywHn74YZWWnvqDyEQZAACAxuGiioQzaAQAAI2CP55RDNLMY63vGx4+fFgjR45Uenq6QkNDFRIS4rUBAAA0OJaftiBU68zjmDFjtG/fPv3mN79RWlqaHI7grI4OAACA2qv14HHDhg1677331L17dz90BwAAoB5iwoxHrW9bZ2RkyDKcbQcAAICGpdaDxzlz5uiRRx7Rl19+6YfuAAAA1D8UCT+j1retb731Vp04cUJt27ZVVFSUwsLCvF4/cuSIbZ0DAABA/VLrweOcOXP80A00KKbFv20uDmy5zOIchkUBrGqz/jnCfP8YOWOizQ5abXYSVS3NymRZoWbXorypWVz4MbN/JlckGk6kK2xiFJZ/4odGcTPjOhrFTWv+qVGcnaq+jjQLNPzoQsvMCsU7jp0wirNchj9ApnGmAvT7AsCFq/XgcfTo0f7oBwAAAIKA0eCxtLTUUxD89KoyNaFwOAAAaHCYbe1hNHhs2rSpDh06pOTkZCUkJJyztqNlWXI4HHLZfUsDAAAgwPwxwaVBT5hZu3atEhMTPf9PYXAAAIDGyWjw2LdvX+3du1etW7dWv379/NwlAACAeihIM4V2M67z2LZtW7Vu3Vp33nmn/vrXv2r//v3+7BcAAADqIePZ1mvXrlVubq5yc3P1t7/9TZWVlWrTpo369++vrKwsZWVlKSUlxZ99BQAACAwmzHgYDx779evnuWVdXl6ujRs3egaTL774oqqqqtShQwft3LnTX30FAABAgNW6zqMkRUZGqn///rrmmmuUlZWlt99+W88//7x27dpld/8AAAACjtnWZ9Rq8FhZWalNmzZp3bp1ys3N1ebNm5WRkaE+ffroj3/8o/r27euvfiKYmK4EYbqyhM0st9lPa0h0lH0HNaxQYCWa1Ul1R5gtk3OsRbhRnMssTJWxZucRbfhI9IlUs/aqXGG+gyStPFh/V5hxuAy/Bwx/LJxllWaBLrOfR6vspFGcu9LwuAAaLOPBY//+/bV582a1bt1affv21S9+8QstWbJEaWlp/uwfAABA4PHMo4fx4PG9995TWlqa+vfvr379+qlv375q1qyZP/sGAABQL3Db+gzj+4ZHjx7Vn/70J0VFRel3v/ud0tPT1aVLF02cOFH/+Mc/9NVXX/mznwAAAKgHjDOP0dHRGjx4sAYPHixJOnbsmDZs2KB169bpqaee0ogRI3TppZdqx44dfussAABAQHDb2uOCZyxER0crMTFRiYmJatq0qUJDQ/Xpp3X/EDoAAADqjnHm0e12a+vWrcrNzdW6dev0/vvvq6ysTD/4wQ+UlZWluXPnKisry599BQAACAwyjx7Gg8eEhASVlZUpNTVVWVlZeuaZZ9SvXz+1bdvWn/0DAABAPWI8eHz66aeVlZWldu3a+bM/AAAA9Q6zrc8wHjz+4he/8Gc/zuvLL7/UY489prVr16qoqEjp6em644479Otf/1rh4YbVjVF3TIt/21xM3BFiVjjbNM60sLejWaLvoAizItdlbROM4kwLSYcfM/uMq6LNPpOIo2a/6Syn2WcXXmIUJrfhj/nB/QbXQpK7q9nn4jR8LHxLRZXPmLBSs7bi9vpuS5IcldVGcdax42ZxLpdRnO0/3wCCzgUtT1jXdu3aJbfbreeff16XXHKJduzYofHjx6usrEyzZ88OdPcAAEBDxzOPHkExePxuiSBJatOmjQoKCjR//nwGjwAAwP8YPHoExeDxXEpKSpSYeP5bVBUVFaqoqPB8XVpa6u9uAQAANGgXXOcxkHbv3q3nnnvO53OYs2bNUnx8vGfLyMioox4CAICG5PSEGbu3YBTQweMjjzwih8Nx3m3Xrl1e7zlw4IAGDx6sW265RePHjz9v+zk5OSopKfFshYWF/jwdAACABi+gt60feughjRkz5rwxbdq08fz/wYMHlZWVpauvvlp/+tOffLYfERGhiIiIi+0mAABo7Hjm0SOgg8ekpCQlJSUZxR44cEBZWVnq2bOnFi1aJKczKO+4AwAABLWgGIEdOHBA/fr1U8uWLTV79mx99dVXKioqUlFRUaC7BgAAGoH68Mzju+++qxtvvFHp6elyOBx67bXXvF4fM2bMWY//fbdajSQdOXJEI0aMUFxcnBISEjRu3DgdP25WD/a0oJhtvWrVKu3evVu7d+9WixYtvF6zrCDN+TZkNhf/NmVcJDzM8NveNC7SdwXr8ox4o6aqm5gV1z7R3Oyziz5sdi2ii8wKRIdWGP68ucziKuPNrlmTI2afS1GS2efybrnZcftFmp3Hk/uv9xmT8JlZW03+bVY53VF2wijOZfhHwRFqVsjeqjYrYg7AfmVlZerWrZvuvPNO3XzzzeeMGTx4sBYtWuT5+vuP740YMUKHDh3SqlWrVFVVpbFjx+quu+7SkiVLjPsRFIPHMWPG+Hw2EgAAwG/8+Mzj90sJ1jRnY8iQIRoyZMh5m4yIiFBqauo5X/v000+1YsUKbdmyRb169ZIkPffcc7r++us1e/ZspaenG3U7KG5bAwAABJTlp01SRkaGV2nBWbNmXXA3c3NzlZycrPbt22vChAn65ptvPK/l5eUpISHBM3CUpAEDBsjpdGrz5s3GxwiKzCMAAEBDVVhYqLi4OM/XF1opZvDgwbr55pvVunVr7dmzR7/61a80ZMgQ5eXlKSQkREVFRUpOTvZ6T2hoqBITE2s1j4TBIwAAgA+O/2x2tylJcXFxXoPHC3Xbbbd5/r9Lly7q2rWr2rZtq9zcXF177bUX3f5p3LYGAABogNq0aaPmzZtr9+7dkqTU1FQVFxd7xVRXV+vIkSM1Pid5LgweAQAAfPHjM4/+sn//fn3zzTdKS0uTJGVmZuro0aPKz8/3xKxdu1Zut1u9e/c2bpfb1gAAAEHg+PHjniyiJO3du1fbtm1TYmKiEhMT9eijj2r48OFKTU3Vnj179PDDD+uSSy7RoEGDJEkdO3bU4MGDNX78eC1YsEBVVVWaOHGibrvtNuOZ1hKZRwAAAJ/qQ5HwrVu3qkePHurRo4ckadKkSerRo4emTZumkJAQffzxx/rxj3+sdu3aady4cerZs6fee+89rwk4L730kjp06KBrr71W119/va655hqjJZ+/i8wj6j1nuO8i3KcCDR9lDjX7tnc0TTCKq06M9hnjDjHsm+EvkoTdZoWandVmDTorzYqEhx8+ZhTnjok0a6/ErFh3RTOzmYdNt5td24U9+hjFNUtbYRT36TuX+IxJP1Bh1JbDbVhkv+ykWXuGxb/dFeVmxzUt7m+6WAAAY/369Tvv4igrV6702UZiYmKtCoKfC4NHAAAAX/xYJDzYMHgEAAAwEaSDPbvxzCMAAACMkXkEAADw4UImuJi0GYzIPAIAAMAYmUcAAABfmDDjQeYRAAAAxsg8AgAA+MAzj2eQeQQAAIAxMo8IGEeI2eoiCjH7N44zNsasvegoozBXM7P2Kpv6XgEn5ITZCi4R35r9MzSs1GyFmZDjZquGOMrN2tOx40ZhzlLDOMNr4agyW62kKtr3aj+StOn9jkZx/90xySiu6We++xd21GyFGVWbfa+4j5eZtWf3Si+sHIPGimcePcg8AgAAwBiZRwAAAB945vEMBo8AAAC+cNvag9vWAAAAMEbmEQAAwBcyjx5kHgEAAGCMzCMAAIAPTJg5g8wjAAAAjJF5hDmH2b81TIt/O5wOozjj4t9hYUZhVoTvot6S5I4w+/EIPV7tMybs25NGbanKrEC0w7CQtEqPGYVZ5WYFrB3hZp+xTK+ZIWel789Ykpp8bVbsPPaLCKM412dmRcITdx31GeMsMSzqfdLwe8WweL77hFmheNOfb4qEo9HimUcPMo8AAAAwRuYRAADAB4dlyWHZmyq0u726wuARAADAF25be3DbGgAAAMbIPAIAAPhAqZ4zyDwCAADAGJlHAAAAX3jm0YPMIwAAAIyReUTghBp++xkWHZdh8W+FmBUnDyk1K5xt8k8wR5lhoebjZoWkrSqzYtimxb/lMiz87DIsYu40/Hep4feAaVH0iBNm5xsf1sysvSNm1835banPGOvIUaO2rCqzgujuykqjOFOmRfstw/r0QEPDM49nkHkEAACAMTKPAAAAvvDMoweDRwAAAB+4bX0Gt60BAABgjMwjAACAL9y29iDzCAAAAGNkHgEAAAwE6zOKdiPzCAAAAGNkHiE5zP4N4Qw3K8JtGRaSdjZpYhRn7NhxozCHK8os7uTJi+mNF/Ni3WafnWVaINrw2trN9HwdEYb/jA8LM4sz/Fya/LvEKM5xwux7wPrWd3umPxemxb8dhsXzrWqzAvCm/QMaLcs6tdndZhAi8wgAAABjZB4BAAB8oM7jGUGXeayoqFD37t3lcDi0bdu2QHcHAAA0BpaftiAUdIPHhx9+WOnp6YHuBgAAQKMUVLet3377bb3zzjv65z//qbfffttnfEVFhSoqzjy4X1pa6s/uAQCABsrhPrXZ3WYwCprM4+HDhzV+/Hj97//+r6KizGbLzpo1S/Hx8Z4tIyPDz70EAABo2IJi8GhZlsaMGaO7775bvXr1Mn5fTk6OSkpKPFthYaEfewkAABosnnn0COjg8ZFHHpHD4TjvtmvXLj333HM6duyYcnJyatV+RESE4uLivDYAAABcuIA+8/jQQw9pzJgx541p06aN1q5dq7y8PEVERHi91qtXL40YMUIvvviiH3sJAAAaO0r1nBHQwWNSUpKSkpJ8xj377LP67W9/6/n64MGDGjRokP7+97+rd+/e/uxiUHOEGq7KYcrpMAtrEm3vcauqzOJCDb+dDVeOcRuuWCOX7yeeTVcNcYaZnYPpaiCWu9ooznS1EtPzCIk3y/Jb1Wb907dHjcIcEWarIDmchquzfPOtUZwJt40rFknmK8cAgN2C4pnHli1bqnPnzp6tXbt2kqS2bduqRYsWAe4dAABo8E4vT2j3VgvvvvuubrzxRqWnp8vhcOi11177XhctTZs2TWlpaWrSpIkGDBigzz//3CvmyJEjGjFihOLi4pSQkKBx48bp+HHDZMl/BMXgEQAAIJBO37a2e6uNsrIydevWTXPnzj3n60899ZSeffZZLViwQJs3b1Z0dLQGDRqk8vJyT8yIESO0c+dOrVq1SsuWLdO7776ru+66q1b9CKo6j6f98Ic/lBWki4kDAAB81/frUEdERJw1z0OShgwZoiFDhpyzDcuyNGfOHE2dOlXDhg2TJP3lL39RSkqKXnvtNd1222369NNPtWLFCm3ZssVTvea5557T9ddfr9mzZxsvwkLmEQAAwBc/lurJyMjwqks9a9asWndv7969Kioq0oABAzz74uPj1bt3b+Xl5UmS8vLylJCQ4FX2cMCAAXI6ndq8ebPxsYIy8wgAANBQFBYWepUTPFfW0ZeioiJJUkpKitf+lJQUz2tFRUVKTk72ej00NFSJiYmeGBMMHgEAAHzwZ6meYKtFzW1rAACAIJeamirp1HLO33X48GHPa6mpqSouLvZ6vbq6WkeOHPHEmGDwCAAA4Es9KNVzPq1bt1ZqaqrWrFnj2VdaWqrNmzcrMzNTkpSZmamjR48qPz/fE7N27Vq53e5a1c3mtnV94jAby5sWdDZmGRYbdht+kxsWfrYMi47rpGGRcEPGBbarDAtY28i0CLcxw+8pq9rwMzZsz328zKw9Q85Is+d/jIuOf/2NUZhpYW/L9GfDhOFnbPxzC6DBOH78uHbv3u35eu/evdq2bZsSExPVsmVLPfDAA/rtb3+rSy+9VK1bt9ZvfvMbpaen66abbpIkdezYUYMHD9b48eO1YMECVVVVaeLEibrtttuMZ1pLDB4BAAB8qg/LE27dulVZWVmerydNmiRJGj16tBYvXqyHH35YZWVluuuuu3T06FFdc801WrFihSIjIz3veemllzRx4kRde+21cjqdGj58uJ599tla9YPBIwAAgC/fKa1ja5u10K9fv/PWuXY4HJo5c6ZmzpxZY0xiYqKWLFlSuwN/D888AgAAwBiZRwAAAB/qw23r+oLMIwAAAIyReQQAAPDFbZlXHalNm0GIzCMAAACMkXkEAADwpR7Mtq4vGDzWJ4ZFfy2zGtfGnOHhZoEhNieqK80KU7vLK4ziTIt/2y4QxZrtLiRtWqDetLC7IdOC98bFustOmB3X8Dwo/g0AZ2PwCAAA4INDfphtbW9zdYbBIwAAgC82r0XtaTMIMWEGAAAAxsg8AgAA+ECR8DPIPAIAAMAYmUcAAABfKNXjQeYRAAAAxsg8AgAA+OCwLDlsnh1td3t1hcFjMLK7iLBh8W+rwqxYtwwLP5uyqs2KidfrIsx29y1AhaRNi2Y7ww2Lf1dWGsWZFvU2LTpue0F5CnsDaEQYPAIAAPji/s9md5tBiMEjAACAD9y2PoMJMwAAADBG5hEAAMAXSvV4kHkEAACAMTKPAAAAvljWqc3uNoMQmUcAAAAYI/MIAADgg8M6tdndZjAi8wgAAABjZB4vkiM0zCjO9hUtDBivtmG6cowh01VDjJmuzlKfBWoFkgCtumO8KpBpe8Y/P2ZxAVuJBkDw4plHjwbwVxkAAAB1hcwjAACADw73qc3uNoMRg0cAAABfuG3twW1rAAAAGCPzCAAA4AvLE3qQeQQAAIAxMo8AAAA+OCxLDpufUbS7vbpC5hEAAADGyDxeJFuLIdtcDNvuQs0BK9YdqALbDUGAPju7i2vbXdSb4t8Aao3Z1h5kHgEAAGCMzCMAAIAvliS7b+YEZ+KRwSMAAIAvTJg5I6huW7/11lvq3bu3mjRpoqZNm+qmm24KdJcAAAAalaDJPP7zn//U+PHj9cQTT6h///6qrq7Wjh07At0tAADQGFjyw4QZe5urK0ExeKyurtb999+vp59+WuPGjfPs79SpUwB7BQAA0PgExW3rDz/8UAcOHJDT6VSPHj2UlpamIUOG+Mw8VlRUqLS01GsDAACotdOleuzeglBQDB6/+OILSdKMGTM0depULVu2TE2bNlW/fv105MiRGt83a9YsxcfHe7aMjIy66jIAAECDFNDB4yOPPCKHw3HebdeuXXK7T82N//Wvf63hw4erZ8+eWrRokRwOh1555ZUa28/JyVFJSYlnKywsrKtTuzCW294tUP0DbGa5LaMNAPzG7actCAX0mceHHnpIY8aMOW9MmzZtdOjQIUnezzhGRESoTZs22rdvX43vjYiIUEREhC19BQAAQIAHj0lJSUpKSvIZ17NnT0VERKigoEDXXHONJKmqqkpffvmlWrVq5e9uAgCARo46j2cExTOPcXFxuvvuuzV9+nS98847Kigo0IQJEyRJt9xyS4B7BwAAGrx6MGFmxowZZz3e16FDB8/r5eXlys7OVrNmzRQTE6Phw4fr8OHDdn8SwVGqR5KefvpphYaGauTIkTp58qR69+6ttWvXqmnTpoHuGgAAQJ247LLLtHr1as/XoaFnhnIPPvig3nrrLb3yyiuKj4/XxIkTdfPNN+v999+3tQ9BM3gMCwvT7NmzNXv27EB3BQAANDb+KK1zAe2FhoYqNTX1rP0lJSVauHChlixZov79+0uSFi1apI4dO2rTpk266qqrLrq7pwXFbWsAAICG6vs1qSsqKmqM/fzzz5Wenq42bdpoxIgRnonD+fn5qqqq0oABAzyxHTp0UMuWLZWXl2drfxk8AgAA+OLHZx4zMjK86lLPmjXrnF3o3bu3Fi9erBUrVmj+/Pnau3ev/uu//kvHjh1TUVGRwsPDlZCQ4PWelJQUFRUV2fpRBM1tawAAgIaosLBQcXFxnq9rKjM4ZMgQz/937dpVvXv3VqtWrfR///d/atKkid/7eRqZRwD1HwXqAQSaH4uEx8XFeW2mNaoTEhLUrl077d69W6mpqaqsrNTRo0e9Yg4fPnzOZyQvBoNHAACAIHT8+HHt2bNHaWlp6tmzp8LCwrRmzRrP6wUFBdq3b58yMzNtPS63rQEAAHyoD0XCf/nLX+rGG29Uq1atdPDgQU2fPl0hISG6/fbbFR8fr3HjxmnSpElKTExUXFyc7r33XmVmZto601pi8AgAAOBbPSjVs3//ft1+++365ptvlJSUpGuuuUabNm3yrNb3zDPPyOl0avjw4aqoqNCgQYM0b948e/ssBo8AAABB4eWXXz7v65GRkZo7d67mzp3r134weAQAAPDFbUkOmzOPbta2BgAAQANH5hEAAMCXevDMY31B5hEAAADGyDwCAAD45IfMo8g8AgAAoIEj8wgAAOALzzx6MHgEAADwxW3J9tvMlOoBAABAQ0fmEQAAwBfLfWqzu80gROYRAAAAxsg8AgAA+MKEGQ8yjwAAADBG5hEAAMAXZlt7kHkEAACAMTKPAAAAvvDMoweDRwAAAF8s+WHwaG9zdYXb1gAAADBG5hEAAMAXblt7kHkEAACAMTKPAAAAvrjdkmxeTtDN8oQAAABo4Mg8AgAA+MIzjx5kHgEAAGCMzCMAAIAvZB49GDwCAAD4wtrWHty2BgAAgDEyjwAAAD5YlluWZW9pHbvbqytkHgEAAGCMzCMAAIAvlmX/M4pBOmGGzCMAAACMkXkEAADwxfLDbGsyjwAAAGjoyDwCAAD44nZLDptnRwfpbGsGjwAAAL5w29qD29YAAAAwRuYRAADAB8vtlmXzbWuKhAMAAKDBI/MIAADgC888egRN5vGzzz7TsGHD1Lx5c8XFxemaa67RunXrAt0tAACARiVoBo833HCDqqurtXbtWuXn56tbt2664YYbVFRUFOiuAQCAhs5t+WcLQkExePz666/1+eef65FHHlHXrl116aWX6sknn9SJEye0Y8eOQHcPAACg0QiKZx6bNWum9u3b6y9/+Ysuv/xyRURE6Pnnn1dycrJ69uxZ4/sqKipUUVHh+bq0tLQuugsAABoay5Jkd5Hw4Mw8BsXg0eFwaPXq1brpppsUGxsrp9Op5ORkrVixQk2bNq3xfbNmzdKjjz5ahz0FAABo2AJ62/qRRx6Rw+E477Zr1y5ZlqXs7GwlJyfrvffe0wcffKCbbrpJN954ow4dOlRj+zk5OSopKfFshYWFdXh2AACgobDcll+2YOSwrMDlTL/66it98803541p06aN3nvvPQ0cOFDffvut4uLiPK9deumlGjdunB555BGj45WWlio+Pl79NEyhjrCL6jsAAKgb1VaVcvW6SkpKvMYBdeH02CEr5Gbbxw7VVpXWuV4NyHldjIDetk5KSlJSUpLPuBMnTkiSnE7vRKnT6ZTbHZzV2QEAAIJRUMy2zszMVNOmTTV69Gj961//0meffabJkydr7969Gjp0aKC7BwAAGrj6ctt67ty5+uEPf6jIyEj17t1bH3zwgR/O9vyCYvDYvHlzrVixQsePH1f//v3Vq1cvbdiwQa+//rq6desW6O4BAAD43d///ndNmjRJ06dP14cffqhu3bpp0KBBKi4urtN+BPSZx7rGM48AAASf+vDMoz/GDrU9r969e+uKK67QH//4R0mS2+1WRkaG7r33XuP5H3YIilI9djk9Tq5Wle3LUwIAAP+oVpWkM3/HA9YHmw9/+ry+X4c6IiJCERERXvsqKyuVn5+vnJwczz6n06kBAwYoLy/P3o750KgGj8eOHZMkbdDyAPcEAADU1rFjxxQfH1+nxwwPD1dqaqo2FPln7BATE6OMjAyvfdOnT9eMGTO89n399ddyuVxKSUnx2p+SkqJdu3b5pW81aVSDx/T0dBUWFio2NlYOhyMgfSgtLVVGRoYKCwuDalq+HRrzuUuN+/wb87lLjfv8OffGee6SvedvWZaOHTum9PR0m3pnLjIyUnv37lVlZaVf2rcs66wxyfezjvVNoxo8Op1OtWjRItDdkCTFxcU1yl8mUuM+d6lxn39jPnepcZ8/5944z12y7/zrOuP4XZGRkYqMjAzY8aVTk4dDQkJ0+PBhr/2HDx9WampqnfYlKGZbAwAANGbh4eHq2bOn1qxZ49nndru1Zs0aZWZm1mlfGlXmEQAAIFhNmjRJo0ePVq9evXTllVdqzpw5Kisr09ixY+u0Hwwe61hERISmT59e759n8IfGfO5S4z7/xnzuUuM+f869cZ67xPn7w6233qqvvvpK06ZNU1FRkbp3764VK1acNYnG3xpVnUcAAABcHJ55BAAAgDEGjwAAADDG4BEAAADGGDwCAADAGINHmz3++OO6+uqrFRUVpYSEhHPG7Nu3T0OHDlVUVJSSk5M1efJkVVdXn7fdI0eOaMSIEYqLi1NCQoLGjRun48eP++EM7JObmyuHw3HObcuWLTW+r1+/fmfF33333XXYc3v88Ic/POs8nnzyyfO+p7y8XNnZ2WrWrJliYmI0fPjwswrCBoMvv/xS48aNU+vWrdWkSRO1bdtW06dP97lCQzBf+7lz5+qHP/yhIiMj1bt3b33wwQfnjX/llVfUoUMHRUZGqkuXLlq+PPiWTZ01a5auuOIKxcbGKjk5WTfddJMKCgrO+57FixefdY0DXXz5Qs2YMeOsc+nQocN539MQrrt07t9vDodD2dnZ54xvSNcdDB5tV1lZqVtuuUUTJkw45+sul0tDhw5VZWWlNm7cqBdffFGLFy/WtGnTztvuiBEjtHPnTq1atUrLli3Tu+++q7vuussfp2Cbq6++WocOHfLa/vu//1utW7dWr169zvve8ePHe73vqaeeqqNe22vmzJle53HvvfeeN/7BBx/Um2++qVdeeUXr16/XwYMHdfPNN9dRb+2za9cuud1uPf/889q5c6eeeeYZLViwQL/61a98vjcYr/3f//53TZo0SdOnT9eHH36obt26adCgQSouLj5n/MaNG3X77bdr3Lhx+uijj3TTTTfppptu0o4dO+q45xdn/fr1ys7O1qZNm7Rq1SpVVVVp4MCBKisrO+/74uLivK7xv//97zrqsf0uu+wyr3PZsGFDjbEN5bpL0pYtW7zOe9WqVZKkW265pcb3NKTr3uhZ8ItFixZZ8fHxZ+1fvny55XQ6raKiIs+++fPnW3FxcVZFRcU52/rkk08sSdaWLVs8+95++23L4XBYBw4csL3v/lJZWWklJSVZM2fOPG9c3759rfvvv79uOuVHrVq1sp555hnj+KNHj1phYWHWK6+84tn36aefWpKsvLw8P/Swbj311FNW69atzxsTrNf+yiuvtLKzsz1fu1wuKz093Zo1a9Y543/2s59ZQ4cO9drXu3dv6xe/+IVf++lvxcXFliRr/fr1NcbU9LsxGE2fPt3q1q2bcXxDve6WZVn333+/1bZtW8vtdp/z9YZ03WFZZB7rWF5enrp06eJV0HPQoEEqLS3Vzp07a3xPQkKCV7ZuwIABcjqd2rx5s9/7bJc33nhD33zzjVEl/JdeeknNmzdX586dlZOToxMnTtRBD+335JNPqlmzZurRo4eefvrp8z6ekJ+fr6qqKg0YMMCzr0OHDmrZsqXy8vLqort+VVJSosTERJ9xwXbtKysrlZ+f73XdnE6nBgwYUON1y8vL84qXTv0eCPbrXFJSIkk+r/Px48fVqlUrZWRkaNiwYTX+7gsGn3/+udLT09WmTRuNGDFC+/btqzG2oV73yspK/fWvf9Wdd94ph8NRY1xDuu6NHSvM1LGioqKzKsGf/rqoqKjG9yQnJ3vtCw0NVWJiYo3vqY8WLlyoQYMGqUWLFueN+/nPf65WrVopPT1dH3/8saZMmaKCggK9+uqrddRTe9x33326/PLLlZiYqI0bNyonJ0eHDh3S73//+3PGFxUVKTw8/KxnZVNSUoLqOp/L7t279dxzz2n27NnnjQvGa//111/L5XKd8+d6165d53xPTb8Hgvk6u91uPfDAA/rRj36kzp071xjXvn17vfDCC+ratatKSko0e/ZsXX311dq5c6fP3w31Te/evbV48WK1b99ehw4d0qOPPqr/+q//0o4dOxQbG3tWfEO87pL02muv6ejRoxozZkyNMQ3pukPctjYxZcoUS9J5t08//dTrPTWl6MePH28NHDjQa19ZWZklyVq+fPk5j//4449b7dq1O2t/UlKSNW/evAs/sQt0IZ9HYWGh5XQ6rX/84x+1Pt6aNWssSdbu3bvtOoULdiHnftrChQut0NBQq7y8/Jyvv/TSS1Z4ePhZ+6+44grr4YcftvU8LtSFnP/+/futtm3bWuPGjav18erTta/JgQMHLEnWxo0bvfZPnjzZuvLKK8/5nrCwMGvJkiVe++bOnWslJyf7rZ/+dvfdd1utWrWyCgsLa/W+yspKq23bttbUqVP91LO68+2331pxcXHW//t//++crzfE625ZljVw4EDrhhtuqNV7GtJ1b4zIPBp46KGHzvsvKklq06aNUVupqalnzcI8PZs2NTW1xvd8/8H76upqHTlypMb3+NOFfB6LFi1Ss2bN9OMf/7jWx+vdu7ekU9mrtm3b1vr9drqY74XevXururpaX375pdq3b3/W66mpqaqsrNTRo0e9so+HDx8OyHU+l9qe/8GDB5WVlaWrr75af/rTn2p9vPp07WvSvHlzhYSEnDUr/nzXLTU1tVbx9d3EiRM9E/lqm0UKCwtTjx49tHv3bj/1ru4kJCSoXbt2NZ5LQ7vukvTvf/9bq1evrvXdgYZ03RsjBo8GkpKSlJSUZEtbmZmZevzxx1VcXOy5Fb1q1SrFxcWpU6dONb7n6NGjys/PV8+ePSVJa9euldvt9vxxrUu1/Twsy9KiRYs0atQohYWF1fp427ZtkySlpaXV+r12u5jvhW3btsnpdJ71CMJpPXv2VFhYmNasWaPhw4dLkgoKCrRv3z5lZmZecJ/tVJvzP3DggLKystSzZ08tWrRITmftH7GuT9e+JuHh4erZs6fWrFmjm266SdKpW7hr1qzRxIkTz/mezMxMrVmzRg888IBn36pVq+rNdTZlWZbuvfdeLV26VLm5uWrdunWt23C5XNq+fbuuv/56P/Swbh0/flx79uzRyJEjz/l6Q7nu37Vo0SIlJydr6NChtXpfQ7rujVKgU58Nzb///W/ro48+sh599FErJibG+uijj6yPPvrIOnbsmGVZllVdXW117tzZGjhwoLVt2zZrxYoVVlJSkpWTk+NpY/PmzVb79u2t/fv3e/YNHjzY6tGjh7V582Zrw4YN1qWXXmrdfvvtdX5+F2L16tU13s7dv3+/1b59e2vz5s2WZVnW7t27rZkzZ1pbt2619u7da73++utWmzZtrD59+tR1ty/Kxo0brWeeecbatm2btWfPHuuvf/2rlZSUZI0aNcoT8/1zt6xTt/5atmxprV271tq6dauVmZlpZWZmBuIULsr+/futSy65xLr22mut/fv3W4cOHfJs341pKNf+5ZdftiIiIqzFixdbn3zyiXXXXXdZCQkJnqoKI0eOtB555BFP/Pvvv2+FhoZas2fPtj799FNr+vTpVlhYmLV9+/ZAncIFmTBhghUfH2/l5uZ6XeMTJ054Yr5/7o8++qi1cuVKa8+ePVZ+fr512223WZGRkdbOnTsDcQoX5aGHHrJyc3OtvXv3Wu+//741YMAAq3nz5lZxcbFlWQ33up/mcrmsli1bWlOmTDnrtYZ83WFZDB5tNnr06HM+B7Zu3TpPzJdffmkNGTLEatKkidW8eXProYcesqqqqjyvr1u3zpJk7d2717Pvm2++sW6//XYrJibGiouLs8aOHesZkNZ3t99+u3X11Vef87W9e/d6fT779u2z+vTpYyUmJloRERHWJZdcYk2ePNkqKSmpwx5fvPz8fKt3795WfHy8FRkZaXXs2NF64oknvJ53/P65W5ZlnTx50rrnnnuspk2bWlFRUdZPfvITrwFXsFi0aFGNz0Se1tCu/XPPPWe1bNnSCg8Pt6688kpr06ZNntf69u1rjR492iv+//7v/6x27dpZ4eHh1mWXXWa99dZbddzji1fTNV60aJEn5vvn/sADD3g+p5SUFOv666+3Pvzww7rvvA1uvfVWKy0tzQoPD7d+8IMfWLfeeqvX87kN9bqftnLlSkuSVVBQcNZrDfm6w7IclmVZdZjoBAAAQBCjziMAAACMMXgEAACAMQaPAAAAMMbgEQAAAMYYPAIAAMAYg0cAAAAYY/AIAAAAYwweAQAAYIzBIwBb5ebmyuFw6OjRoxfVzpgxYzxrRdtpxowZcjgccjgcmjNnju3tn8+YMWM8x37ttdfq9NgAYBcGjwDOacGCBYqNjVV1dbVn3/HjxxUWFqZ+/fp5xZ4eMO7Zs0dXX321Dh06pPj4eL/2b/78+UpISFBhYaHX/nvvvVft2rXTiRMnanzvZZddpkOHDumuu+6ypS8vvviirrnmGp9xf/jDH3To0CFbjgkAgcLgEcA5ZWVl6fjx49q6datn33vvvafU1FRt3rxZ5eXlnv3r1q1Ty5Yt1bZtW4WHhys1NVUOh8Ov/bv77rt15ZVXaty4cZ59a9as0fz587V48WJFRUXV+N7Q0FClpqaeN6Y2Xn/9df34xz/2GRcfH6/U1FRbjgkAgcLgEcA5tW/fXmlpacrNzfXsy83N1bBhw9S6dWtt2rTJa39WVpbn/79723rx4sVKSEjQypUr1bFjR8XExGjw4MFeGTiXy6VJkyYpISFBzZo108MPPyzLss7bP4fDoYULF2rz5s1asGCBSktLdeedd2rSpEm6+uqra32+DodDzz//vG644QZFRUWpY8eOysvL0+7du9WvXz9FR0fr6quv1p49e7zeV15ernfeecczeJw3b54uvfRSRUZGKiUlRT/96U9r3RcAqM8YPAKoUVZWltatW+f5et26derXr5/69u3r2X/y5Elt3rzZM3g8lxMnTmj27Nn63//9X7377rvat2+ffvnLX3pe/5//+R8tXrxYL7zwgjZs2KAjR45o6dKlPvuXkZGhOXPmaPLkybrjjjsUExOjxx577ILP97HHHtOoUaO0bds2dejQQT//+c/1i1/8Qjk5Odq6dassy9LEiRO93rNmzRr94Ac/UIcOHbR161bdd999mjlzpgoKCrRixQr16dPngvsDAPVRaKA7AKD+ysrK0gMPPKDq6mqdPHlSH330kfr27auqqiotWLBAkpSXl6eKiorzDh5Px7dt21aSNHHiRM2cOdPz+pw5c5STk6Obb75Z0qnnLVeuXGnUx7Fjx+pPf/qT3nzzTW3evFkREREXeroaO3asfvazn0mSpkyZoszMTP3mN7/RoEGDJEn333+/xo4d6/We796y3rdvn6Kjo3XDDTcoNjZWrVq1Uo8ePS64PwBQH5F5BFCjfv36qaysTFu2bNF7772ndu3aKSkpSX379vU895ibm6s2bdqoZcuWNbYTFRXlGThKUlpamoqLiyVJJSUlOnTokHr37u15PTQ0VL169TLq47/+9S99+OGHioqK0nvvvXeBZ3pK165dPf+fkpIiSerSpYvXvvLycpWWlkqSLMvSm2++6Rk8XnfddWrVqpXatGmjkSNH6qWXXjrvxB0ACEYMHgHU6JJLLlGLFi20bt06rVu3Tn379pUkpaenKyMjQxs3btS6devUv3//87YTFhbm9bXD4fD5TKOJyspKjRo1SiNGjNC8efM0depUFRQUXHB73+3n6Qk/59rndrslSR988IGqq6s9z1jGxsbqww8/1N/+9jelpaVp2rRp6tat20WXLQKA+oTBI4DzysrKUm5urnJzc71K9PTp00dvv/22Pvjgg/PesvYlPj5eaWlp2rx5s2dfdXW18vPzfb535syZOnLkiJ555hmNHj1a1113ncaOHesZ3Pnb66+/rqFDhyokJMSzLzQ0VAMGDNBTTz2ljz/+WF9++aXWrl1bJ/0BgLrA4BHAeWVlZWnDhg3atm2bJ/MoSX379tXzzz+vysrKixo8SqeeJXzyySf12muvadeuXbrnnnt8Zuu2bNmi3/3ud1q4cKGnpuTzzz+vgoICPfPMMxfVH1NvvPGGV4meZcuW6dlnn9W2bdv073//W3/5y1/kdrvVvn37OukPANQFJswAOK+srCydPHlSHTp08DwHKJ0aPB47dsxT0udiPPTQQzp06JBGjx4tp9OpO++8Uz/5yU9UUlJyzviKigqNHj1aY8eO1cCBAz3709LS9Nxzz2ncuHG64YYb/Dpo27Nnj3bv3u2ZTCNJCQkJevXVVzVjxgyVl5fr0ksv1d/+9jdddtllfusHANQ1h2XHg0cAECRmzJih1157Tdu2bbuodn7/+99r9erVWr58ea3f63A4tHTpUr8svwgA/sZtawCNzvbt2xUTE6N58+ZdcBstWrRQTk5Ord5z9913KyYm5oKPCQD1AZlHAI3KkSNHdOTIEUlSUlKS39fg/q7i4mJPmZ+0tDRFR0fX2bEBwC4MHgEAAGCM29YAAAAwxuARAAAAxhg8AgAAwBiDRwAAABhj8AgAAABjDB4BAABgjMEjAAAAjDF4BAAAgLH/DyBsfvi/OxvsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(df['Wx'], df['Wy'], bins=(50, 50), vmax=400)\n",
    "plt.colorbar()\n",
    "plt.xlabel('Wind X [m/s]')\n",
    "plt.ylabel('Wind Y [m/s]')\n",
    "ax = plt.gca()\n",
    "ax.axis('tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_8im1ttOWlRB"
   },
   "source": [
    "#### Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7YE21HKK40zQ"
   },
   "source": [
    "Similarly, the `Date Time` column is very useful, but not in this string form. Start by converting it to seconds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:12.790953Z",
     "iopub.status.busy": "2024-08-16T02:37:12.790718Z",
     "iopub.status.idle": "2024-08-16T02:37:12.896970Z",
     "shell.execute_reply": "2024-08-16T02:37:12.896329Z"
    },
    "id": "LIFf-VjMfnh3"
   },
   "outputs": [],
   "source": [
    "timestamp_s = date_time.map(pd.Timestamp.timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EC_pnM1D5Sgc"
   },
   "source": [
    "Similar to the wind direction, the time in seconds is not a useful model input. Being weather data, it has clear daily and yearly periodicity. There are many ways you could deal with periodicity.\n",
    "\n",
    "You can get usable signals by using sine and cosine transforms to clear \"Time of day\" and \"Time of year\" signals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:12.901184Z",
     "iopub.status.busy": "2024-08-16T02:37:12.900546Z",
     "iopub.status.idle": "2024-08-16T02:37:12.911113Z",
     "shell.execute_reply": "2024-08-16T02:37:12.910517Z"
    },
    "id": "MBfX6CDwax73"
   },
   "outputs": [],
   "source": [
    "day = 24*60*60\n",
    "year = (365.2425)*day\n",
    "\n",
    "df['Day sin'] = np.sin(timestamp_s * (2 * np.pi / day))\n",
    "df['Day cos'] = np.cos(timestamp_s * (2 * np.pi / day))\n",
    "df['Year sin'] = np.sin(timestamp_s * (2 * np.pi / year))\n",
    "df['Year cos'] = np.cos(timestamp_s * (2 * np.pi / year))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:12.914356Z",
     "iopub.status.busy": "2024-08-16T02:37:12.913890Z",
     "iopub.status.idle": "2024-08-16T02:37:13.071986Z",
     "shell.execute_reply": "2024-08-16T02:37:13.071420Z"
    },
    "id": "mXBbTJZfuuTC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Time of day signal')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAIjCAYAAADoYWivAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAConklEQVR4nOzdd3xUVf7/8dfMpHdCGoFACB0poYbQhVCUFVBUUJQigmBlsfJd17q/taxtVQQFKQoCih0VaVKkJPTeISQEUiCk95n5/XFTzAJJgCRn7uTzfDzmkcPMnTvvoeWTO+d8jsFqtVoRQgghhBDCThhVBxBCCCGEEKI6SYErhBBCCCHsihS4QgghhBDCrkiBK4QQQggh7IoUuEIIIYQQwq5IgSuEEEIIIeyKFLhCCCGEEMKuSIErhBBCCCHsihS4QgghhBDCrkiBK4SwCxMmTCA0NFR1jBuSlJTE3XffTf369TEYDHzwwQfX9fzY2FgMBgMLFy6skXw345VXXsFgMKiOUap///70799fdQwhRA1zUB1ACCGupaqF0R9//FHDSWrW3//+d37//XdefvllgoKC6Nq1q+pIQgiha1LgCiFs1pdfflnu11988QVr1qy54v42bdowd+5cLBZLbcarNuvXr2fEiBE888wzqqNUuxdffJEXXnhBdQwhRB0jBa4QwmY98MAD5X69fft21qxZc8X9epecnIyPj4/qGDXCwcEBBwf5ViOEqF0yB1cIYRf+dw5uybzUd955h1mzZhEWFoabmxuDBw8mPj4eq9XK66+/TqNGjXB1dWXEiBGkpqZecd7ffvuNPn364O7ujqenJ8OGDePQoUNVynT69GnuuecefH19cXNzo0ePHvzyyy+ljy9cuBCDwYDVamXWrFkYDIZKp2WkpaUxYcIEvL298fHxYfz48aSlpV1x3P79+5kwYQJhYWG4uLgQFBTEQw89xKVLl0qP+eOPPzAYDHz//fdXPP+rr77CYDCwbdu2a2YpLCzk1VdfpUWLFri4uFC/fn169+7NmjVrSo+52hzc3NxcnnzySfz8/PD09GT48OEkJCRgMBh45ZVXrnjuyZMnmTBhAj4+Pnh7ezNx4kRycnLKnXPBggUMGDCAgIAAnJ2dadu2LbNnz67w91IIYb/kx2ohhF1bsmQJBQUFPPHEE6SmpvL2229z7733MmDAADZs2MDzzz/PyZMn+eijj3jmmWeYP39+6XO//PJLxo8fz5AhQ3jrrbfIyclh9uzZ9O7dmz179lS4qC0pKYmePXuSk5PDk08+Sf369Vm0aBHDhw9nxYoV3HnnnfTt25cvv/ySBx98kEGDBjFu3LgK34vVamXEiBH8+eefTJ06lTZt2vD9998zfvz4K45ds2YNp0+fZuLEiQQFBXHo0CE+++wzDh06xPbt2zEYDPTv35+QkBCWLFnCnXfeecXvW7NmzYiMjLxmnldeeYU33niDhx9+mO7du5ORkcHOnTvZvXs3gwYNuubzJkyYwNdff82DDz5Ijx492LhxI8OGDbvm8ffeey9NmzbljTfeYPfu3cybN4+AgADeeuut0mNmz57NLbfcwvDhw3FwcODnn3/m0UcfxWKx8Nhjj1X02yqEsEdWIYTQiccee8x6rf+2xo8fb23SpEnpr8+cOWMFrP7+/ta0tLTS+2fOnGkFrB07drQWFhaW3n/fffdZnZycrHl5eVar1WrNzMy0+vj4WCdPnlzudRITE63e3t5X3P+/pk+fbgWsmzdvLr0vMzPT2rRpU2toaKjVbDaX3g9YH3vssUrf/w8//GAFrG+//XbpfUVFRdY+ffpYAeuCBQtK78/Jybni+UuXLrUC1k2bNpXeN3PmTKuzs3O536Pk5GSrg4OD9eWXX64wT8eOHa3Dhg2r8JiXX3653J/Zrl27rIB1+vTp5Y6bMGGCFSj3miXPfeihh8ode+edd1rr169f7r6rvd8hQ4ZYw8LCyt3Xr18/a79+/SrMLITQP5miIISwa/fccw/e3t6lv46IiAC0+b1/nRsaERFBQUEBCQkJgHYFNC0tjfvuu4+LFy+W3kwmExEREZV2bvj111/p3r07vXv3Lr3Pw8ODKVOmEBsby+HDh6/7vfz66684ODgwbdq00vtMJhNPPPHEFce6urqWjvPy8rh48SI9evQAYPfu3aWPjRs3jvz8fFasWFF63/LlyykqKqp0rrOPjw+HDh3ixIkTVX4Pq1atAuDRRx8td//V3kOJqVOnlvt1nz59uHTpEhkZGaX3/fX9pqenc/HiRfr168fp06dJT0+vcj4hhH2QAlcIYdcaN25c7tclxW5ISMhV7798+TJAadE2YMAA/P39y91Wr15NcnJyha979uxZWrVqdcX9bdq0KX38ep09e5YGDRrg4eFR7v6rvU5qaipPPfUUgYGBuLq64u/vT9OmTQHKFXytW7emW7duLFmypPS+JUuW0KNHD5o3b15hntdee420tDRatmxJ+/btefbZZ9m/f3+l78FoNJZmKVHRa/3vn2G9evWAsj8rgC1bthAVFYW7uzs+Pj74+/vzf//3f1e8XyFE3SBzcIUQds1kMl3X/VarFaC05diXX35JUFDQFcfZemeAe++9l61bt/Lss88SHh6Oh4cHFouFoUOHXtFObdy4cTz11FOcO3eO/Px8tm/fzscff1zpa/Tt25dTp07x448/snr1aubNm8f777/PnDlzePjhh6vtvVT2Z3Xq1CkGDhxI69atee+99wgJCcHJyYlff/2V999/X7ft44QQN862/4cWQghFmjVrBkBAQABRUVHX/fwmTZpw7NixK+4/evRo6eM3cs5169aRlZVV7iru/77O5cuXWbduHa+++iovvfRS6f3XmkowZswYZsyYwdKlS8nNzcXR0ZHRo0dXKZOvry8TJ05k4sSJZGVl0bdvX1555ZVrFrhNmjTBYrFw5swZWrRoUXr/yZMnq/R6V/Pzzz+Tn5/PTz/9VO5qr943ABFC3DiZoiCEEFcxZMgQvLy8+Pe//01hYeEVj6ekpFT4/Ntvv52YmJhybbays7P57LPPCA0NpW3btted6fbbb6eoqKhc+yuz2cxHH31U7riSK54lVzhLXGsLYD8/P2677TYWL17MkiVLGDp0KH5+fpXm+WvLMdDmGDdv3pz8/PxrPmfIkCEAfPLJJ+Xu/9/3cD2u9n7T09NZsGDBDZ9TCKFvcgVXCCGuwsvLi9mzZ/Pggw/SuXNnxowZg7+/P3Fxcfzyyy/06tWrwo/xX3jhBZYuXcptt93Gk08+ia+vL4sWLeLMmTN8++23GI3Xf33hjjvuoFevXrzwwgvExsbStm1bvvvuuyvmmHp5edG3b1/efvttCgsLadiwIatXr+bMmTPXPPe4ceO4++67AXj99derlKdt27b079+fLl264Ovry86dO1mxYgWPP/74NZ/TpUsXRo0axQcffMClS5dK24QdP34cqPr2zH81ePBgnJycuOOOO3jkkUfIyspi7ty5BAQEcOHChes+nxBC/6TAFUKIa7j//vsJDg7mzTff5D//+Q/5+fk0bNiQPn36MHHixAqfGxgYyNatW3n++ef56KOPyMvLo0OHDvz8888V9nytiNFo5KeffmL69OksXrwYg8HA8OHDeffdd+nUqVO5Y7/66iueeOIJZs2ahdVqZfDgwfz2228EBwdf9dx33HEH9erVw2KxMHz48CrlefLJJ/npp59YvXo1+fn5NGnShH/96188++yzFT7viy++ICgoiKVLl/L9998TFRXF8uXLadWqFS4uLlX7zfiLVq1asWLFCl588UWeeeYZgoKCmDZtGv7+/jz00EPXfT4hhP4ZrP/7GZYQQog6p6ioiODgYO644w4+//zzWn/9vXv30qlTJxYvXszYsWNr/fWFEPZF5uAKIYTghx9+ICUlpdLd1KpDbm7uFfd98MEHGI1G+vbtW+OvL4SwfzJFQQgh6rDo6Gj279/P66+/TqdOnejXr1+Nv+bbb7/Nrl27uPXWW3FwcOC3337jt99+Y8qUKVf0JxZCiBshUxSEEKIOmzBhAosXLyY8PJyFCxfSrl27Gn/NNWvW8Oqrr3L48GGysrJo3LgxDz74IP/4xz9svr+wEEIfpMAVQgghhBB2RebgCiGEEEIIuyIFrhBCCCGEsCt1crKTxWLh/PnzeHp63lBTcSGEEEIIUbOsViuZmZkEBwdf9+Y4dbLAPX/+vKzUFUIIIYTQgfj4eBo1anRdz6mTBa6npyeg/YZ5eXkpTiOEEEIIIf5XRkYGISEhpXXb9aiTBW7JtAQvLy8pcIUQQgghbNiNTCeVRWZCCCGEEMKuSIErhBBCCCHsihS4QgghhBDCrkiBK4QQQggh7IoUuEIIIYQQwq5IgSuEEEIIIeyKFLhCCCGEEMKuSIErhBBCCCHsihS4QgghhBDCrkiBK4QQQggh7IoUuEIIIYQQwq5IgSuEEEIIIeyKFLhCCCGEEMKuSIErhBBCCCHsSo0WuJs2beKOO+4gODgYg8HADz/8UOlzNmzYQOfOnXF2dqZ58+YsXLjwimNmzZpFaGgoLi4uREREEBMTU/3hhRBCCCGELtVogZudnU3Hjh2ZNWtWlY4/c+YMw4YN49Zbb2Xv3r1Mnz6dhx9+mN9//730mOXLlzNjxgxefvlldu/eTceOHRkyZAjJyck19TaEEEIIIYSOGKxWq7VWXshg4Pvvv2fkyJHXPOb555/nl19+4eDBg6X3jRkzhrS0NFatWgVAREQE3bp14+OPPwbAYrEQEhLCE088wQsvvFClLBkZGXh7e5Oeno6Xl9eNvylRZ+UWmNkbn0ajeq6E+LqpjiOEEEJAZhKkHIEG4eDqozrNTbuZes2hhjLdkG3bthEVFVXuviFDhjB9+nQACgoK2LVrFzNnzix93Gg0EhUVxbZt26553vz8fPLz80t/nZGRUb3BRZ2QnJnH+iPJrD2SxOYTF8kvsgDQOsiTqDaBRLUNpENDb4xGg+KkQggh6gSrFZKPwLFf4dhvkLBTu9/oAE16QqvbodVtUC9UaUwVbKrATUxMJDAwsNx9gYGBZGRkkJuby+XLlzGbzVc95ujRo9c87xtvvMGrr75aI5mF/bJarRxPymLtkSTWHE5ib3xaucf9PZ25lJXP0cRMjiZm8vEfJ/H3dCaqTQBRbQLp1dwPF0eTmvBCCCHsk7kQzm7VCtpjv0La2fKPewZD5nk4s0m7rXoBAtpqhW6r2yG4Mxjtv8eATRW4NWXmzJnMmDGj9NcZGRmEhIQoTCRsVaHZQsyZVNYcTmLd0STiU3PLPd4xxIdBbQKIahtIq0BP0nIK2XA8mbWHk9l4PIWUzHyWxsSzNCYeF0cjfVr4M6hNILe2DsDf01nRuxJCCKFruWlwcq1W1J5YA/npZY+ZnCGsv1bAthwKXg3g0ik4vko7/uxWSD6s3Ta/C+4B0GqoVuw27QdO9jnNzqYK3KCgIJKSksrdl5SUhJeXF66urphMJkwm01WPCQoKuuZ5nZ2dcXaW4kJcXXpuIRuOJbP2SDIbjiWTmVdU+pizg5Hezf2IahvIwNYBBHi5lHtuPXcn7uzUiDs7NSK/yEz06VTWHkli7eEkzqfnseawdvXXYIBOIT5EtQ1kUJtAmgd4YDDIVAYhhBDXcDkWjq3SrtKe3QKWsu9NuPlpxWyr26DZreDkXv659ZtB5GPaLSe1uDj+FU6shexk2P2FdnNw1Z5fUhx7BNTqW6xJNlXgRkZG8uuvv5a7b82aNURGRgLg5OREly5dWLduXeliNYvFwrp163j88cdrO67QsfjUHNYcTmLtkSRizqRSZClba1nf3YmBxdMMerfww82pav9MnB1M9G3pT9+W/rw6/BYOX8hg7WFtzu6BhHR2x6WxOy6Nt1cdo0l9N23ebptAuoXWw8Fk/x8XCSGEqIDFAuf3lM2nTT5U/nG/VmXTDBp1BWMVp8C5+UKHe7VbUQGc/bN4esNvkB5f/Hq/AgbtvCWv4d8adHwhpka7KGRlZXHy5EkAOnXqxHvvvcett96Kr68vjRs3ZubMmSQkJPDFF18AWpuwdu3a8dhjj/HQQw+xfv16nnzySX755ReGDBkCaG3Cxo8fz6effkr37t354IMP+Prrrzl69OgVc3OvRboo1D0Wi5X9CemsLS5qjyZmlnu8RYAHUW21gjM8xAdTNS8Uu5Cey7riBWpbT16iwGwpfczb1ZFbW/kT1TaQfi398XRxrNbXFkIIYaMKc+H0Rq3APL4Ksv7yCbXBCI17Fhect2lXZauT1QpJB8vm8p7fU/7xeqFli9QaR4Kp9r833Uy9VqMF7oYNG7j11luvuH/8+PEsXLiQCRMmEBsby4YNG8o95+9//zuHDx+mUaNG/POf/2TChAnlnv/xxx/zn//8h8TERMLDw/nwww+JiIiocq5aL3BzL0N8DLQcUvOvJUrlF5n588RFbcrAkWRSMss6aZiMBrqF1iu9ihrq517BmapXdn4Rm0+ksOZwMuuPJnE5p7D0MUeTgR5h9YlqE8jgWwJp4O1aa7mEEELUguxLZVdpT62Hor+s9XDyhOYDtcKyxSDt6mttyThfNm/39EYwl33PxMUbWgzWit3mUdqvayOSrRa4tqpWC9zCPFh8lzbJe+ib0GNqzb6eACAxPY8JC2LKXan1cHagXytt0Vf/Vv74uDkpTKgxW6zsjrvM2sNJrDmSxOmU7NLHHE0G/nN3R0Z2aqgwoRBCiGpzeiMsfwDy/9Ku1KtR2VXa0N7gYANrhvKz4PQfWrF7fBXkXCp7zOgIM46Ah3+Nx5AC9zrVaoFrMcNvz8GOedqvez0FA1+pEy06VDmelMmE+TGcT8+jnpsjwzsGE9U2kIim9XFysO3f91MpWaw7ksQv+y+w75y2Snbmba2Z0jdMFqUJIYSeHVgB308FS6E2n7bdKK2oDWpv23NdLWY4t6PsqrPJGab9WSsvLQXudar1KQpWK/z5Hqx7Tft1+3thxCxwUH8F0d7siE1l0sIdZOQVEebvzqKJ3XW505jFYuVfvxxh/pYzAEzsFco/h7WVTSSEEEKPtn4Eq1/Uxm1Hwp2fgqNLhU+xWXnpupiiYNuXs+yFwQB9noaRs8FgggNfw1f3Qn5m5c8VVbbq4AXGzosmI6+Izo19+HZqT10WtwBGo4GX7mjLP25vA8CCLbE8sWwPeYVmxcmEEEJUmcUCq/6vrLiNmAp3L9BvcQu1VtzeLClwa1P4/XD/1+Dors1tWXC7tm+0uGlfbItl2pLdFBRZiGoTyJKHe1DPXf9XyCf3DeO/Y8JxNBn4Zf8Fxs+PIT23sPInCiGEUKsoH757GLbP0n496DVtLY5MUawV8rtc21pEwYSV4O4Pifvh8yi4eFJ1Kt2yWq28veooL/14CKsV7uvemDkPdMbVyX62yB0R3pCFE7vj4exA9JlURn+6jcT0PNWxhBBCXEteOiweBQe/BaMD3PmZtgbHlufa2hkpcFVo2BkmrYZ6TSEtDj4fBPE7VKfSnUKzhae/2ccnG04BMGNQS/59Zzu73DShV3M/lj/SA39PZ44mZnLXJ1s4niRTXIQQwuZkXNA+oY3dDE4eMPYb6Dhadao6x/4qAb3wDYNJayC4E+SmwqI7tC35RJVk5xcxadFOvtudgMlo4K1R7XlyYAu77jRwS7A3303rSZi/O+fT87h79lZ2xKaqjiWEEKJEyjHtolXSQXAPgIm/QrMBqlPVSVLgquThD+NXQvNBWqPnZffBrkWqU9m8lMx8xny2nU3HU3B1NDF3XBdGd2usOlatCPF149upPenc2IeMvCLGzotm1cELqmMJIYSI2w6fD9a2v63fHB5eAw06qk5VZ0mBq5qzB9y3FMIfAKsFfn4SNryptRYTV4i9mM2o2Vs5kJCOr7sTS6f0YEDrqm3RbC/quTux5OEeRLUJpKDIwrQlu/liW6zqWEIIUXcdWQlfjIC8NGjYFR5arW11K5SRAtcWmBxhxMfQ5xnt1xvegJ+fAnOR2lw2Zm98GqNmbyUuNYcQX1dWTI0kPMRHdSwlXJ1MzHmgM/d1b4zVCi/9eIi3Vx2lDra1FkIItXZ8Dl8/CEV50HIojP8Z3OurTlXnSYFrKwwGGPhPGPYuGIywe5G2nV9BjupkNuGPo8nc99l2LmUX0K6hF99N60WYv4fqWEo5mIz8+852zBjUEoBPNpzi6W/2UWi2KE4mhBB1gNUK616HX2Zon8B2Hgejl4CTPvuv2xspcG1Nt4fh3i/BwQWO/wZfDIfsS5U/z459vTOeh7/YSW6hmT4t/Fg2JRJ/TxvYq9sGGAwGnhzYgrdGtcdkNPDd7gQmLdpJdr5c/RdCiBpjLoQfH4fN72i/7j8T7vgQTA5qc4lSUuDaojZ/g3E/gouPtv/z/MFwOVZ1qlpntVr5aN0JnluxH7PFyl2dGvL5+G54OMt/IP9rdLfGzB3XBVdHE5uOpzDms+2kZOarjiWEEPYnPwuW3gd7F2ufuN7xX+j/gvS4tTFS4Nqqxj20XrneIXDppLYy88I+1alqjdli5cUfDvLumuMATOvfjHfv7YiTg/yVvZYBrQNZOqUHvu5OHEhIZ9TsrcRezFYdSwgh7EdWCiz6G5xcAw6uMOYr6DJBdSpxFVIt2DL/Vlqv3MB2kJWkNY4+tV51qhqXV2hm2uJdLImOw2CAV4ffwvNDW9t1j9vqEh7iw4qpkYT4uhKXmsOo2VvZG5+mOpYQQuhf6mntE9Xze8DVV1tM1uo21anENUiBa+u8GmiNokP7QEEWLLkH9n+tOlWNuZxdwNh50aw+nISTg5FZ93dmfM9Q1bF0Jczfg++m9aJdQy8uZRdw32fb+eNosupYQgihXwm7Yd4grcj1aax9whrSTXUqUQEpcPXAxRse+BZuuQssRfDdZNjyX7vrlXvucg53z9nKrrOX8XJx4MuHunN7+waqY+mSv6czy6ZE0qeFH7mFZh7+Yidf74xXHUsIIfTnxFpY+DfIuQhB7bVPVv1aqE4lKiEFrl44OMOoz6HHY9qv17wEq2aCxT5aQh0+n8Fdn2zlVEo2DbxdWDGtJxFh0kfwZng4O/D5+G7c1akhZouV51bs56N1J6RXrhBCVNXer2DpaCjMhrD+MOFX8AxSnUpUgRS4emI0wtB/w+B/ab+Ong0rJkJhntpcN2nryYvc++k2kjPzaRXoyXeP9qRloKfqWHbBycHIu/d2ZFr/ZgC8u+Y4L/5wELNFilwhhLgmqxU2vQM/TNM+OW1/L9z/Dbh4qU4mqkgKXD3q+YR2NdfoCId/gMWjIDdNdaob8tO+84xfEENWfhHdm/ry9dRIGni7qo5lVwwGA88Pbc2rw2/BYIAl0XFMXbyLvEKz6mhCCGF7LGb49RlY/7r2615PwZ2fgoOT2lziukiBq1ft79bm5Tp5wtk/4at7dbe17/qjSTy5dA+FZivD2jfgi4e64+3qqDqW3RrfM5RP7u+Mk4ORNYeTeGLpHpmuIIQQ/2v1i7BjHmCAoW/BoNe0T1CFrsifmJ6F9YOHfgNnL4iPhj/fU52oyi5m5fPciv0AjO4awkf3dcLF0aQ4lf27rX0DvnyoO04mrchdtkMWngkhRKmT62D7J9p41DzoMVVtHnHDpMDVu6D2MOxdbbzhTTi3U22eKrBarTy/Yj8XswpoHeTJqyNuwWiUHre1JSKsPs8NbQXAaz8f5nRKluJEQghhA7IvwQ+PauPuU7RPSoVuSYFrD9rfA+1GgdWstRDLt+2C5auYONYdTcbJZOSDMeFy5VaBh3o1pWez+uQWmvn78r0Umu2jG4cQQtwQqxVWPgVZieDXCqJeVZ1I3CQpcO2BwaBdxfVqqDWh/v3/VCe6plMpWby+8jAAzw1tResgWZGqgtFo4N17O+Ll4sC+c+l8tO6E6khCCKHO3iVw5Gdt8fZdn4GTm+pE4iZJgWsvXOvBnXMAA+xeBEd/UZ3oCoVmC39fvpe8Qgu9mtfnoV5NVUeq0xp4u/Lvu9oD8PEfJ9l1NlVxIiGEUCD1NPz2vDYe8A8IDlcaR1QPKXDtSdO+0PNxbfzTE5CZpDbP//hw3Qn2n0vH29WRd+7pKPNubcDfOgRzV6eGWKwwfflesvL11YlDCCFuirkIvnsECrKgSS/o+aTqRKKaSIFrbwb8EwLbQ84l+PExm9nOd2dsKrP+OAnAv+9sL71ubcgrI26hoY8r8am5vPrTIdVxhBCi9vz5HpyLAWdv7VNQo6wJsRdS4NobB2cYNRdMznByTXEvP7Uy8wr5+9d7sVhhVOdGDOvQQHUk8RdeLo68PzocowG+2XWO3w5cUB1JCCFq3rldWvch0Nax+DRWm0dUKylw7VFAG60xNWgNq1OOKY3zyk+HiU/NpVE9V14Z3lZpFnF13Zv6lm7nO/P7AySm63v7ZyGEqFB+Fnz3sNZ9qN3d0OEe1YlENZMC1151nwLNBkBRHnz7MBQVKInxy/4LfLv7HEYDvD86HE8X2anMVj01sCXtG3qTllPIsyv2YbHYxvQWIYSodr//n7a4zKsRDHtHdRpRA6TAtVdGI4z4BFx9IXE/bPh3rUdITM/j/74/AMCj/ZvTLdS31jOIqnNyMPL+6HBcHI1sPnGRhVtjVUcSQojqd/QXrdsQBrhzttaFSNgdKXDtmVcDuOO/2vjPDyB2S629tMVi5Zlv9pGeW0iHRt48FdWi1l5b3LjmAR78Y5g2jeTNVUc5lpipOJEQQlSjzCStyxBAzye07kPCLkmBa+/aDodODwBW+P4RyEuvlZddsDWWP09exMVRuyroaJK/anrxQERjBrQOoKDIwlPL9pBfZFYdSQghbp7VqnUXyrmkdRsa8KLqRKIGSdVRFwx9E+qFQno8/Ppsjb/c0cQM3lp1FIAXh7Wlmb9Hjb+mqD4Gg4G3RnWgvrsTRxMzeXf1cdWRhBDi5u2Yp3UXcnDRug05OKtOJGqQFLh1gbMn3DUXDEbYvxwOrKixl8orNDN92V4KiiwMbB3A2Ahpu6JH/p7OvDWqAwBzN59m68mLihMJIcRNSDmmdRUCrctQQBu1eUSNkwK3rgjpDn2Lr96unAFp8TXyMu/8foyjiZn4eTjx1t0dMBhktzK9imobyP0RjbFaYcbX+0jPKVQdSQghrl9RQXE3oTxoNhC6TVadSNQCKXDrkr7PQsMukJ8OP0wDi6VaT7/l5EXm/XkGgLdGdcDPQz7+0bsXh7WhqZ87iRl5/N8PB7DayM54QghRZRv+rXUTcvWFEbO0LkPC7smfcl1ictSmKji6Q+xm2PZxtZ06LaeAp7/eB8DYiMYMbBNYbecW6rg5OfDB6HBMRgO/7L/AD3sTVEcSQoiqi92idRECrauQl+ykWVdIgVvX1G8GQ9/Qxuteg8QDN31Kq9XKP74/SGJGHmF+7vxjmMxtsicdQ3yYPlBr8/bSD4eIT81RnEgIIaogL13rHoRV6ybUdrjqRKIWSYFbF3UeB62GgaUQvp0Mhbk3dbrv9yTwy4ELOBgNfDAmHDcnh2oKKmzFtP7N6NKkHpn5RTz99T7MssuZEMLW/fqs1j2oXlOtm5CoU2qlwJ01axahoaG4uLgQERFBTEzMNY/t378/BoPhituwYcNKj5kwYcIVjw8dOrQ23op9MBhg+IfgHgApR2Dtqzd8qvjUHF768RAAfx/Ukg6NfKoppLAlDiYjH4wOx8PZgZjYVD7ddEp1JCGEuLYDK7SuQQaTNjXP2VN1IlHLarzAXb58OTNmzODll19m9+7ddOzYkSFDhpCcnHzV47/77jsuXLhQejt48CAmk4l77rmn3HFDhw4td9zSpUtr+q3YF3c/GPmJNo6eDSfXXfcpzBYrM77eS1Z+EV2b1GNqv2bVHFLYkhBfN14ZfgsA760+zoFztbNpiBBCXJf0c/DLDG3c91kI6aY2j1Cixgvc9957j8mTJzNx4kTatm3LnDlzcHNzY/78+Vc93tfXl6CgoNLbmjVrcHNzu6LAdXZ2LndcvXqyl/R1azGorF3KD9Mg+9J1PX3OxlPsiL2Mh7MD7xcvRBL2bVTnhtzePogii5Wnlu8ht0B2ORNC2BCLBb6fqs2/bdgV+j6jOpFQpEYL3IKCAnbt2kVUVFTZCxqNREVFsW3btiqd4/PPP2fMmDG4u7uXu3/Dhg0EBATQqlUrpk2bxqVL1y7O8vPzycjIKHcTxQa9Bn6tICsJVj6lbWVYBfvPpfH+Gm2Hq1eH30KIr1tNphQ2wmAw8P9GtifQy5nTKdn8+9cjqiMJIUSZbR9rXYIc3eGuz7TuQaJOqtEC9+LFi5jNZgIDy7eMCgwMJDExsdLnx8TEcPDgQR5++OFy9w8dOpQvvviCdevW8dZbb7Fx40Zuu+02zOarX01644038Pb2Lr2FhITc+JuyN05u2n8CRkc48jPsXVLpU3IKipi+bC9FFivD2jfgrs4NayGosBX13J14556OAHy5/Sx/HL36dCMhhKhViQe07kCgdQuqL9Pm6jKb7qLw+eef0759e7p3717u/jFjxjB8+HDat2/PyJEjWblyJTt27GDDhg1XPc/MmTNJT08vvcXH18wuXroVHA4D/qGNf3seUk9XePi/fz3C6YvZBHo58//ubCe7ldVBfVr481CvpgA8u2IfF7PyFScSQtRphblaVyBLodYlqPM41YmEYjVa4Pr5+WEymUhKSip3f1JSEkFBQRU+Nzs7m2XLljFp0qRKXycsLAw/Pz9Onjx51cednZ3x8vIqdxP/o+eT0KQXFGTBd4+Aueiqh60/msTi7XEAvHtPOD5uTrWZUtiQ54a2olWgJxezCnjhW9nlTAih0NpXta5AHoFalyC58FLn1WiB6+TkRJcuXVi3rmyFvsViYd26dURGRlb43G+++Yb8/HweeOCBSl/n3LlzXLp0iQYNZIeSG2Y0wZ1zwNkbzsXA5nevOORiVj7PrdgPwMO9m9K7hV9tpxQ2xMXRxAdjwnEyGVl7JImlMfLJiBBCgZPrtG5AACM+0boEiTqvxqcozJgxg7lz57Jo0SKOHDnCtGnTyM7OZuLEiQCMGzeOmTNnXvG8zz//nJEjR1K/fv1y92dlZfHss8+yfft2YmNjWbduHSNGjKB58+YMGTKkpt+OffNpDMOKC9uNb8G5naUPWa1Wnl+xn4tZBbQO8uSZIa0UhRS2pE0DL54bqv1deH3lYU6nZClOJISoU7IvaV2AALpPgRZRFR8v6owaL3BHjx7NO++8w0svvUR4eDh79+5l1apVpQvP4uLiuHDhQrnnHDt2jD///POq0xNMJhP79+9n+PDhtGzZkkmTJtGlSxc2b96Ms7NzTb8d+9fhHmh3N1jN8N1kyNcKlq9i4lh3NBknk5EPxoTj4mhSHFTYiod6NaVns/rkFpr5+/K9FJotqiMJIeoCq1Xr/pOVpHUDirrxTYuE/TFY6+DEuYyMDLy9vUlPT5f5uFeTexlm94aMc9B5PKci/82wDzeTV2jhxWFteLhPmOqEwsZcSM9lyPubyMgr4skBzZkxWK7wCyFq2J7F8ONjWhegh9dqC6aFXbmZes2muygIRVzrwZ2zAQPsXsSyL2aTV2ihV/P6pSvnhfirBt6u/Puu9gB8/MdJdp1NVZxICGHXUk9rXX9A6wIkxa34H1Lgiqtr2hd6PgHA1Iz/0tQlh3fu6YhRdisT1/C3DsHc1akhFitMX76XnIKrd+IQQoibYrFo3X4KsrTuPz2fVJ1I2CApcMU1JXR+miPWJtQ3ZDI/7A8aeLuqjiRs3CsjbqGhjyvxqbl8vvmM6jhCCHt0cIXW7cfJU+v+Y5Q1IeJKUuCKa3pv/VleLXwQgNDY5XDplOJEwtZ5uTjy/G2tAfh002kuyQYQQojqVJQP61/Xxn3+rnX/EeIqpMAVV3U0MYPv9pxju6UtGY36Y7AUwfp/qY4ldOBv7RvQvqE3WflFfLT+6puvCCHEDdnxOaTFgWcDiJimOo2wYVLgiqt6e9UxrFYY1r4BXn/7f4ABDn0HCbtVRxM2zmg08ELxVdwl0WeJu5SjOJEQwi7kpcOm/2jj/jPByU1tHmHTpMAVV9h++hLrjyZjMhq0DR2C2kHHMdqDa1/Weg8KUYFezf3o08KPQrOVd9ccUx1HCGEPtnwIuang1xLCx6pOI2ycFLiiHKvVypu/HQXgvu4hNPVz1x649f/A5ARnNsGpdRWcQQjN80O1q7g/7j3PwYR0xWmEELqWcQG2zdLGA18Gk4PaPMLmSYEryll1MJG98Wm4OZl4cmCLsgd8GmvbIAKseUVr0yJEBdo19GZEeDAAb606qjiNEELXNr4JRbkQEgGth6lOI3RAClxRqtBs4T+/ax8nP9wnjABPl/IH9HkanL0h6YDWpkWISjwzuBWOJgObT1xk84kU1XGEEHqUchx2f6mNo14Fg/RjF5WTAleU+npnPKcvZlPf3YnJfa6yY5mbL/Sero3Xv661axGiAiG+bjzQowmgXcW1WGT+thDiOq1/DaxmaHU7NIlUnUbohBS4AoCcgiI+WHsCgCcGNMfTxfHqB0ZM1dqzpMVp7VqEqMTjtzbHw9mBgwkZ/Lz/vOo4Qgg9iY+BIz+DwQgDX1KdRuiIFLgCgM83nyElM5/Gvm7cH9Hk2gc6uWntWUBr15Ini4dExep7OPNI3zAA3ll9jIIimb8thKgCqxXWvKyNw++HgDZq8whdkQJXcCkrn083nQbgmSGtcHKo5K9F+FitTUtuqta2RYhKTOrTFH9PZ+JTc/kq+qzqOEIIPTj+O8RtBQcX6P9/qtMInZECV/DxHyfJyi+iXUMv/ta+QeVPMDlA1CvaeNssyEys0XxC/9ycHJgepXXl+HD9STLzChUnEkLYNIsZ1r6ijSOmgndDpXGE/kiBW8fFXcph8XbtitoLQ9tgNFZxdWqr27V2LUW5sOGNGkwo7MW9XUMI83MnNbuAucWfGAghxFXtWwopR8DFp2xxsxDXQQrcOu7dNccoNFvp08KP3i38qv5Eg0Fr1wJa+5aU4zUTUNgNR5ORZ4e0AmDu5jMkZ+YpTiSEsEmFufDHv7Vxn6fBtZ7aPEKXpMCtww4mpPPjXm1Ve8muU9elSaR2Jddq1tq4CFGJoe2CCA/xIbfQzIfrTqiOI4SwRTGfQUYCeDUq22BIiOskBW4dVrK71IjwYNo19L6xkwx8WWvfcuRniN9RjemEPTIYDLxwm/bD1NKYeE6nZClOJISwKbmXYfO72njAP8DRpeLjhbgGKXDrqD9PXGTziYs4mgw8PajVjZ8ooLXWVQFgzUtaWxchKtAjrD4DWgdgtlh5d7VMbRFC/MWf72vtJwPaQofRqtMIHZMCtw6yWKy8ueoIAGMjmtC4vtvNnbD/TK2NS9xWra2LEJV4bmgrDAb45cAF9sanqY4jhLAF6edg+xxtHPUKGE1K4wh9kwK3Dlp54AIHEzLwcHbgiQHNb/6E3g21Ni6gtXWxmG/+nMKutQ7yYlTnRgC88esRrHLlXwjxxxtgzocmvaHFYNVphM5JgVvHFBRZeOf3YwA80jeM+h7O1XPi3tO1di4pR2Dfsuo5p7Brfx/UEicHI9FnUtlwPEV1HCGESkmHYd9X2njQq1qnHiFughS4dczSmDjiUnPw83BmUp+m1Xdi13rQ9xlt/Mf/09q8CFGBhj6uTOgZCsBbvx3FbJGruELUWeteA6sF2gyHRl1VpxF2QArcOiQzr7C0NdP0qBa4OTlU7wt0m6y1dclI0Nq8CFGJR/s3w8vFgaOJmfywJ0F1HCGECme3wvHfwGCCgS+pTiPshBS4dcjczWe4lF1AmJ87o7uFVP8LOLpobV1Aa/OSe7n6X0PYFR83Jx69VZsH/t6a4+QVyvxtIeoUq1XrwAPQZTz4tVCbR9gNKXDriOTMPOZt1rZHfXZIKxxNNfRH32G01t4lL11r9yJEJSb0DCXIy4WEtNzSbaOFEHXE0ZVwbgc4ukG/51WnEXZECtw64qN1J8kpMNMxxIeh7YJq7oWMJq29C2jtXtLP1dxrCbvg4mhixqCWAHz8x0nScwsVJxJC1ApzEawt3vI98jHwrMHvTaLOkQK3DjhzMZulMXEAzLytNYaaXp3aYrDW5sWcr7V9EaISd3VuSIsAD9JyCpmz8ZTqOEKI2rDnS7h0AtzqQ88nVacRdkYK3Drgnd+PUWSxMqB1AD3C6tf8CxoMWpsX0Nq+JB2u+dcUuuZgMvL8UG0L3/l/niExPU9xIiFEjSrIhg1vauO+z4GLl9o8wu5IgWvn9san8cuBCxgM2u5RtaZRV63di9WitX8RohID2wTQLbQe+UUWPlgrW/gKYde2z4asRPBpAl0nqk4j7JAUuHbMarXy5m/alrx3dWpE66Ba/gl54Eta25fjv2ltYISogMFg4IXbtKu4X++M52RypuJEQogakX0JtvxXGw/4JzhU04ZDQvyFFLh2bMPxFLafTsXJwciMwS1rP4BfC63tC2htYGQ7VlGJLk18Gdw2EIsV3lp1THUcIURN2PwO5GdAUHtoN0p1GmGnpMC1U2aLlbd+OwrA+MgmNPRxVROk3/Na+5dzO7R2MEJU4rmhrTAaYM3hJHbGpqqOI4SoTpdjIWauNo56FYxShoiaIX+z7NSPexM4mpiJp4sDjxU30lfCM0hr/wJaOxhzkbosQheaB3iWbkTy5m9HscqVfyHsxx//BkshhPWH5gNVpxF2TApcO5RXaObd1doinUf7N8fHzUltoJ5Pgquv1g5m72K1WYQuPDWwJS6ORnaevczaI8mq4wghqsOF/bD/a21c0i9diBoiBa4dWrz9LAlpuQR5uTCxV6jqOFr7l37PaeM/3tDawwhRgSBvFx7q1RSAt1YdpchsUZxICHHT1r4CWLV5t8GdVKcRdk4KXDuTnlvIx3+cBODvg1rg4mhSnKhY14e0djBZiVp7GCEq8Ui/Zvi4OXIyOYtvd8uOeELo2ukNcGodGB1hwIuq04g6QApcO/PpxlOk5RTSIsCDUZ0bqY5TxsFZawcDWnuY7Etq8wib5+3qyOPF88ffX3OC3AKz4kRCiBtiscCal7Vx14fAN0xtHlEnSIFrRxLT85i/5QwAzw1tjYPJxv54243S2sLkZ8Dmd1WnETrwYHEHkMSMPBZujVUdRwhxIw7/ABf2gpMH9H1WdRpRR9hYBSRuxn/XHSev0ELXJvWIahOgOs6VjEatLQzAjrlw+azaPMLmOTuYeLq4h/MnG06SllOgOJEQ4rqYC8t2s+z5JHj4q80j6gwpcO3EyeRMlu+IB2Dm7a0xGAyKE11D84FaexhzAfzx/1SnETowIrwhrYM8ycwrYlbx/HIhhE7sWgiXz4B7QFnLSCFqQa0UuLNmzSI0NBQXFxciIiKIiYm55rELFy7EYDCUu7m4uJQ7xmq18tJLL9GgQQNcXV2JiorixIkTNf02bNrbq45hscLgtoF0aeKrOk7FStrD7P9aaxsjRAVMxrItfBdtPcu5yzmKEwkhqiQ/Eza+pY37Pw/OHmrziDqlxgvc5cuXM2PGDF5++WV2795Nx44dGTJkCMnJ1+5t6eXlxYULF0pvZ8+W/yj77bff5sMPP2TOnDlER0fj7u7OkCFDyMvLq+m3Y5N2nU1l9eEkjAZtFyibF9ypeHtGK6x7VXUaoQP9WvoTGVafArOF99fU7R9mhdCNbbMgO0VbVNZ5vOo0oo6p8QL3vffeY/LkyUycOJG2bdsyZ84c3NzcmD9//jWfYzAYCAoKKr0FBgaWPma1Wvnggw948cUXGTFiBB06dOCLL77g/Pnz/PDDDzX9dmyO1WrlzeItee/tGkLzAE/FiapowItgdICTa+H0RtVphI0zGMqu4n635xxHEzMUJxJCVCgrGbZ+pI0HvgQmR7V5RJ1TowVuQUEBu3btIioqquwFjUaioqLYtm3bNZ+XlZVFkyZNCAkJYcSIERw6dKj0sTNnzpCYmFjunN7e3kRERFzznPn5+WRkZJS72YuNx1PYEXsZF0cj06Naqo5Tdb5hWrsYgPWvg2zHKirRMcSHYe0bYLVSulOfEMJG/fkBFGRBcGdoO1J1GlEH1WiBe/HiRcxmc7krsACBgYEkJiZe9TmtWrVi/vz5/PjjjyxevBiLxULPnj05d05r9F7yvOs55xtvvIG3t3fpLSQk5Gbfms2YveEUAA9ENCHI26WSo21M32fB5AzndsDZrarTCB34+6CWGAyw5nASJ5MzVccRQlxNTqq2uAxgwD/AVhc9C7tmc10UIiMjGTduHOHh4fTr14/vvvsOf39/Pv300xs+58yZM0lPTy+9xcfHV2NidfbEXSb6TCqOJgOT+jRVHef6eQRA+P3aeMt/1WYRutA8wINBbbQfbj/bdFpxGiHEVe34HAqztb7nzQaqTiPqqBotcP38/DCZTCQlJZW7PykpiaCgoCqdw9HRkU6dOnHypNYeqOR513NOZ2dnvLy8yt3swacbtW/wI8Ib0sDbVXGaG9TzCcAAJ36HpMOq0wgdmNq/GQDf70kgMb1uLiwVwmYV5kL0HG3ca7pcvRXK1GiB6+TkRJcuXVi3bl3pfRaLhXXr1hEZGVmlc5jNZg4cOECDBg0AaNq0KUFBQeXOmZGRQXR0dJXPaQ9OpWTx+2FtSsYjfXW87WH9ZtB2uDaWq7iiCjo3rkf3UF8KzdbSnfuEEDZiz2LIuQg+jWXurVCqxqcozJgxg7lz57Jo0SKOHDnCtGnTyM7OZuLEiQCMGzeOmTNnlh7/2muvsXr1ak6fPs3u3bt54IEHOHv2LA8//DCgraaePn06//rXv/jpp584cOAA48aNIzg4mJEjR9b027EZczedxmqFqDYBtAjUSeeEa+k1Xft6cAWk2cf0EVGzpvbXfqj7KjqO9NxCxWmEEACYi8o6J0Q+ASYHtXlEnVbjf/tGjx5NSkoKL730EomJiYSHh7Nq1arSRWJxcXEYjWV19uXLl5k8eTKJiYnUq1ePLl26sHXrVtq2bVt6zHPPPUd2djZTpkwhLS2N3r17s2rVqis2hLBXyRl5fLc7AYCp/ZopTlMNGnaGpn3hzCbY/gkMfUN1ImHjbm0VQKtAT44lZbIk+iyP9m+uOpIQ4siPkHYWXH2h0wOq04g6zmC11r3+TBkZGXh7e5Oenq7L+bhv/naUORtP0aVJPb6d1lN1nOpxch0svgsc3eHvB8HNxndjE8p9u+scT3+zDz8PZ/58/lZcHE2qIwlRd1mt8GlfSNwP/WdC/xdUJxJ24GbqNZvroiAqlpFXyJLt2s5udnH1tkSzAdqK28Js2DFPdRqhA8PDgwn2duFiVn7pJxpCCEVO/6EVt45u0H2K6jRCSIGrN0uj48jML6JFgAcDWweojlN9DIayubjRc7SVuEJUwNFkZFIfbS7u3M2nMVvq3IdRQtiOkkXCncfJJ3DCJkiBqyP5RWY+/1NbNT6lbxhGo521X2k7Ult5m3NJW4krRCXGdAvB29WRMxezWX3o6hu9CCFq2Pk9cHoDGEzQ41HVaYQApMDVlR/2JJCcmU+QlwsjwhuqjlP9TA7aylvQVuKai9TmETbP3dmBcZFNAJiz8RR1cEmBEOqVXL1tNwrqNVGbRYhiUuDqhMVi5dPinZsm9W6Kk4Od/tF1egDc6msrcY/8qDqN0IHxPUNxdjCy71w620+nqo4jRN2SehoOF/9f3esptVmE+As7rZLsz5ojSZxOycbTxYH7IhqrjlNznNyg+yPa+M8PtJW5QlTAz8OZe7uGANpVXCFELdr6MVgt0HwQBLVTnUaIUlLg6oDVai39xv1gjyZ4ONt58+zuk7WVuIn7tZW5QlRicp8wjAbYeDyFw+czVMcRom7ISoG9S7SxXL0VNkYKXB2IOZPKnrg0nByMTOzVVHWcmufmq63EBe0qrhCVaFzfjdvba9t5f7pJruIKUSui50BRHjTsAqG9VacRohwpcHWgZO7t3V0a4e/prDhNLYl8TFuRe2ajtkJXiEqU9IVeuf8C8ak5itMIYefys2DHXG3ca7rW6lEIGyIFro07lpjJ+qPJGAzax7B1hk9jaH+3Ni5ZoStEBdo19KZ3cz/MFmtpOz0hRA3ZvQjy0sG3GbQepjqNEFeQAtfGfVo89/a2dkE09XNXnKaW9XxS+3r4R22lrhCVKLmKu2xHHKnZBYrTCGGnigpg2yxt3OtJMMo22cL2SIFrwxLScvlp33nAzrblraqgdtrKXKtFW6krRCV6Na9Pu4Ze5BVa+GJbrOo4Qting99CRgJ4BEKHMarTCHFVUuDasM83n6HIYqVns/p0aOSjOo4avadrX/cu0VbsClEBg8HAI321HwYXbY0lp0A2CxGiWlksZdPGIqaCo4vaPEJcgxS4Niotp4BlO+IAeKQuXr0t0aQXNOyqrdSNnqM6jdCB29oF0djXjcs5hXy9I151HCHsy4nVkHIEnDyh60Oq0whxTVLg2qgvtp0lp8BM2wZe9G3hpzqOOgZDWX/FHXO1lbtCVMDBZGRyX21B5tzNZygyWxQnEsKOlFy97ToRXH2URhGiIlLg2qC8QjMLt8YC8Ei/MAx1vf1K62FQv7m2Ynf3ItVphA7c06UR9d2dSEjL5ZcDF1THEcI+xMdA3FYwOkKPR1WnEaJCUuDaoG92xpOaXUCjeq4MK25eX6cZTWUdFbbN0lbwClEBF0cTE3qGAjBn42mssuWzEDevZOOdjqPBS743CdsmBa6NKTJb+Gyz1hJrcp8wHEzyRwRAh9Hait2MBDi4QnUaoQMPRjbBzcnEkQsZbDwuCxSFuCkpx+DYL4ABesq2vML2SfVkY347mEh8ai713By5t2uI6ji2w9EFekzTxls+1FbyClEBHzcn7uveGIBPN0ofZSFuytYPta+th4F/S7VZhKgCKXBtiNVqZU7xxg7je4bi6iTNs8vp+pC2cjfliLaSV4hKTOrdFAejgW2nL7EvPk11HCH0KeM87FuujXvJ1VuhD1Lg2pA/T17k0PkMXB1NjI8MVR3H9rh4ayt3AbZ8oDSK0IdgH1eGhwcDlP7wKIS4Tts/AUshNO4JId1VpxGiSqTAtSElH6OO7hZCPXcnxWlsVI9HweQEcdsgLlp1GqEDJRs/rDqUyJmL2YrTCKEzuWmwc6E2Ltl4RwgdkALXRhw4l86fJy9iMhp4uE9T1XFsl1cDbcEZlPVjFKICrYI8GdA6AKsVPtskc3GFuC4750NBJvi30bZOF0InpMC1EXM2aR+f3tGhAY3quSlOY+N6PQUYtBW9KcdUpxE6MLV4N8Bvd58jOTNPcRohdKIwD7bP1sa9ngKjlAxCP+Rvqw04eymb34qb0dfpbXmryq+FtpIXylb2ClGBbqH16NzYh4IiCwu3xKqOI4Q+7F8G2cng1Qja3606jRDXRQpcGzB382ksVujfyp82DbxUx9GHXtO1r/uWayt8haiAwWAo/eHxy+1nycwrVJxICBtnMWstGQEiHwWTo9o8QlwnKXAVu5iVzzc7zwFli2FEFYR0gya9tJW92z9RnUbowKA2gTTzdyczr4ilMXGq4whh246uhNRT4OIDncerTiPEdZMCV7GFW2LJL7LQMcSHHmG+quPoS0k/xp0LtZW+QlTAaDSU/hD5+Z9nyC8yK04khI2yWsu25e0+GZw9lMYR4kZIgatQdn4RX2yLBWBavzAMBoPaQHrTYjAEtNVW+O6crzqN0IERnYIJ9HImKSOfH/fK1BYhrir2Tzi/GxxcoPsjqtMIcUOkwFVoaUwcGXlFNPVzZ1DbINVx9MdgKLuKu322tuJXiAo4O5h4qJfWhu/TjaewWKyKEwlhg0o20gkfCx7+SqMIcaOkwFWkoMjC53+eAWBK3zBMRrl6e0PajdJW+GYnw76lqtMIHbg/ojGeLg6cSslm7ZEk1XGEsC2JB+DkWjAYoecTqtMIccOkwFXk533nuZCeh7+nM3d2aqg6jn6ZHCHyMW289SNt5a8QFfB0ceSBHk0A+FQ2fhCivJLOCW1Hgq9sOiT0SwpcBSwWK58Wb+zwUK+muDiaFCfSuc7jtJW+qae0lb9CVGJiz1CcTEZ2nb3MjthU1XGEsA2Xz8LBb7VxyfQvIXRKClwF/jiWzPGkLDycHbg/orHqOPrn7AHdp2jjPz/QVgALUYEALxdGddE+OZmz4ZTiNELYiG2zwGqGsP4QHK46jRA3RQpcBeZs1L6hjo1ojLerNM+uFt2naCt+z+/WVgALUYnJfcIwGGDd0WSOJ2WqjiOEWtmXYPcX2rhkIx0hdEwK3Fq262wqO2Iv42gy8FBvmd9UbTz8odMD2rhkBbAQFQjz92BIcfeSTzfKXFxRx+2YC0W5ENRBu4IrhM5JgVvL5hR/I72zU0MCvVwUp7EzkY9rK39PrtVWAgtRian9tY0fftybwPm0XMVphFCkIBuiP9XGvadrLRiF0DkpcGvRyeRM1hxOwmCAKbItb/XzbQq33KmNS1YCC1GB8OIdBIssVuYXt+0Tos7ZswRyU6FeKLQZoTqNENVCCtxa9FlxS6JBbQJpHiBbH9aIkpW/B7/VVgQLUYlH+mk/bC6NiSM9p1BxGiFqmbkItn2kjSMfB5OD2jxCVBMpcGtJYnoe3+9JAMo+FhU1oEFHCLtVWwm8bZbqNEIH+rf0p3WQJ9kFZr7cHqs6jhC169D3kBYHbn5l6xiEsANS4NaS+VvOUGi20j3Ul86N66mOY996T9e+7v5CWxksRAUMBgNTi6/iLtgSS16hbBYi6girFbb8VxtHTAVHV7V5hKhGtVLgzpo1i9DQUFxcXIiIiCAmJuaax86dO5c+ffpQr1496tWrR1RU1BXHT5gwAYPBUO42dOjQmn4bNyw9t5CvouMAmNo/THGaOqBpP+1KblGutjJYiEoM69CAhj6uXMouYMWuc6rjCFE7Tq2DpAPg6A7dJqlOI0S1qvECd/ny5cyYMYOXX36Z3bt307FjR4YMGUJycvJVj9+wYQP33Xcff/zxB9u2bSMkJITBgweTkJBQ7rihQ4dy4cKF0tvSpUtr+q3csCXRZ8nKL6JVoCe3tgpQHcf+GQxlfRyjP9VWCAtRAUeTkYf7aG375m4+jdkim4WIOuDPD7SvXcaDm6/SKML2WSxW7p2zjXd+P0Z6ru2vV6jxAve9995j8uTJTJw4kbZt2zJnzhzc3NyYP3/+VY9fsmQJjz76KOHh4bRu3Zp58+ZhsVhYt25dueOcnZ0JCgoqvdWrZ5sf+xeZLSzcEgvAlL5hGKT9Su1oOwLqNdVWBu9bpjqN0IHR3ULwcXPk7KUc1hxOUh1HiJp1YR/EbgajA/R4VHUaoQObTqQQE5vKwq2xmIy2X8vUaIFbUFDArl27iIqKKntBo5GoqCi2bdtWpXPk5ORQWFiIr2/5ny43bNhAQEAArVq1Ytq0aVy6dO25lvn5+WRkZJS71RYHk5HFD0cwsVcow8ODa+116zyjSZtTBtpVXNm+V1TCzcmB+7trW2cv2CItw4SdK+l723YE+ISozSJ0YeHWWADu6doID2fb77ZRowXuxYsXMZvNBAYGlrs/MDCQxMTEKp3j+eefJzg4uFyRPHToUL744gvWrVvHW2+9xcaNG7ntttswm6++OOSNN97A29u79BYSUrv/mFsGevLyHbfgaJI1fbUq/H5w8oSLx+D0H6rTCB14MLIJJqOB6DOpHD5fez8IC1GrslLgwDfaOGKa2ixCF06lZLHhWAoGA4yPDFUdp0psuuJ68803WbZsGd9//z0uLmW7fo0ZM4bhw4fTvn17Ro4cycqVK9mxYwcbNmy46nlmzpxJenp66S0+Pr6W3oFQysULOo3VxtvnqM0idKGBtytD22nb9y7cKldxhZ3atRDMBRDcGRp1VZ1G6MCi4qu3A1oFEOrnrjZMFdVogevn54fJZCIpqfx8tqSkJIKCgip87jvvvMObb77J6tWr6dChQ4XHhoWF4efnx8mTJ6/6uLOzM15eXuVuoo7oPgUwwInf4dIp1WmEDjzUKxSAH/ae51JWvtowQlS3ogLYMU8b95gm2/KKSmXkFZZ2l5nYq6niNFVXowWuk5MTXbp0KbdArGTBWGRk5DWf9/bbb/P666+zatUqunat/KfLc+fOcenSJRo0aFAtuYUdqd8MWgzWxjGfqc0idKFz43q0b+hNQZGFZTvk0x5hZ478BFmJ4BEIbUeqTiN04Osd8eQUmGkR4EGv5vVVx6myGp+iMGPGDObOncuiRYs4cuQI06ZNIzs7m4kTJwIwbtw4Zs6cWXr8W2+9xT//+U/mz59PaGgoiYmJJCYmkpWVBUBWVhbPPvss27dvJzY2lnXr1jFixAiaN2/OkCFDavrtCD3qUbzYbM8SyJN5laJiBoOBicVXcb/cdpZCs0VtICGq0/bZ2teuk8DBSW0WYfPMFitfbNO2vZ/QK1RXnaBqvMAdPXo077zzDi+99BLh4eHs3buXVatWlS48i4uL48KFC6XHz549m4KCAu6++24aNGhQenvnnXcAMJlM7N+/n+HDh9OyZUsmTZpEly5d2Lx5M87OzjX9doQehd0Kfq2gIBP2LlGdRujAsA4N8PNwJjEjj98OVm1BrBA279xOSNgJJifoOlF1GqED648mE5eag7erI3d2aqg6znUxWK11r39SRkYG3t7epKeny3zcumLH5/DLDK037hO7wWjT6yuFDXh/zXH+u+4EnRv78N2jvVTHEeLmffuw1j2h431wpyy8FZW7f+52tp66xCN9w5h5e5taf/2bqdfku7yoGzqOARdvuHwGTqxWnUbowNgejXE0Gdgdl8a++DTVcYS4ORkX4ND32rikR7gQFTiWmMnWU5cwGrQWinojBa6oG5zcofM4bRwtVy5E5QI8Xbijg7Y5S0mDcyF0a+d8sBRB40gIDledRuhASavEIbcE0aiem+I0108KXFF3dJ8CBqO26UPyUdVphA5MKF5stnL/eZIz8tSGEeJGFeZpBS5AxCNqswhduJxdwHe7EwCY0DNUbZgbJAWuqDt8GkPrYdpYruKKKujQyIcuTepRaLayODpOdRwhbsyh7yDnIng1hNZ3qE4jdGDZjnjyiyy0beBF96a+quPcEClwRd1SMvds3zLIvaw2i9CFkqsXX0WfJb/o6tuBC2GzrNay1mDdHgaTg9o8wuYVmS18uS0WgIk6aw32V1LgirqlSS8IbA9FubD7C9VphA4MbRdEkJcLF7MKWLnvQuVPEMKWxG2DxP3g4ApdJqhOI3Rg9eEkzqfnUd/diTs6BquOc8OkwBV1i8FQNgctZi6Yi9TmETbP0WQsXUG8YOsZ6mBnRaFnJdOxOtwLbvr8qFnUrgVbtMVl90c0xsXRpDjNjZMCV9Q97e8Bt/qQHg/HflWdRujAfd0b4+xg5GBCBrvOytQWoRNp8XBkpTaWxWWiCg4mpLMj9jIORgMP9NBfa7C/kgJX1D2OLtCleBcfWWwmqsDX3YmR4douPgu2xKoNI0RV7ZgHVjM07QuBt6hOI3Sg5P+329s3INDLRW2YmyQFrqibuk0CowOc3QIX9qtOI3SgpGXYqkOJnE/LVRtGiMoU5MCuhdo4YprSKEIfUjLz+XnfeUBbXKZ3UuCKuskrGNqO0MbRn6rNInShTQMveoT5YrZY+XL7WdVxhKjY/uWQlwY+TaDlENVphA4sjYmjwGyhY4gPnRrXUx3npkmBK+qukpZhB76B7ItqswhdmNirKaB9I8gtkJZhwkZZrWU/uEc8Akb9LhQStaOgyFL6g/tDdnD1FqTAFXVZo24Q3BnM+bBrgeo0Qgei2gTSqJ4raTmF/Lg3QXUcIa7uzEZIOQKO7tDpAdVphA78euACKZn5BHg6c1u7BqrjVAspcEXdZTBAj+K5aTs+B3Oh2jzC5pmMBsZHhgLaYgxpGSZs0vbixbPh94OLt9osQhcWbI0F4IEeTXBysI/S0D7ehRA3qu1I8AiEzAtw+EfVaYQO3NstBFdHE8eSMtl26pLqOEKUl3oajq/SxtIaTFTB7rjL7ItPw8lk5P6IxqrjVBspcEXd5uAEXSdpY2kZJqrA29WRUV2KW4YVX/UQwmbEzAWs0DwK/FqoTiN0oKQ12B0dg/HzcFYbphpJgStE14lgcoJzO+DcLtVphA5M6KktNlt7JIm4SzmK0whRLD8T9izWxtIaTFRBYnoevx3QtiC3h9ZgfyUFrhAeAdBulDaWq7iiCpoHeNC3pT9WK3yxLVZ1HCE0e5dCfgbUbwHNBqhOI3Rg8fazFFmsdA/1pV1D+5qvLQWuEFA2V+3Q95CZqDaL0IWJPUMBWL4znuz8IrVhhLBYyn5Aj3gEjPLtXVQsr9DMVzFxQNlGNvZE/gUIARDcCUJ6gKVQ66ggRCX6tfQnzM+dzLwivt19TnUcUdedWgepp8DZGzrepzqN0IGf9p0nNbuAYG8XBrcNVB2n2kmBK0SJHsUbP+ycD0X5arMIm2c0GhhffBV34dZYLBZpGSYU2j5b+9r5QXD2UJtF2Dyr1Vq6uOzByFAcTPZXDtrfOxLiRrX+G3g1hJyLcPBb1WmEDozq0ghPZwdOp2Sz6USK6jiirko5rl3BxQDdHladRuhAzJlUjlzIwMXRyH3dQ1THqRFS4ApRwuRY9s1h+2xtu0shKuDh7MA9XbVvDiVXQ4SodTHF2/K2uh18m6rNInSh5P+rOzs1wsfNSW2YGiIFrhB/1WUCOLhA4n6I2646jdCB8T2bYDDAxuMpnErJUh1H1DW5aVr3BJCNHUSVnLucw+rD2mLqCcXTrOyRFLhC/JWbL3S4VxtHz1abRehCk/ruDGwdAMAi2fhB1LY9i6EwGwLaQtO+qtMIHfhy21ksVujVvD6tgjxVx6kxUuAK8b8iihebHVkJafFqswhdmNhL+1h4xa5zZOQVKk4j6gyLuWx6QsRUMBjU5hE2L6egiKUlrcF62vd0FilwhfhfgbdAaB+wmmHHPNVphA70bFafloEe5BSY+XqH/FAkasnxVZAWB671oP09qtMIHfh+TwIZeUU09nVjQPEnT/ZKClwhrqZH8TaXuxZCgWzFKipmMBhKr4Ys2haLWVqGidpQ0hqsywRwclMaRdg+q9XKwuLFZeN7hmIy2vcVfylwhbialkPBpwnkpcGBr1WnETpwZ6eGeLs6Ep+ay/qjyarjCHuXeBBiN4PBJK3BRJX8efIiJ5KzcHcycU/XRqrj1DgpcIW4GqMJuk/RxtvnSMswUSlXJxNjupe0DDujOI2weyVzb9vcAd72X6yIm1dy9fbuLo3wcnFUG6YWSIErxLV0egAc3SHlCJzZpDqN0IFxkaEYDbD11CWOJmaojiPsVfYl2F/8yVLJdCohKhB7MZv1x7RPlsbbcWuwv5ICV4hrcfWB8OI93aPnKI0i9KGhjytDbgkCpGWYqEG7F0FRHjToCCERqtMIHVi0LRarFfq38ifMv25s5SwFrhAVKWkZduw3SD2tNovQhZKWYd/tTuBydoHiNMLumAvLurtETJPWYKJSmXmFfLPzHFD2/1NdIAWuEBXxawHNowArxEjLMFG5bqH1uCXYi/wiC8ukZZiobkd+howEcPeHdnepTiN0YMWuc2TlFxHm706f5n6q49QaKXCFqEzJVdw9X0J+ptoswuZpLcNCAfhyWyxFZovaQMK+RBcvLuv6EDg4q80ibJ7FYi2dLjWxZyhGO28N9ldS4ApRmWYDoX5zyM8o2/NdiArc0TGY+u5OnE/P4/dDSarjCHtxfg/EbwejI3SdpDqN0IENx5OJvZSDp4sDd3WuW902pMAVojJGI3R/RBvHfAoWuSInKubiaGJsRGMAFm6VlmGimmwvXuza7i7wDFSbRejCguLWYKO7huDu7KA2TC2TAleIqgi/D5y94NJJOLVOdRqhA2N7NMHBaGBH7GUOJqSrjiP0LjMJDn6rjSMeUZtF6MLJ5Ew2n7iIwVB3WoP9lRS4QlSFsyd0elAbS8swUQWBXi4M69AAgPmy8YO4WbsWgKUQGnWHhl1UpxE6UHL1NqpNICG+dW8rZylwhaiq7pMBA5xcCynHVacROlCy2GzlvgukZOarDSP0q6gAdnyujeXqraiC9JxCvtudAMDEXqFqwygiBa4QVeXbFFrdpo1LtskUogKdGtcjPMSHArOFr6LjVMcRenXoe8hOBs9gaDtCdRqhA8t3xpFbaKZ1kCeRYfVVx1FCClwhrkfJ1ZO9SyE3TWkUoQ8lV08WR5+loEgWKIrrZLVC9Gxt3G0SmBzV5hE2r8hsYdHWs4D2KZKhjm4GUisF7qxZswgNDcXFxYWIiAhiYmIqPP6bb76hdevWuLi40L59e3799ddyj1utVl566SUaNGiAq6srUVFRnDhxoibfghCapv0goC0UZsOexarTCB24rV0DAjydScnM59cDF1THEXpzbofWHszkDF0mqE4jdGDtkWQS0nKp5+bIyE4NVcdRpsYL3OXLlzNjxgxefvlldu/eTceOHRkyZAjJyclXPX7r1q3cd999TJo0iT179jBy5EhGjhzJwYMHS495++23+fDDD5kzZw7R0dG4u7szZMgQ8vLyavrtiLrOYCi7ihvzKVjMavMIm+fkYOTBHk0AWLDlDFarVXEioSvbi6/edrgH3OvOLlTixi0oXtR6X/fGuDiaFKdRx2Ct4f9tIyIi6NatGx9//DEAFouFkJAQnnjiCV544YUrjh89ejTZ2dmsXLmy9L4ePXoQHh7OnDlzsFqtBAcH8/TTT/PMM88AkJ6eTmBgIAsXLmTMmDGVZsrIyMDb25v09HS8vLyq6Z2KOqMgB95vC7mXYcxX0HqY6kTCxl3KyifyzfUUFFn47tGedG5cT3UkoQfpCfBBe7CaYeqfENRedSJh4w6fz+D2DzdjMhrY/NytBPu4qo50U26mXqvRK7gFBQXs2rWLqKioshc0GomKimLbtm1Xfc62bdvKHQ8wZMiQ0uPPnDlDYmJiuWO8vb2JiIi45jnz8/PJyMgodxPihjm5Qefx2rjk6ooQFajv4czwjsFAWeseISq183OtuG3SW4pbUSUlG8sMbRek++L2ZtVogXvx4kXMZjOBgeV3XAkMDCQxMfGqz0lMTKzw+JKv13PON954A29v79JbSEjIDb0fIUp1exgMJojdDEmHVKcROlCy2Oy3AxdITJfpVKIShbmwc4E2ltZgogouZeXzw97zAEysgxs7/K860UVh5syZpKenl97i4+NVRxJ65xMCbf6mjWXjB1EFtwR70z3UlyKLlcXbz6qOI2zdgRWQmwrejWUalKiSZTviKSiy0L6hN12ayDSoGi1w/fz8MJlMJCUllbs/KSmJoKCgqz4nKCiowuNLvl7POZ2dnfHy8ip3E+KmRUzTvu7/GrIvqc0idKHkKu5XMXHkFcoCRXENVmvZD87dJ4Ox7i4UElVTaLbw5TbtB+eJvepua7C/qtEC18nJiS5durBu3brS+ywWC+vWrSMyMvKqz4mMjCx3PMCaNWtKj2/atClBQUHljsnIyCA6Ovqa5xSiRjTuAUEdoCgPdi9SnUbowKC2gTT0cSU1u4Cf9p1XHUfYqrNbIOkgOLpB5wdVpxE6sOpgIokZefh5OJduEV7X1fgUhRkzZjB37lwWLVrEkSNHmDZtGtnZ2UycOBGAcePGMXPmzNLjn3rqKVatWsW7777L0aNHeeWVV9i5cyePP/44AAaDgenTp/Ovf/2Ln376iQMHDjBu3DiCg4MZOXJkTb8dIcoYDNCj+CrujnlgLlSbR9g8B5ORByNLWobFSsswcXUli1c7jgFX+ahZVK6kNdjYiMY4O8gVf6iFAnf06NG88847vPTSS4SHh7N3715WrVpVukgsLi6OCxfKmp/37NmTr776is8++4yOHTuyYsUKfvjhB9q1a1d6zHPPPccTTzzBlClT6NatG1lZWaxatQoXF5eafjtClNduFLj7Q0YCHPlZdRqhA2O6heDiaOTIhQyiz6SqjiNszeWzcKx4c6PusrhMVG5ffBq749JwNBkY26Ox6jg2o8b74Noi6YMrqtX6/web3oaQHjDpd9VphA7M/O4AS2PiGHpLEHMe7KI6jrAlq1+ErR9B2K0w7gfVaYQO/H35Xr7fk8CdnRry/uhw1XGqlc32wRWiTug2CYwOEL9d21JTiEqULDZbfTiR+NQctWGE7SjIht1faOOS6U9CVCA5I4+V+7X5/BOkNVg5UuAKcbM8g+CWO7Vx9KdqswhdaBnoSe/mflisSMswUWbfUshLB98waD5IdRqhA0ui4yg0W+nc2IeOIT6q49gUKXCFqA4lLcMOfgtZyWqzCF0oudqyNCaOnIIitWGEelZr2Q/I3R8Bo3x7FhXLLzKzJLqkNVhTxWlsj/wLEqI6NOoCjbqBuQB2zledRujAgNYBNKnvRkZeEd/tTlAdR6h2aj1cPA5OnhB+v+o0QgdW7rvAxawCgrxcGNru6vsA1GVS4ApRXSKmal93fA5FBWqzCJtnNBoYFxkKwMKt0jKsziu5ettpLLjI4mdRMavVysKtsQA8GNkER5OUc/9LfkeEqC5tR4BnA8hOhkPfq04jdOCero1wdzJxMjmLP09eVB1HqHLpFJz4HTBA9ymq0wgd2HX2MgcS0nFyMHJfd2kNdjVS4ApRXUyOWkcFgOjZ2pw6ISrg5eLIPV1DAFi4JVZtGKFOydXblkOgfjO1WYQuLCi+ejsyPBhfdye1YWyUFLhCVKcuE8HkrLULO7dDdRqhA+OKdzZbfyyZ2IvZitOIWpeXAXuXaOMI2dhBVO58Wi6rDiYCMKGnLC67FilwhahO7n7Q/h5tXLLdphAVCPP34NZW/litlM6pE3XI3iVQkAV+rbTNHYSoxJfbz2K2WIlo6kvbYJmvfS1S4ApR3Uquwhz+EdJldbyo3ITiFj8rdp0jM69QcRpRayyWsukJEY+AwaA2j7B5eYVmlsbEAdIarDJS4ApR3Rp0gCa9wGqGnZ+rTiN0oG8LP5r5u5OVX8SKXedUxxG15cRquHwGXLyh4xjVaYQO/LAngbScQhr6uDKobaDqODZNClwhakJJy7CdC6AwV20WYfMMBkPpVdxFW2OxWGSBYp0QXTyNqfN4cHJXm0XYvL+2Bhvfswkmo1zxr4gUuELUhFa3g3cI5KbCgRWq0wgduKtTQzxdHIi9lMOG47Ibnt1LPgqnN4DBCN0nq04jdGDb6UscTczE1dHE6K7SGqwyUuAKURNMDmXftKLnSMswUSl3ZwfGdNNahi2QlmH2L3qO9rX1MPCRYkVUruT/hVFdGuLt5qg2jA5IgStETek8DhzdIOkgxP6pOo3QgXGRoRgNsPnERU4kZaqOI2pKTirsW6aNS6YzCVGB+NQc1h5JAmBCz1C1YXRCClwhaoprPegwWhuXXK0RogIhvm5EtdEWjkjLMDu250soyoXA9tqCVCEqsWhrLFYr9GnhR/MAT9VxdEEKXCFqUsnVmWO/wuWzarMIXShp/fPd7gTSc6RlmN0xF0HMXG3cY6q0BhOVys4vYvnOeAAm9gpVG0ZHpMAVoiYFtIaw/mC1wI65qtMIHegR5kvrIE9yC80s3xmnOo6obsd+hfR4cKsP7e5WnUbowHe7z5GZV0RTP3f6twxQHUc3pMAVoqZFTNO+7v4CCmQrVlExg8FQepVm0dazFJktagOJ6lUyXanLRHB0UZtF2DyLxcqCktZgkU0wSmuwKpMCV4ia1mIw1GsKeemwb6nqNEIHRoQ3pJ6bIwlpuaw9Ii3D7MaF/XB2CxgdoNsk1WmEDmw+eZHTKdl4ODswqksj1XF0RQpcIWqa0Vi2fW/0p9IyTFTKxdHEfd211lELtpxRnEZUm5JteduOAK9gtVmELpT8+7+nayM8XaQ12PWQAleI2hA+Fpw84eJxOLVedRqhAw9GajsVRZ9J5fD5DNVxxM3KvggHvtHGJdOWhKjA6ZQsNhxLwWCA8ZGhquPojhS4QtQGFy/oNFYbl1zFEaICDbxdGdouCICFW+Uqru7tXADmfAjuDI26qk4jdGBR8dzbAa0CCPWTrZyvlxS4QtSW7lMAA5z4HS6dUp1G6MBDxYvNfth7nktZ+WrDiBtnLoQd87Rxj2nSGkxUKiOvkBW7zgFlrQPF9ZECV4jaUr+ZtuAM5CquqJLOjevRvqE3BUUWlu2IVx1H3KjDP0JWIngEQtuRqtMIHfhm5zmyC8y0CPCgV/P6quPokhS4QtSmksVme5dAnsyrFBX7a8uwL7edpVBahulTSWuwrpPAwUltFmHzzBZr6fSECb1CMcgV/xsiBa4QtanZAPBrBQVZWpErRCWGdWiAn4cziRl5rDqYqDqOuF7ndsG5HWBygq4TVacROvDH0WTiUnPwcnHgzk4NVcfRLSlwhahNBkP5lmEWs9o8wuY5O5gYGyEtw3Qrerb2td0o8JBdqETlFhQvKr2ve2PcnBwUp9EvKXCFqG0dx4CLN1w+AyfWqE4jdGBsj8Y4mgzsjktjX3ya6jiiqjIuwKHvtXHJD7ZCVOBYYiZbTl7CaNBaBYobJwWuELXNyR06j9PGJVd3hKhAgKcLf+ugbQywsHhuntCBnfPBUgQhPSC4k+o0QgdK/n0PbhtEo3puasPonBS4QqjQbTIYjHB6AyQfVZ1G6EDJYrOV+8+TnJGnNoyoXFG+VuAC9JiqNovQhbScAr7fU9IaLFRtGDsgBa4QKtRrAq1u18YlK6yFqECHRj50aVKPQrOVJdFxquOIyhz8FnIugldDaH2H6jRCB5btiCev0EKbBl50b+qrOo7uSYErhCo9irfr3LcMclLVZhG6MKFnKABLos+SXyQLFG2W1Qrbi6cfdXsYTLJQSFSsyGzhi+LpCROlNVi1kAJXCFWa9ILAdlCUC3u+VJ1G6MDQdkEEeblwMauAlfsuqI4jriVuOyTuBwcX6DJBdRqhA6sPJ3E+PQ9fdyeGdwxWHccuSIErhCoGA0QUz82LmQvmIrV5hM1zNBlLV1Yv3BqL1WpVnEhcVcni0Q73gpt81Cwqt3BLLAD3d2+Mi6NJbRg7IQWuECq1vwfc6kN6PBz7RXUaoQP3dW+Ms4ORAwnp7Dp7WXUc8b/S4uHISm0cIYvLROUOJqQTE5uKg9EgrcGqkRS4Qqjk+JePMKM/VRpF6IOvuxMjw7XdjRZIyzDbs2MeWM3QtC8E3qI6jdCBktZgt7dvQKCXi9owdkQKXCFU6/YwGB3g7Ba4sF91GqEDE4pbCK06mMj5tFy1YUSZghzYtVAby9VbUQUXs/L5ae95oOzftageUuAKoZpXMLQdoY3lKq6ogjYNvOgR5ovZYuXL7WdVxxElDnwNeWng0wRaDlWdRujAV9FxFJgtdAzxoXPjeqrj2BUpcIWwBSVXew58A9kX1WYRujChZ1MAlsbEkVcoLcOUs1phe3FP6+5TwCgLhUTFCoosLC7+AXVicQtAUX2kwBXCFjTqBsGdwZwPOxeoTiN0YFDbQBrVcyUtp5Af9iSojiPObISUI+DoDp0eUJ1G6MBvBy+QnJlPgKczt7dvoDqO3anRAjc1NZWxY8fi5eWFj48PkyZNIisrq8Ljn3jiCVq1aoWrqyuNGzfmySefJD09vdxxBoPhituyZctq8q0IUbP+2jJsxzwwF6rNI2yeyWhgfGQoIC3DbELJ9KLw+8HVR2kUoQ8LiluDPdCjCU4Ocr2xutXo7+jYsWM5dOgQa9asYeXKlWzatIkpU6Zc8/jz589z/vx53nnnHQ4ePMjChQtZtWoVkyZNuuLYBQsWcOHChdLbyJEja/CdCFELbrkTPAIhKxEO/6g6jdCBe7uG4Opo4mhiJttOX1Idp+5KPQ3HftPGEY+ozSJ0YU/cZfbGp+FkMnJf98aq49ilGitwjxw5wqpVq5g3bx4RERH07t2bjz76iGXLlnH+/PmrPqddu3Z8++233HHHHTRr1owBAwbw//7f/+Pnn3+mqKh8E3wfHx+CgoJKby4u0lpD6JyDE3Qt/mGuZJtPISrg7ebIqC7FLcOKrwYJBWLmAlZoHgV+LVSnETpQ8u/1jo7B+Hs6qw1jp2qswN22bRs+Pj507dq19L6oqCiMRiPR0dFVPk96ejpeXl44OJTfy/uxxx7Dz8+P7t27M3/+/Ao/nsvPzycjI6PcTQib1HUimJwgYSec26k6jdCBCcWLU9YeSSI+NUdtmLooPxP2LNbG0hpMVEFSRh6/HtC22p4orcFqTI0VuImJiQQEBJS7z8HBAV9fXxITE6t0josXL/L6669fMa3htdde4+uvv2bNmjWMGjWKRx99lI8++uia53njjTfw9vYuvYWEhFz/GxKiNngEQLtR2jh6jtosQheaB3jSp4UfVissko0fat/epZCfAfWbQ7OBqtMIHVi8/SxFFivdQuvRrqG36jh267oL3BdeeOGqi7z+ejt69OhNB8vIyGDYsGG0bduWV155pdxj//znP+nVqxedOnXi+eef57nnnuM///nPNc81c+ZM0tPTS2/x8fE3nU+IGlMyh+/Q95BxQW0WoQsP9dJahi3fGU9WflElR4tqY7FATPHisoipYJSFQqJieYVmvoqOA2Bi8b9bUTMcKj+kvKeffpoJEyZUeExYWBhBQUEkJyeXu7+oqIjU1FSCgoIqfH5mZiZDhw7F09OT77//HkdHxwqPj4iI4PXXXyc/Px9n5yvnsjg7O1/1fiFsUnAnaBwJcdsgejYMek11ImHj+rX0J8zPndMXs1kWE8fDfcJUR6objv0Kl06Cszd0HKM6jdCBb3ef41J2AQ19XBncNlB1HLt23QWuv78//v7+lR4XGRlJWloau3btokuXLgCsX78ei8VCRETENZ+XkZHBkCFDcHZ25qeffqrS4rG9e/dSr149KWKF/ej1lFbg7lwAfZ4GF/kYS1yb0Whgct8wZn53gM//PMO4yFBpO1TTrFbY8oE27jYJnD2VxhG2z2yxMnfTaQAm9W6Kg0n+jdakGvvdbdOmDUOHDmXy5MnExMSwZcsWHn/8ccaMGUNwcDAACQkJtG7dmpiYGEArbgcPHkx2djaff/45GRkZJCYmkpiYiNms7dTz888/M2/ePA4ePMjJkyeZPXs2//73v3niiSdq6q0IUftaDAH/1trcPtn4QVTBnZ0a4u/pzIX0PH7ed/VONaIaxW2DczvA5CyLy0SV/H4okdhLOfi4OTKmu6wFqmk1+uPDkiVLaN26NQMHDuT222+nd+/efPbZZ6WPFxYWcuzYMXJytJW/u3fvJjo6mgMHDtC8eXMaNGhQeiuZN+vo6MisWbOIjIwkPDycTz/9lPfee4+XX365Jt+KELXLaISeT2rj7bOhKF9tHmHzXBxNpSuyP910CotFNn6oUX9+oH0Nvw885aNmUTGr1cqcjacAGNejCW5O1/0BurhOBmsd3P4mIyMDb2/v0hZkQtikogL4MBwyEuCOD6HLeNWJhI1Lzy2k15vrycov4vPxXRnYRgqvGpF0GGZHAgZ4YhfUb6Y6kbBxW09e5P550bg4Gtny/ADqe8iUyqq4mXpNJoAIYascnKDHo9p464faim0hKuDt6sjYCG1XpJKrRaIGbP1Q+9p2uBS3okpmF/97vLdriBS3tUQKXCFsWZfx2gKzSyfh2C+q0wgdeKh3UxxNBnbEXmbX2VTVcexPWjwc+EYb93pKbRahC4fOp7P5xEWMBpgsHU5qjRS4QtgyZ0/o9rA2/vMDbeW2EBUI9HLhzk7a9r1zNp5WnMYObf8ELEUQ2gcadlGdRujAp8X/Dod1CCbE101xmrpDClwhbF3EVG2ldsJOOLtFdRqhA1P6NsNggDWHkziZnKk6jv3ISYVdi7Rx7+lKowh9iE/NYeV+ravJI33l6m1tkgJXCFvnEQCdxmrjLf9Vm0XoQvMADwYVLzD7bJNcxa02Oz6HwmwIbC/b8ooqmbf5NBYr9GnhJ9vy1jIpcIXQg8jHwWCEE6sh6ZDqNEIHHumnLX76fk8Ciel5itPYgcJciJ6jjXs9BQaD2jzC5l3Kymf5Tq3F6dR+shixtkmBK4Qe1G8GbYZrY7mKK6qgS5N6dA/1pdBsZf6WM6rj6N+exZBzEXwawy13qk4jdGDRtrPkFVpo39Cbns3qq45T50iBK4RelKzYPrAC0uLUZhG6MLW/Nufvq+g40nMLFafRMXMRbPtYG0c+ASZp0i8qllNQxBfbYgHt6q1BrvjXOilwhdCLhp2haV+wmmHbJ6rTCB24tVUArQI9ycovYkn0WdVx9OvIj3A5Flx9y+bDC1GB5TviScsppEl9N4a2C1Idp06SAlcIPek1Xfu6e5G2oluIChgMBqYUr9ye/2cseYVmxYl0yGot25Y34hFwclcaR9i+QrOFeZu1aUGT+4RhMsrVWxWkwBVCT5oNgKD2UJgDO+apTiN0YHh4MMHeLlzMyue73Qmq4+jP6Q2QuB8c3aD7FNVphA6s3H+ehLRc/DycuLtLI9Vx6iwpcIXQE4Oh7Cpu9BwoyFEaR9g+R5ORScW7J3226RRmi2wWcl22fKB97fQguPkqjSJsn9VqLd3YYWKvprg4mhQnqrukwBVCb9qOBJ8mkHMJ9i5RnUbowJhuIXi7OhJ7KYfVhxJVx9GP83u0K7gGE0Q+pjqN0IENx1M4mpiJu5OJByKaqI5Tp0mBK4TemByg5xPaeOuH2gpvISrg7uzAuEjtm+2cjaewypbPVVPSkq/dKKgnxYqo3JwNpwC4r3tjvN0cFaep26TAFUKPwseCW32tXdjhH1SnETowvmcozg5G9p1LZ9vpS6rj2L7U03D4R21c0qJPiArsibtM9JlUHE0GJvVpqjpOnScFrhB65OQG3R/Rxls+0FZ6C1EBPw9n7u0aAlA6R1BUYOvHYLVA8ygIaqc6jdCBkn9XI8Ib0sDbVXEaIQWuEHrVfbK2sjvxAJxarzqN0IHJfcIwGmDj8RQOn89QHcd2ZaWUzW8vWdQpRAVOpWTx+2Ftfvsjxa35hFpS4AqhV26+0Hm8Npbte0UVNK7vxu3tGwDw6aZTitPYsJhPoSgPGnaB0N6q0wgdmLvpNFYrRLUJoEWgp+o4AilwhdC3yEe1Fd5nNmorvoWoxNR+zQBYuf8C8anSZu4K+VkQM1cb93pKa80nRAWSM/JKe0yX/PsS6kmBK4Se+TSG9ndr45LdloSoQLuG3vRp4YfZYuXzP8+ojmN7di+CvDTwbQat/6Y6jdCB+VtiKTBb6NqkHl1DpVeyrZACVwi9K1nhfeQnuCQfO4vKPdJXu8q0bEccqdkFitPYEHMhbJuljXs9CUZp0i8qlpFXyJLtZwF4RK7e2hQpcIXQu8BboMVgbcX3to9VpxE60Kt5fdo19CKv0MKirbGq49iOAysgIwE8AqHDGNVphA58FR1HZn4RLQI8GNg6QHUc8RdS4AphD0qu4u5ZAlnJarMIm2cwGErnCn6xLZacAtksBIulbLFmxFRwdFGbR9i8/CIz84un+UzpG4bRKPO1bYkUuELYgya9oGFXMOdD9BzVaYQO3NauAY193bicU8jXO+JVx1HvxGpIOQJOntD1IdVphA78sCeB5Mx8grxcGBHeUHUc8T+kwBXCHhgM0Hu6Nt4xD/IzlcYRts9kNDC5uF/n3M1nKDRbFCdSrOTqbdeJ4OqjNIqwfRaLlU83aRs7TOrdFCcHKadsjfyJCGEvWt0O9ZtDXjrsWqQ6jdCBe7o0ws/DiYS0XH7Zf0F1HHXiYyBuKxgdocc01WmEDqw+nMTplGy8XBy4L6Kx6jjiKqTAFcJeGE3Q80ltvG0WFMnqeFExF0cTE3qGAjBn4ymsdXXL55IWex1Hg1ew0ijC9lmtVuZs1DrWPBjZBA9nB8WJxNVIgSuEPek4BjyCIPM8HFyhOo3QgQd6NMHNycTRxEw2Hk9RHaf2pRyHY78ABuj5lOo0QgdizqSyNz4NJwcjE3o2VR1HXIMUuELYEwfnso9Yt/xXWxkuRAV83Jy4r7v2EWvJVak6ZWvx3NvWw8C/pdosQhdK/p3c3aUR/p7OitOIa5ECVwh703UiOHtBylE48bvqNEIHJvVuioPRwPbT2pWpOiPjPOxbro17ydVbUbmjiRn8cSwFowGm9AlTHUdUQApcIeyNi7dW5IJs3yuqJNjHtbTN0ad16Sru9k/AUgiNe0JId9VphA58tlHrnHBbuwaE+rkrTiMqIgWuEPYoYhqYnCB+O8RtV51G6MAj/bSrUasOJXI6JUtxmlqQmwY7F2rjkhZ7QlQgIS2Xn/adB8r+vQjbJQWuEPbIqwF0GK2NS/p7ClGBloGeDGwdgNUKczefVh2n5u2cDwWZ4N8Gmg9SnUbowLzNpymyWOnZrD4dGvmojiMqIQWuEPaq11OAAY79CslHVacROjC1v7Z977e7EkjOzFOcpgYV5sH22dq411NglG+FomKXswtYFqPt+FeyzbWwbfKvWgh75ddCWxkOsPUjtVmELnQL9aVLk3oUmC0s2BKrOk7N2b8MspPBqxG0v1t1GqEDX24/S26hmbYNvOjTwk91HFEFUuAKYc96Tde+7l8O6QlKowh9eKR4+97F28+SmVeoOE0NsJhhy4faOPJRMDmqzSNsXm6BmYVbYwFt7q3BYFAbSFSJFLhC2LOQbtCkl7ZSfPsnqtMIHYhqE0jzAA8y84r4KjpOdZzqd3QlpJ4CFx/oPF51GqED3+yKJzW7gBBfV4a1b6A6jqgiKXCFsHclV3F3LdRWjgtRAaPRwJTiq7jzt5whv8isOFE1slrLWud1nwzOHkrjCNtXZLaULrqc3CcMB5OUTXohf1JC2LsWgyCgLRRkwc7PVacROjAiPJhAL2eSMvL5cc951XGqT+yfcH43OLhA90dUpxE68OvBROJTc/F1d+KeLiGq44jrIAWuEPbOYCjbpWn7HG0FuRAVcHYwMal3UwDmbDqFxWJVnKiabPlA+xo+Fjz8lUYRts9qtTJng7bxyfjIUFydTIoTieshBa4QdUG7UeAdoq0c3/eV6jRCB+7r3hhPFwdOp2Sz9kiS6jg3L/EAnFwLBiP0fEJ1GqEDf568yOELGbg6mhgX2UR1HHGdpMAVoi4wOULkY9p460faSnIhKuDp4siDPbRv6nM2nsJq1flV3JLOCW1Hgm9TpVGEPswp3rZ6TPcQ6rk7KU4jrleNFripqamMHTsWLy8vfHx8mDRpEllZFW8B2b9/fwwGQ7nb1KlTyx0TFxfHsGHDcHNzIyAggGeffZaioqKafCtC6F+nB7WV46mn4cjPqtMIHZjQKxQnByO749LYEXtZdZwbd/ksHPxWG5dM1xGiAgfOpbPl5CVMRkPpdB2hLzVa4I4dO5ZDhw6xZs0aVq5cyaZNm5gyZUqlz5s8eTIXLlwovb399tulj5nNZoYNG0ZBQQFbt25l0aJFLFy4kJdeeqkm34oQ+ufsAd2L//1t+UBbUS5EBQI8XRjVuRFQdjVLl7bNAqsZwvpDcLjqNEIHSv6+D+8YTKN6borTiBtRYwXukSNHWLVqFfPmzSMiIoLevXvz0UcfsWzZMs6fr3hVrpubG0FBQaU3Ly+v0sdWr17N4cOHWbx4MeHh4dx22228/vrrzJo1i4KCgpp6O0LYh4hHwMEVzu+B2M2q0wgdmNI3DIMB1h9N5lhipuo41y/7Euz+QhuXtMwTogKxF7P57eAFQNvYQehTjRW427Ztw8fHh65du5beFxUVhdFoJDo6usLnLlmyBD8/P9q1a8fMmTPJyckpd9727dsTGBhYet+QIUPIyMjg0KFDVz1ffn4+GRkZ5W5C1EnuftDpAW288W25iisq1dTPndvaBQHw8R8nFae5Ads/gaJcaNBRu4IrRCVmbziFxQq3tvKndZBX5U8QNqnGCtzExEQCAgLK3efg4ICvry+JiYnXfN7999/P4sWL+eOPP5g5cyZffvklDzzwQLnz/rW4BUp/fa3zvvHGG3h7e5feQkKkl52ow3o9CSYn7QruqXWq0wgdeOzW5gD8vO88BxPSFae5DpmJ2vQEgL7Pai3zhKjAiaRMvtkVD8DjA1ooTiNuxnUXuC+88MIVi8D+93b06NEbDjRlyhSGDBlC+/btGTt2LF988QXff/89p07d+PyvmTNnkp6eXnqLj4+/4XMJoXs+jcvm4q55BSwWpXGE7bsl2JuR4cEAvLXqxv9/r3Ub3tSu3jbqDq3/pjqN0IG3fz+GxQpDbgmkS5N6quOIm+BwvU94+umnmTBhQoXHhIWFERQURHJycrn7i4qKSE1NJSgoqMqvFxERAcDJkydp1qwZQUFBxMTElDsmKUnr0Xit8zo7O+Ps7Fzl1xTC7vV5WpuXmHQADq6ADveqTiRs3NODW/HLgQtsPnGRzSdS6NPCxjdKuHiibO7toFfl6q2o1M7YVNYcTsJogGeHtFYdR9yk676C6+/vT+vWrSu8OTk5ERkZSVpaGrt27Sp97vr167FYLKVFa1Xs3bsXgAYNGgAQGRnJgQMHyhXPa9aswcvLi7Zt217v2xGibnLzhd7TtfH616EoX2kcYftCfN14oLgv7lurjtr+7mbrXtU6J7S8DZr0VJ1G2Dir1cqbv2mfTozuFkLzAA/FicTNqrE5uG3atGHo0KFMnjyZmJgYtmzZwuOPP86YMWMIDtY+6kpISKB169alV2RPnTrF66+/zq5du4iNjeWnn35i3Lhx9O3blw4dOgAwePBg2rZty4MPPsi+ffv4/fffefHFF3nsscfkKq0Q1yNiGng2gLQ42PG56jRCBx6/tTkezg4cTMjg5/0Vd8NRKn6H1uvZYISol1WnETqw5nASO89exsXRyPSolqrjiGpQo31wlyxZQuvWrRk4cCC33347vXv35rPPPit9vLCwkGPHjpV2SXBycmLt2rUMHjyY1q1b8/TTTzNq1Ch+/rmsKb3JZGLlypWYTCYiIyN54IEHGDduHK+99lpNvhUh7I+TG/SfqY03/QfydLR4SChR38OZqcVtk95ZfYyCIhucv221wprivujh90NAG7V5hM0rMlt4+/djAEzq3ZRALxfFiUR1MFh1v//i9cvIyMDb25v09PRyPXaFqHPMRTA7Ei4ehz7PwMB/qk4kbFxOQRH9/rOBlMx8XrmjLRN62dguT8d/h6/uBQcXeGIXeDdSnUjYuOU74nj+2wP4uDmy6blb8XJxVB1JFLuZeq1Gr+AKIWycyQEGFn+Eu20WZFxQm0fYPDcnB6ZHae2TPlx/ksy8QsWJ/sJihrWvaOOIR6S4FZXKLTDz/poTgDYFR4pb+yEFrhB1XethEBKhtVPa+KbqNEIH7u0aQpifO6nZBczddFp1nDL7lkHyYXDxgd5/V51G6MCCrWdIzMijoY8rD0Y2UR1HVCMpcIWo6wwGiHpVG+/+ElKOq80jbJ6jycizQ1oBMHfzGZIz8xQnAgpz4Y//p437PA2u0sNUVOxydgGzN2g99p8e3BJnB5PiRKI6SYErhIAmkdDqdq2t0npZsCkqN7RdEOEhPuQWmvlw3QnVcSBmLmQkgFejso1MhKjAJxtOkplXRJsGXowMb6g6jqhmUuAKITQDX9LaKh35GeJjKj9e1GkGg4EXbtOa4S+Nied0Spa6MLmXYfO72vjW/wNHWQUvKnbucg6Ltp4F4PmhrTAaZSMQeyMFrhBCE9BGa6sEsOZlrd2SEBXoEVafAa0DMFusvLta4dSWP9+HvDTwbwMdx6jLIXTj/TUnKDBbiAyrT7+WNr4rn7ghUuAKIcr0/z+tvVLcVq3dkhCVeG5oKwwG+OXABfbGp9V+gPRzsH2ONo56BYwyj1JU7MiFDL7bcw6AF25rjUG2cbZLUuAKIcp4N4SIqdp47Sta2yUhKtA6yIu7OmntuN749Qi13lp9wxtgzocmvaDlkNp9baFLb686itUKw9o3oGOIj+o4ooZIgSuEKK/3dK3NUsoRre2SEJWYMbglTg5Gos+ksuF4Su29cPIR2PuVNo56VesIIkQFtp26xB/HUnAwGnimuBOIsE9S4AohynOtp7VZAq3tUmGu2jzC5jX0cWVCz1AA3vrtKGZLLV3FXfsqWC3Q5g4I6VY7ryl0y2q18uaqowDc170xTf3cFScSNUkKXCHElbpP0dotZSRAzGeq0wgdeLR/MzxdHDiamMkPexJq/gXPboXjv4HBVLYbnxAV+O1gIvvi03BzMvHEwOaq44gaJgWuEOJKji4w4B/aePO7WhsmISrg4+bEo/21ouG9NcfJK6zB+dtWq9bpA6DzOPBrUXOvJexCodnCf34/BsDDfcII8JRWcvZOClwhxNV1GA0BbSEvXWvDJEQlJvYKJcjLhYS0XBZvP1tzL3T0FzgXA45u0P+FmnsdYTeW74jnzMVs6rs7MaVvmOo4ohZIgSuEuDqjSWu7BFobpvRzSuMI2+fiaGLGoJYAfPzHSdJzC6v/RcxFsK54a+kej4JnUPW/hrAr2flFfLBW223vyYEt8HB2UJxI1AYpcIUQ19ZisNZ+yZwPf7yhOo3Qgbs6N6RFgAdpOYV8uvFU9b/A3sVw8Ti4+kKvJ6v//MLuzP/zDBez8mns68Z93RurjiNqiRS4QohrMxhg0GvaeN9XkHRYbR5h8xxMRp4bqm3hO3/LGRLT86rv5AU5ZT9o9XsOXLyr79zCLl3KyufTTacBeGZIK5wcpOypK+RPWghRsUZdoc1wrR3TutdUpxE6ENUmgK5N6pFXaOGDtdW4hW/0bMhKBJ/G0PWh6juvsFsfrT9JVn4R7Rp68bf2DVTHEbVIClwhROUGvqS1Yzr+m9aeSYgKGAwGZt6uXcX9emc8J5Mzb/6k2Zfgzw+08YB/goPzzZ9T2LW4SzksidYWO74wtA1Go2wEUpdIgSuEqJxfC60dE8Cal7Q2TUJUoEsTXwa3DcRihbdXHbv5E25+F/IzILA9tLv75s8n7N67a45RaLbSp4UfvVv4qY4japkUuEKIqun/gtaW6dwOOLpSdRqhA88NbYXRAKsPJ7HrbOqNn+jyWdgxVxsPegWM8q1LVOxgQjo/7j0PwPPFc8JF3SL/SwghqsYzCCIf08ZrX9XaNQlRgeYBntzbNQSAN349ivVGr/z/8W8wF0DTftBsYDUmFPbqreIteUeEB9OuoSxGrIukwBVCVF3PJ7X2TJdOwJ4vVacROjA9qiXODkZ2nr3M2iPJ13+CxAOwf7k2jnpF6+whRAU2n0hh84mLOJoMPD2oleo4QhEpcIUQVefipbVnAtjwJhRkq80jbF6QtwsP9W4KwNurjlJktlzfCda+AljhlrugYedqzyfsi8ViLb16OzaiCY3ruylOJFSRAlcIcX26PqS1acpKhO2zVacROjC1XzN83Bw5kZzFd7sTqv7E0xvh5FowOsCAF2suoLAbKw9c4GBCBh7ODjwxoLnqOEIhKXCFENfHwRkGvKSNt/xXa98kRAW8XR15/Fat2HhvzXFyC8yVP8lqhbUva+OuD0H9ZjWYUNiDgiIL7/yudex4pG8Y9T2klVxdJgWuEOL6tRsFQe21tk2b31GdRujAAz2a0NDHlcSMPBZuja38CYe+h/N7wMkD+j5X4/mE/n0VfZa41Bz8PJyZ1Kep6jhCMSlwhRDXz2iEqFe1ccxcuByrNI6wfS6OJmYMagnAJxtOkpZTcO2DzYVlu+b1fAI8/GshodCzzLxCPlx/EoDpUS1wc3JQnEioJgWuEOLGNBugtW2yFGptnISoxMhODWkd5ElmXhGfbDh17QN3LYTLZ8Ddv6w1nRAVmLv5DKnZBYT5uTO6W4jqOMIGSIErhLgxBgMMKr6Ku/9ruLBfbR5h80xGA8/fpjXdX7g1loS03CsPys+CjW9p437Pg7NnLSYUepScmce8zacBeHZIKxxNUtoIKXCFEDcjuJM2HxdrcTsnISrWv6U/PcJ8KSiy8N7q41cesO1jyE4B3zDoMqHW8wn9+XDdCXIKzHQM8WFouyDVcYSNkAJXCHFzBryotXE6tQ5Ob1CdRtg4g8HAC7e1AeC7Pec4mphR9mBWMmz9SBsP+CeYHBUkFHpyOiWLpTHxAMy8rTUG2QhEFJMCVwhxc3zDtDZOAGteBst1NvIXdU54iA+3tw/CaoW3Vx0re2DTf6AgS/tkoO1IZfmEfry7+jhmi5VbW/nTI6y+6jjChkiBK4S4eX2f09o5XdgLh39QnUbowDODW2EyGlh/NJntpy9B6mnYOV97MOpVrVOHEBXYG5/GLwcuYDDAc0Nbq44jbIz8DyKEuHke/tDzSW287jWtzZMQFQjz9+C+7tpq9zd/O4p1/b/AUgTNoyCsn+J0wtZZrVbe/O0IAHd1akSbBl6KEwlbIwWuEKJ6RD6mtXW6fEZr8yREJZ4c2AJXRxPmc7sxHPwWMEDUK6pjCR3YcDyF7adTcXIwMmNwS9VxhA2SAlcIUT2cPbS2TgAb3oS8dLV5hM0L8HRhcu9QZjp8BYC53T3aDnlCVKDQbOGt344CMD5S2yFPiP8lBa4Qovp0mQC+zSDnIvwq26uKyj3quZGepsPkWx351DRGdRyhAx+vP8nRxEy8XBx4tH9z1XGEjZICVwhRfUyOMPITMBhh/zI4+K3qRMKWpRzHZf3LALxZNIb/xOSx9dRFxaGELdt19jIf/6FtyfuvO9tTz91JcSJhq6TAFUJUr8Y9oM/T2njl3yE9QW0eYZuKCuC7h6EoF8JuJb/zw1it8PTX+0jPkUWK4kpZ+UXM+HovZouVkeHBDO8YrDqSsGFS4Aohql+/5yG4szYP94ep0htXXGnDG3BhH7jWg5GzefGOdjT1c+dCeh4v/ngQq9WqOqGwMa/9fIizl3Jo6OPKayPbqY4jbJwUuEKI6mdyhLvmgqMbnNkE22epTiRsydmt8Of72viOD8GrAW5ODrw/OhyT0cDP+87z497zajMKm7Lq4AW+3nkOgwHeu7cjXi6yy52oWI0WuKmpqYwdOxYvLy98fHyYNGkSWVlZ1zw+NjYWg8Fw1ds333xTetzVHl+2bFlNvhUhxPXyaw5D/q2N170GiQfU5hG2IS8dvnsEsEL4A9B2eOlD4SE+PDWwBQD//OEg5y7nKAopbElSRh4vfKf9/zG1XzMiZMcyUQU1WuCOHTuWQ4cOsWbNGlauXMmmTZuYMmXKNY8PCQnhwoUL5W6vvvoqHh4e3HbbbeWOXbBgQbnjRo4cWZNvRQhxI7pMgJa3gbkAvp0MhXmqEwnVfn0O0uOgXijc9uYVDz/avxmdG/uQmV/EjK/3YbbIVIW6zGKx8sw3+0jLKeSWYC/+HiU9b0XV1FiBe+TIEVatWsW8efOIiIigd+/efPTRRyxbtozz56/+0ZPJZCIoKKjc7fvvv+fee+/Fw8Oj3LE+Pj7ljnNxcamptyKEuFEGAwz/SNsAIuUIrHtVdSKh0sFvte4aBiPc+Rk4e15xiIPJyPujw3F3MhFzJpXPNp1WEFTYii+2xbL5xEWcHYz8d0w4Tg4ys1JUTY39Tdm2bRs+Pj507dq19L6oqCiMRiPR0dFVOseuXbvYu3cvkyZNuuKxxx57DD8/P7p37878+fMrXJCQn59PRkZGuZsQopZ4+MOI4jm42z+BU+vV5hFqpCdoXTUA+jwDjSOueWiT+u68PPwWAN5bc4yDCbJpSF10PCmTN4o3dPjHsDY0D7jyByIhrqXGCtzExEQCAgLK3efg4ICvry+JiYlVOsfnn39OmzZt6NmzZ7n7X3vtNb7++mvWrFnDqFGjePTRR/noo4+ueZ433ngDb2/v0ltISMj1vyEhxI1rOQS6Fv+g+sOjkJOqNo+oXRaL1k0jL13rrtGv8k1A7unSiKG3BFFotvLUsj3kFphrIaiwFflFZp5atpf8Igv9WvrzYI8mqiMJnbnuAveFF1645kKwktvRo0dvOlhubi5fffXVVa/e/vOf/6RXr1506tSJ559/nueee47//Oc/1zzXzJkzSU9PL73Fx8ffdD4hxHUa/C+o3wIyL8DPT4G0gao7tn+iddNwdNO6a5gqXwFvMBh44672BHg6cyolmzd+O1ILQYWteG/1cY5cyMDX3Yn/3NMBg8GgOpLQmesucJ9++mmOHDlS4S0sLIygoCCSk5PLPbeoqIjU1FSCgoIqfZ0VK1aQk5PDuHHjKj02IiKCc+fOkZ+ff9XHnZ2d8fLyKncTQtQyJzcYNReMDnDk/7d353FV1nn/x1+HXWWT7QCu4IYrmiai2SaujeVSLk2NWlquv9D2mZKxuzLrrttqHEvTdO5bTZu0slJzSxv3NFxSUclCE3FBQEBkOdfvj2vEmExBwQsO7+fjcR6Pc851znW96etFHy+/1+f7OSQutDqR3Awn916ee91rqtldo5Rq1/Lgvx+IBuAfW35m/cFT1/iGOIPNyWeY9a059/q1Aa0J8dE9NlJ2bmX9QnBwMMHBwdf8XGxsLBkZGezcuZP27dsDsG7dOhwOBzExvz/36pI5c+Zw7733lupYiYmJ1K5dG09Pz2v/ACJinfB2cNefzbZhK56BBp0hIMLqVFJRCvLM7hlF+dCsD9wyrMy7uL1pMCO6NOTDTT/x9D/3sCq+K4He+l3vrDJzC3hyyW4MA4Z2rEePlte+ICZyJRU2B7d58+b06tWLUaNGsX37djZt2sT48eMZMmQI4eHm8nq//PILUVFRbN++vcR3jxw5wsaNGxk5cuRv9rt8+XI++OAD9u3bx5EjR5g5cyavvvoqEyZMqKgfRUTKU5d4qB8L+dmw7HEoKrQ6kVSUtVPM7hm1QsxuGtf5z8zP9oqiqd2bM9kXeW7pXq1y5sRe/GwfqZl5NAysyQv3tLA6jlRhFdpvY8GCBURFRdGtWzf69OnDbbfdxqxZs4q3FxQUkJSURG5uyWbec+fOpW7duvTo0eM3+3R3d2fGjBnExsbStm1b3n//fd566y0SEhIq8kcRkfLi4gr93wcPHzi27fKKVuJckteZc2/B7KJRK+i6d+Xl7sr0we3wcHVh9f40Fu/QfRTO6LPEX/h89wlcXWxmqzjPMv8js0gxm1EN/yqclZWFn58fmZmZmo8rYpXdH5lXcG2u8OhqqNve6kRSXnLTYWZn84bCW0fCPW+Wy25nbUzm1a8OUsPdla+e6EpEUK1y2a9Y7/i5XHpP/5bzFwuZGNeUJ+KaWB1JKoEbqdfUMVlErNFmMLTsD0YRLB0F+TlWJ5LyYBhml4zzqWbXjO7/VW67HnlbJLGRgVwoKCJ+cSIFRY5y27dYp8hhMGnJbs5fLKRdfX/G3dXI6kjiBFTgiog1bDa45y3wCYf0ZFj1F6sTSXnYvcjskuHiZnbN8KhZbrt2cbHx5qBofL3c2H0sg3fXHSm3fYt1Zm38ke1H06nl4cr0wW1xc1VpIjdOf4pExDo1A6D/e+bznR9C0gpr88iNST8KXz1tPr/rL2bXjHIW7l+DV/q3BuBv6w6z8+dz5X4MuXn2/ZLJW6uTAEi4tyUNAjXtRMqHClwRsVbkHRA73nz+2XjIVq/TKqmo0JxTnZ8N9TtDlycq7FB9o8Pp364ODgMmLk4k+6I6cVRFF/KLeOKj7ykoMujVMpQH2te1OpI4ERW4ImK9bpMhpCXknoHPxmmVs6roX/9jdsXw9DWvyru4VujhptzXkjr+NUhJz+Wl5T9U6LGkYry24gDJp3MI8fHk1QGttVqZlCsVuCJiPTdPc76mqycc/hq+m2N1IimL4zvhm6nm8z7/DbUbVPghfb3ceWtQNDYbLPnuOCv3pVb4MaX8rE86xfwtPwPwxgPRBNTysDiROBsVuCJSOdhbQtxfzeerXoDThyyNI6WUn2N2wTCKoOUAaDPoph06JjKQ0XeYd9w/t3QvaVl5N+3Ycv3OZl/k6Y/3ADC8c0PuaHrtFUtFykoFrohUHjGjIfJOKLwAS0dCYb7VieRaVv3F7ILhWwf+8NZ1r1Z2vSbGNaVluC8ZuQU89fFuHA5Nb6nMDMPguaV7OZN9kSYh3jzXO8rqSOKkVOCKSOXh4gL9ZkKN2pC6Gza8ZnUiuZqkFWb3C2yXx+0m83Bz4e0hbfF0c+Hbw2eYv+Wnm55BSm/xjmOs3p+Gu6uN6UPa4uVesXO1pfpSgSsilYtvOPxhuvn827fg582WxpHfkX3K7HoB0Hm82Q3DIo1DfHjhnuYATF1xkENp5y3LIr/v6JkcpizfD8DTPZvRMtzP4kTizFTgikjl07IftP0jYMDSxyEv0+pE8muGYRa3uWfA3gruftHqRDzUqQF3Ngsmv9DBEx8lcrGwyOpI8isFRQ7iFydyoaCI2MhARt4WaXUkcXIqcEWkcur1Gvg3gMwU+OoZq9PIr303Bw6vMrteDJhtdsGwmM1m4/X72xBQy4MDqVm89bVuUqxM/rbuCLuPZeDj5cabg6JxcVFLMKlYKnBFpHLy8oUBs8DmAns+gn2fWJ1IwOxuseoF83n3KWBvYW2eXwnx8eK1AeYqZ7O+/ZHNyWcsTiQAO38+x7vrDgPwSv/WhPvXsDiRVAcqcEWk8qrfCbo+aT7/YiJk/mJtnuquMP/f3S0uQORd0PFxqxP9Ro+WoQztWA/DgCeX7CYzt8DqSNVa9sVCJi5OxGFAv7bh3BsdbnUkqSZU4IpI5XbHsxDezpyH++locDisTlR9bXjN7G5Ro7bZNcGlcv4v5IV7WtAwsCapmXn85dO9GFoZzzIvLf+BlPRc6vjXYMp9rayOI9VI5fztJCJyias7DPgA3GvC0Y2w9e9WJ6qeft5sdrUAs8uFb5ilca6mlqcb/zO4La4uNr7Yk8pniSesjlQtrdyXypLvjmOzwZuDovGr4W51JKlGVOCKSOUX1Bh6vmI+XzsFTu61Nk91k5dpdrPAMLtbtOxndaJrale/Nv/v7iYAvPjpPo6l51qcqHpJy8rj+aXmefr47Y3oFBlocSKpblTgikjV0H4ENO0NRfnwv/3hxPdWJ6oesk/B/HvNbha1G0LvaVYnKrVxdzXilvr+nL9YyNDZW/nxdLbVkaqFY+m5DJm1lXO5BbQM92VS96ZWR5JqSAWuiFQNNhvcNwNC20DOafjwHjiyxupUzu1sMszpDqmJUDMQBv0DPH2sTlVqbq4uvPvgLTQMrMnxcxcYOHMzu1LOWR3Lqe37JZP+f9/M0TM51PGvwYwHb8HDTaWG3Hz6UyciVUetQBj+JUTeCQU5sHAwJC6yOpVzOr7TLG7P/WReuX10NYRFW52qzOr41+CfYzoTXdePc7kFPDh7K2v2p1kdyyltPHSawe9v4Uz2RZqH+bJ0bGcaBtWyOpZUUypwRaRq8fKFBz+G1oPAUWh2Vvj2TXN1LSkfh76G+X+A3LMQ1tYsbgMbWZ3qugV5e7LosU7c1SyYvAIHj/3vd3y0PcXqWE5l2ffHeWTeDnLyi+jSOJAlj3fC7utldSypxlTgikjV4+YB/d+HLk+Yr9e+BF89DQ4tz3rDdv0vLBoCBbnQqJt5xdw7xOpUN6ymhxuz/tSBQR3q4jDguaV7mb7mkFqI3SDDMHhvQzITF++m0GFwb3Q4Hw7viI+XOiaItVTgikjV5OIC3V+CXtMAG+yYDR8Pg4I8q5NVTYYBG96Az8eDUQTRQ+HBxeDpbXWycuPu6sK0gW2YcHdjAKavOcyfl+2lsEi9la9HkcNgyvL9vLbiIACjukYwfXBbzbmVSkF/CkWkaus0Gu6fC64ecGC52WHhgm4kKhNHEXw5Cda/bL7u+qS5kIOr812Fs9lsPNmjGS/3a4WLDRZtP8bo/9vJhXxd/S+LvIIiJizaxbzNPwHwwj3N+cs9LXBxsVkbTOTfVOCKSNXXagA8tBQ8/SBlM8ztBRnHrE5VNeTnwuKH4bu5gA36/Dd0m2x2rXBiD3VqwMyH2uPp5sKaA6d48IOtpOfkWx2rSsi8UMCf5m7nq70n8XB14d2h7RjZNdLqWCIlqMAVEecQ0RUeWQE+4XD6IMzpAWk/WJ2qcstNh3/cB0lfgqun2Qas4yirU900PVuGsmBkDH413Pk+JYP7Z27WghDXkJp5gQfe28z2o+n4eLox75Fb6RsdbnUskd9QgSsizsPeEkauhuAoOH8C5vaGo99anapyykiBuT3h+Hbw8oM/fQot7rU61U3XoWEAn4yJpY5/DX48k8OAmZvZ90um1bEqpUNp5xnw980cSssmxMeTJaNj6dwoyOpYIlekAldEnItfXRixAurHwsVM+L8BsG+p1akql9Q98EF3OHMIfOvAI6ugQWerU1mmcYgPS8d2JirUh9PnLzJk1lb+dfiM1bEqle1H07l/5mZSM/NoHOLN0rGdaR7ma3Uskd+lAldEnE/NAHj4U2je11za95+PwNaZVqeqHH7cAB/2geyTENLC7HEb0tzqVJaz+3qxZHQssZGBZF8sZPiH2/n0+1+sjlUpfLU3lYfmbCMrr5D2DWrzz9Gx1K1d0+pYIlelAldEnJO7FzwwH24dBRiw8jn4+kVwVOOWUHv/Cf83EPLPQ4PbzCvdfnWsTlVp+Hq5F88pLXQYxC9O5P0NydW6V+78zT8xbuEu8gsd9GhhZ8HIGPxrelgdS+SaVOCKiPNycYU+b0C3BPP15ndg2eNQWA3vlt/8LnzyKDgKoEU/eOgTqOFvdapKx9PNlbcHt2XkbREATF1xkJe+2I/DUb2KXMMwmLbyIAmf/4BhwEOd6jPzofZ4ubtaHU2kVFTgiohzs9mg6yTo9x64uMHeJbDwAcjLsjrZzeFwwMo/w9cvmK9jxsD9H5pXuOWKXFxsvPCHFrxwjzl148NNPzFh0ffkFVSPXrkFRQ6eXLKbmd8kA/BUj6b8132tcFWPW6lCVOCKSPXQdigMXQzuteDHb2BeHzh/0upUFavwonnVdusM83X3l6DXVHMVOLmmkV0jeWdoO9xdbXy5N5Vhc7eTeaHA6lgVKvtiIY/M28HS73/B1cXGG/e3YfzdTbA5eV9kcT76LSci1UeTOBj+BdQKhpN7YU53OHPY6lQVIy/TnG/7w1LzyvWA2dDlCadfwKG83RsdzvwRHfH2dGPb0XQGvbeFk5nOuRy02UFiC98ePkMNd1c+GNaBBzrUszqWyHVRgSsi1UudW+DRryEg0uwFO6cHHNthdarylZVqdkr46Vvw8IY/fgxtBlmdqsrq3DiIJY/HEuLjSVLaeQb8fROH0s5bHatcHT2Tw4CZm9j3SxaBtTz46LFO3NUsxOpYItdNBa6IVD8BkfDI1xB+C1xIh/l9IWmF1anKx+kk88p02j6oFQIjvoJGd1udqsprEe7L0rGdiQyuxYnMPO6fuZkdP6VbHatcJB7LYODMzRxLv0D9gJp8MqYz0fX8rY4lckNU4IpI9eQdDMOWQ+PuUHgBPnoQds6zOtWNSdlqXpHOPAaBjc1V3cKirU7lNOrWrsknozvTvkFtsvIK+eMH21i5L9XqWDdk3cE0hs7aSnpOPm3q+vHJmM40DKpldSyRG6YCV0SqL09vGLoI2j4EhgOWPwGrJ0NmFWvwfyEDvvsQ/nEf5GVAnQ7mFeraDS0O5nxq1/JgwcgYurewk1/oYMyCXcxYf4TT5y9aHa1M0nPymb3xR0b9YycXCoq4vWkwi0Z1ItjH0+poIuXCZlTDDtZZWVn4+fmRmZmJr6+WGhSp9gwD1r8CG9+4/F5YNDTrA816Q2ibyndz1rmfIGklJH0FP28CR6H5ftNeZhswD600VZEKixxM/vwHFm5LAcw/Hu3q+RPXwk735nYah3hXus4DyaezWbM/jTUH0tj58zkutfYdeEtdXhvYGndXXfOSyuVG6jUVuCpwReSSvf+E7bPh2DbgV78afeuYhW6z3tCwK7hZcJXL4YAT35sFbdIKOPVDye1BzaDNA9BlIri63fx81ZBhGCzcnsLiHcfYczyzxLYGgTWJa24nrrmdWxvWxs2C4rGwyMGulAzWHEhjzf40fjyTU2J7izBfHuhQl+GdG1a6YlwEVOCWmQpcEbmq7NNw+GuzmExeBwW5l7d5eEPjbubV3SY9oGZAxeUouAA/bjBzHFoJ2WmXt9lcoH7ny4V3YKOKyyHXdDIzj7UHzUJyU/JZ8gsvLwntV8Odu5oFE9fCzh1Ng/Hxcq+wHNkXC/n20GlWH0hj/cFTnMu93LfX3dVGp8hAerSwc3dzO3X8a1RYDpHyoAK3jFTgikipFVyAo99C0pfmlIDsXy0OYXOB+rH/LjL7lE+RmX0KDq0yr9ImrzNvgLvEw+dXxXX3ii2u5brlXCzk28NnWL0/jXUH065YZMY1t9OteQh1a9/4VJLUzAusOXCKNfvT2JJ8lvyiksX13VEhxDW3c3vToAotrkXKW6UscF955RW+/PJLEhMT8fDwICMj45rfMQyDhIQEZs+eTUZGBl26dGHmzJk0adKk+DPp6elMmDCB5cuX4+LiwsCBA3n77bfx9vYudTYVuCJyXRwOSP3eLD6TVpituH4tqOnlYrfureDieu19GobZ2uvS1IPjOyg5PaLur6ZH3GbN9Ai5bkUOg10p51izP43VB9L48XTJaQLNw3zp3jyEuBZ2WoX74VKK5XANw+CHE1nm1IMDaez7peSy0w0Ca9K9uZ24FnY6NLBmeoRIeaiUBW5CQgL+/v4cP36cOXPmlKrAnTZtGlOnTmX+/PlERETw4osvsnfvXvbv34+Xl7lueu/evUlNTeX999+noKCAESNGcOutt7Jw4cJSZ1OBKyLl4tzP5tSBpK/gp39dvtELoGagecNXs94QeZfZseGSokJI2fLvQvkrOHe05H7D2v7qBrfWle8GN7luyaezWXsgjTX7T/Hdz+nFN3oB2H096dbcvEkttlEgXu6X/4J0sbCIrT+mF98klvqr1dRsNrilfm3imtvp3iKERsGV7wY3ketRKQvcS+bNm0d8fPw1C1zDMAgPD+fJJ5/kqaeeAiAzMxO73c68efMYMmQIBw4coEWLFuzYsYMOHToAsHLlSvr06cPx48cJDw8vVSYVuCJS7vIy4cgas2g9/LX5+hJXT4i8AyJuh9Q9/96e8avtHhBxh1nQNu0FfnVueny5+dJz8ll/8BRrDqSx8dBpcvKLirfV9HCla5Mgbm0YwK6Uc2xIKrm9hru5Pa6FnbujQgjy1pV9cT43Uq9Vmlttjx49ysmTJ4mLiyt+z8/Pj5iYGLZs2cKQIUPYsmUL/v7+xcUtQFxcHC4uLmzbto3+/ftfcd8XL17k4sXLPQqzsrKu+DkRkevm5QetBpqPogJz0YWkFebc3XM/mUXt4a8vf75GwOUrvI3uLnmFV6qFgFoeDGxfl4Ht617xCu2qH9JY9cPlGwtDfMwrvD1a/PYKr4iUVGkK3JMnzRs37HZ7ifftdnvxtpMnTxISUnJtbDc3NwICAoo/cyVTp05lypQp5ZxYROR3uLpDRFfz0fOVy3NsU7ZCcDNz+kG9jqWboyvVgqebK3c0DeaOpsG8dF/L4jm2u49l0KqOH3HN7bSuU7o5uiJSxgL3ueeeY9q0aVf9zIEDB4iKirqhUOXt+eefZ9KkScWvs7KyqFevnoWJRKTasNkgJMp8iJSCzWajVR0/WtXxszqKSJVVpgL3ySefZPjw4Vf9TGRk5HUFCQ0NBSAtLY2wsLDi99PS0mjbtm3xZ06dOlXie4WFhaSnpxd//0o8PT3x9NT8JBEREZHqoEwFbnBwMMHBwRUSJCIigtDQUNauXVtc0GZlZbFt2zbGjBkDQGxsLBkZGezcuZP27dsDsG7dOhwOBzExMRWSS0RERESqlgprjpeSkkJiYiIpKSkUFRWRmJhIYmIi2dnZxZ+Jiopi2bJlgPlPMvHx8bz88st8/vnn7N27lz/96U+Eh4fTr18/AJo3b06vXr0YNWoU27dvZ9OmTYwfP54hQ4aUuoOCiIiIiDi3CrvJbPLkycyfP7/4dbt27QBYv349d955JwBJSUlkZl5upfPMM8+Qk5PDY489RkZGBrfddhsrV64s7oELsGDBAsaPH0+3bt2KF3p45513KurHEBEREZEqRkv1qg+uiIiISKVzI/Wa1u8TEREREaeiAldEREREnIoKXBERERFxKipwRURERMSpqMAVEREREaeiAldEREREnIoKXBERERFxKipwRURERMSpqMAVEREREaeiAldEREREnIoKXBERERFxKipwRURERMSpqMAVEREREafiZnUAKxiGAUBWVpbFSURERETkSi7VaZfqtrKolgXu+fPnAahXr57FSURERETkas6fP4+fn1+ZvmMzrqcsruIcDgcnTpzAx8cHm81W4cfLysqiXr16HDt2DF9f3wo/ntwcGlfnozF1ThpX56MxdT5XGlPDMDh//jzh4eG4uJRtVm21vILr4uJC3bp1b/pxfX19dSI6IY2r89GYOieNq/PRmDqf/xzTsl65vUQ3mYmIiIiIU1GBKyIiIiJORQXuTeDp6UlCQgKenp5WR5FypHF1PhpT56RxdT4aU+dT3mNaLW8yExERERHnpSu4IiIiIuJUVOCKiIiIiFNRgSsiIiIiTkUFroiIiIg4FRW4N8GMGTNo2LAhXl5exMTEsH37dqsjyXX661//is1mK/GIioqyOpaU0caNG+nbty/h4eHYbDY+/fTTEtsNw2Dy5MmEhYVRo0YN4uLiOHz4sDVhpVSuNabDhw//zbnbq1cva8JKqUydOpVbb70VHx8fQkJC6NevH0lJSSU+k5eXx7hx4wgMDMTb25uBAweSlpZmUWIpjdKM65133vmb83X06NFlOo4K3Aq2ePFiJk2aREJCArt27SI6OpqePXty6tQpq6PJdWrZsiWpqanFj3/9619WR5IyysnJITo6mhkzZlxx++uvv84777zDe++9x7Zt26hVqxY9e/YkLy/vJieV0rrWmAL06tWrxLm7aNGim5hQymrDhg2MGzeOrVu3snr1agoKCujRowc5OTnFn5k4cSLLly/n448/ZsOGDZw4cYIBAwZYmFqupTTjCjBq1KgS5+vrr79etgMZUqE6duxojBs3rvh1UVGRER4ebkydOtXCVHK9EhISjOjoaKtjSDkCjGXLlhW/djgcRmhoqPHGG28Uv5eRkWF4enoaixYtsiChlNV/jqlhGMawYcOM++67z5I8Uj5OnTplAMaGDRsMwzDPS3d3d+Pjjz8u/syBAwcMwNiyZYtVMaWM/nNcDcMw7rjjDuOJJ564of3qCm4Fys/PZ+fOncTFxRW/5+LiQlxcHFu2bLEwmdyIw4cPEx4eTmRkJH/84x9JSUmxOpKUo6NHj3Ly5MkS562fnx8xMTE6b6u4b775hpCQEJo1a8aYMWM4e/as1ZGkDDIzMwEICAgAYOfOnRQUFJQ4V6Oioqhfv77O1SrkP8f1kgULFhAUFESrVq14/vnnyc3NLdN+3cotofzGmTNnKCoqwm63l3jfbrdz8OBBi1LJjYiJiWHevHk0a9aM1NRUpkyZQteuXdm3bx8+Pj5Wx5NycPLkSYArnreXtknV06tXLwYMGEBERATJycn8+c9/pnfv3mzZsgVXV1er48k1OBwO4uPj6dKlC61atQLMc9XDwwN/f/8Sn9W5WnVcaVwBHnzwQRo0aEB4eDh79uzh2WefJSkpiaVLl5Z63ypwRcqgd+/exc/btGlDTEwMDRo0YMmSJTz66KMWJhORqxkyZEjx89atW9OmTRsaNWrEN998Q7du3SxMJqUxbtw49u3bp3senMzvjetjjz1W/Lx169aEhYXRrVs3kpOTadSoUan2rSkKFSgoKAhXV9ff3NGZlpZGaGioRamkPPn7+9O0aVOOHDlidRQpJ5fOTZ23zi0yMpKgoCCdu1XA+PHj+eKLL1i/fj1169Ytfj80NJT8/HwyMjJKfF7natXwe+N6JTExMQBlOl9V4FYgDw8P2rdvz9q1a4vfczgcrF27ltjYWAuTSXnJzs4mOTmZsLAwq6NIOYmIiCA0NLTEeZuVlcW2bdt03jqR48ePc/bsWZ27lZhhGIwfP55ly5axbt06IiIiSmxv37497u7uJc7VpKQkUlJSdK5WYtca1ytJTEwEKNP5qikKFWzSpEkMGzaMDh060LFjR6ZPn05OTg4jRoywOppch6eeeoq+ffvSoEEDTpw4QUJCAq6urgwdOtTqaFIG2dnZJa4EHD16lMTERAICAqhfvz7x8fG8/PLLNGnShIiICF588UXCw8Pp16+fdaHlqq42pgEBAUyZMoWBAwcSGhpKcnIyzzzzDI0bN6Znz54WpparGTduHAsXLuSzzz7Dx8eneF6tn58fNWrUwM/Pj0cffZRJkyYREBCAr68vEyZMIDY2lk6dOlmcXn7PtcY1OTmZhQsX0qdPHwIDA9mzZw8TJ07k9ttvp02bNqU/0A31YJBSeffdd4369esbHh4eRseOHY2tW7daHUmu0+DBg42wsDDDw8PDqFOnjjF48GDjyJEjVseSMlq/fr0B/OYxbNgwwzDMVmEvvviiYbfbDU9PT6Nbt25GUlKStaHlqq42prm5uUaPHj2M4OBgw93d3WjQoIExatQo4+TJk1bHlqu40ngCxocfflj8mQsXLhhjx441ateubdSsWdPo37+/kZqaal1ouaZrjWtKSopx++23GwEBAYanp6fRuHFj4+mnnzYyMzPLdBzbvw8mIiIiIuIUNAdXRERERJyKClwRERERcSoqcEVERETEqajAFRERERGnogJXRERERJyKClwRERERcSoqcEVERETEqajAFRERERGnogJXRMQiw4cPt2T533nz5mGz2bDZbMTHxxe/37BhQ6ZPn37V7176nr+/f4VmFBG5EW5WBxARcUY2m+2q2xMSEnj77bexajFJX19fkpKSqFWrVpm+l5qayuLFi0lISKigZCIiN04FrohIBUhNTS1+vnjxYiZPnkxSUlLxe97e3nh7e1sRDTAL8NDQ0DJ/LzQ0FD8/vwpIJCJSfjRFQUSkAoSGhhY//Pz8igvKSw9vb+/fTFG48847mTBhAvHx8dSuXRu73c7s2bPJyclhxIgR+Pj40LhxY1asWFHiWPv27aN37954e3tjt9t5+OGHOXPmzHXlzs3N5ZFHHsHHx4f69esza9asG/nPICJiCRW4IiKVyPz58wkKCmL79u1MmDCBMWPG8MADD9C5c2d27dpFjx49ePjhh8nNzQUgIyODu+++m3bt2vHdd9+xcuVK0tLSGDRo0HUd/80336RDhw58//33jB07ljFjxpS48iwiUhWowBURqUSio6N54YUXaNKkCc8//zxeXl4EBQUxatQomjRpwuTJkzl79ix79uwB4G9/+xvt2rXj1VdfJSoqinbt2jF37lzWr1/PoUOHynz8Pn36MHbsWBo3bsyzzz5LUFAQ69evL+8fU0SkQmkOrohIJdKmTZvi566urgQGBtK6devi9+x2OwCnTp0CYPfu3axfv/6K83mTk5Np2rTpdR//0rSKS8cSEakqVOCKiFQi7u7uJV7bbLYS713qzuBwOADIzs6mb9++TJs27Tf7CgsLK5fjXzqWiEhVoQJXRKQKu+WWW/jkk09o2LAhbm76lS4iApqDKyJSpY0bN4709HSGDh3Kjh07SE5OZtWqVYwYMYKioiKr44mIWEIFrohIFRYeHs6mTZsoKiqiR48etG7dmvj4ePz9/XFx0a94EamebIZVy+iIiIgl5s2bR3x8PBkZGZZ8X0Skoumv9yIi1VBmZibe3t48++yzZfqet7c3o0ePrqBUIiLlQ1dwRUSqmfPnz5OWlgaAv78/QUFBpf7ukSNHALOFWURERIXkExG5USpwRURERMSpaIqCiIiIiDgVFbgiIiIi4lRU4IqIiIiIU1GBKyIiIiJORQWuiIiIiDgVFbgiIiIi4lRU4IqIiIiIU1GBKyIiIiJO5f8D4OdFl5WjY6UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(df['Day sin'])[:25])\n",
    "plt.plot(np.array(df['Day cos'])[:25])\n",
    "plt.xlabel('Time [h]')\n",
    "plt.title('Time of day signal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HiurzTGQgf_D"
   },
   "source": [
    "This gives the model access to the most important frequency features. In this case you knew ahead of time which frequencies were important.\n",
    "\n",
    "If you don't have that information, you can determine which frequencies are important by extracting features with <a href=\"https://en.wikipedia.org/wiki/Fast_Fourier_transform\" class=\"external\">Fast Fourier Transform</a>. To check the assumptions, here is the `tf.signal.rfft` of the temperature over time. Note the obvious peaks at frequencies near `1/year` and `1/day`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:13.075536Z",
     "iopub.status.busy": "2024-08-16T02:37:13.074863Z",
     "iopub.status.idle": "2024-08-16T02:37:15.629237Z",
     "shell.execute_reply": "2024-08-16T02:37:15.628542Z"
    },
    "id": "EN4U1fcMiTYs"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723775833.614540   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775833.618414   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775833.622101   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775833.625816   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775833.638786   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775833.642295   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775833.645790   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775833.649212   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775833.652657   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775833.656173   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775833.659653   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1723775833.663146   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.884491   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.886647   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.888756   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.890846   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.892883   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.894854   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.896838   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.898826   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.900762   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.902728   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.904708   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.906685   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.945076   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.947150   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.949185   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.951205   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.953161   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.955132   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.957121   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.959115   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.961075   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.963543   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.965909   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723775834.968304   80658 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fft = tf.signal.rfft(df['T (degC)'])\n",
    "f_per_dataset = np.arange(0, len(fft))\n",
    "\n",
    "n_samples_h = len(df['T (degC)'])\n",
    "hours_per_year = 24*365.2524\n",
    "years_per_dataset = n_samples_h/(hours_per_year)\n",
    "\n",
    "f_per_year = f_per_dataset/years_per_dataset\n",
    "plt.step(f_per_year, np.abs(fft))\n",
    "plt.xscale('log')\n",
    "plt.ylim(0, 400000)\n",
    "plt.xlim([0.1, max(plt.xlim())])\n",
    "plt.xticks([1, 365.2524], labels=['1/Year', '1/day'])\n",
    "_ = plt.xlabel('Frequency (log scale)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2rbL8bSGDHy3"
   },
   "source": [
    "### Split the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qoFJZmXBaxCc"
   },
   "source": [
    "You'll use a `(70%, 20%, 10%)` split for the training, validation, and test sets. Note the data is **not** being randomly shuffled before splitting. This is for two reasons:\n",
    "\n",
    "1. It ensures that chopping the data into windows of consecutive samples is still possible.\n",
    "2. It ensures that the validation/test results are more realistic, being evaluated on the data collected after the model was trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:15.632490Z",
     "iopub.status.busy": "2024-08-16T02:37:15.632220Z",
     "iopub.status.idle": "2024-08-16T02:37:15.636752Z",
     "shell.execute_reply": "2024-08-16T02:37:15.636119Z"
    },
    "id": "ia-MPAHxbInX"
   },
   "outputs": [],
   "source": [
    "column_indices = {name: i for i, name in enumerate(df.columns)}\n",
    "\n",
    "n = len(df)\n",
    "train_df = df[0:int(n*0.7)]\n",
    "val_df = df[int(n*0.7):int(n*0.9)]\n",
    "test_df = df[int(n*0.9):]\n",
    "\n",
    "num_features = df.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-eFckdUUHWmT"
   },
   "source": [
    "### Normalize the data\n",
    "\n",
    "It is important to scale features before training a neural network. Normalization is a common way of doing this scaling: subtract the mean and divide by the standard deviation of each feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mxbIic5TMlxx"
   },
   "source": [
    "The mean and standard deviation should only be computed using the training data so that the models have no access to the values in the validation and test sets.\n",
    "\n",
    "It's also arguable that the model shouldn't have access to future values in the training set when training, and that this normalization should be done using moving averages. That's not the focus of this tutorial, and the validation and test sets ensure that you get (somewhat) honest metrics. So, in the interest of simplicity this tutorial uses a simple average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:15.639977Z",
     "iopub.status.busy": "2024-08-16T02:37:15.639735Z",
     "iopub.status.idle": "2024-08-16T02:37:15.676215Z",
     "shell.execute_reply": "2024-08-16T02:37:15.675492Z"
    },
    "id": "Eji6njXvHusN"
   },
   "outputs": [],
   "source": [
    "train_mean = train_df.mean()\n",
    "train_std = train_df.std()\n",
    "\n",
    "train_df = (train_df - train_mean) / train_std\n",
    "val_df = (val_df - train_mean) / train_std\n",
    "test_df = (test_df - train_mean) / train_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G6ufs8kk9JQw"
   },
   "source": [
    "Now, peek at the distribution of the features. Some features do have long tails, but there are no obvious errors like the `-9999` wind velocity value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:15.679903Z",
     "iopub.status.busy": "2024-08-16T02:37:15.679638Z",
     "iopub.status.idle": "2024-08-16T02:37:21.877113Z",
     "shell.execute_reply": "2024-08-16T02:37:21.876437Z"
    },
    "id": "T0UYEnkwm8Fe"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmpfs/tmp/ipykernel_80658/3214313372.py:5: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  _ = ax.set_xticklabels(df.keys(), rotation=90)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_std = (df - train_mean) / train_std\n",
    "df_std = df_std.melt(var_name='Column', value_name='Normalized')\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.violinplot(x='Column', y='Normalized', data=df_std)\n",
    "_ = ax.set_xticklabels(df.keys(), rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZBBmdxZ2HgfJ"
   },
   "source": [
    "## Data windowing\n",
    "\n",
    "The models in this tutorial will make a set of predictions based on a window of consecutive samples from the data.\n",
    "\n",
    "The main features of the input windows are:\n",
    "\n",
    "- The width (number of time steps) of the input and label windows.\n",
    "- The time offset between them.\n",
    "- Which features are used as inputs, labels, or both.\n",
    "\n",
    "This tutorial builds a variety of models (including Linear, DNN, CNN and RNN models), and uses them for both:\n",
    "\n",
    "- *Single-output*, and *multi-output* predictions.\n",
    "- *Single-time-step* and *multi-time-step* predictions.\n",
    "\n",
    "This section focuses on implementing the data windowing so that it can be reused for all of those models.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YAhGUVx1jtOy"
   },
   "source": [
    "Depending on the task and type of model you may want to generate a variety of data windows. Here are some examples:\n",
    "\n",
    "1. For example, to make a single prediction 24 hours into the future, given 24 hours of history, you might define a window like this:\n",
    "\n",
    "  ![One prediction 24 hours into the future.](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/raw_window_24h.png?raw=1)\n",
    "\n",
    "2. A model that makes a prediction one hour into the future, given six hours of history, would need a window like this:\n",
    "\n",
    "  ![One prediction one hour into the future.](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/raw_window_1h.png?raw=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sa2BbfNZt8wy"
   },
   "source": [
    "The rest of this section defines a `WindowGenerator` class. This class can:\n",
    "\n",
    "1. Handle the indexes and offsets as shown in the diagrams above.\n",
    "1. Split windows of features into `(features, labels)` pairs.\n",
    "2. Plot the content of the resulting windows.\n",
    "3. Efficiently generate batches of these windows from the training, evaluation, and test data, using `tf.data.Dataset`s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rfx3jGjyziUF"
   },
   "source": [
    "### 1. Indexes and offsets\n",
    "\n",
    "Start by creating the `WindowGenerator` class. The `__init__` method includes all the necessary logic for the input and label indices.\n",
    "\n",
    "It also takes the training, evaluation, and test DataFrames as input. These will be converted to `tf.data.Dataset`s of windows later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:21.881074Z",
     "iopub.status.busy": "2024-08-16T02:37:21.880821Z",
     "iopub.status.idle": "2024-08-16T02:37:21.887324Z",
     "shell.execute_reply": "2024-08-16T02:37:21.886729Z"
    },
    "id": "Kem30j8QHxyW"
   },
   "outputs": [],
   "source": [
    "class WindowGenerator():\n",
    "  def __init__(self, input_width, label_width, shift,\n",
    "               train_df=train_df, val_df=val_df, test_df=test_df,\n",
    "               label_columns=None):\n",
    "    # Store the raw data.\n",
    "    self.train_df = train_df\n",
    "    self.val_df = val_df\n",
    "    self.test_df = test_df\n",
    "\n",
    "    # Work out the label column indices.\n",
    "    self.label_columns = label_columns\n",
    "    if label_columns is not None:\n",
    "      self.label_columns_indices = {name: i for i, name in\n",
    "                                    enumerate(label_columns)}\n",
    "    self.column_indices = {name: i for i, name in\n",
    "                           enumerate(train_df.columns)}\n",
    "\n",
    "    # Work out the window parameters.\n",
    "    self.input_width = input_width\n",
    "    self.label_width = label_width\n",
    "    self.shift = shift\n",
    "\n",
    "    self.total_window_size = input_width + shift\n",
    "\n",
    "    self.input_slice = slice(0, input_width)\n",
    "    self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n",
    "\n",
    "    self.label_start = self.total_window_size - self.label_width\n",
    "    self.labels_slice = slice(self.label_start, None)\n",
    "    self.label_indices = np.arange(self.total_window_size)[self.labels_slice]\n",
    "\n",
    "  def __repr__(self):\n",
    "    return '\\n'.join([\n",
    "        f'Total window size: {self.total_window_size}',\n",
    "        f'Input indices: {self.input_indices}',\n",
    "        f'Label indices: {self.label_indices}',\n",
    "        f'Label column name(s): {self.label_columns}'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yVJgblsYzL1g"
   },
   "source": [
    "Here is code to create the 2 windows shown in the diagrams at the start of this section:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:21.890814Z",
     "iopub.status.busy": "2024-08-16T02:37:21.890418Z",
     "iopub.status.idle": "2024-08-16T02:37:21.894995Z",
     "shell.execute_reply": "2024-08-16T02:37:21.894393Z"
    },
    "id": "IsM5kRkz0UwK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total window size: 48\n",
       "Input indices: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
       "Label indices: [47]\n",
       "Label column name(s): ['T (degC)']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w1 = WindowGenerator(input_width=24, label_width=1, shift=24,\n",
    "                     label_columns=['T (degC)'])\n",
    "w1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:21.898029Z",
     "iopub.status.busy": "2024-08-16T02:37:21.897523Z",
     "iopub.status.idle": "2024-08-16T02:37:21.902152Z",
     "shell.execute_reply": "2024-08-16T02:37:21.901590Z"
    },
    "id": "viwKsYeAKFUn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total window size: 7\n",
       "Input indices: [0 1 2 3 4 5]\n",
       "Label indices: [6]\n",
       "Label column name(s): ['T (degC)']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2 = WindowGenerator(input_width=6, label_width=1, shift=1,\n",
    "                     label_columns=['T (degC)'])\n",
    "w2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kJaUyTWQJd-L"
   },
   "source": [
    "### 2. Split\n",
    "\n",
    "Given a list of consecutive inputs, the `split_window` method will convert them to a window of inputs and a window of labels.\n",
    "\n",
    "The example `w2` you define earlier will be split like this:\n",
    "\n",
    "![The initial window is all consecutive samples, this splits it into an (inputs, labels) pairs](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/split_window.png?raw=1)\n",
    "\n",
    "This diagram doesn't show the `features` axis of the data, but this `split_window` function also handles the `label_columns` so it can be used for both the single output and multi-output examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:21.905359Z",
     "iopub.status.busy": "2024-08-16T02:37:21.905135Z",
     "iopub.status.idle": "2024-08-16T02:37:21.909773Z",
     "shell.execute_reply": "2024-08-16T02:37:21.909151Z"
    },
    "id": "W4KbxfzqkXPW"
   },
   "outputs": [],
   "source": [
    "def split_window(self, features):\n",
    "  inputs = features[:, self.input_slice, :]\n",
    "  labels = features[:, self.labels_slice, :]\n",
    "  if self.label_columns is not None:\n",
    "    labels = tf.stack(\n",
    "        [labels[:, :, self.column_indices[name]] for name in self.label_columns],\n",
    "        axis=-1)\n",
    "\n",
    "  # Slicing doesn't preserve static shape information, so set the shapes\n",
    "  # manually. This way the `tf.data.Datasets` are easier to inspect.\n",
    "  inputs.set_shape([None, self.input_width, None])\n",
    "  labels.set_shape([None, self.label_width, None])\n",
    "\n",
    "  return inputs, labels\n",
    "\n",
    "WindowGenerator.split_window = split_window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G6U6VtVuM15s"
   },
   "source": [
    "Try it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:21.912753Z",
     "iopub.status.busy": "2024-08-16T02:37:21.912533Z",
     "iopub.status.idle": "2024-08-16T02:37:21.930611Z",
     "shell.execute_reply": "2024-08-16T02:37:21.929951Z"
    },
    "id": "YeCWbq6KLmL7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All shapes are: (batch, time, features)\n",
      "Window shape: (3, 7, 19)\n",
      "Inputs shape: (3, 6, 19)\n",
      "Labels shape: (3, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "# Stack three slices, the length of the total window.\n",
    "example_window = tf.stack([np.array(train_df[:w2.total_window_size]),\n",
    "                           np.array(train_df[100:100+w2.total_window_size]),\n",
    "                           np.array(train_df[200:200+w2.total_window_size])])\n",
    "\n",
    "example_inputs, example_labels = w2.split_window(example_window)\n",
    "\n",
    "print('All shapes are: (batch, time, features)')\n",
    "print(f'Window shape: {example_window.shape}')\n",
    "print(f'Inputs shape: {example_inputs.shape}')\n",
    "print(f'Labels shape: {example_labels.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xtMk1ffk2Mmd"
   },
   "source": [
    "Typically, data in TensorFlow is packed into arrays where the outermost index is across examples (the \"batch\" dimension). The middle indices are the \"time\" or \"space\" (width, height) dimension(s). The innermost indices are the features.\n",
    "\n",
    "The code above took a batch of three 7-time step windows with 19 features at each time step. It splits them into a batch of 6-time step 19-feature inputs, and a 1-time step 1-feature label. The label only has one feature because the `WindowGenerator` was initialized with `label_columns=['T (degC)']`. Initially, this tutorial will build models that predict single output labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tFZukGXrJoGo"
   },
   "source": [
    "### 3. Plot\n",
    "\n",
    "Here is a plot method that allows a simple visualization of the split window:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:21.933658Z",
     "iopub.status.busy": "2024-08-16T02:37:21.933435Z",
     "iopub.status.idle": "2024-08-16T02:37:21.936385Z",
     "shell.execute_reply": "2024-08-16T02:37:21.935838Z"
    },
    "id": "fmgd1qkYUWT7"
   },
   "outputs": [],
   "source": [
    "w2.example = example_inputs, example_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:21.939468Z",
     "iopub.status.busy": "2024-08-16T02:37:21.939192Z",
     "iopub.status.idle": "2024-08-16T02:37:21.945612Z",
     "shell.execute_reply": "2024-08-16T02:37:21.945035Z"
    },
    "id": "jIrYccI-Hm3B"
   },
   "outputs": [],
   "source": [
    "def plot(self, model=None, plot_col='T (degC)', max_subplots=3):\n",
    "  inputs, labels = self.example\n",
    "  plt.figure(figsize=(12, 8))\n",
    "  plot_col_index = self.column_indices[plot_col]\n",
    "  max_n = min(max_subplots, len(inputs))\n",
    "  for n in range(max_n):\n",
    "    plt.subplot(max_n, 1, n+1)\n",
    "    plt.ylabel(f'{plot_col} [normed]')\n",
    "    plt.plot(self.input_indices, inputs[n, :, plot_col_index],\n",
    "             label='Inputs', marker='.', zorder=-10)\n",
    "\n",
    "    if self.label_columns:\n",
    "      label_col_index = self.label_columns_indices.get(plot_col, None)\n",
    "    else:\n",
    "      label_col_index = plot_col_index\n",
    "\n",
    "    if label_col_index is None:\n",
    "      continue\n",
    "\n",
    "    plt.scatter(self.label_indices, labels[n, :, label_col_index],\n",
    "                edgecolors='k', label='Labels', c='#2ca02c', s=64)\n",
    "    if model is not None:\n",
    "      predictions = model(inputs)\n",
    "      plt.scatter(self.label_indices, predictions[n, :, label_col_index],\n",
    "                  marker='X', edgecolors='k', label='Predictions',\n",
    "                  c='#ff7f0e', s=64)\n",
    "\n",
    "    if n == 0:\n",
    "      plt.legend()\n",
    "\n",
    "  plt.xlabel('Time [h]')\n",
    "\n",
    "WindowGenerator.plot = plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HXvctEuK68vX"
   },
   "source": [
    "This plot aligns inputs, labels, and (later) predictions based on the time that the item refers to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:21.948646Z",
     "iopub.status.busy": "2024-08-16T02:37:21.948382Z",
     "iopub.status.idle": "2024-08-16T02:37:22.504148Z",
     "shell.execute_reply": "2024-08-16T02:37:22.503488Z"
    },
    "id": "XjTqUnglOOni"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w2.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UqiqcPOldPG6"
   },
   "source": [
    "You can plot the other columns, but the example window `w2` configuration only has labels for the `T (degC)` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:22.508048Z",
     "iopub.status.busy": "2024-08-16T02:37:22.507802Z",
     "iopub.status.idle": "2024-08-16T02:37:22.790948Z",
     "shell.execute_reply": "2024-08-16T02:37:22.790341Z"
    },
    "id": "EBRe4wnlfCH8"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w2.plot(plot_col='p (mbar)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xCvD-UaUzYMw"
   },
   "source": [
    "### 4. Create `tf.data.Dataset`s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kLO3SFR9Osdf"
   },
   "source": [
    "Finally, this `make_dataset` method will take a time series DataFrame and convert it to a `tf.data.Dataset` of `(input_window, label_window)` pairs using the `tf.keras.utils.timeseries_dataset_from_array` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:22.794616Z",
     "iopub.status.busy": "2024-08-16T02:37:22.794336Z",
     "iopub.status.idle": "2024-08-16T02:37:22.798638Z",
     "shell.execute_reply": "2024-08-16T02:37:22.798046Z"
    },
    "id": "35qoSQeRVfJg"
   },
   "outputs": [],
   "source": [
    "def make_dataset(self, data):\n",
    "  data = np.array(data, dtype=np.float32)\n",
    "  ds = tf.keras.utils.timeseries_dataset_from_array(\n",
    "      data=data,\n",
    "      targets=None,\n",
    "      sequence_length=self.total_window_size,\n",
    "      sequence_stride=1,\n",
    "      shuffle=True,\n",
    "      batch_size=32,)\n",
    "\n",
    "  ds = ds.map(self.split_window)\n",
    "\n",
    "  return ds\n",
    "\n",
    "WindowGenerator.make_dataset = make_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LvsxQwJaCift"
   },
   "source": [
    "The `WindowGenerator` object holds training, validation, and test data.\n",
    "\n",
    "Add properties for accessing them as `tf.data.Dataset`s using the `make_dataset` method you defined earlier. Also, add a standard example batch for easy access and plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:22.801744Z",
     "iopub.status.busy": "2024-08-16T02:37:22.801515Z",
     "iopub.status.idle": "2024-08-16T02:37:22.806464Z",
     "shell.execute_reply": "2024-08-16T02:37:22.805875Z"
    },
    "id": "2jZ2KkqGCfzu"
   },
   "outputs": [],
   "source": [
    "@property\n",
    "def train(self):\n",
    "  return self.make_dataset(self.train_df)\n",
    "\n",
    "@property\n",
    "def val(self):\n",
    "  return self.make_dataset(self.val_df)\n",
    "\n",
    "@property\n",
    "def test(self):\n",
    "  return self.make_dataset(self.test_df)\n",
    "\n",
    "@property\n",
    "def example(self):\n",
    "  \"\"\"Get and cache an example batch of `inputs, labels` for plotting.\"\"\"\n",
    "  result = getattr(self, '_example', None)\n",
    "  if result is None:\n",
    "    # No example batch was found, so get one from the `.train` dataset\n",
    "    result = next(iter(self.train))\n",
    "    # And cache it for next time\n",
    "    self._example = result\n",
    "  return result\n",
    "\n",
    "WindowGenerator.train = train\n",
    "WindowGenerator.val = val\n",
    "WindowGenerator.test = test\n",
    "WindowGenerator.example = example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fF_Vj6Iw3Y2w"
   },
   "source": [
    "Now, the `WindowGenerator` object gives you access to the `tf.data.Dataset` objects, so you can easily iterate over the data.\n",
    "\n",
    "The `Dataset.element_spec` property tells you the structure, data types, and shapes of the dataset elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:22.809563Z",
     "iopub.status.busy": "2024-08-16T02:37:22.809305Z",
     "iopub.status.idle": "2024-08-16T02:37:23.910099Z",
     "shell.execute_reply": "2024-08-16T02:37:23.909399Z"
    },
    "id": "daJ0-U383YVs"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorSpec(shape=(None, 6, 19), dtype=tf.float32, name=None),\n",
       " TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Each element is an (inputs, label) pair.\n",
    "w2.train.element_spec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XKTx3_Z7ua-n"
   },
   "source": [
    "Iterating over a `Dataset` yields concrete batches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:23.913642Z",
     "iopub.status.busy": "2024-08-16T02:37:23.913349Z",
     "iopub.status.idle": "2024-08-16T02:37:24.052797Z",
     "shell.execute_reply": "2024-08-16T02:37:24.052067Z"
    },
    "id": "6gtKXEgf4Iml"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inputs shape (batch, time, features): (32, 6, 19)\n",
      "Labels shape (batch, time, features): (32, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "for example_inputs, example_labels in w2.train.take(1):\n",
    "  print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n",
    "  print(f'Labels shape (batch, time, features): {example_labels.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LyuGuJUgjUK3"
   },
   "source": [
    "## Single step models\n",
    "\n",
    "The simplest model you can build on this sort of data is one that predicts a single feature's value—1 time step (one hour) into the future based only on the current conditions.\n",
    "\n",
    "So, start by building models to predict the `T (degC)` value one hour into the future.\n",
    "\n",
    "![Predict the next time step](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/narrow_window.png?raw=1)\n",
    "\n",
    "Configure a `WindowGenerator` object to produce these single-step `(input, label)` pairs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:24.056634Z",
     "iopub.status.busy": "2024-08-16T02:37:24.055997Z",
     "iopub.status.idle": "2024-08-16T02:37:24.061083Z",
     "shell.execute_reply": "2024-08-16T02:37:24.060520Z"
    },
    "id": "G5QX1G1JTPCr"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total window size: 2\n",
       "Input indices: [0]\n",
       "Label indices: [1]\n",
       "Label column name(s): ['T (degC)']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_step_window = WindowGenerator(\n",
    "    input_width=1, label_width=1, shift=1,\n",
    "    label_columns=['T (degC)'])\n",
    "single_step_window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RKTm8ajVGw4N"
   },
   "source": [
    "The `window` object creates `tf.data.Dataset`s from the training, validation, and test sets, allowing you to easily iterate over batches of data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:24.064047Z",
     "iopub.status.busy": "2024-08-16T02:37:24.063821Z",
     "iopub.status.idle": "2024-08-16T02:37:24.201547Z",
     "shell.execute_reply": "2024-08-16T02:37:24.200860Z"
    },
    "id": "Do4ILUaBF8oc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inputs shape (batch, time, features): (32, 1, 19)\n",
      "Labels shape (batch, time, features): (32, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "for example_inputs, example_labels in single_step_window.train.take(1):\n",
    "  print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n",
    "  print(f'Labels shape (batch, time, features): {example_labels.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D1bbPiR3VAm_"
   },
   "source": [
    "### Baseline\n",
    "\n",
    "Before building a trainable model it would be good to have a performance baseline as a point for comparison with the later more complicated models.\n",
    "\n",
    "This first task is to predict temperature one hour into the future, given the current value of all features. The current values include the current temperature.\n",
    "\n",
    "So, start with a model that just returns the current temperature as the prediction, predicting \"No change\". This is a reasonable baseline since temperature changes slowly. Of course, this baseline will work less well if you make a prediction further in the future.\n",
    "\n",
    "![Send the input to the output](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/baseline.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:24.205259Z",
     "iopub.status.busy": "2024-08-16T02:37:24.204722Z",
     "iopub.status.idle": "2024-08-16T02:37:24.209336Z",
     "shell.execute_reply": "2024-08-16T02:37:24.208673Z"
    },
    "id": "9TybQaIsi3yg"
   },
   "outputs": [],
   "source": [
    "class Baseline(tf.keras.Model):\n",
    "  def __init__(self, label_index=None):\n",
    "    super().__init__()\n",
    "    self.label_index = label_index\n",
    "\n",
    "  def call(self, inputs):\n",
    "    if self.label_index is None:\n",
    "      return inputs\n",
    "    result = inputs[:, :, self.label_index]\n",
    "    return result[:, :, tf.newaxis]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0vb3f948i8p8"
   },
   "source": [
    "Instantiate and evaluate this model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:24.212939Z",
     "iopub.status.busy": "2024-08-16T02:37:24.212377Z",
     "iopub.status.idle": "2024-08-16T02:37:25.560821Z",
     "shell.execute_reply": "2024-08-16T02:37:25.560109Z"
    },
    "id": "IS3-QKc4sX0D"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/439\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:27\u001b[0m 337ms/step - loss: 0.0075 - mean_absolute_error: 0.0657"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723775844.435380   80829 service.cc:146] XLA service 0x7eff1c004a90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1723775844.435411   80829 service.cc:154]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
      "I0000 00:00:1723775844.435416   80829 service.cc:154]   StreamExecutor device (1): Tesla T4, Compute Capability 7.5\n",
      "I0000 00:00:1723775844.435419   80829 service.cc:154]   StreamExecutor device (2): Tesla T4, Compute Capability 7.5\n",
      "I0000 00:00:1723775844.435422   80829 service.cc:154]   StreamExecutor device (3): Tesla T4, Compute Capability 7.5\n",
      "I0000 00:00:1723775844.614710   80829 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 38/439\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0103 - mean_absolute_error: 0.0724    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 80/439\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0110 - mean_absolute_error: 0.0746"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m122/439\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0113 - mean_absolute_error: 0.0755"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m164/439\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0116 - mean_absolute_error: 0.0760"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m207/439\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0117 - mean_absolute_error: 0.0762"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m250/439\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0118 - mean_absolute_error: 0.0763"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m291/439\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0118 - mean_absolute_error: 0.0763"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m335/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0119 - mean_absolute_error: 0.0765"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m382/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0120 - mean_absolute_error: 0.0767"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m428/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0121 - mean_absolute_error: 0.0769"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m439/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0121 - mean_absolute_error: 0.0769"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m439/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0121 - mean_absolute_error: 0.0769\n"
     ]
    }
   ],
   "source": [
    "baseline = Baseline(label_index=column_indices['T (degC)'])\n",
    "\n",
    "baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n",
    "                 metrics=[tf.keras.metrics.MeanAbsoluteError()])\n",
    "\n",
    "val_performance = {}\n",
    "performance = {}\n",
    "val_performance['Baseline'] = baseline.evaluate(single_step_window.val, return_dict=True)\n",
    "performance['Baseline'] = baseline.evaluate(single_step_window.test, verbose=0, return_dict=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nhBxQcCSs7Ec"
   },
   "source": [
    "That printed some performance metrics, but those don't give you a feeling for how well the model is doing.\n",
    "\n",
    "The `WindowGenerator` has a plot method, but the plots won't be very interesting with only a single sample.\n",
    "\n",
    "So, create a wider `WindowGenerator` that generates windows 24 hours of consecutive inputs and labels at a time. The new `wide_window` variable doesn't change the way the model operates. The model still makes predictions one hour into the future based on a single input time step. Here, the `time` axis acts like the `batch` axis: each prediction is made independently with no interaction between time steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:25.565206Z",
     "iopub.status.busy": "2024-08-16T02:37:25.564652Z",
     "iopub.status.idle": "2024-08-16T02:37:25.570032Z",
     "shell.execute_reply": "2024-08-16T02:37:25.569424Z"
    },
    "id": "C8jNR5uuJ5Zp"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total window size: 25\n",
       "Input indices: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
       "Label indices: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]\n",
       "Label column name(s): ['T (degC)']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wide_window = WindowGenerator(\n",
    "    input_width=24, label_width=24, shift=1,\n",
    "    label_columns=['T (degC)'])\n",
    "\n",
    "wide_window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZAnj7CFZkuYv"
   },
   "source": [
    "This expanded window can be passed directly to the same `baseline` model without any code changes. This is possible because the inputs and labels have the same number of time steps, and the baseline just forwards the input to the output:\n",
    "\n",
    "![One prediction 1h into the future, ever hour.](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/last_window.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:25.573572Z",
     "iopub.status.busy": "2024-08-16T02:37:25.573029Z",
     "iopub.status.idle": "2024-08-16T02:37:25.704058Z",
     "shell.execute_reply": "2024-08-16T02:37:25.703359Z"
    },
    "id": "sGKdvdg087qs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (32, 24, 19)\n",
      "Output shape: (32, 24, 1)\n"
     ]
    }
   ],
   "source": [
    "print('Input shape:', wide_window.example[0].shape)\n",
    "print('Output shape:', baseline(wide_window.example[0]).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SKqQHX1K0JW-"
   },
   "source": [
    "By plotting the baseline model's predictions, notice that it is simply the labels shifted right by one hour:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:25.707851Z",
     "iopub.status.busy": "2024-08-16T02:37:25.707202Z",
     "iopub.status.idle": "2024-08-16T02:37:26.100135Z",
     "shell.execute_reply": "2024-08-16T02:37:26.099469Z"
    },
    "id": "jQyAPVLgWTOZ"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wide_window.plot(baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e93TLUhfAVg2"
   },
   "source": [
    "In the above plots of three examples the single step model is run over the course of 24 hours. This deserves some explanation:\n",
    "\n",
    "- The blue `Inputs` line shows the input temperature at each time step. The model receives all features, this plot only shows the temperature.\n",
    "- The green `Labels` dots show the target prediction value. These dots are shown at the prediction time, not the input time. That is why the range of labels is shifted 1 step relative to the inputs.\n",
    "- The orange `Predictions` crosses are the model's prediction's for each output time step. If the model were predicting perfectly the predictions would land directly on the `Labels`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E4aOJScj52Yu"
   },
   "source": [
    "### Linear model\n",
    "\n",
    "The simplest **trainable** model you can apply to this task is to insert linear transformation between the input and output. In this case the output from a time step only depends on that step:\n",
    "\n",
    "![A single step prediction](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/narrow_window.png?raw=1)\n",
    "\n",
    "A `tf.keras.layers.Dense` layer with no `activation` set is a linear model. The layer only transforms the last axis of the data from `(batch, time, inputs)` to `(batch, time, units)`; it is applied independently to every item across the `batch` and `time` axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:26.104718Z",
     "iopub.status.busy": "2024-08-16T02:37:26.104311Z",
     "iopub.status.idle": "2024-08-16T02:37:26.109121Z",
     "shell.execute_reply": "2024-08-16T02:37:26.108443Z"
    },
    "id": "6341OXuQ5xA9"
   },
   "outputs": [],
   "source": [
    "linear = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(units=1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:26.112109Z",
     "iopub.status.busy": "2024-08-16T02:37:26.111734Z",
     "iopub.status.idle": "2024-08-16T02:37:26.862460Z",
     "shell.execute_reply": "2024-08-16T02:37:26.861743Z"
    },
    "id": "KwaOM8RucUSn"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (32, 1, 19)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape: (32, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "print('Input shape:', single_step_window.example[0].shape)\n",
    "print('Output shape:', linear(single_step_window.example[0]).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OMZTYIj3bYLg"
   },
   "source": [
    "This tutorial trains many models, so package the training procedure into a function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:26.865774Z",
     "iopub.status.busy": "2024-08-16T02:37:26.865507Z",
     "iopub.status.idle": "2024-08-16T02:37:26.870443Z",
     "shell.execute_reply": "2024-08-16T02:37:26.869796Z"
    },
    "id": "CbCL6VIrk-Gt"
   },
   "outputs": [],
   "source": [
    "MAX_EPOCHS = 20\n",
    "\n",
    "def compile_and_fit(model, window, patience=2):\n",
    "  early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                                                    patience=patience,\n",
    "                                                    mode='min')\n",
    "\n",
    "  model.compile(loss=tf.keras.losses.MeanSquaredError(),\n",
    "                optimizer=tf.keras.optimizers.Adam(),\n",
    "                metrics=[tf.keras.metrics.MeanAbsoluteError()])\n",
    "\n",
    "  history = model.fit(window.train, epochs=MAX_EPOCHS,\n",
    "                      validation_data=window.val,\n",
    "                      callbacks=[early_stopping])\n",
    "  return history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OobVjM-schwj"
   },
   "source": [
    "Train the model and evaluate its performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:26.873803Z",
     "iopub.status.busy": "2024-08-16T02:37:26.873262Z",
     "iopub.status.idle": "2024-08-16T02:37:51.919237Z",
     "shell.execute_reply": "2024-08-16T02:37:51.918393Z"
    },
    "id": "9agbz2qB9bLS"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m21:51\u001b[0m 856ms/step - loss: 2.1417 - mean_absolute_error: 1.1814"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  39/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 2.5030 - mean_absolute_error: 1.2593     "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  79/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 2.2471 - mean_absolute_error: 1.1913"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 123/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 2.0225 - mean_absolute_error: 1.1219"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 164/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.8576 - mean_absolute_error: 1.0669"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 207/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.7156 - mean_absolute_error: 1.0171"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 246/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.6083 - mean_absolute_error: 0.9780"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 285/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.5164 - mean_absolute_error: 0.9435"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 324/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.4367 - mean_absolute_error: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 364/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.3650 - mean_absolute_error: 0.8842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 404/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.3016 - mean_absolute_error: 0.8583"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 445/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.2435 - mean_absolute_error: 0.8341"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 486/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.1914 - mean_absolute_error: 0.8119"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 527/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.1442 - mean_absolute_error: 0.7913"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 568/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.1013 - mean_absolute_error: 0.7720"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 609/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.0619 - mean_absolute_error: 0.7540"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 650/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.0257 - mean_absolute_error: 0.7371"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 691/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.9923 - mean_absolute_error: 0.7211"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 732/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.9612 - mean_absolute_error: 0.7060"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 773/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.9324 - mean_absolute_error: 0.6916"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 812/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.9067 - mean_absolute_error: 0.6787"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 851/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.8826 - mean_absolute_error: 0.6663"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 891/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.8594 - mean_absolute_error: 0.6542"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 933/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.8365 - mean_absolute_error: 0.6421"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 974/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.8154 - mean_absolute_error: 0.6308"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1017/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.7946 - mean_absolute_error: 0.6195"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1059/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.7754 - mean_absolute_error: 0.6090"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1099/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.7581 - mean_absolute_error: 0.5993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1139/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.7416 - mean_absolute_error: 0.5901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1179/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.7260 - mean_absolute_error: 0.5812"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1221/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.7104 - mean_absolute_error: 0.5722"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1261/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6962 - mean_absolute_error: 0.5640"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1301/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6826 - mean_absolute_error: 0.5561"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1341/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6696 - mean_absolute_error: 0.5484"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1379/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6578 - mean_absolute_error: 0.5414"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1422/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6450 - mean_absolute_error: 0.5337"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1462/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6336 - mean_absolute_error: 0.5269"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1502/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6227 - mean_absolute_error: 0.5202"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6143 - mean_absolute_error: 0.5151"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.6140 - mean_absolute_error: 0.5149 - val_loss: 0.0133 - val_mean_absolute_error: 0.0868\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:28\u001b[0m 57ms/step - loss: 0.0389 - mean_absolute_error: 0.1103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  37/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0164 - mean_absolute_error: 0.0905   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  78/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0148 - mean_absolute_error: 0.0886"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 117/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0142 - mean_absolute_error: 0.0878"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 156/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0138 - mean_absolute_error: 0.0870"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 195/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0135 - mean_absolute_error: 0.0863"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 236/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0132 - mean_absolute_error: 0.0859"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 276/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0131 - mean_absolute_error: 0.0855"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 316/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0129 - mean_absolute_error: 0.0851"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 357/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0128 - mean_absolute_error: 0.0847"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 398/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0127 - mean_absolute_error: 0.0844"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 439/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0126 - mean_absolute_error: 0.0841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 481/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0125 - mean_absolute_error: 0.0838"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 526/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0124 - mean_absolute_error: 0.0836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 568/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0123 - mean_absolute_error: 0.0833"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 610/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0123 - mean_absolute_error: 0.0831"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 650/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0122 - mean_absolute_error: 0.0829"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 689/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0122 - mean_absolute_error: 0.0827"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 727/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0121 - mean_absolute_error: 0.0826"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 766/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0121 - mean_absolute_error: 0.0824"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 806/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0120 - mean_absolute_error: 0.0822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 846/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0120 - mean_absolute_error: 0.0820"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 888/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0119 - mean_absolute_error: 0.0819"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 930/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0119 - mean_absolute_error: 0.0817"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 974/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0119 - mean_absolute_error: 0.0815"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1016/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0118 - mean_absolute_error: 0.0814"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1056/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0118 - mean_absolute_error: 0.0812"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1095/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0118 - mean_absolute_error: 0.0811"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1136/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0117 - mean_absolute_error: 0.0810"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1178/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0117 - mean_absolute_error: 0.0808"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1219/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0117 - mean_absolute_error: 0.0807"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1259/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0116 - mean_absolute_error: 0.0806"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1301/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0116 - mean_absolute_error: 0.0805"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1342/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0116 - mean_absolute_error: 0.0803"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1384/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0115 - mean_absolute_error: 0.0802"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1424/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0115 - mean_absolute_error: 0.0801"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1465/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0115 - mean_absolute_error: 0.0800"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1506/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0114 - mean_absolute_error: 0.0799"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0114 - mean_absolute_error: 0.0798 - val_loss: 0.0090 - val_mean_absolute_error: 0.0699\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:23\u001b[0m 54ms/step - loss: 0.0079 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  39/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0090 - mean_absolute_error: 0.0685   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  79/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0701"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 121/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 162/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 203/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 244/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 285/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 325/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 369/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 409/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0703"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 448/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0703"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 488/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0703"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 533/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 574/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 615/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 656/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 698/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 739/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 779/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 819/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 858/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 896/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 935/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 974/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1012/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1053/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1093/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1132/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1170/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1209/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1248/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1288/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1330/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1370/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1411/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1451/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1492/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0093 - mean_absolute_error: 0.0704 - val_loss: 0.0087 - val_mean_absolute_error: 0.0684\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:25\u001b[0m 56ms/step - loss: 0.0128 - mean_absolute_error: 0.0845"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  39/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0112 - mean_absolute_error: 0.0726   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  78/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0104 - mean_absolute_error: 0.0718"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 117/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0100 - mean_absolute_error: 0.0711"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 156/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0097 - mean_absolute_error: 0.0706"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 195/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0095 - mean_absolute_error: 0.0702"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 236/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0094 - mean_absolute_error: 0.0701"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 276/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0094 - mean_absolute_error: 0.0700"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 316/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0094 - mean_absolute_error: 0.0699"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 354/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0698"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 392/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0698"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 431/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 469/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 509/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 550/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 592/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 633/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 674/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 715/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 756/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 798/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 840/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 882/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 924/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 965/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1006/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1047/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1089/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1131/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1173/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1214/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1255/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1294/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1334/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1373/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1412/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1452/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1493/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0091 - mean_absolute_error: 0.0696 - val_loss: 0.0087 - val_mean_absolute_error: 0.0681\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:22\u001b[0m 54ms/step - loss: 0.0044 - mean_absolute_error: 0.0547"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  38/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0102 - mean_absolute_error: 0.0700   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  78/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0099 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 118/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0096 - mean_absolute_error: 0.0701"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 158/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0094 - mean_absolute_error: 0.0699"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 198/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 238/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 279/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 321/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 362/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 402/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 441/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 481/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 524/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 564/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 605/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 645/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 687/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 725/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 763/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 802/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 843/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 884/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 924/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 964/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1005/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1046/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1086/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1127/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1167/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1208/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1250/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1290/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1330/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1369/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1411/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1452/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1492/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1533/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0091 - mean_absolute_error: 0.0695 - val_loss: 0.0086 - val_mean_absolute_error: 0.0684\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:24\u001b[0m 55ms/step - loss: 0.0195 - mean_absolute_error: 0.0948"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  38/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0101 - mean_absolute_error: 0.0705   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  75/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0098 - mean_absolute_error: 0.0705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 115/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0096 - mean_absolute_error: 0.0702"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 157/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0094 - mean_absolute_error: 0.0698"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 199/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 243/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 281/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 320/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 359/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 399/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 438/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 479/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 520/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 561/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 601/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 645/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 687/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 726/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 766/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 805/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 843/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 882/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 921/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 960/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1001/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1041/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1080/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1122/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1165/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1208/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1250/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1291/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1331/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1372/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1414/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1455/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1497/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0091 - mean_absolute_error: 0.0695 - val_loss: 0.0086 - val_mean_absolute_error: 0.0683\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:24\u001b[0m 55ms/step - loss: 0.0121 - mean_absolute_error: 0.0801"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  43/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0102 - mean_absolute_error: 0.0704   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  82/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0098 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 124/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0096 - mean_absolute_error: 0.0701"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 163/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0094 - mean_absolute_error: 0.0698"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 203/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 246/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 286/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 327/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 369/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 412/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 456/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0692"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 499/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0090 - mean_absolute_error: 0.0692"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 540/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0090 - mean_absolute_error: 0.0692"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 582/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0090 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 624/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0090 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 667/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0090 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 710/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0090 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 753/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0090 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 797/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0090 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 835/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 871/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 911/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 951/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 990/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1031/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1070/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1110/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1151/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1191/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1231/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1269/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1308/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1349/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1389/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1429/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1468/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1507/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0091 - mean_absolute_error: 0.0694 - val_loss: 0.0086 - val_mean_absolute_error: 0.0685\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:22\u001b[0m 54ms/step - loss: 0.0093 - mean_absolute_error: 0.0665"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  41/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0107 - mean_absolute_error: 0.0710   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  84/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0100 - mean_absolute_error: 0.0706"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 124/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0097 - mean_absolute_error: 0.0702"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 164/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0094 - mean_absolute_error: 0.0698"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 204/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 247/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 288/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 329/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 369/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 409/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 449/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 489/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 530/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 571/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 612/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 652/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 693/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 733/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 775/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 818/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 858/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 897/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 934/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 975/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1015/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0693"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1055/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1095/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1137/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1181/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1223/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1262/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1300/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1339/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1382/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1423/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1464/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1505/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0091 - mean_absolute_error: 0.0694 - val_loss: 0.0086 - val_mean_absolute_error: 0.0681\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:26\u001b[0m 57ms/step - loss: 0.0300 - mean_absolute_error: 0.0890"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  43/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0113 - mean_absolute_error: 0.0715   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  82/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0104 - mean_absolute_error: 0.0710"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 122/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0100 - mean_absolute_error: 0.0704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 162/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0097 - mean_absolute_error: 0.0701"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 204/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0095 - mean_absolute_error: 0.0698"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 248/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0094 - mean_absolute_error: 0.0697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 288/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0094 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 330/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0696"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 371/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0093 - mean_absolute_error: 0.0695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 411/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 451/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 495/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0092 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 535/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 575/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 615/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 655/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 695/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 734/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 773/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 813/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 854/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 895/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 935/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 975/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1014/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1054/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1094/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1134/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1175/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1216/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1256/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1295/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1334/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1374/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1415/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1454/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1494/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0091 - mean_absolute_error: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0091 - mean_absolute_error: 0.0694 - val_loss: 0.0087 - val_mean_absolute_error: 0.0687\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/439\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m26s\u001b[0m 61ms/step - loss: 0.0151 - mean_absolute_error: 0.0865"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 39/439\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0083 - mean_absolute_error: 0.0656  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 79/439\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0084 - mean_absolute_error: 0.0661"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m122/439\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0085 - mean_absolute_error: 0.0665"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m165/439\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0085 - mean_absolute_error: 0.0668"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m209/439\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0086 - mean_absolute_error: 0.0672"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m256/439\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0086 - mean_absolute_error: 0.0675"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m299/439\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0086 - mean_absolute_error: 0.0677"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m342/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0086 - mean_absolute_error: 0.0678"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m386/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0086 - mean_absolute_error: 0.0679"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m431/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0087 - mean_absolute_error: 0.0680"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m439/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0087 - mean_absolute_error: 0.0680\n"
     ]
    }
   ],
   "source": [
    "history = compile_and_fit(linear, single_step_window)\n",
    "\n",
    "val_performance['Linear'] = linear.evaluate(single_step_window.val, return_dict=True)\n",
    "performance['Linear'] = linear.evaluate(single_step_window.test, verbose=0, return_dict=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7U9XukYh8beN"
   },
   "source": [
    "Like the `baseline` model, the linear model can be called on batches of wide windows. Used this way the model makes a set of independent predictions on consecutive time steps. The `time` axis acts like another `batch` axis. There are no interactions between the predictions at each time step.\n",
    "\n",
    "![A single step prediction](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/wide_window.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:51.923625Z",
     "iopub.status.busy": "2024-08-16T02:37:51.922917Z",
     "iopub.status.idle": "2024-08-16T02:37:51.930574Z",
     "shell.execute_reply": "2024-08-16T02:37:51.929851Z"
    },
    "id": "K9UVM5Sw9KQN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (32, 24, 19)\n",
      "Output shape: (32, 24, 1)\n"
     ]
    }
   ],
   "source": [
    "print('Input shape:', wide_window.example[0].shape)\n",
    "print('Output shape:', linear(wide_window.example[0]).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X-CGj85oKaOG"
   },
   "source": [
    "Here is the plot of its example predictions on the `wide_window`, note how in many cases the prediction is clearly better than just returning the input temperature, but in a few cases it's worse:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:51.934033Z",
     "iopub.status.busy": "2024-08-16T02:37:51.933419Z",
     "iopub.status.idle": "2024-08-16T02:37:52.330400Z",
     "shell.execute_reply": "2024-08-16T02:37:52.329720Z"
    },
    "id": "bCC8VVo-OvwV"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wide_window.plot(linear)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Is51vU8EMl6c"
   },
   "source": [
    "One advantage to linear models is that they're relatively simple to  interpret.\n",
    "You can pull out the layer's weights and visualize the weight assigned to each input:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:52.334464Z",
     "iopub.status.busy": "2024-08-16T02:37:52.334170Z",
     "iopub.status.idle": "2024-08-16T02:37:52.516090Z",
     "shell.execute_reply": "2024-08-16T02:37:52.515475Z"
    },
    "id": "d4uCTbsmK8VI"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x = range(len(train_df.columns)),\n",
    "        height=linear.layers[0].kernel[:,0].numpy())\n",
    "axis = plt.gca()\n",
    "axis.set_xticks(range(len(train_df.columns)))\n",
    "_ = axis.set_xticklabels(train_df.columns, rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ylng7215boIY"
   },
   "source": [
    "Sometimes the model doesn't even place the most weight on the input `T (degC)`. This is one of the risks of random initialization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W18e6da1cNbw"
   },
   "source": [
    "### Dense\n",
    "\n",
    "Before applying models that actually operate on multiple time-steps, it's worth checking the performance of deeper, more powerful, single input step models.\n",
    "\n",
    "Here's a model similar to the `linear` model, except it stacks several a few `Dense` layers between the input and the output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:37:52.519913Z",
     "iopub.status.busy": "2024-08-16T02:37:52.519283Z",
     "iopub.status.idle": "2024-08-16T02:38:15.445950Z",
     "shell.execute_reply": "2024-08-16T02:38:15.445222Z"
    },
    "id": "Z86WkYp7cNAD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m45:10\u001b[0m 2s/step - loss: 1.1741 - mean_absolute_error: 0.8577"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  36/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.5730 - mean_absolute_error: 0.5364  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  72/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.3825 - mean_absolute_error: 0.4079"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 108/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.2954 - mean_absolute_error: 0.3417"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 144/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.2442 - mean_absolute_error: 0.3004"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 180/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.2100 - mean_absolute_error: 0.2716"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 216/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1854 - mean_absolute_error: 0.2501"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 252/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1666 - mean_absolute_error: 0.2335"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 288/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1519 - mean_absolute_error: 0.2201"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 324/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1399 - mean_absolute_error: 0.2091"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 361/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1297 - mean_absolute_error: 0.1995"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 401/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1205 - mean_absolute_error: 0.1907"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 440/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1129 - mean_absolute_error: 0.1833"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 476/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1069 - mean_absolute_error: 0.1774"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 513/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1014 - mean_absolute_error: 0.1720"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 549/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0967 - mean_absolute_error: 0.1673"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 585/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0925 - mean_absolute_error: 0.1630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 621/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0887 - mean_absolute_error: 0.1592"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 657/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0852 - mean_absolute_error: 0.1557"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 694/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0820 - mean_absolute_error: 0.1524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 731/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0791 - mean_absolute_error: 0.1493"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 768/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0765 - mean_absolute_error: 0.1465"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 804/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0741 - mean_absolute_error: 0.1440"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 841/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0718 - mean_absolute_error: 0.1416"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 878/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0697 - mean_absolute_error: 0.1394"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 914/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0677 - mean_absolute_error: 0.1373"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 950/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0660 - mean_absolute_error: 0.1354"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 986/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0643 - mean_absolute_error: 0.1336"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1023/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0627 - mean_absolute_error: 0.1319"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1059/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0612 - mean_absolute_error: 0.1303"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1095/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0598 - mean_absolute_error: 0.1288"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1132/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0585 - mean_absolute_error: 0.1273"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1170/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0572 - mean_absolute_error: 0.1259"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1208/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0559 - mean_absolute_error: 0.1245"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1245/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0548 - mean_absolute_error: 0.1232"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1283/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0537 - mean_absolute_error: 0.1220"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1321/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0526 - mean_absolute_error: 0.1208"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1358/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0517 - mean_absolute_error: 0.1197"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1396/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0507 - mean_absolute_error: 0.1186"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1433/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0498 - mean_absolute_error: 0.1176"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1470/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0490 - mean_absolute_error: 0.1167"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1506/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0482 - mean_absolute_error: 0.1158"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0476 - mean_absolute_error: 0.1151"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - loss: 0.0476 - mean_absolute_error: 0.1151 - val_loss: 0.0084 - val_mean_absolute_error: 0.0669\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:25\u001b[0m 56ms/step - loss: 0.0064 - mean_absolute_error: 0.0582"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  33/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0089 - mean_absolute_error: 0.0664   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  70/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0087 - mean_absolute_error: 0.0664"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 107/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0087 - mean_absolute_error: 0.0667"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 143/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0086 - mean_absolute_error: 0.0668"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 181/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0086 - mean_absolute_error: 0.0667"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 221/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0086 - mean_absolute_error: 0.0667"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 258/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0085 - mean_absolute_error: 0.0665"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 294/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0085 - mean_absolute_error: 0.0664"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 331/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0085 - mean_absolute_error: 0.0663"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 369/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0084 - mean_absolute_error: 0.0662"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 407/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0084 - mean_absolute_error: 0.0661"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 445/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0084 - mean_absolute_error: 0.0659"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 482/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0083 - mean_absolute_error: 0.0659"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 519/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0083 - mean_absolute_error: 0.0658"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 557/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0083 - mean_absolute_error: 0.0657"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 594/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0083 - mean_absolute_error: 0.0657"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 630/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0083 - mean_absolute_error: 0.0656"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 668/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0083 - mean_absolute_error: 0.0656"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 706/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0083 - mean_absolute_error: 0.0655"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 743/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0655"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 779/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0654"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 815/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0654"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 852/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0654"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 889/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0653"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 925/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0653"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 962/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0653"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 999/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0652"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1035/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0652"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1071/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0652"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1107/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0652"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1144/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0652"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1181/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0652"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1217/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0652"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1254/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0651"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1291/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0651"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1328/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0651"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1366/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0651"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1403/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0651"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1440/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0650"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1478/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0650"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1514/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0650"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0081 - mean_absolute_error: 0.0650 - val_loss: 0.0071 - val_mean_absolute_error: 0.0604\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:26\u001b[0m 57ms/step - loss: 0.0096 - mean_absolute_error: 0.0701"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  38/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0629   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  73/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 110/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0080 - mean_absolute_error: 0.0630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 148/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0080 - mean_absolute_error: 0.0630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 187/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0079 - mean_absolute_error: 0.0630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 227/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0079 - mean_absolute_error: 0.0630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 264/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0079 - mean_absolute_error: 0.0628"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 300/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0078 - mean_absolute_error: 0.0627"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 337/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0078 - mean_absolute_error: 0.0627"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 374/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0078 - mean_absolute_error: 0.0626"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 411/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0077 - mean_absolute_error: 0.0625"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 448/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0077 - mean_absolute_error: 0.0625"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 485/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0077 - mean_absolute_error: 0.0624"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 521/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0077 - mean_absolute_error: 0.0624"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 558/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0077 - mean_absolute_error: 0.0624"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 594/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0077 - mean_absolute_error: 0.0624"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 630/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0077 - mean_absolute_error: 0.0623"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 665/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0077 - mean_absolute_error: 0.0623"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 701/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0077 - mean_absolute_error: 0.0623"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 737/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0623"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 774/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0622"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 810/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0622"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 847/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0622"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 884/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0622"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 921/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0621"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 958/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0621"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 993/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0621"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1032/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0621"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1069/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0621"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1107/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0620"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1144/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0620"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1182/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0620"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1219/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0620"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1256/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0620"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1294/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0620"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1332/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0620"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1370/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0620"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1406/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0619"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1442/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0619"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1478/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0619"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1516/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0619"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0075 - mean_absolute_error: 0.0619 - val_loss: 0.0071 - val_mean_absolute_error: 0.0611\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:26\u001b[0m 57ms/step - loss: 0.0093 - mean_absolute_error: 0.0729"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  36/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0652   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  73/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0645"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 110/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0079 - mean_absolute_error: 0.0637"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 147/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0078 - mean_absolute_error: 0.0632"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 185/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0077 - mean_absolute_error: 0.0628"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 226/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0077 - mean_absolute_error: 0.0624"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 265/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0622"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 304/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0620"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 342/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0618"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 379/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0617"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 416/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0616"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 453/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0614"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 489/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0614"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 526/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0613"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 564/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0612"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 601/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0612"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 636/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0611"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 672/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0611"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 708/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0610"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 744/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0610"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 781/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0609"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 817/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0609"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 855/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0609"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 892/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0608"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 928/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0608"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 964/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0608"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1003/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1039/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1079/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1118/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1157/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1195/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1233/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1269/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1305/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1343/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1380/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1417/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0606"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1453/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0606"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1490/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0606"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1526/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0606"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0072 - mean_absolute_error: 0.0606 - val_loss: 0.0066 - val_mean_absolute_error: 0.0564\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:24\u001b[0m 55ms/step - loss: 0.0065 - mean_absolute_error: 0.0647"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  35/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0605   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  71/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0611"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 108/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0613"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 145/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0076 - mean_absolute_error: 0.0612"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 183/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0610"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 220/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0608"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 258/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0606"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 293/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0604"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 329/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0603"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 364/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0602"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 400/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0600"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 438/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0600"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 477/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0599"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 514/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0599"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 552/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0599"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 590/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0598"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 630/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0598"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 668/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0598"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 707/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0598"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 744/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0598"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 780/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0598"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 816/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0598"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 852/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0598"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 889/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0598"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 925/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0598"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 960/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 995/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1031/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1068/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1106/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1144/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1180/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1217/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1254/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1291/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1329/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0596"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1366/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0596"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1402/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0596"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1438/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0596"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1474/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0596"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1511/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0596"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0071 - mean_absolute_error: 0.0596 - val_loss: 0.0065 - val_mean_absolute_error: 0.0561\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:22\u001b[0m 54ms/step - loss: 0.0165 - mean_absolute_error: 0.0842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  40/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0085 - mean_absolute_error: 0.0615   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  78/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0079 - mean_absolute_error: 0.0605"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 116/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0077 - mean_absolute_error: 0.0600"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 156/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 194/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0595"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 232/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0593"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 269/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0592"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 305/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0591"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 342/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0590"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 379/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0589"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 415/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0588"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 451/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0588"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 488/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0588"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 525/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 562/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 600/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 637/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 674/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 709/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 747/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 784/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 822/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 859/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 896/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 932/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 967/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1004/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1040/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1077/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1114/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1151/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1187/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1223/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1258/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1295/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1331/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1368/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1407/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1444/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1481/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1516/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0069 - mean_absolute_error: 0.0587 - val_loss: 0.0065 - val_mean_absolute_error: 0.0564\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m   1/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:28\u001b[0m 57ms/step - loss: 0.0055 - mean_absolute_error: 0.0559"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  36/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0074 - mean_absolute_error: 0.0601   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  73/1534\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.0073 - mean_absolute_error: 0.0597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 109/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0594"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 146/1534\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0593"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 184/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0592"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 225/1534\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0072 - mean_absolute_error: 0.0591"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 265/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0590"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 302/1534\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0589"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 340/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0588"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 379/1534\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0588"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 416/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 452/1534\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 488/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0586"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 525/1534\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0586"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 563/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0586"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 601/1534\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0586"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 640/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0585"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 677/1534\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0585"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 713/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0585"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 750/1534\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0585"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 786/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0585"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 822/1534\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0585"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 859/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0585"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 895/1534\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0585"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 931/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0585"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 968/1534\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1006/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1043/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1079/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1115/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1153/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1192/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1231/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1270/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1306/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1342/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1380/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1417/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1454/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1491/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1529/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1534/1534\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0069 - mean_absolute_error: 0.0584 - val_loss: 0.0068 - val_mean_absolute_error: 0.0572\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/439\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m28s\u001b[0m 65ms/step - loss: 0.0041 - mean_absolute_error: 0.0466"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 40/439\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0075 - mean_absolute_error: 0.0571  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 79/439\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0071 - mean_absolute_error: 0.0568"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m120/439\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0568"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m160/439\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0070 - mean_absolute_error: 0.0569"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m200/439\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0569"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m241/439\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0069 - mean_absolute_error: 0.0569"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m281/439\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0068 - mean_absolute_error: 0.0569"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m321/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0068 - mean_absolute_error: 0.0570"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m360/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0068 - mean_absolute_error: 0.0570"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m400/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0068 - mean_absolute_error: 0.0570"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m439/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0068 - mean_absolute_error: 0.0570\n"
     ]
    }
   ],
   "source": [
    "dense = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(units=64, activation='relu'),\n",
    "    tf.keras.layers.Dense(units=64, activation='relu'),\n",
    "    tf.keras.layers.Dense(units=1)\n",
    "])\n",
    "\n",
    "history = compile_and_fit(dense, single_step_window)\n",
    "\n",
    "val_performance['Dense'] = dense.evaluate(single_step_window.val, return_dict=True)\n",
    "performance['Dense'] = dense.evaluate(single_step_window.test, verbose=0, return_dict=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j5dv_whJdswH"
   },
   "source": [
    "### Multi-step dense\n",
    "\n",
    "A single-time-step model has no context for the current values of its inputs. It can't see how the input features are changing over time. To address this issue the model needs access to multiple time steps when making predictions:\n",
    "\n",
    "![Three time steps are used for each prediction.](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/conv_window.png?raw=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zac-ti8agbJ7"
   },
   "source": [
    "The `baseline`, `linear` and `dense` models handled each time step independently. Here the model will take multiple time steps as input to produce a single output.\n",
    "\n",
    "Create a `WindowGenerator` that will produce batches of three-hour inputs and one-hour labels:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gtN4BwZ37niR"
   },
   "source": [
    "Note that the `Window`'s `shift` parameter is relative to the end of the two windows.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:38:15.450848Z",
     "iopub.status.busy": "2024-08-16T02:38:15.449997Z",
     "iopub.status.idle": "2024-08-16T02:38:15.455835Z",
     "shell.execute_reply": "2024-08-16T02:38:15.455206Z"
    },
    "id": "lBh0j5djUKY2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total window size: 4\n",
       "Input indices: [0 1 2]\n",
       "Label indices: [3]\n",
       "Label column name(s): ['T (degC)']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CONV_WIDTH = 3\n",
    "conv_window = WindowGenerator(\n",
    "    input_width=CONV_WIDTH,\n",
    "    label_width=1,\n",
    "    shift=1,\n",
    "    label_columns=['T (degC)'])\n",
    "\n",
    "conv_window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:38:15.458935Z",
     "iopub.status.busy": "2024-08-16T02:38:15.458308Z",
     "iopub.status.idle": "2024-08-16T02:38:15.961227Z",
     "shell.execute_reply": "2024-08-16T02:38:15.960559Z"
    },
    "id": "dCQ5gvs68Xkd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Given 3 hours of inputs, predict 1 hour into the future.')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conv_window.plot()\n",
    "plt.suptitle(\"Given 3 hours of inputs, predict 1 hour into the future.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "We0HdMxKeqB_"
   },
   "source": [
    "You could train a `dense` model on a multiple-input-step window by adding a `tf.keras.layers.Flatten` as the first layer of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:38:15.965375Z",
     "iopub.status.busy": "2024-08-16T02:38:15.964760Z",
     "iopub.status.idle": "2024-08-16T02:38:15.972273Z",
     "shell.execute_reply": "2024-08-16T02:38:15.971670Z"
    },
    "id": "oNQnUOkOnC1G"
   },
   "outputs": [],
   "source": [
    "multi_step_dense = tf.keras.Sequential([\n",
    "    # Shape: (time, features) => (time*features)\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(units=32, activation='relu'),\n",
    "    tf.keras.layers.Dense(units=32, activation='relu'),\n",
    "    tf.keras.layers.Dense(units=1),\n",
    "    # Add back the time dimension.\n",
    "    # Shape: (outputs) => (1, outputs)\n",
    "    tf.keras.layers.Reshape([1, -1]),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:38:15.975589Z",
     "iopub.status.busy": "2024-08-16T02:38:15.975013Z",
     "iopub.status.idle": "2024-08-16T02:38:16.080134Z",
     "shell.execute_reply": "2024-08-16T02:38:16.079414Z"
    },
    "id": "cayD74luo4Vq"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (32, 3, 19)\n",
      "Output shape: (32, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "print('Input shape:', conv_window.example[0].shape)\n",
    "print('Output shape:', multi_step_dense(conv_window.example[0]).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:38:16.083488Z",
     "iopub.status.busy": "2024-08-16T02:38:16.083210Z",
     "iopub.status.idle": "2024-08-16T02:38:41.550893Z",
     "shell.execute_reply": "2024-08-16T02:38:41.550016Z"
    },
    "id": "fu91yEbRo9-J"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/438\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m28s\u001b[0m 64ms/step - loss: 0.0044 - mean_absolute_error: 0.0532"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 38/438\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0085 - mean_absolute_error: 0.0606  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 83/438\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0086 - mean_absolute_error: 0.0609"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m125/438\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0085 - mean_absolute_error: 0.0609"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m169/438\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0085 - mean_absolute_error: 0.0610"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m213/438\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0084 - mean_absolute_error: 0.0609"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m256/438\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0083 - mean_absolute_error: 0.0609"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m298/438\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0609"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m341/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0082 - mean_absolute_error: 0.0609"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m383/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0081 - mean_absolute_error: 0.0608"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m430/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0080 - mean_absolute_error: 0.0608"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m438/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0080 - mean_absolute_error: 0.0608\n"
     ]
    }
   ],
   "source": [
    "history = compile_and_fit(multi_step_dense, conv_window)\n",
    "\n",
    "IPython.display.clear_output()\n",
    "val_performance['Multi step dense'] = multi_step_dense.evaluate(conv_window.val, return_dict=True)\n",
    "performance['Multi step dense'] = multi_step_dense.evaluate(conv_window.test, verbose=0, return_dict=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:38:41.554951Z",
     "iopub.status.busy": "2024-08-16T02:38:41.554236Z",
     "iopub.status.idle": "2024-08-16T02:38:41.962838Z",
     "shell.execute_reply": "2024-08-16T02:38:41.962182Z"
    },
    "id": "tnqdXYT6pkEh"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conv_window.plot(multi_step_dense)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gWfrsP8mq8lV"
   },
   "source": [
    "The main down-side of this approach is that the resulting model can only be executed on input windows of exactly this shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:38:41.966655Z",
     "iopub.status.busy": "2024-08-16T02:38:41.966181Z",
     "iopub.status.idle": "2024-08-16T02:38:41.971893Z",
     "shell.execute_reply": "2024-08-16T02:38:41.971240Z"
    },
    "id": "j-q6tz5Yq8Jk"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (32, 24, 19)\n",
      "\n",
      "ValueError:Exception encountered when calling Sequential.call().\n",
      "\n",
      "\u001b[1mInput 0 of layer \"dense_4\" is incompatible with the layer: expected axis -1 of input shape to have value 57, but received input with shape (32, 456)\u001b[0m\n",
      "\n",
      "Arguments received by Sequential.call():\n",
      "  • inputs=tf.Tensor(shape=(32, 24, 19), dtype=float32)\n",
      "  • training=None\n",
      "  • mask=None\n"
     ]
    }
   ],
   "source": [
    "print('Input shape:', wide_window.example[0].shape)\n",
    "try:\n",
    "  print('Output shape:', multi_step_dense(wide_window.example[0]).shape)\n",
    "except Exception as e:\n",
    "  print(f'\\n{type(e).__name__}:{e}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bvvajm3ip_8V"
   },
   "source": [
    "The convolutional models in the next section fix this problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CrpU6gwSJome"
   },
   "source": [
    "### Convolution neural network\n",
    "\n",
    "A convolution layer (`tf.keras.layers.Conv1D`) also takes multiple time steps as input to each prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cdLBwoaHmsWb"
   },
   "source": [
    "Below is the **same** model as `multi_step_dense`, re-written with a convolution.\n",
    "\n",
    "Note the changes:\n",
    "* The `tf.keras.layers.Flatten` and the first `tf.keras.layers.Dense` are replaced by a `tf.keras.layers.Conv1D`.\n",
    "* The `tf.keras.layers.Reshape` is no longer necessary since the convolution keeps the time axis in its output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:38:41.975421Z",
     "iopub.status.busy": "2024-08-16T02:38:41.974832Z",
     "iopub.status.idle": "2024-08-16T02:38:41.981326Z",
     "shell.execute_reply": "2024-08-16T02:38:41.980715Z"
    },
    "id": "5azaMBj4ac9t"
   },
   "outputs": [],
   "source": [
    "conv_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv1D(filters=32,\n",
    "                           kernel_size=(CONV_WIDTH,),\n",
    "                           activation='relu'),\n",
    "    tf.keras.layers.Dense(units=32, activation='relu'),\n",
    "    tf.keras.layers.Dense(units=1),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ftaH6B5ECRiK"
   },
   "source": [
    "Run it on an example batch to check that the model produces outputs with the expected shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:38:41.984497Z",
     "iopub.status.busy": "2024-08-16T02:38:41.984122Z",
     "iopub.status.idle": "2024-08-16T02:38:42.015576Z",
     "shell.execute_reply": "2024-08-16T02:38:42.014876Z"
    },
    "id": "5YNgt1-e98lH"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv model on `conv_window`\n",
      "Input shape: (32, 3, 19)\n",
      "Output shape: (32, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"Conv model on `conv_window`\")\n",
    "print('Input shape:', conv_window.example[0].shape)\n",
    "print('Output shape:', conv_model(conv_window.example[0]).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5m4kC-jGCY3x"
   },
   "source": [
    "Train and evaluate it on the ` conv_window` and it should give performance similar to the `multi_step_dense` model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:38:42.018560Z",
     "iopub.status.busy": "2024-08-16T02:38:42.018331Z",
     "iopub.status.idle": "2024-08-16T02:39:25.391185Z",
     "shell.execute_reply": "2024-08-16T02:39:25.390048Z"
    },
    "id": "QDVWdm4paUW7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/438\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m27s\u001b[0m 63ms/step - loss: 0.0046 - mean_absolute_error: 0.0502"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 36/438\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0051 - mean_absolute_error: 0.0528  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 75/438\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0053 - mean_absolute_error: 0.0532"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m114/438\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0054 - mean_absolute_error: 0.0533"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m154/438\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0055 - mean_absolute_error: 0.0533"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m192/438\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0056 - mean_absolute_error: 0.0534"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m231/438\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0056 - mean_absolute_error: 0.0535"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m270/438\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0057 - mean_absolute_error: 0.0537"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m307/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0058 - mean_absolute_error: 0.0538"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m344/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0058 - mean_absolute_error: 0.0538"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m382/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0059 - mean_absolute_error: 0.0539"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m420/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0059 - mean_absolute_error: 0.0540"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m438/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0059 - mean_absolute_error: 0.0540\n"
     ]
    }
   ],
   "source": [
    "history = compile_and_fit(conv_model, conv_window)\n",
    "\n",
    "IPython.display.clear_output()\n",
    "val_performance['Conv'] = conv_model.evaluate(conv_window.val, return_dict=True)\n",
    "performance['Conv'] = conv_model.evaluate(conv_window.test, verbose=0, return_dict=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sYRipDeXs0Kr"
   },
   "source": [
    "The difference between this `conv_model` and the `multi_step_dense` model is that the `conv_model` can be run on inputs of any length. The convolutional layer is applied to a sliding window of inputs:\n",
    "\n",
    "![Executing a convolutional model on a sequence](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/wide_conv_window.png?raw=1)\n",
    "\n",
    "If you run it on wider input, it produces wider output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:39:25.395237Z",
     "iopub.status.busy": "2024-08-16T02:39:25.394927Z",
     "iopub.status.idle": "2024-08-16T02:39:25.483901Z",
     "shell.execute_reply": "2024-08-16T02:39:25.483016Z"
    },
    "id": "hoqccxx9r5jF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wide window\n",
      "Input shape: (32, 24, 19)\n",
      "Labels shape: (32, 24, 1)\n",
      "Output shape: (32, 22, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0000 00:00:1723775965.411205   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.430143   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.431321   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.432466   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.433586   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.434754   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.435894   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.437041   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.438188   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.439356   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.440494   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.441646   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.442802   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.443962   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.459825   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n"
     ]
    }
   ],
   "source": [
    "print(\"Wide window\")\n",
    "print('Input shape:', wide_window.example[0].shape)\n",
    "print('Labels shape:', wide_window.example[1].shape)\n",
    "print('Output shape:', conv_model(wide_window.example[0]).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h_WGxtLIHhRF"
   },
   "source": [
    "Note that the output is shorter than the input. To make training or plotting work, you need the labels, and prediction to have the same length. So build a `WindowGenerator` to produce wide windows with a few extra input time steps so the label and prediction lengths match:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:39:25.487388Z",
     "iopub.status.busy": "2024-08-16T02:39:25.487056Z",
     "iopub.status.idle": "2024-08-16T02:39:25.493585Z",
     "shell.execute_reply": "2024-08-16T02:39:25.492756Z"
    },
    "id": "_VPvJ_VwTc0f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total window size: 27\n",
       "Input indices: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
       " 24 25]\n",
       "Label indices: [ 3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26]\n",
       "Label column name(s): ['T (degC)']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LABEL_WIDTH = 24\n",
    "INPUT_WIDTH = LABEL_WIDTH + (CONV_WIDTH - 1)\n",
    "wide_conv_window = WindowGenerator(\n",
    "    input_width=INPUT_WIDTH,\n",
    "    label_width=LABEL_WIDTH,\n",
    "    shift=1,\n",
    "    label_columns=['T (degC)'])\n",
    "\n",
    "wide_conv_window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:39:25.496668Z",
     "iopub.status.busy": "2024-08-16T02:39:25.496424Z",
     "iopub.status.idle": "2024-08-16T02:39:25.655089Z",
     "shell.execute_reply": "2024-08-16T02:39:25.654066Z"
    },
    "id": "gtqlWYXeKXej"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wide conv window\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (32, 26, 19)\n",
      "Labels shape: (32, 24, 1)\n",
      "Output shape: (32, 24, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0000 00:00:1723775965.630979   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.632244   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.633402   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.634575   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.635713   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.636852   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.637993   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.639168   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.640323   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.641462   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.642607   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.643764   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.644903   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.646054   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n",
      "W0000 00:00:1723775965.647249   80658 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced\n"
     ]
    }
   ],
   "source": [
    "print(\"Wide conv window\")\n",
    "print('Input shape:', wide_conv_window.example[0].shape)\n",
    "print('Labels shape:', wide_conv_window.example[1].shape)\n",
    "print('Output shape:', conv_model(wide_conv_window.example[0]).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yzxbbS56cSBV"
   },
   "source": [
    "Now, you can plot the model's predictions on a wider window. Note the 3 input time steps before the first prediction. Every prediction here is based on the 3 preceding time steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:39:25.658669Z",
     "iopub.status.busy": "2024-08-16T02:39:25.658385Z",
     "iopub.status.idle": "2024-08-16T02:39:26.093968Z",
     "shell.execute_reply": "2024-08-16T02:39:26.093294Z"
    },
    "id": "gR7VyL45UuEe"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wide_conv_window.plot(conv_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H4crpOcoMlSe"
   },
   "source": [
    "### Recurrent neural network\n",
    "\n",
    "A Recurrent Neural Network (RNN) is a type of neural network well-suited to time series data. RNNs process a time series step-by-step, maintaining an internal state from time-step to time-step.\n",
    "\n",
    "You can learn more in the [Text generation with an RNN](https://www.tensorflow.org/text/tutorials/text_generation) tutorial and the [Recurrent Neural Networks (RNN) with Keras](https://www.tensorflow.org/guide/keras/rnn) guide.\n",
    "\n",
    "In this tutorial, you will use an RNN layer called Long Short-Term Memory (`tf.keras.layers.LSTM`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vfQbHSMb1ATa"
   },
   "source": [
    "An important constructor argument for all Keras RNN layers, such as `tf.keras.layers.LSTM`, is the `return_sequences` argument. This setting can configure the layer in one of two ways:\n",
    "\n",
    "1. If `False`, the default, the layer only returns the output of the final time step, giving the model time to warm up its internal state before making a single prediction:\n",
    "\n",
    "![An LSTM warming up and making a single prediction](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/lstm_1_window.png?raw=1)\n",
    "\n",
    "2. If `True`, the layer returns an output for each input. This is useful for:\n",
    "  * Stacking RNN layers.\n",
    "  * Training a model on multiple time steps simultaneously.\n",
    "\n",
    "![An LSTM making a prediction after every time step](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/lstm_many_window.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:39:26.098158Z",
     "iopub.status.busy": "2024-08-16T02:39:26.097908Z",
     "iopub.status.idle": "2024-08-16T02:39:26.107912Z",
     "shell.execute_reply": "2024-08-16T02:39:26.107312Z"
    },
    "id": "DXKLCJy8nWNU"
   },
   "outputs": [],
   "source": [
    "lstm_model = tf.keras.models.Sequential([\n",
    "    # Shape [batch, time, features] => [batch, time, lstm_units]\n",
    "    tf.keras.layers.LSTM(32, return_sequences=True),\n",
    "    # Shape => [batch, time, features]\n",
    "    tf.keras.layers.Dense(units=1)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F124B00KZcLC"
   },
   "source": [
    "With `return_sequences=True`, the model can be trained on 24 hours of data at a time.\n",
    "\n",
    "Note: This will give a pessimistic view of the model's performance. On the first time step, the model has no access to previous steps and, therefore, can't do any better than the simple `linear` and `dense` models shown earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:39:26.111207Z",
     "iopub.status.busy": "2024-08-16T02:39:26.110971Z",
     "iopub.status.idle": "2024-08-16T02:39:26.344663Z",
     "shell.execute_reply": "2024-08-16T02:39:26.343963Z"
    },
    "id": "eZEROCQVYV6q"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (32, 24, 19)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape: (32, 24, 1)\n"
     ]
    }
   ],
   "source": [
    "print('Input shape:', wide_window.example[0].shape)\n",
    "print('Output shape:', lstm_model(wide_window.example[0]).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:39:26.347833Z",
     "iopub.status.busy": "2024-08-16T02:39:26.347582Z",
     "iopub.status.idle": "2024-08-16T02:40:23.904870Z",
     "shell.execute_reply": "2024-08-16T02:40:23.904040Z"
    },
    "id": "uvdWRl1e9WJl"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/438\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m28s\u001b[0m 65ms/step - loss: 0.0062 - mean_absolute_error: 0.0525"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 24/438\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0058 - mean_absolute_error: 0.0527  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 49/438\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 74/438\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0523"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m100/438\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0523"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m127/438\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m153/438\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m178/438\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m204/438\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m230/438\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m257/438\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m283/438\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m309/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m336/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m362/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m387/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m414/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m438/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0057 - mean_absolute_error: 0.0524\n"
     ]
    }
   ],
   "source": [
    "history = compile_and_fit(lstm_model, wide_window)\n",
    "\n",
    "IPython.display.clear_output()\n",
    "val_performance['LSTM'] = lstm_model.evaluate(wide_window.val, return_dict=True)\n",
    "performance['LSTM'] = lstm_model.evaluate(wide_window.test, verbose=0, return_dict=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:40:23.909062Z",
     "iopub.status.busy": "2024-08-16T02:40:23.908785Z",
     "iopub.status.idle": "2024-08-16T02:40:24.324574Z",
     "shell.execute_reply": "2024-08-16T02:40:24.323790Z"
    },
    "id": "NwAOWCVgB26e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wide_window.plot(lstm_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pYglOCKehi8F"
   },
   "source": [
    "### Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2pCk0_rwhi8H"
   },
   "source": [
    "With this dataset typically each of the models does slightly better than the one before it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:40:24.329120Z",
     "iopub.status.busy": "2024-08-16T02:40:24.328857Z",
     "iopub.status.idle": "2024-08-16T02:40:24.333159Z",
     "shell.execute_reply": "2024-08-16T02:40:24.332571Z"
    },
    "id": "dMPev9Nzd4mD"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<MeanAbsoluteError name=mean_absolute_error>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = lstm_model.metrics[1]\n",
    "cm.metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:40:24.336571Z",
     "iopub.status.busy": "2024-08-16T02:40:24.335986Z",
     "iopub.status.idle": "2024-08-16T02:40:24.340579Z",
     "shell.execute_reply": "2024-08-16T02:40:24.339960Z"
    },
    "id": "6is3g113eIIa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Baseline': {'loss': 0.012845644727349281,\n",
       "  'mean_absolute_error': 0.07846628874540329},\n",
       " 'Linear': {'loss': 0.008695926517248154,\n",
       "  'mean_absolute_error': 0.06866316497325897},\n",
       " 'Dense': {'loss': 0.006793886888772249,\n",
       "  'mean_absolute_error': 0.05716359242796898},\n",
       " 'Multi step dense': {'loss': 0.007616413291543722,\n",
       "  'mean_absolute_error': 0.06059327721595764},\n",
       " 'Conv': {'loss': 0.006222909316420555,\n",
       "  'mean_absolute_error': 0.05451442673802376},\n",
       " 'LSTM': {'loss': 0.0056776562705636024,\n",
       "  'mean_absolute_error': 0.05233458802103996}}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:40:24.343438Z",
     "iopub.status.busy": "2024-08-16T02:40:24.343215Z",
     "iopub.status.idle": "2024-08-16T02:40:24.505374Z",
     "shell.execute_reply": "2024-08-16T02:40:24.504735Z"
    },
    "id": "JjEkt488hi8I"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(len(performance))\n",
    "width = 0.3\n",
    "metric_name = 'mean_absolute_error'\n",
    "val_mae = [v[metric_name] for v in val_performance.values()]\n",
    "test_mae = [v[metric_name] for v in performance.values()]\n",
    "\n",
    "plt.ylabel('mean_absolute_error [T (degC), normalized]')\n",
    "plt.bar(x - 0.17, val_mae, width, label='Validation')\n",
    "plt.bar(x + 0.17, test_mae, width, label='Test')\n",
    "plt.xticks(ticks=x, labels=performance.keys(),\n",
    "           rotation=45)\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:40:24.509285Z",
     "iopub.status.busy": "2024-08-16T02:40:24.508690Z",
     "iopub.status.idle": "2024-08-16T02:40:24.512363Z",
     "shell.execute_reply": "2024-08-16T02:40:24.511797Z"
    },
    "id": "cBMCpsdphi8L"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline    : 0.0852\n",
      "Linear      : 0.0663\n",
      "Dense       : 0.0584\n",
      "Multi step dense: 0.0633\n",
      "Conv        : 0.0543\n",
      "LSTM        : 0.0533\n"
     ]
    }
   ],
   "source": [
    "for name, value in performance.items():\n",
    "  print(f'{name:12s}: {value[metric_name]:0.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b5rUJ_2YMWzG"
   },
   "source": [
    "### Multi-output models\n",
    "\n",
    "The models so far all predicted a single output feature, `T (degC)`, for a single time step.\n",
    "\n",
    "All of these models can be converted to predict multiple features just by changing the number of units in the output layer and adjusting the training windows to include all features in the `labels` (`example_labels`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:40:24.515300Z",
     "iopub.status.busy": "2024-08-16T02:40:24.515052Z",
     "iopub.status.idle": "2024-08-16T02:40:24.653362Z",
     "shell.execute_reply": "2024-08-16T02:40:24.652590Z"
    },
    "id": "9Gk0Z91xjOwv"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inputs shape (batch, time, features): (32, 24, 19)\n",
      "Labels shape (batch, time, features): (32, 24, 19)\n"
     ]
    }
   ],
   "source": [
    "single_step_window = WindowGenerator(\n",
    "    # `WindowGenerator` returns all features as labels if you\n",
    "    # don't set the `label_columns` argument.\n",
    "    input_width=1, label_width=1, shift=1)\n",
    "\n",
    "wide_window = WindowGenerator(\n",
    "    input_width=24, label_width=24, shift=1)\n",
    "\n",
    "for example_inputs, example_labels in wide_window.train.take(1):\n",
    "  print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n",
    "  print(f'Labels shape (batch, time, features): {example_labels.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XmcjHfDskX1N"
   },
   "source": [
    "Note above that the `features` axis of the labels now has the same depth as the inputs, instead of `1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9k7S5IHNhSNF"
   },
   "source": [
    "#### Baseline\n",
    "\n",
    "The same baseline model (`Baseline`) can be used here, but this time repeating all features instead of selecting a specific `label_index`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:40:24.657246Z",
     "iopub.status.busy": "2024-08-16T02:40:24.656790Z",
     "iopub.status.idle": "2024-08-16T02:40:24.666615Z",
     "shell.execute_reply": "2024-08-16T02:40:24.665881Z"
    },
    "id": "sqqB9W-pjr5i"
   },
   "outputs": [],
   "source": [
    "baseline = Baseline()\n",
    "baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n",
    "                 metrics=[tf.keras.metrics.MeanAbsoluteError()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:40:24.669644Z",
     "iopub.status.busy": "2024-08-16T02:40:24.669367Z",
     "iopub.status.idle": "2024-08-16T02:40:26.031999Z",
     "shell.execute_reply": "2024-08-16T02:40:26.031236Z"
    },
    "id": "ltQdgaqQjQWu"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/438\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:08\u001b[0m 293ms/step - loss: 0.0831 - mean_absolute_error: 0.1571"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 41/438\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0875 - mean_absolute_error: 0.1589    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 84/438\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0878 - mean_absolute_error: 0.1590"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m128/438\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0880 - mean_absolute_error: 0.1590"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m170/438\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0882 - mean_absolute_error: 0.1591"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m213/438\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0883 - mean_absolute_error: 0.1591"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m257/438\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0884 - mean_absolute_error: 0.1591"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m297/438\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0884 - mean_absolute_error: 0.1591"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m338/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0885 - mean_absolute_error: 0.1591"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m379/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0885 - mean_absolute_error: 0.1591"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m422/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0885 - mean_absolute_error: 0.1591"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m438/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0885 - mean_absolute_error: 0.1591"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m438/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0885 - mean_absolute_error: 0.1591\n"
     ]
    }
   ],
   "source": [
    "val_performance = {}\n",
    "performance = {}\n",
    "val_performance['Baseline'] = baseline.evaluate(wide_window.val, return_dict=True)\n",
    "performance['Baseline'] = baseline.evaluate(wide_window.test, verbose=0, return_dict=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dfbCrf5q3P6n"
   },
   "source": [
    "#### Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:40:26.036208Z",
     "iopub.status.busy": "2024-08-16T02:40:26.035925Z",
     "iopub.status.idle": "2024-08-16T02:40:26.043266Z",
     "shell.execute_reply": "2024-08-16T02:40:26.042461Z"
    },
    "id": "NdpzH1dYjdIN"
   },
   "outputs": [],
   "source": [
    "dense = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(units=64, activation='relu'),\n",
    "    tf.keras.layers.Dense(units=64, activation='relu'),\n",
    "    tf.keras.layers.Dense(units=num_features)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:40:26.047045Z",
     "iopub.status.busy": "2024-08-16T02:40:26.046233Z",
     "iopub.status.idle": "2024-08-16T02:41:14.157780Z",
     "shell.execute_reply": "2024-08-16T02:41:14.157061Z"
    },
    "id": "6uHuU9Cd3PTo"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/439\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m27s\u001b[0m 63ms/step - loss: 0.0662 - mean_absolute_error: 0.1301"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 37/439\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0697 - mean_absolute_error: 0.1328  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 80/439\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0694 - mean_absolute_error: 0.1326"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m121/439\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0693 - mean_absolute_error: 0.1325"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m161/439\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0691 - mean_absolute_error: 0.1323"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m202/439\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0690 - mean_absolute_error: 0.1321"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m244/439\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0689 - mean_absolute_error: 0.1319"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m286/439\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0687 - mean_absolute_error: 0.1316"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m327/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0686 - mean_absolute_error: 0.1315"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m369/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0685 - mean_absolute_error: 0.1313"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m414/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0684 - mean_absolute_error: 0.1312"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m439/439\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0684 - mean_absolute_error: 0.1312\n"
     ]
    }
   ],
   "source": [
    "history = compile_and_fit(dense, single_step_window)\n",
    "\n",
    "IPython.display.clear_output()\n",
    "val_performance['Dense'] = dense.evaluate(single_step_window.val, return_dict=True)\n",
    "performance['Dense'] = dense.evaluate(single_step_window.test, verbose=0, return_dict=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dsc9pur_mHsx"
   },
   "source": [
    "#### RNN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:41:14.162151Z",
     "iopub.status.busy": "2024-08-16T02:41:14.161521Z",
     "iopub.status.idle": "2024-08-16T02:43:12.094671Z",
     "shell.execute_reply": "2024-08-16T02:43:12.093559Z"
    },
    "id": "4QbGLMyomXaz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/438\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m27s\u001b[0m 64ms/step - loss: 0.0667 - mean_absolute_error: 0.1213"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 27/438\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0614 - mean_absolute_error: 0.1192  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 53/438\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0607 - mean_absolute_error: 0.1188"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 79/438\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0604 - mean_absolute_error: 0.1187"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m105/438\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0605 - mean_absolute_error: 0.1187"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m132/438\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0606 - mean_absolute_error: 0.1189"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m158/438\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0607 - mean_absolute_error: 0.1191"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m184/438\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0608 - mean_absolute_error: 0.1192"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m210/438\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0609 - mean_absolute_error: 0.1193"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m236/438\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0609 - mean_absolute_error: 0.1194"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m263/438\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0610 - mean_absolute_error: 0.1194"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m290/438\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0610 - mean_absolute_error: 0.1195"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m316/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0610 - mean_absolute_error: 0.1195"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m343/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0611 - mean_absolute_error: 0.1195"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m369/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0611 - mean_absolute_error: 0.1196"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m396/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0611 - mean_absolute_error: 0.1196"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m423/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0612 - mean_absolute_error: 0.1196"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m438/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0612 - mean_absolute_error: 0.1196\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 4min 33s, sys: 51.8 s, total: 5min 25s\n",
      "Wall time: 1min 57s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "wide_window = WindowGenerator(\n",
    "    input_width=24, label_width=24, shift=1)\n",
    "\n",
    "lstm_model = tf.keras.models.Sequential([\n",
    "    # Shape [batch, time, features] => [batch, time, lstm_units]\n",
    "    tf.keras.layers.LSTM(32, return_sequences=True),\n",
    "    # Shape => [batch, time, features]\n",
    "    tf.keras.layers.Dense(units=num_features)\n",
    "])\n",
    "\n",
    "history = compile_and_fit(lstm_model, wide_window)\n",
    "\n",
    "IPython.display.clear_output()\n",
    "val_performance['LSTM'] = lstm_model.evaluate( wide_window.val, return_dict=True)\n",
    "performance['LSTM'] = lstm_model.evaluate( wide_window.test, verbose=0, return_dict=True)\n",
    "\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UwhY2f_Nn0_K"
   },
   "source": [
    "<a id=\"residual\"></a>\n",
    "\n",
    "#### Advanced: Residual connections\n",
    "\n",
    "The `Baseline` model from earlier took advantage of the fact that the sequence doesn't change drastically from time step to time step. Every model trained in this tutorial so far was randomly initialized, and then had to learn that the output is a a small change from the previous time step.\n",
    "\n",
    "While you can get around this issue with careful initialization, it's  simpler to build this into the model structure.\n",
    "\n",
    "It's common in time series analysis to build models that instead of predicting the next value, predict how the value will change in the next time step. Similarly, <a href=\"https://arxiv.org/abs/1512.03385\" class=\"external\">residual networks</a>—or ResNets—in deep learning refer to architectures where each layer adds to the model's accumulating result.\n",
    "\n",
    "That is how you take advantage of the knowledge that the change should be small.\n",
    "\n",
    "![A model with a residual connection](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/residual.png?raw=1)\n",
    "\n",
    "Essentially, this initializes the model to match the `Baseline`. For this task it helps models converge faster, with slightly better performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yP58A_ORx0kM"
   },
   "source": [
    "This approach can be used in conjunction with any model discussed in this tutorial.\n",
    "\n",
    "Here, it is being applied to the LSTM model, note the use of the `tf.initializers.zeros` to ensure that the initial predicted changes are small, and don't overpower the residual connection. There are no symmetry-breaking concerns for the gradients here, since the `zeros` are only used on the last layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:43:12.098962Z",
     "iopub.status.busy": "2024-08-16T02:43:12.098182Z",
     "iopub.status.idle": "2024-08-16T02:43:12.103167Z",
     "shell.execute_reply": "2024-08-16T02:43:12.102258Z"
    },
    "id": "7YlfnDQC22TQ"
   },
   "outputs": [],
   "source": [
    "class ResidualWrapper(tf.keras.Model):\n",
    "  def __init__(self, model):\n",
    "    super().__init__()\n",
    "    self.model = model\n",
    "\n",
    "  def call(self, inputs, *args, **kwargs):\n",
    "    delta = self.model(inputs, *args, **kwargs)\n",
    "\n",
    "    # The prediction for each time step is the input\n",
    "    # from the previous time step plus the delta\n",
    "    # calculated by the model.\n",
    "    return inputs + delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:43:12.106544Z",
     "iopub.status.busy": "2024-08-16T02:43:12.105957Z",
     "iopub.status.idle": "2024-08-16T02:43:55.912783Z",
     "shell.execute_reply": "2024-08-16T02:43:55.912018Z"
    },
    "id": "NNeH02pspc9B"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/438\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m28s\u001b[0m 65ms/step - loss: 0.0711 - mean_absolute_error: 0.1259"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 25/438\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0631 - mean_absolute_error: 0.1193  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 51/438\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0627 - mean_absolute_error: 0.1189"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 77/438\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0627 - mean_absolute_error: 0.1189"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m102/438\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0628 - mean_absolute_error: 0.1189"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m128/438\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0627 - mean_absolute_error: 0.1189"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m153/438\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0627 - mean_absolute_error: 0.1188"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m179/438\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0626 - mean_absolute_error: 0.1188"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m205/438\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0625 - mean_absolute_error: 0.1187"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m230/438\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0625 - mean_absolute_error: 0.1187"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m255/438\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0624 - mean_absolute_error: 0.1186"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m281/438\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0623 - mean_absolute_error: 0.1186"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m307/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0623 - mean_absolute_error: 0.1185"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m334/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0623 - mean_absolute_error: 0.1185"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m360/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0623 - mean_absolute_error: 0.1185"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m386/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0622 - mean_absolute_error: 0.1184"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m411/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0622 - mean_absolute_error: 0.1184"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m437/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0622 - mean_absolute_error: 0.1184"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m438/438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0622 - mean_absolute_error: 0.1184\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 1min 40s, sys: 19.1 s, total: 1min 59s\n",
      "Wall time: 43.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "residual_lstm = ResidualWrapper(\n",
    "    tf.keras.Sequential([\n",
    "    tf.keras.layers.LSTM(32, return_sequences=True),\n",
    "    tf.keras.layers.Dense(\n",
    "        num_features,\n",
    "        # The predicted deltas should start small.\n",
    "        # Therefore, initialize the output layer with zeros.\n",
    "        kernel_initializer=tf.initializers.zeros())\n",
    "]))\n",
    "\n",
    "history = compile_and_fit(residual_lstm, wide_window)\n",
    "\n",
    "IPython.display.clear_output()\n",
    "val_performance['Residual LSTM'] = residual_lstm.evaluate(wide_window.val, return_dict=True)\n",
    "performance['Residual LSTM'] = residual_lstm.evaluate(wide_window.test, verbose=0, return_dict=True)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I42Er9Du6co1"
   },
   "source": [
    "#### Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LZxR38P_6pUi"
   },
   "source": [
    "Here is the overall performance for these multi-output models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:43:55.916521Z",
     "iopub.status.busy": "2024-08-16T02:43:55.916265Z",
     "iopub.status.idle": "2024-08-16T02:43:56.067971Z",
     "shell.execute_reply": "2024-08-16T02:43:56.067386Z"
    },
    "id": "6XgTK9tnr7rc"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(len(performance))\n",
    "width = 0.3\n",
    "\n",
    "metric_name = 'mean_absolute_error'\n",
    "val_mae = [v[metric_name] for v in val_performance.values()]\n",
    "test_mae = [v[metric_name] for v in performance.values()]\n",
    "\n",
    "plt.bar(x - 0.17, val_mae, width, label='Validation')\n",
    "plt.bar(x + 0.17, test_mae, width, label='Test')\n",
    "plt.xticks(ticks=x, labels=performance.keys(),\n",
    "           rotation=45)\n",
    "plt.ylabel('MAE (average over all outputs)')\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:43:56.071176Z",
     "iopub.status.busy": "2024-08-16T02:43:56.070666Z",
     "iopub.status.idle": "2024-08-16T02:43:56.074535Z",
     "shell.execute_reply": "2024-08-16T02:43:56.073893Z"
    },
    "id": "URz3ajCc6kBj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline       : 0.1638\n",
      "Dense          : 0.1308\n",
      "LSTM           : 0.1215\n",
      "Residual LSTM  : 0.1193\n"
     ]
    }
   ],
   "source": [
    "for name, value in performance.items():\n",
    "  print(f'{name:15s}: {value[metric_name]:0.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Vt2MJhNxwPU"
   },
   "source": [
    "The above performances are averaged across all model outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eYokb7Om2YbK"
   },
   "source": [
    "## Multi-step models\n",
    "\n",
    "Both the single-output and multiple-output models in the previous sections made **single time step predictions**, one hour into the future.\n",
    "\n",
    "This section looks at how to expand these models to make **multiple time step predictions**.\n",
    "\n",
    "In a multi-step prediction, the model needs to learn to predict a range of future values. Thus, unlike a single step model, where only a single future point is predicted, a multi-step model predicts a sequence of the future values.\n",
    "\n",
    "There are two rough approaches to this:\n",
    "\n",
    "1. Single shot predictions where the entire time series is predicted at once.\n",
    "2. Autoregressive predictions where the model only makes single step predictions and its output is fed back as its input.\n",
    "\n",
    "In this section all the models will predict **all the features across all output time steps**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WFsDAwVt4_rq"
   },
   "source": [
    "For the multi-step model, the training data again consists of hourly samples. However, here, the models will learn to predict 24 hours into the future, given 24 hours of the past.\n",
    "\n",
    "Here is a `Window` object that generates these slices from the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:43:56.077825Z",
     "iopub.status.busy": "2024-08-16T02:43:56.077375Z",
     "iopub.status.idle": "2024-08-16T02:43:56.548732Z",
     "shell.execute_reply": "2024-08-16T02:43:56.548037Z"
    },
    "id": "1cFYtsz6XiGw"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total window size: 48\n",
       "Input indices: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
       "Label indices: [24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47]\n",
       "Label column name(s): None"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "OUT_STEPS = 24\n",
    "multi_window = WindowGenerator(input_width=24,\n",
    "                               label_width=OUT_STEPS,\n",
    "                               shift=OUT_STEPS)\n",
    "\n",
    "multi_window.plot()\n",
    "multi_window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5lg8SInh9Jzd"
   },
   "source": [
    "### Baselines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "axwpoWYOApJL"
   },
   "source": [
    "A simple baseline for this task is to repeat the last input time step for the required number of output time steps:\n",
    "\n",
    "![Repeat the last input, for each output step](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/multistep_last.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:43:56.552750Z",
     "iopub.status.busy": "2024-08-16T02:43:56.552464Z",
     "iopub.status.idle": "2024-08-16T02:43:58.608886Z",
     "shell.execute_reply": "2024-08-16T02:43:58.608226Z"
    },
    "id": "_5iaHSaJ9Rxv"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/437\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:11\u001b[0m 303ms/step - loss: 0.6484 - mean_absolute_error: 0.5045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 44/437\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6169 - mean_absolute_error: 0.4922    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 89/437\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6219 - mean_absolute_error: 0.4952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m132/437\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6239 - mean_absolute_error: 0.4965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m177/437\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6256 - mean_absolute_error: 0.4976"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m220/437\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6266 - mean_absolute_error: 0.4983"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m264/437\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6270 - mean_absolute_error: 0.4987"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m306/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6270 - mean_absolute_error: 0.4989"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m349/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6270 - mean_absolute_error: 0.4990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m394/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6271 - mean_absolute_error: 0.4991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m437/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6271 - mean_absolute_error: 0.4992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m437/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6271 - mean_absolute_error: 0.4992\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class MultiStepLastBaseline(tf.keras.Model):\n",
    "  def call(self, inputs):\n",
    "    return tf.tile(inputs[:, -1:, :], [1, OUT_STEPS, 1])\n",
    "\n",
    "last_baseline = MultiStepLastBaseline()\n",
    "last_baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n",
    "                      metrics=[tf.keras.metrics.MeanAbsoluteError()])\n",
    "\n",
    "multi_val_performance = {}\n",
    "multi_performance = {}\n",
    "\n",
    "multi_val_performance['Last'] = last_baseline.evaluate(multi_window.val, return_dict=True)\n",
    "multi_performance['Last'] = last_baseline.evaluate(multi_window.test, verbose=0, return_dict=True)\n",
    "multi_window.plot(last_baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AvHZ93ObAfMA"
   },
   "source": [
    "Since this task is to predict 24 hours into the future, given 24 hours of the past, another simple approach is to repeat the previous day, assuming tomorrow will be similar:\n",
    "\n",
    "![Repeat the previous day](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/multistep_repeat.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:43:58.612920Z",
     "iopub.status.busy": "2024-08-16T02:43:58.612625Z",
     "iopub.status.idle": "2024-08-16T02:44:00.296055Z",
     "shell.execute_reply": "2024-08-16T02:44:00.295388Z"
    },
    "id": "L8Y1uMhGwIRs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/437\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:49\u001b[0m 250ms/step - loss: 0.4145 - mean_absolute_error: 0.3752"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 43/437\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.4137 - mean_absolute_error: 0.3905    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 85/437\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.4186 - mean_absolute_error: 0.3930"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m129/437\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.4190 - mean_absolute_error: 0.3932"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m173/437\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.4200 - mean_absolute_error: 0.3935"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m216/437\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.4208 - mean_absolute_error: 0.3938"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m257/437\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.4218 - mean_absolute_error: 0.3941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m299/437\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.4227 - mean_absolute_error: 0.3944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m342/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.4233 - mean_absolute_error: 0.3946"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m384/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.4236 - mean_absolute_error: 0.3947"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m432/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.4240 - mean_absolute_error: 0.3948"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m437/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.4240 - mean_absolute_error: 0.3948"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m437/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.4241 - mean_absolute_error: 0.3948\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class RepeatBaseline(tf.keras.Model):\n",
    "  def call(self, inputs):\n",
    "    return inputs\n",
    "\n",
    "repeat_baseline = RepeatBaseline()\n",
    "repeat_baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n",
    "                        metrics=[tf.keras.metrics.MeanAbsoluteError()])\n",
    "\n",
    "multi_val_performance['Repeat'] = repeat_baseline.evaluate(multi_window.val, return_dict=True)\n",
    "multi_performance['Repeat'] = repeat_baseline.evaluate(multi_window.test, verbose=0, return_dict=True)\n",
    "multi_window.plot(repeat_baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tbndS-ct9C2Q"
   },
   "source": [
    "### Single-shot models\n",
    "\n",
    "One high-level approach to this problem is to use a \"single-shot\" model, where the model makes the entire sequence prediction in a single step.\n",
    "\n",
    "This can be implemented efficiently as a `tf.keras.layers.Dense` with `OUT_STEPS*features` output units. The model just needs to reshape that output to the required `(OUTPUT_STEPS, features)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NCKS4m1VKrDQ"
   },
   "source": [
    "#### Linear\n",
    "\n",
    "A simple linear model based on the last input time step does better than either baseline, but is underpowered. The model needs to predict `OUTPUT_STEPS` time steps, from a single input time step with a linear projection. It can only capture a low-dimensional slice of the behavior, likely based mainly on the time of day and time of year.\n",
    "\n",
    "![Predict all timesteps from the last time-step](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/multistep_dense.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:44:00.300423Z",
     "iopub.status.busy": "2024-08-16T02:44:00.299899Z",
     "iopub.status.idle": "2024-08-16T02:44:26.772739Z",
     "shell.execute_reply": "2024-08-16T02:44:26.772038Z"
    },
    "id": "kfRz_WVhIQcd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/437\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m27s\u001b[0m 63ms/step - loss: 0.2524 - mean_absolute_error: 0.3003"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 41/437\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2516 - mean_absolute_error: 0.3044  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 80/437\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2534 - mean_absolute_error: 0.3051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m121/437\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2544 - mean_absolute_error: 0.3052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m162/437\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2550 - mean_absolute_error: 0.3054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m204/437\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2550 - mean_absolute_error: 0.3054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m246/437\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2549 - mean_absolute_error: 0.3053"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m284/437\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2548 - mean_absolute_error: 0.3052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m321/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2547 - mean_absolute_error: 0.3051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m357/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2547 - mean_absolute_error: 0.3051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m394/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2547 - mean_absolute_error: 0.3051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m437/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.2547 - mean_absolute_error: 0.3051\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_linear_model = tf.keras.Sequential([\n",
    "    # Take the last time-step.\n",
    "    # Shape [batch, time, features] => [batch, 1, features]\n",
    "    tf.keras.layers.Lambda(lambda x: x[:, -1:, :]),\n",
    "    # Shape => [batch, 1, out_steps*features]\n",
    "    tf.keras.layers.Dense(OUT_STEPS*num_features,\n",
    "                          kernel_initializer=tf.initializers.zeros()),\n",
    "    # Shape => [batch, out_steps, features]\n",
    "    tf.keras.layers.Reshape([OUT_STEPS, num_features])\n",
    "])\n",
    "\n",
    "history = compile_and_fit(multi_linear_model, multi_window)\n",
    "\n",
    "IPython.display.clear_output()\n",
    "multi_val_performance['Linear'] = multi_linear_model.evaluate(multi_window.val, return_dict=True)\n",
    "multi_performance['Linear'] = multi_linear_model.evaluate(multi_window.test, verbose=0, return_dict=True)\n",
    "multi_window.plot(multi_linear_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zi2TMHk2IRrh"
   },
   "source": [
    "#### Dense\n",
    "\n",
    "Adding a `tf.keras.layers.Dense` between the input and output gives the linear model more power, but is still only based on a single input time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:44:26.776904Z",
     "iopub.status.busy": "2024-08-16T02:44:26.776649Z",
     "iopub.status.idle": "2024-08-16T02:45:00.604477Z",
     "shell.execute_reply": "2024-08-16T02:45:00.603535Z"
    },
    "id": "jezm-BKaGj91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/437\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m26s\u001b[0m 62ms/step - loss: 0.1709 - mean_absolute_error: 0.2667"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 38/437\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2145 - mean_absolute_error: 0.2803  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 77/437\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2149 - mean_absolute_error: 0.2798"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m116/437\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2148 - mean_absolute_error: 0.2796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m155/437\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2151 - mean_absolute_error: 0.2797"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m196/437\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2152 - mean_absolute_error: 0.2796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m240/437\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2153 - mean_absolute_error: 0.2796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m282/437\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2156 - mean_absolute_error: 0.2797"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m323/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2157 - mean_absolute_error: 0.2797"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m363/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2158 - mean_absolute_error: 0.2797"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m404/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2160 - mean_absolute_error: 0.2797"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m437/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.2161 - mean_absolute_error: 0.2798\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_dense_model = tf.keras.Sequential([\n",
    "    # Take the last time step.\n",
    "    # Shape [batch, time, features] => [batch, 1, features]\n",
    "    tf.keras.layers.Lambda(lambda x: x[:, -1:, :]),\n",
    "    # Shape => [batch, 1, dense_units]\n",
    "    tf.keras.layers.Dense(512, activation='relu'),\n",
    "    # Shape => [batch, out_steps*features]\n",
    "    tf.keras.layers.Dense(OUT_STEPS*num_features,\n",
    "                          kernel_initializer=tf.initializers.zeros()),\n",
    "    # Shape => [batch, out_steps, features]\n",
    "    tf.keras.layers.Reshape([OUT_STEPS, num_features])\n",
    "])\n",
    "\n",
    "history = compile_and_fit(multi_dense_model, multi_window)\n",
    "\n",
    "IPython.display.clear_output()\n",
    "multi_val_performance['Dense'] = multi_dense_model.evaluate(multi_window.val, return_dict=True)\n",
    "multi_performance['Dense'] = multi_dense_model.evaluate(multi_window.test, verbose=0, return_dict=True)\n",
    "multi_window.plot(multi_dense_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "icsBAjCzMaMl"
   },
   "source": [
    "#### CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "34lCZrWYNBwd"
   },
   "source": [
    "A convolutional model makes predictions based on a fixed-width history, which may lead to better performance than the dense model since it can see how things are changing over time:\n",
    "\n",
    "![A convolutional model sees how things change over time](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/multistep_conv.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:45:00.609367Z",
     "iopub.status.busy": "2024-08-16T02:45:00.608715Z",
     "iopub.status.idle": "2024-08-16T02:45:36.778873Z",
     "shell.execute_reply": "2024-08-16T02:45:36.778194Z"
    },
    "id": "0xJoIP6PMWMI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/437\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m26s\u001b[0m 62ms/step - loss: 0.2232 - mean_absolute_error: 0.2737"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 40/437\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2116 - mean_absolute_error: 0.2784  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 79/437\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2119 - mean_absolute_error: 0.2788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m118/437\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2122 - mean_absolute_error: 0.2790"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m155/437\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2122 - mean_absolute_error: 0.2790"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m193/437\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2123 - mean_absolute_error: 0.2790"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m233/437\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2125 - mean_absolute_error: 0.2791"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m274/437\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2127 - mean_absolute_error: 0.2791"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m315/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2128 - mean_absolute_error: 0.2792"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m354/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2130 - mean_absolute_error: 0.2792"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m392/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2131 - mean_absolute_error: 0.2793"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m431/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2132 - mean_absolute_error: 0.2794"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m437/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.2133 - mean_absolute_error: 0.2794\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CONV_WIDTH = 3\n",
    "multi_conv_model = tf.keras.Sequential([\n",
    "    # Shape [batch, time, features] => [batch, CONV_WIDTH, features]\n",
    "    tf.keras.layers.Lambda(lambda x: x[:, -CONV_WIDTH:, :]),\n",
    "    # Shape => [batch, 1, conv_units]\n",
    "    tf.keras.layers.Conv1D(256, activation='relu', kernel_size=(CONV_WIDTH)),\n",
    "    # Shape => [batch, 1,  out_steps*features]\n",
    "    tf.keras.layers.Dense(OUT_STEPS*num_features,\n",
    "                          kernel_initializer=tf.initializers.zeros()),\n",
    "    # Shape => [batch, out_steps, features]\n",
    "    tf.keras.layers.Reshape([OUT_STEPS, num_features])\n",
    "])\n",
    "\n",
    "history = compile_and_fit(multi_conv_model, multi_window)\n",
    "\n",
    "IPython.display.clear_output()\n",
    "\n",
    "multi_val_performance['Conv'] = multi_conv_model.evaluate(multi_window.val, return_dict=True)\n",
    "multi_performance['Conv'] = multi_conv_model.evaluate(multi_window.test, verbose=0, return_dict=True)\n",
    "multi_window.plot(multi_conv_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "weBjeZAFJOP4"
   },
   "source": [
    "#### RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8022xOKxOO92"
   },
   "source": [
    "A recurrent model can learn to use a long history of inputs, if it's relevant to the predictions the model is making. Here the model will accumulate internal state for 24 hours, before making a single prediction for the next 24 hours.\n",
    "\n",
    "In this single-shot format, the LSTM only needs to produce an output at the last time step, so set `return_sequences=False` in `tf.keras.layers.LSTM`.\n",
    "\n",
    "![The LSTM accumulates state over the input window, and makes a single prediction for the next 24 hours](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/multistep_lstm.png?raw=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:45:36.783126Z",
     "iopub.status.busy": "2024-08-16T02:45:36.782879Z",
     "iopub.status.idle": "2024-08-16T02:47:01.682062Z",
     "shell.execute_reply": "2024-08-16T02:47:01.681340Z"
    },
    "id": "Bf1ks6RTzF64"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/437\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m27s\u001b[0m 64ms/step - loss: 0.2065 - mean_absolute_error: 0.2797"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 24/437\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2136 - mean_absolute_error: 0.2837  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 50/437\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2127 - mean_absolute_error: 0.2833"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 76/437\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2124 - mean_absolute_error: 0.2831"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m101/437\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2131 - mean_absolute_error: 0.2836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m126/437\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2139 - mean_absolute_error: 0.2842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m151/437\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2145 - mean_absolute_error: 0.2847"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m177/437\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2149 - mean_absolute_error: 0.2849"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m203/437\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2150 - mean_absolute_error: 0.2851"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m229/437\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2152 - mean_absolute_error: 0.2852"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m255/437\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2153 - mean_absolute_error: 0.2854"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m281/437\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2155 - mean_absolute_error: 0.2855"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m307/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2156 - mean_absolute_error: 0.2856"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m334/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2157 - mean_absolute_error: 0.2856"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m360/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2158 - mean_absolute_error: 0.2857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m385/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2159 - mean_absolute_error: 0.2857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m409/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2159 - mean_absolute_error: 0.2857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m435/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2160 - mean_absolute_error: 0.2858"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m437/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2160 - mean_absolute_error: 0.2858\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_lstm_model = tf.keras.Sequential([\n",
    "    # Shape [batch, time, features] => [batch, lstm_units].\n",
    "    # Adding more `lstm_units` just overfits more quickly.\n",
    "    tf.keras.layers.LSTM(32, return_sequences=False),\n",
    "    # Shape => [batch, out_steps*features].\n",
    "    tf.keras.layers.Dense(OUT_STEPS*num_features,\n",
    "                          kernel_initializer=tf.initializers.zeros()),\n",
    "    # Shape => [batch, out_steps, features].\n",
    "    tf.keras.layers.Reshape([OUT_STEPS, num_features])\n",
    "])\n",
    "\n",
    "history = compile_and_fit(multi_lstm_model, multi_window)\n",
    "\n",
    "IPython.display.clear_output()\n",
    "\n",
    "multi_val_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.val, return_dict=True)\n",
    "multi_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.test, verbose=0, return_dict=True)\n",
    "multi_window.plot(multi_lstm_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d5n-1cDW12Vo"
   },
   "source": [
    "### Advanced: Autoregressive model\n",
    "\n",
    "The above models all predict the entire output sequence in a single step.\n",
    "\n",
    "In some cases it may be helpful for the model to decompose this prediction into individual time steps. Then, each model's output can be fed back into itself at each step and predictions can be made conditioned on the previous one, like in the classic <a href=\"https://arxiv.org/abs/1308.0850\" class=\"external\">Generating Sequences With Recurrent Neural Networks</a>.\n",
    "\n",
    "One clear advantage to this style of model is that it can be set up to produce output with a varying length.\n",
    "\n",
    "You could take any of the single-step multi-output models trained in the first half of this tutorial and run in an autoregressive feedback loop, but here you'll focus on building a model that's been explicitly trained to do that.\n",
    "\n",
    "![Feedback a model's output to its input](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/multistep_autoregressive.png?raw=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PKRreBbULRXY"
   },
   "source": [
    "#### RNN\n",
    "\n",
    "This tutorial only builds an autoregressive RNN model, but this pattern could be applied to any model that was designed to output a single time step.\n",
    "\n",
    "The model will have the same basic form as the single-step LSTM models from earlier: a `tf.keras.layers.LSTM` layer followed by a `tf.keras.layers.Dense` layer that converts the `LSTM` layer's outputs to model predictions.\n",
    "\n",
    "A `tf.keras.layers.LSTM` is a `tf.keras.layers.LSTMCell` wrapped in the higher level `tf.keras.layers.RNN` that manages the state and sequence results for you (Check out the [Recurrent Neural Networks (RNN) with Keras](https://www.tensorflow.org/guide/keras/rnn) guide for details).\n",
    "\n",
    "In this case, the model has to manually manage the inputs for each step, so it uses `tf.keras.layers.LSTMCell` directly for the lower level, single time step interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:47:01.686195Z",
     "iopub.status.busy": "2024-08-16T02:47:01.685928Z",
     "iopub.status.idle": "2024-08-16T02:47:01.690626Z",
     "shell.execute_reply": "2024-08-16T02:47:01.689999Z"
    },
    "id": "s5tz3Nu0R5JG"
   },
   "outputs": [],
   "source": [
    "class FeedBack(tf.keras.Model):\n",
    "  def __init__(self, units, out_steps):\n",
    "    super().__init__()\n",
    "    self.out_steps = out_steps\n",
    "    self.units = units\n",
    "    self.lstm_cell = tf.keras.layers.LSTMCell(units)\n",
    "    # Also wrap the LSTMCell in an RNN to simplify the `warmup` method.\n",
    "    self.lstm_rnn = tf.keras.layers.RNN(self.lstm_cell, return_state=True)\n",
    "    self.dense = tf.keras.layers.Dense(num_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:47:01.693558Z",
     "iopub.status.busy": "2024-08-16T02:47:01.693327Z",
     "iopub.status.idle": "2024-08-16T02:47:01.701736Z",
     "shell.execute_reply": "2024-08-16T02:47:01.701169Z"
    },
    "id": "2OXVM9G1U7xR"
   },
   "outputs": [],
   "source": [
    "feedback_model = FeedBack(units=32, out_steps=OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ph5uFSfTUNho"
   },
   "source": [
    "The first method this model needs is a `warmup` method to initialize its internal state based on the inputs. Once trained, this state will capture the relevant parts of the input history. This is equivalent to the single-step `LSTM` model from earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:47:01.705096Z",
     "iopub.status.busy": "2024-08-16T02:47:01.704570Z",
     "iopub.status.idle": "2024-08-16T02:47:01.708251Z",
     "shell.execute_reply": "2024-08-16T02:47:01.707592Z"
    },
    "id": "vM2K_LLdRjDZ"
   },
   "outputs": [],
   "source": [
    "def warmup(self, inputs):\n",
    "  # inputs.shape => (batch, time, features)\n",
    "  # x.shape => (batch, lstm_units)\n",
    "  x, *state = self.lstm_rnn(inputs)\n",
    "\n",
    "  # predictions.shape => (batch, features)\n",
    "  prediction = self.dense(x)\n",
    "  return prediction, state\n",
    "\n",
    "FeedBack.warmup = warmup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6JkaSYaZ9eB7"
   },
   "source": [
    "This method returns a single time-step prediction and the internal state of the `LSTM`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:47:01.710989Z",
     "iopub.status.busy": "2024-08-16T02:47:01.710765Z",
     "iopub.status.idle": "2024-08-16T02:47:01.891617Z",
     "shell.execute_reply": "2024-08-16T02:47:01.890955Z"
    },
    "id": "w9Fz6NTKXXwU"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([32, 19])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction, state = feedback_model.warmup(multi_window.example[0])\n",
    "prediction.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S_ZdvPjdX3y3"
   },
   "source": [
    "With the `RNN`'s state, and an initial prediction you can now continue iterating the model feeding the predictions at each step back as the input.\n",
    "\n",
    "The simplest approach for collecting the output predictions is to use a Python list and a `tf.stack` after the loop."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yotTad3nZXQU"
   },
   "source": [
    "Note: Stacking a Python list like this only works with eager-execution, using `Model.compile(..., run_eagerly=True)` for training, or with a fixed length output. For a dynamic output length, you would need to use a `tf.TensorArray` instead of a Python list, and `tf.range` instead of the Python `range`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:47:01.895126Z",
     "iopub.status.busy": "2024-08-16T02:47:01.894848Z",
     "iopub.status.idle": "2024-08-16T02:47:01.899981Z",
     "shell.execute_reply": "2024-08-16T02:47:01.899403Z"
    },
    "id": "g1GRDu3mZtr9"
   },
   "outputs": [],
   "source": [
    "def call(self, inputs, training=None):\n",
    "  # Use a TensorArray to capture dynamically unrolled outputs.\n",
    "  predictions = []\n",
    "  # Initialize the LSTM state.\n",
    "  prediction, state = self.warmup(inputs)\n",
    "\n",
    "  # Insert the first prediction.\n",
    "  predictions.append(prediction)\n",
    "\n",
    "  # Run the rest of the prediction steps.\n",
    "  for n in range(1, self.out_steps):\n",
    "    # Use the last prediction as input.\n",
    "    x = prediction\n",
    "    # Execute one lstm step.\n",
    "    x, state = self.lstm_cell(x, states=state,\n",
    "                              training=training)\n",
    "    # Convert the lstm output to a prediction.\n",
    "    prediction = self.dense(x)\n",
    "    # Add the prediction to the output.\n",
    "    predictions.append(prediction)\n",
    "\n",
    "  # predictions.shape => (time, batch, features)\n",
    "  predictions = tf.stack(predictions)\n",
    "  # predictions.shape => (batch, time, features)\n",
    "  predictions = tf.transpose(predictions, [1, 0, 2])\n",
    "  return predictions\n",
    "\n",
    "FeedBack.call = call"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ubop-YWp15XW"
   },
   "source": [
    "Test run this model on the example inputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:47:01.902902Z",
     "iopub.status.busy": "2024-08-16T02:47:01.902664Z",
     "iopub.status.idle": "2024-08-16T02:47:02.055792Z",
     "shell.execute_reply": "2024-08-16T02:47:02.055202Z"
    },
    "id": "Xja83zEYaM2D"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape (batch, time, features):  (32, 24, 19)\n"
     ]
    }
   ],
   "source": [
    "print('Output shape (batch, time, features): ', feedback_model(multi_window.example[0]).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qMs0rYB8be9M"
   },
   "source": [
    "Now, train the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:47:02.058857Z",
     "iopub.status.busy": "2024-08-16T02:47:02.058604Z",
     "iopub.status.idle": "2024-08-16T02:49:34.079265Z",
     "shell.execute_reply": "2024-08-16T02:49:34.078552Z"
    },
    "id": "VBRVG2hnNyrO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/437\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m27s\u001b[0m 63ms/step - loss: 0.2044 - mean_absolute_error: 0.2868"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 20/437\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.2239 - mean_absolute_error: 0.2994  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 40/437\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.2252 - mean_absolute_error: 0.2986"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/437\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2250 - mean_absolute_error: 0.2982"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 81/437\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2249 - mean_absolute_error: 0.2979"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m102/437\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2246 - mean_absolute_error: 0.2977"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m123/437\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2243 - mean_absolute_error: 0.2976"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m144/437\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2241 - mean_absolute_error: 0.2974"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m164/437\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2240 - mean_absolute_error: 0.2973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m185/437\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2241 - mean_absolute_error: 0.2973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m206/437\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2242 - mean_absolute_error: 0.2973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m225/437\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2242 - mean_absolute_error: 0.2973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m245/437\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2242 - mean_absolute_error: 0.2973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m266/437\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2243 - mean_absolute_error: 0.2973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m286/437\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2243 - mean_absolute_error: 0.2973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m306/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2243 - mean_absolute_error: 0.2974"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m325/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2243 - mean_absolute_error: 0.2974"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m345/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2243 - mean_absolute_error: 0.2974"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m365/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2244 - mean_absolute_error: 0.2974"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m384/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2244 - mean_absolute_error: 0.2974"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m404/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2244 - mean_absolute_error: 0.2973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m423/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.2244 - mean_absolute_error: 0.2973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m437/437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.2244 - mean_absolute_error: 0.2973\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "history = compile_and_fit(feedback_model, multi_window)\n",
    "\n",
    "IPython.display.clear_output()\n",
    "\n",
    "multi_val_performance['AR LSTM'] = feedback_model.evaluate(multi_window.val, return_dict=True)\n",
    "multi_performance['AR LSTM'] = feedback_model.evaluate(multi_window.test, verbose=0, return_dict=True)\n",
    "multi_window.plot(feedback_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hGjcJsAQJUkI"
   },
   "source": [
    "### Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sODAwr2ndtDB"
   },
   "source": [
    "There are clearly diminishing returns as a function of model complexity on this problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:49:34.083772Z",
     "iopub.status.busy": "2024-08-16T02:49:34.083511Z",
     "iopub.status.idle": "2024-08-16T02:49:34.235168Z",
     "shell.execute_reply": "2024-08-16T02:49:34.234510Z"
    },
    "id": "WZwWBA8S6B3L"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(len(multi_performance))\n",
    "width = 0.3\n",
    "\n",
    "metric_name = 'mean_absolute_error'\n",
    "val_mae = [v[metric_name] for v in multi_val_performance.values()]\n",
    "test_mae = [v[metric_name] for v in multi_performance.values()]\n",
    "\n",
    "plt.bar(x - 0.17, val_mae, width, label='Validation')\n",
    "plt.bar(x + 0.17, test_mae, width, label='Test')\n",
    "plt.xticks(ticks=x, labels=multi_performance.keys(),\n",
    "           rotation=45)\n",
    "plt.ylabel(f'MAE (average over all times and outputs)')\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zq3hUsedCEmJ"
   },
   "source": [
    "The metrics for the multi-output models in the first half of this tutorial show the performance averaged across all output features. These performances are similar but also averaged across output time steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-16T02:49:34.238636Z",
     "iopub.status.busy": "2024-08-16T02:49:34.237999Z",
     "iopub.status.idle": "2024-08-16T02:49:34.241900Z",
     "shell.execute_reply": "2024-08-16T02:49:34.241245Z"
    },
    "id": "jKq3eAIvH4Db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last    : 0.5157\n",
      "Repeat  : 0.3774\n",
      "Linear  : 0.2980\n",
      "Dense   : 0.2765\n",
      "Conv    : 0.2732\n",
      "LSTM    : 0.2767\n",
      "AR LSTM : 0.2910\n"
     ]
    }
   ],
   "source": [
    "for name, value in multi_performance.items():\n",
    "  print(f'{name:8s}: {value[metric_name]:0.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MpBFwfnaHP23"
   },
   "source": [
    "The gains achieved going from a dense model to convolutional and recurrent models are only a few percent (if any), and the autoregressive model performed clearly worse. So these more complex approaches may not be worth while on **this** problem, but there was no way to know without trying, and these models could be helpful for **your** problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pOzaIRYBhqwg"
   },
   "source": [
    "## Next steps\n",
    "\n",
    "This tutorial was a quick introduction to time series forecasting using TensorFlow.\n",
    "\n",
    "To learn more, refer to:\n",
    "\n",
    "- Chapter 15 of <a href=\"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/\" class=\"external\">Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow</a>, 2nd Edition.\n",
    "- Chapter 6 of <a href=\"https://www.manning.com/books/deep-learning-with-python\" class=\"external\">Deep Learning with Python</a>.\n",
    "- Lesson 8 of <a href=\"https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187\" class=\"external\">Udacity's intro to TensorFlow for deep learning</a>, including the <a href=\"https://github.com/tensorflow/examples/tree/master/courses/udacity_intro_to_tensorflow_for_deep_learning\" class=\"external\">exercise notebooks</a>.\n",
    "\n",
    "Also, remember that you can implement any <a href=\"https://otexts.com/fpp2/index.html\" class=\"external\">classical time series model</a> in TensorFlow—this tutorial just focuses on TensorFlow's built-in functionality.\n"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "time_series.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}