{
 "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": "2024-01-11T20:36:54.201663Z",
     "iopub.status.busy": "2024-01-11T20:36:54.201407Z",
     "iopub.status.idle": "2024-01-11T20:36:54.205716Z",
     "shell.execute_reply": "2024-01-11T20:36:54.205035Z"
    },
    "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": [
    "# 時系列予測"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "11Ilg92myRcw"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td><a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/structured_data/time_series\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">TensorFlow.org で表示</a></td>\n",
    "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/tutorials/structured_data/time_series.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Google Colab で実行</a></td>\n",
    "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ja/tutorials/structured_data/time_series.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">GitHub でソースを表示</a></td>\n",
    "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ja/tutorials/structured_data/time_series.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">ノートブックをダウンロード</a></td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GU8C5qm_4vZb"
   },
   "source": [
    "このチュートリアルは、TensorFlow を使用した時系列予測を紹介します。畳み込みおよび回帰ニューラルネットワーク（CNN および RNN）を含む様々なスタイルのモデルを構築します。\n",
    "\n",
    "ここでは、2 つの主要部分をサブセクションとともに説明しています。\n",
    "\n",
    "- 単一の時間ステップの予測\n",
    "    - 単一の特徴量。\n",
    "    - すべての特徴量。\n",
    "- 複数のステップの予測\n",
    "    - シングルショット: すべての予測を一度に行います。\n",
    "    - 自動回帰: 一度に 1 つの予測を行い、出力をモデルにフィードし直します。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XVhK72Pu1cJL"
   },
   "source": [
    "## セットアップ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:36:54.209412Z",
     "iopub.status.busy": "2024-01-11T20:36:54.209165Z",
     "iopub.status.idle": "2024-01-11T20:36:57.238060Z",
     "shell.execute_reply": "2024-01-11T20:36:57.237226Z"
    },
    "id": "7rZnJaGTWQw0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-11 20:36:55.595591: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-01-11 20:36:55.595635: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-01-11 20:36:55.597247: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] 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": [
    "## 気象データセット\n",
    "\n",
    "このチュートリアルでは、<a href=\"https://www.bgc-jena.mpg.de\" class=\"external\">マックス・プランク生物地球化学研究所</a>が記録した<a href=\"https://www.bgc-jena.mpg.de/wetter/\" class=\"external\">気象の時系列データセット</a>を使用します。\n",
    "\n",
    "このデータセットには、気温、気圧、および湿度といった 14 個特徴量が含まれます。これらは、2003 年から 10 分ごとに収集されたデータです。効率化を図るために、2009 年から 2016 年までに収集されたデータのみを使用します。このセクションのデータセットは、「<a href=\"https://www.manning.com/books/deep-learning-with-python\" class=\"external\">Deep Learning with Python</a>」向けに著者 François Chollet 本人によって準備されました。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:36:57.242241Z",
     "iopub.status.busy": "2024-01-11T20:36:57.241804Z",
     "iopub.status.idle": "2024-01-11T20:36:57.712578Z",
     "shell.execute_reply": "2024-01-11T20:36:57.711898Z"
    },
    "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",
      "    8192/13568290 [..............................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5226496/13568290 [==========>...................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11026432/13568290 [=======================>......] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "13568290/13568290 [==============================] - 0s 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": [
    "このチュートリアルでは、**時間ごとの予測**のみを使用するため、10 分間隔のデータを 1 時間間隔にサブサンプリングしましょう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:36:57.716336Z",
     "iopub.status.busy": "2024-01-11T20:36:57.716089Z",
     "iopub.status.idle": "2024-01-11T20:36:58.583463Z",
     "shell.execute_reply": "2024-01-11T20:36:58.582737Z"
    },
    "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": [
    "データをのぞいてみましょう。最初の数行は、次のようになっています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:36:58.587601Z",
     "iopub.status.busy": "2024-01-11T20:36:58.587314Z",
     "iopub.status.idle": "2024-01-11T20:36:58.606849Z",
     "shell.execute_reply": "2024-01-11T20:36:58.606274Z"
    },
    "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": [
    "時が経過するにつれ、いくつかの特徴量は次のように変化しています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:36:58.610253Z",
     "iopub.status.busy": "2024-01-11T20:36:58.609806Z",
     "iopub.status.idle": "2024-01-11T20:37:01.097788Z",
     "shell.execute_reply": "2024-01-11T20:37:01.096967Z"
    },
    "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": [
    "### 検査とクリーンアップ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yhmZXJew6GlS"
   },
   "source": [
    "次に、データセットの統計を確認してみましょう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:01.102069Z",
     "iopub.status.busy": "2024-01-11T20:37:01.101475Z",
     "iopub.status.idle": "2024-01-11T20:37:01.166035Z",
     "shell.execute_reply": "2024-01-11T20:37:01.165425Z"
    },
    "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": [
    "#### 風速"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i47LiW5DCVsP"
   },
   "source": [
    "風速の `min` 値（`wv (m/s)`）と最大値（`max. wv (m/s)`）列が目立つはずです。この `-9999` は誤りの可能性があります。\n",
    "\n",
    "これとは別に、風向の列があるため、風速はゼロより大きい値（`>=0`）でなければなりません。これをゼロに置き換えましょう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:01.169633Z",
     "iopub.status.busy": "2024-01-11T20:37:01.169373Z",
     "iopub.status.idle": "2024-01-11T20:37:01.178345Z",
     "shell.execute_reply": "2024-01-11T20:37:01.177704Z"
    },
    "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": [
    "### 特徴量エンジニアリング\n",
    "\n",
    "モデルの構築を始める前に、データを理解しておくことが重要です。また、モデルに適切にフォーマットされたデータを渡していることも確認する必要があります。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FYyEaqiD6j4s"
   },
   "source": [
    "#### 風\n",
    "\n",
    "データの最後の列にある `wd (deg)` は、角度単位の風向を示します。角度は、モデル入力には適していません。360° と 0° は互いに近く、スムーズに回り込む必要があります。風が吹いていない場合は、向きは関係ありません。\n",
    "\n",
    "現時点では、風のデータの分布は次のようになっています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:01.181442Z",
     "iopub.status.busy": "2024-01-11T20:37:01.181206Z",
     "iopub.status.idle": "2024-01-11T20:37:01.409328Z",
     "shell.execute_reply": "2024-01-11T20:37:01.408706Z"
    },
    "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": [
    "ただし、風向と風速の列を風の**ベクトル**に変換すると、モデルを解釈しやすくなります。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:01.412351Z",
     "iopub.status.busy": "2024-01-11T20:37:01.412118Z",
     "iopub.status.idle": "2024-01-11T20:37:01.425771Z",
     "shell.execute_reply": "2024-01-11T20:37:01.425185Z"
    },
    "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": [
    "モデルを正しく解釈する上で、風ベクトルの分布ははるかに単純です。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:01.428933Z",
     "iopub.status.busy": "2024-01-11T20:37:01.428688Z",
     "iopub.status.idle": "2024-01-11T20:37:01.658979Z",
     "shell.execute_reply": "2024-01-11T20:37:01.658299Z"
    },
    "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": 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",
      "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": [
    "#### 時刻"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7YE21HKK40zQ"
   },
   "source": [
    "同様に、`Date Time` 列は非常に便利ですが、この文字列の形態ではそうでもありません。そこで、秒に変換することにします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:01.662032Z",
     "iopub.status.busy": "2024-01-11T20:37:01.661799Z",
     "iopub.status.idle": "2024-01-11T20:37:01.765677Z",
     "shell.execute_reply": "2024-01-11T20:37:01.765070Z"
    },
    "id": "LIFf-VjMfnh3"
   },
   "outputs": [],
   "source": [
    "timestamp_s = date_time.map(pd.Timestamp.timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EC_pnM1D5Sgc"
   },
   "source": [
    "風向と同様に、秒単位の時間は、使いやすいモデル入力ではありません。気象データであるため、明確な日単位および年単位の周期性があります。周期の操作には、様々な方法があります。\n",
    "\n",
    "使いやすい信号は、サインとコサインで明確な「Time of day（時刻）」と「Time of year（時期）」信号に変換して取得できます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:01.768809Z",
     "iopub.status.busy": "2024-01-11T20:37:01.768563Z",
     "iopub.status.idle": "2024-01-11T20:37:01.779161Z",
     "shell.execute_reply": "2024-01-11T20:37:01.778571Z"
    },
    "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": "2024-01-11T20:37:01.782410Z",
     "iopub.status.busy": "2024-01-11T20:37:01.781938Z",
     "iopub.status.idle": "2024-01-11T20:37:01.961893Z",
     "shell.execute_reply": "2024-01-11T20:37:01.961325Z"
    },
    "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": 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",
      "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": [
    "こうすることで、モデルはほとんどの重要な頻度特徴量にアクセスできるようになります。この場合、前もってどの頻度が重要であるかがわかっていました。\n",
    "\n",
    "その情報がない場合は、<a href=\"https://en.wikipedia.org/wiki/Fast_Fourier_transform\" class=\"external\">高速フーリエ変換</a>で特徴量を抽出し、どの周波数が重要であるかを判断することができます。予想を確認するために、以下に、時間の経過に伴う気温の `tf.signal.rfft` を示します。`1/year` と `1/day` に近い周波数で明確なピークに注意してください。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:01.964876Z",
     "iopub.status.busy": "2024-01-11T20:37:01.964648Z",
     "iopub.status.idle": "2024-01-11T20:37:04.574473Z",
     "shell.execute_reply": "2024-01-11T20:37:04.573816Z"
    },
    "id": "EN4U1fcMiTYs"
   },
   "outputs": [
    {
     "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": [
    "### データを分割する"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qoFJZmXBaxCc"
   },
   "source": [
    "トレーニング、検証、およびテスト用のセットとして、`(70%, 20%, 10%)` に分割したものを使用します。データの分割前に、ランダムに**シャッフルされていない**ことに注意してください。これには、次の 2 つの理由があります。\n",
    "\n",
    "1. 連続したサンプルの期間にデータが分割されていることを確実にするため。\n",
    "2. 検証/テストの結果がより現実的で、モデルがトレーニングされた後に収集されたデータを評価できるようにするため。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:04.578090Z",
     "iopub.status.busy": "2024-01-11T20:37:04.577816Z",
     "iopub.status.idle": "2024-01-11T20:37:04.582633Z",
     "shell.execute_reply": "2024-01-11T20:37:04.582039Z"
    },
    "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": [
    "### データを正規化する\n",
    "\n",
    "ニューラルネットワークをトレーニングする前に特徴量をスケーリングすることが重要です。正規化は、このスケーリングを行うための一般的な方法です。平均を減算して各特徴量の標準偏差で除算します。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mxbIic5TMlxx"
   },
   "source": [
    "平均と標準偏差は、モデルが検証とテストのセットにある値にアクセスできないように、トレーニングデータを使用してのみ計算する必要があります。\n",
    "\n",
    "また、モデルがトレーニング中にトレーニングセットの未来の値にアクセスしないことと、この正規化が、移動する平均を使用して行われるようにすることにも論拠があります。このことは、このチュートリアルの焦点ではなく、検証とテストのセットによって、（ある程度）正直な指標が得られるようになっています。そのため、単純化するために、このチュートリアルでは、単純な平均を使用しています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:04.585855Z",
     "iopub.status.busy": "2024-01-11T20:37:04.585625Z",
     "iopub.status.idle": "2024-01-11T20:37:04.622135Z",
     "shell.execute_reply": "2024-01-11T20:37:04.621416Z"
    },
    "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": [
    "では、特徴量の分布をみてみましょう。いくつかの特徴量には実際にロングテールがありますが、`-9999` の風速値のような明確な誤差はありません。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:04.626135Z",
     "iopub.status.busy": "2024-01-11T20:37:04.625862Z",
     "iopub.status.idle": "2024-01-11T20:37:10.850021Z",
     "shell.execute_reply": "2024-01-11T20:37:10.849309Z"
    },
    "id": "T0UYEnkwm8Fe"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmpfs/tmp/ipykernel_566122/3214313372.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": [
    "## データのウィンドウ処理\n",
    "\n",
    "このチュートリアルのモデルは、データの連続するサンプルのウィンドウに基づいてあるセットの予測を立てます。\n",
    "\n",
    "入力ウィンドウの主な特徴量は次の通りです。\n",
    "\n",
    "- 入力とラベルウィンドウの幅（時間ステップ数）\n",
    "- それらの時間オフセット\n",
    "- どの特徴量が入力かラベル、またはこの両方として使用されているか\n",
    "\n",
    "このチュートリアルは、様々なモデル（線形、DNN、CNN、および RNN モデル）を構築し、次の両方に対して使用します。\n",
    "\n",
    "- *単一出力*および*複数出力*予測。\n",
    "- *単一時間ステップ*と*複数時間ステップ*予測。\n",
    "\n",
    "このセクションでは、こういったすべてのモデルに再利用できるようにデータウィンドウ処理を実装することに焦点を当てています。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YAhGUVx1jtOy"
   },
   "source": [
    "モデルのタスクと種類に応じて、様々なデータウィンドウを生成するようにするとよいでしょう。次にいくつかのサンプルを示します。\n",
    "\n",
    "1. たとえば、24 時間先の単一予測を立てるには、過去 24 時間の履歴を指定し、次のようにウィンドウを定義することができます。\n",
    "\n",
    "![24 時間先の予測。](images/raw_window_24h.png)\n",
    "\n",
    "1. 1 時間先の予測を立てるモデルは、過去 6 時間の履歴を指定した場合、次のようにウィンドウを定義する必要があります。\n",
    "\n",
    "![1 時間先の予測。](images/raw_window_1h.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sa2BbfNZt8wy"
   },
   "source": [
    "このセクションの残りの部分では、`WindowGenerator` クラスを定義します。このクラスは、次の項目を行えます。\n",
    "\n",
    "1. インデックスとオフセットを、上記の図に示されるように処理する。\n",
    "2. 特徴量のウィンドウを `(features, labels)` ペアに分割する。\n",
    "3. 結果のウィンドウのコンテンツをプロットする。\n",
    "4. トレーニング、評価、およびテストデータからのこれらのウィンドウのバッチを、`tf.data.Dataset` を使用して効率的に生成する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rfx3jGjyziUF"
   },
   "source": [
    "### 1. インデックスとオフセット\n",
    "\n",
    "`WindowGenerator` クラスの作成から始めます。`__init__` メソッドには、入力とラベルインデックスに必要なすべての論理が含まれます。\n",
    "\n",
    "また、トレーニング、評価、およびテストの DataFrames を入力として取ります。これらは、後でウィンドウの `tf.data.Dataset` に変換されます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:10.854363Z",
     "iopub.status.busy": "2024-01-11T20:37:10.853843Z",
     "iopub.status.idle": "2024-01-11T20:37:10.860426Z",
     "shell.execute_reply": "2024-01-11T20:37:10.859803Z"
    },
    "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": [
    "以下は、このセクションの始めの図に示された 2 つのウィンドウを作成するコードです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:10.863416Z",
     "iopub.status.busy": "2024-01-11T20:37:10.863180Z",
     "iopub.status.idle": "2024-01-11T20:37:10.867900Z",
     "shell.execute_reply": "2024-01-11T20:37:10.867333Z"
    },
    "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": "2024-01-11T20:37:10.871139Z",
     "iopub.status.busy": "2024-01-11T20:37:10.870597Z",
     "iopub.status.idle": "2024-01-11T20:37:10.875160Z",
     "shell.execute_reply": "2024-01-11T20:37:10.874599Z"
    },
    "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. 分割\n",
    "\n",
    "リストの連続入力がある場合、`split_window` メソッドはこれらを入力のウィンドウとラベルのウィンドウに変換します。\n",
    "\n",
    "前に定義した例の `w2` は次のように分割されます。\n",
    "\n",
    "![初期のウィンドウはすべて連続したサンプルで、これを (inputs, labels) ペアに分割](images/split_window.png)\n",
    "\n",
    "この図は、データの `features` 軸を表示しませんが、この `split_window` 関数は、`label_columns` も処理するため、単一出力と複数出力の例の両方に使用できます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:10.878520Z",
     "iopub.status.busy": "2024-01-11T20:37:10.877862Z",
     "iopub.status.idle": "2024-01-11T20:37:10.882558Z",
     "shell.execute_reply": "2024-01-11T20:37:10.881982Z"
    },
    "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": [
    "次を試します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:10.885737Z",
     "iopub.status.busy": "2024-01-11T20:37:10.885269Z",
     "iopub.status.idle": "2024-01-11T20:37:10.905115Z",
     "shell.execute_reply": "2024-01-11T20:37:10.904471Z"
    },
    "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": [
    "通常 TensorFlow のデータは、最も外側のインデックスがサンプル全体である配列にパックされます（「batch」次元）。中央のインデックスは、「time」または「space」（width, height）次元です。最も内側のインデックスは特徴量です。\n",
    "\n",
    "上記のコードは、バッチ 3、7 時間ステップウィンドウ、各時間ステップに 19 個の特徴量を取りました。これを 6 時間ステップ、19 個の特徴量入力、および 1 時間ステップ 1 特徴量ラベルに分割しました。ラベルには、`WindowGenerator` が `label_columns=['T (degC)']` で初期化されたため、1 つの特徴量しかありません。最初に、このチュートリアルは単一出力ラベルを予測するモデルを構築します。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tFZukGXrJoGo"
   },
   "source": [
    "### 3. プロット\n",
    "\n",
    "以下は、分割ウィンドウを単純に視覚化できる描画（plot）メソッドです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:10.908322Z",
     "iopub.status.busy": "2024-01-11T20:37:10.908054Z",
     "iopub.status.idle": "2024-01-11T20:37:10.911353Z",
     "shell.execute_reply": "2024-01-11T20:37:10.910744Z"
    },
    "id": "fmgd1qkYUWT7"
   },
   "outputs": [],
   "source": [
    "w2.example = example_inputs, example_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:10.914589Z",
     "iopub.status.busy": "2024-01-11T20:37:10.914007Z",
     "iopub.status.idle": "2024-01-11T20:37:10.920426Z",
     "shell.execute_reply": "2024-01-11T20:37:10.919850Z"
    },
    "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": [
    "この図は、入力、ラベル、および（後の）予測を、項目が参照する時間に基づいて整列します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:10.923424Z",
     "iopub.status.busy": "2024-01-11T20:37:10.923189Z",
     "iopub.status.idle": "2024-01-11T20:37:11.333410Z",
     "shell.execute_reply": "2024-01-11T20:37:11.332779Z"
    },
    "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": [
    "ほかの列をプロットすることはできますが、サンプルウィンドウ `w2` 構成には、`T (degC)` 列のラベルしかありません。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:11.337362Z",
     "iopub.status.busy": "2024-01-11T20:37:11.336747Z",
     "iopub.status.idle": "2024-01-11T20:37:11.655699Z",
     "shell.execute_reply": "2024-01-11T20:37:11.655077Z"
    },
    "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. `tf.data.Dataset` を作成する"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kLO3SFR9Osdf"
   },
   "source": [
    "最後に、この `make_dataset` メソッドは時系列 DataFrame を取って、`tf.keras.utils.timeseries_dataset_from_array` 関数を使用して `(input_window, label_window)` ペアの `tf.data.Dataset` に変換します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:11.659238Z",
     "iopub.status.busy": "2024-01-11T20:37:11.658983Z",
     "iopub.status.idle": "2024-01-11T20:37:11.663068Z",
     "shell.execute_reply": "2024-01-11T20:37:11.662501Z"
    },
    "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": [
    "`WindowGenerator` オブジェクトには、トレーニング、検証、およびテストのデータが含まれます。\n",
    "\n",
    "これらにアクセスするためのプロパティを `tf.data.Dataset` として追加し、前に定義した`make_dataset` メソッドを使用します。また、標準のサンプルバッチを追加して、簡単にアクセスして描画できるようにします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:11.666113Z",
     "iopub.status.busy": "2024-01-11T20:37:11.665895Z",
     "iopub.status.idle": "2024-01-11T20:37:11.671026Z",
     "shell.execute_reply": "2024-01-11T20:37:11.670455Z"
    },
    "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": [
    "`WindowGenerator` オブジェクトにより、`tf.data.Dataset` オブジェクトにアクセスできるようになったため、データを簡単にイテレートできるようになりました。\n",
    "\n",
    "`Dataset.element_spec` プロパティは、データセット要素の構造、データ型、および形状を示します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:11.674153Z",
     "iopub.status.busy": "2024-01-11T20:37:11.673712Z",
     "iopub.status.idle": "2024-01-11T20:37:12.761317Z",
     "shell.execute_reply": "2024-01-11T20:37:12.760635Z"
    },
    "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": [
    "`Dataset` をイテレートすると、具象バッチを得られます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:12.764778Z",
     "iopub.status.busy": "2024-01-11T20:37:12.764514Z",
     "iopub.status.idle": "2024-01-11T20:37:12.908225Z",
     "shell.execute_reply": "2024-01-11T20:37:12.907471Z"
    },
    "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": [
    "## 単一ステップモデル\n",
    "\n",
    "このようなデータで構築できる最も単純なモデルは、現在の条件のみに基づいて、1 時間ステップ（1 時間）先までの単一の特徴量の値を予測するモデルです。\n",
    "\n",
    "そのため、1 時間先までの `T (degC)` 値を予測するモデルを構築することにします。\n",
    "\n",
    "![次の時間ステップを予測する](images/narrow_window.png)\n",
    "\n",
    "`WindowGenerator` オブジェクトを構成して、これらの単一ステップ `(input, label)` ペアを生成します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:12.912256Z",
     "iopub.status.busy": "2024-01-11T20:37:12.911581Z",
     "iopub.status.idle": "2024-01-11T20:37:12.916992Z",
     "shell.execute_reply": "2024-01-11T20:37:12.916364Z"
    },
    "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": [
    "`window` オブジェクトは、トレーニング、検証、およびテストのセットから `tf.data.Datasets` 作成し、データのバッチを簡単にイテレートできるようにします。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:12.920263Z",
     "iopub.status.busy": "2024-01-11T20:37:12.920024Z",
     "iopub.status.idle": "2024-01-11T20:37:13.063421Z",
     "shell.execute_reply": "2024-01-11T20:37:13.062590Z"
    },
    "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": [
    "### 基準\n",
    "\n",
    "トレーニング可能なモデルを構築する前に、後のより複雑なモデルと比較するための基準ポイントとして、パフォーマンス基準を設定します。\n",
    "\n",
    "最初のタスクは、すべての特徴量の現在の値がある場合に、1 時間先までの気温を予測することです。現在の値には、現在の気温が含まれます。\n",
    "\n",
    "では、現在の気温のみを予測として返し、「変化なし」と予測するモデルから始めましょう。気温はゆっくりと変化するため、これは合理的な基準と言えます。もちろん、予測をずっと先まで行う場合は、この基準はあまり機能しなくなります。\n",
    "\n",
    "![入力を出力に送信する](images/baseline.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:13.067134Z",
     "iopub.status.busy": "2024-01-11T20:37:13.066811Z",
     "iopub.status.idle": "2024-01-11T20:37:13.071826Z",
     "shell.execute_reply": "2024-01-11T20:37:13.071092Z"
    },
    "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": [
    "このモデルをインスタンス化して評価します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:13.075160Z",
     "iopub.status.busy": "2024-01-11T20:37:13.074897Z",
     "iopub.status.idle": "2024-01-11T20:37:14.908538Z",
     "shell.execute_reply": "2024-01-11T20:37:14.907746Z"
    },
    "id": "IS3-QKc4sX0D"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/439 [..............................] - ETA: 4:02 - loss: 0.0198 - mean_absolute_error: 0.0958"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 30/439 [=>............................] - ETA: 0s - loss: 0.0133 - 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\b\b\b\b\b\b\b\b\r",
      " 61/439 [===>..........................] - ETA: 0s - loss: 0.0129 - mean_absolute_error: 0.0792"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 91/439 [=====>........................] - ETA: 0s - loss: 0.0130 - mean_absolute_error: 0.0789"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "121/439 [=======>......................] - ETA: 0s - loss: 0.0131 - mean_absolute_error: 0.0794"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "150/439 [=========>....................] - ETA: 0s - loss: 0.0133 - mean_absolute_error: 0.0798"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "180/439 [===========>..................] - ETA: 0s - loss: 0.0130 - mean_absolute_error: 0.0790"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "210/439 [=============>................] - ETA: 0s - loss: 0.0130 - mean_absolute_error: 0.0788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "240/439 [===============>..............] - ETA: 0s - loss: 0.0128 - mean_absolute_error: 0.0785"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "270/439 [=================>............] - ETA: 0s - loss: 0.0129 - mean_absolute_error: 0.0786"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "361/439 [=======================>......] - ETA: 0s - loss: 0.0129 - mean_absolute_error: 0.0787"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "392/439 [=========================>....] - ETA: 0s - loss: 0.0129 - mean_absolute_error: 0.0785"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "423/439 [===========================>..] - ETA: 0s - loss: 0.0128 - mean_absolute_error: 0.0784"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "439/439 [==============================] - 1s 2ms/step - loss: 0.0128 - mean_absolute_error: 0.0785\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)\n",
    "performance['Baseline'] = baseline.evaluate(single_step_window.test, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nhBxQcCSs7Ec"
   },
   "source": [
    "これによって何らかのパフォーマンスメトリックが出力されましたが、このモデルがどれくらいうまく機能しているかに対する感触は得られません。\n",
    "\n",
    "`WindowGenerator` には描画メソッドがありますが、サンプルが 1 つしかないのであれば、興味深いプロットにはなりません。\n",
    "\n",
    "そこで、24 時間連続入力とラベルのウィンドウを一度に生成するより幅の広い `WindowGenerator` を作成することにします。新しい `wide_window` 変数によってモデルの動作が変わることはありません。モデルは引き続き、1 つの入力時間ステップに基づいて 1 時間先の予測を立てます。ここでは、`time` 軸は `batch` 軸として機能します。各予測は、時間ステップ間で相互作用のない独立した予測となります。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:14.912475Z",
     "iopub.status.busy": "2024-01-11T20:37:14.912196Z",
     "iopub.status.idle": "2024-01-11T20:37:14.918346Z",
     "shell.execute_reply": "2024-01-11T20:37:14.917400Z"
    },
    "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": [
    "コードを変更することなく、上記の拡張されたウィンドウを同じ `baseline` モデルに直接渡すことができます。これは、入力とラベルに同じ数の時間ステップがあり、基準は入力を出力に転送するだけであるため、可能です。\n",
    "\n",
    "![1 時間先の 1 時間ごとの予測。](images/last_window.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:14.921780Z",
     "iopub.status.busy": "2024-01-11T20:37:14.921532Z",
     "iopub.status.idle": "2024-01-11T20:37:15.052998Z",
     "shell.execute_reply": "2024-01-11T20:37:15.051895Z"
    },
    "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": [
    "基準モデルの予測を描画すると、ラベルが 1 時間右に移動しただけであることがわかります。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:15.056893Z",
     "iopub.status.busy": "2024-01-11T20:37:15.056605Z",
     "iopub.status.idle": "2024-01-11T20:37:15.493119Z",
     "shell.execute_reply": "2024-01-11T20:37:15.492214Z"
    },
    "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": [
    "上記の 3 つの例の図には、単一ステップモデルが 24 時間分実行されています。これには、次のような理由があります。\n",
    "\n",
    "- 青い `Inputs` の線は、各時間ステップの入力気温を示します。モデルはすべての特徴量を受け取りますが、この図は気温のみを示します。\n",
    "- 緑色の `Labels` の点は、ターゲットの予測値を示します。これらの点は入力時間ではなく、予測時間に示されます。そのため、ラベルの範囲は入力に対して 1 ステップ移動しています。\n",
    "- オレンジ色の `Predictions` の十字は、各出力時間ステップのモデルの予測です。モデルが完璧に予測しているのであれば、予測は `Labels` に着地します。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E4aOJScj52Yu"
   },
   "source": [
    "### 線形モデル\n",
    "\n",
    "このタスクに適用できる最も単純な**トレーニング可能な**モデルは、入力と出力間に線形変換を挿入することです。この場合、ある時間ステップの出力は、そのステップのみに依存します。\n",
    "\n",
    "![単一ステップの予測](images/narrow_window.png)\n",
    "\n",
    "`activation` セットのない `tf.keras.layers.Dense` レイヤーは線形モデルです。レイヤーはデータの最後の軸のみを `(batch, time, inputs)` から `(batch, time, units)` に変換するため、`batch` と `time` 軸の各項目に独立して適用されます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:15.497368Z",
     "iopub.status.busy": "2024-01-11T20:37:15.497118Z",
     "iopub.status.idle": "2024-01-11T20:37:15.506138Z",
     "shell.execute_reply": "2024-01-11T20:37:15.505546Z"
    },
    "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": "2024-01-11T20:37:15.509137Z",
     "iopub.status.busy": "2024-01-11T20:37:15.508864Z",
     "iopub.status.idle": "2024-01-11T20:37:15.695431Z",
     "shell.execute_reply": "2024-01-11T20:37:15.694477Z"
    },
    "id": "KwaOM8RucUSn"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (32, 1, 19)\n",
      "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": [
    "このチュートリアルは、多くのモデルをトレーニングするため、トレーニング手順を関数にパッケージ化します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:15.699044Z",
     "iopub.status.busy": "2024-01-11T20:37:15.698763Z",
     "iopub.status.idle": "2024-01-11T20:37:15.704132Z",
     "shell.execute_reply": "2024-01-11T20:37:15.703376Z"
    },
    "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": [
    "モデルをトレーニングしてそのパフォーマンスを評価します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:15.707142Z",
     "iopub.status.busy": "2024-01-11T20:37:15.706908Z",
     "iopub.status.idle": "2024-01-11T20:37:54.032907Z",
     "shell.execute_reply": "2024-01-11T20:37:54.032074Z"
    },
    "id": "9agbz2qB9bLS"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 17:21 - loss: 6.3810 - mean_absolute_error: 2.1376"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  21/1534 [..............................] - ETA: 3s - loss: 5.2649 - mean_absolute_error: 1.8399   "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1705005436.429000  566323 device_compiler.h:186] 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\b\b\b\b\b\b\b\b\r",
      "  42/1534 [..............................] - ETA: 3s - loss: 4.9675 - mean_absolute_error: 1.7836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  64/1534 [>.............................] - ETA: 3s - loss: 4.7516 - mean_absolute_error: 1.7433"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  85/1534 [>.............................] - ETA: 3s - loss: 4.4440 - mean_absolute_error: 1.6840"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 106/1534 [=>............................] - ETA: 3s - loss: 4.1653 - mean_absolute_error: 1.6243"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 127/1534 [=>............................] - ETA: 3s - loss: 3.8735 - mean_absolute_error: 1.5585"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 149/1534 [=>............................] - ETA: 3s - loss: 3.6597 - mean_absolute_error: 1.5115"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 171/1534 [==>...........................] - ETA: 3s - loss: 3.4349 - mean_absolute_error: 1.4589"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 193/1534 [==>...........................] - ETA: 3s - loss: 3.2319 - mean_absolute_error: 1.4117"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 213/1534 [===>..........................] - ETA: 3s - loss: 3.0590 - mean_absolute_error: 1.3672"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 235/1534 [===>..........................] - ETA: 3s - loss: 2.8875 - mean_absolute_error: 1.3233"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 257/1534 [====>.........................] - ETA: 3s - loss: 2.7239 - mean_absolute_error: 1.2789"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 279/1534 [====>.........................] - ETA: 3s - loss: 2.5966 - mean_absolute_error: 1.2446"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 301/1534 [====>.........................] - ETA: 2s - loss: 2.4767 - mean_absolute_error: 1.2124"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 323/1534 [=====>........................] - ETA: 2s - loss: 2.3678 - mean_absolute_error: 1.1817"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 345/1534 [=====>........................] - ETA: 2s - loss: 2.2650 - mean_absolute_error: 1.1513"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 366/1534 [======>.......................] - ETA: 2s - loss: 2.1764 - mean_absolute_error: 1.1249"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 387/1534 [======>.......................] - ETA: 2s - loss: 2.0909 - mean_absolute_error: 1.0985"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 409/1534 [======>.......................] - ETA: 2s - loss: 2.0103 - mean_absolute_error: 1.0731"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 433/1534 [=======>......................] - ETA: 2s - loss: 1.9298 - mean_absolute_error: 1.0471"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 454/1534 [=======>......................] - ETA: 2s - loss: 1.8641 - mean_absolute_error: 1.0253"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 475/1534 [========>.....................] - ETA: 2s - loss: 1.8019 - mean_absolute_error: 1.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 497/1534 [========>.....................] - ETA: 2s - loss: 1.7404 - mean_absolute_error: 0.9827"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 518/1534 [=========>....................] - ETA: 2s - loss: 1.6856 - mean_absolute_error: 0.9631"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 540/1534 [=========>....................] - ETA: 2s - loss: 1.6305 - mean_absolute_error: 0.9430"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 562/1534 [=========>....................] - ETA: 2s - loss: 1.5809 - mean_absolute_error: 0.9252"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 583/1534 [==========>...................] - ETA: 2s - loss: 1.5334 - mean_absolute_error: 0.9065"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 604/1534 [==========>...................] - ETA: 2s - loss: 1.4887 - mean_absolute_error: 0.8891"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 625/1534 [===========>..................] - ETA: 2s - loss: 1.4464 - mean_absolute_error: 0.8722"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 647/1534 [===========>..................] - ETA: 2s - loss: 1.4052 - mean_absolute_error: 0.8559"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 668/1534 [============>.................] - ETA: 2s - loss: 1.3676 - mean_absolute_error: 0.8406"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 690/1534 [============>.................] - ETA: 2s - loss: 1.3305 - mean_absolute_error: 0.8254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 712/1534 [============>.................] - ETA: 1s - loss: 1.2949 - mean_absolute_error: 0.8104"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 733/1534 [=============>................] - ETA: 1s - loss: 1.2628 - mean_absolute_error: 0.7968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 755/1534 [=============>................] - ETA: 1s - loss: 1.2304 - mean_absolute_error: 0.7828"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 776/1534 [==============>...............] - ETA: 1s - loss: 1.2012 - mean_absolute_error: 0.7699"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 797/1534 [==============>...............] - ETA: 1s - loss: 1.1730 - mean_absolute_error: 0.7572"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 818/1534 [==============>...............] - ETA: 1s - loss: 1.1463 - mean_absolute_error: 0.7452"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 840/1534 [===============>..............] - ETA: 1s - loss: 1.1195 - mean_absolute_error: 0.7330"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 862/1534 [===============>..............] - ETA: 1s - loss: 1.0939 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 884/1534 [================>.............] - ETA: 1s - loss: 1.0693 - mean_absolute_error: 0.7096"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 906/1534 [================>.............] - ETA: 1s - loss: 1.0456 - mean_absolute_error: 0.6982"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 927/1534 [=================>............] - ETA: 1s - loss: 1.0240 - mean_absolute_error: 0.6877"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 948/1534 [=================>............] - ETA: 1s - loss: 1.0034 - mean_absolute_error: 0.6778"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 969/1534 [=================>............] - ETA: 1s - loss: 0.9836 - mean_absolute_error: 0.6681"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 990/1534 [==================>...........] - ETA: 1s - loss: 0.9645 - mean_absolute_error: 0.6587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1011/1534 [==================>...........] - ETA: 1s - loss: 0.9460 - mean_absolute_error: 0.6495"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1032/1534 [===================>..........] - ETA: 1s - loss: 0.9283 - mean_absolute_error: 0.6407"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1052/1534 [===================>..........] - ETA: 1s - loss: 0.9120 - mean_absolute_error: 0.6324"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1073/1534 [===================>..........] - ETA: 1s - loss: 0.8954 - mean_absolute_error: 0.6239"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1095/1534 [====================>.........] - ETA: 1s - loss: 0.8786 - mean_absolute_error: 0.6152"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1117/1534 [====================>.........] - ETA: 0s - loss: 0.8625 - mean_absolute_error: 0.6068"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1138/1534 [=====================>........] - ETA: 0s - loss: 0.8477 - mean_absolute_error: 0.5991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1160/1534 [=====================>........] - ETA: 0s - loss: 0.8327 - mean_absolute_error: 0.5912"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1181/1534 [======================>.......] - ETA: 0s - loss: 0.8188 - mean_absolute_error: 0.5838"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1202/1534 [======================>.......] - ETA: 0s - loss: 0.8056 - mean_absolute_error: 0.5768"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1223/1534 [======================>.......] - ETA: 0s - loss: 0.7926 - mean_absolute_error: 0.5699"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1244/1534 [=======================>......] - ETA: 0s - loss: 0.7801 - mean_absolute_error: 0.5632"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1265/1534 [=======================>......] - ETA: 0s - loss: 0.7679 - mean_absolute_error: 0.5567"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1286/1534 [========================>.....] - ETA: 0s - loss: 0.7561 - mean_absolute_error: 0.5503"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1308/1534 [========================>.....] - ETA: 0s - loss: 0.7443 - mean_absolute_error: 0.5439"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1329/1534 [========================>.....] - ETA: 0s - loss: 0.7333 - mean_absolute_error: 0.5379"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1350/1534 [=========================>....] - ETA: 0s - loss: 0.7227 - mean_absolute_error: 0.5323"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1371/1534 [=========================>....] - ETA: 0s - loss: 0.7123 - mean_absolute_error: 0.5266"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1393/1534 [==========================>...] - ETA: 0s - loss: 0.7018 - mean_absolute_error: 0.5208"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1414/1534 [==========================>...] - ETA: 0s - loss: 0.6920 - mean_absolute_error: 0.5155"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1435/1534 [===========================>..] - ETA: 0s - loss: 0.6825 - mean_absolute_error: 0.5103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1456/1534 [===========================>..] - ETA: 0s - loss: 0.6733 - mean_absolute_error: 0.5051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1477/1534 [===========================>..] - ETA: 0s - loss: 0.6643 - mean_absolute_error: 0.5003"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1498/1534 [============================>.] - ETA: 0s - loss: 0.6555 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1520/1534 [============================>.] - ETA: 0s - loss: 0.6466 - mean_absolute_error: 0.4902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 5s 3ms/step - loss: 0.6413 - mean_absolute_error: 0.4871 - val_loss: 0.0313 - val_mean_absolute_error: 0.1402\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 1:00 - loss: 0.0268 - mean_absolute_error: 0.1296"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  22/1534 [..............................] - ETA: 3s - loss: 0.0314 - mean_absolute_error: 0.1341  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  43/1534 [..............................] - ETA: 3s - loss: 0.0348 - mean_absolute_error: 0.1407"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  65/1534 [>.............................] - ETA: 3s - loss: 0.0349 - mean_absolute_error: 0.1414"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  86/1534 [>.............................] - ETA: 3s - loss: 0.0360 - mean_absolute_error: 0.1431"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 108/1534 [=>............................] - ETA: 3s - loss: 0.0351 - mean_absolute_error: 0.1422"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 130/1534 [=>............................] - ETA: 3s - loss: 0.0349 - mean_absolute_error: 0.1420"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 152/1534 [=>............................] - ETA: 3s - loss: 0.0344 - mean_absolute_error: 0.1406"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 174/1534 [==>...........................] - ETA: 3s - loss: 0.0338 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 196/1534 [==>...........................] - ETA: 3s - loss: 0.0331 - mean_absolute_error: 0.1379"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 218/1534 [===>..........................] - ETA: 3s - loss: 0.0329 - mean_absolute_error: 0.1374"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 240/1534 [===>..........................] - ETA: 3s - loss: 0.0328 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 262/1534 [====>.........................] - ETA: 2s - loss: 0.0324 - mean_absolute_error: 0.1366"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 285/1534 [====>.........................] - ETA: 2s - loss: 0.0320 - mean_absolute_error: 0.1357"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 306/1534 [====>.........................] - ETA: 2s - loss: 0.0319 - mean_absolute_error: 0.1353"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 328/1534 [=====>........................] - ETA: 2s - loss: 0.0316 - mean_absolute_error: 0.1346"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 350/1534 [=====>........................] - ETA: 2s - loss: 0.0312 - mean_absolute_error: 0.1340"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 371/1534 [======>.......................] - ETA: 2s - loss: 0.0309 - mean_absolute_error: 0.1331"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 392/1534 [======>.......................] - ETA: 2s - loss: 0.0306 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 413/1534 [=======>......................] - ETA: 2s - loss: 0.0304 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 435/1534 [=======>......................] - ETA: 2s - loss: 0.0301 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 457/1534 [=======>......................] - ETA: 2s - loss: 0.0297 - mean_absolute_error: 0.1302"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 478/1534 [========>.....................] - ETA: 2s - loss: 0.0294 - mean_absolute_error: 0.1296"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 499/1534 [========>.....................] - ETA: 2s - loss: 0.0293 - mean_absolute_error: 0.1291"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 520/1534 [=========>....................] - ETA: 2s - loss: 0.0289 - mean_absolute_error: 0.1282"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 542/1534 [=========>....................] - ETA: 2s - loss: 0.0288 - mean_absolute_error: 0.1278"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 563/1534 [==========>...................] - ETA: 2s - loss: 0.0287 - mean_absolute_error: 0.1275"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 585/1534 [==========>...................] - ETA: 2s - loss: 0.0283 - mean_absolute_error: 0.1267"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 607/1534 [==========>...................] - ETA: 2s - loss: 0.0281 - mean_absolute_error: 0.1261"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 629/1534 [===========>..................] - ETA: 2s - loss: 0.0279 - mean_absolute_error: 0.1255"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 651/1534 [===========>..................] - ETA: 2s - loss: 0.0276 - mean_absolute_error: 0.1250"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 673/1534 [============>.................] - ETA: 2s - loss: 0.0275 - mean_absolute_error: 0.1246"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 695/1534 [============>.................] - ETA: 1s - loss: 0.0273 - mean_absolute_error: 0.1240"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 717/1534 [=============>................] - ETA: 1s - loss: 0.0270 - mean_absolute_error: 0.1234"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 738/1534 [=============>................] - ETA: 1s - loss: 0.0268 - mean_absolute_error: 0.1227"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 760/1534 [=============>................] - ETA: 1s - loss: 0.0265 - mean_absolute_error: 0.1222"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 781/1534 [==============>...............] - ETA: 1s - loss: 0.0264 - mean_absolute_error: 0.1219"
     ]
    },
    {
     "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\b\b\b\b\b\b\b\b\b\b\r",
      " 802/1534 [==============>...............] - ETA: 1s - loss: 0.0263 - mean_absolute_error: 0.1214"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 823/1534 [===============>..............] - ETA: 1s - loss: 0.0261 - mean_absolute_error: 0.1211"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 845/1534 [===============>..............] - ETA: 1s - loss: 0.0260 - mean_absolute_error: 0.1206"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 867/1534 [===============>..............] - ETA: 1s - loss: 0.0258 - mean_absolute_error: 0.1202"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 889/1534 [================>.............] - ETA: 1s - loss: 0.0256 - mean_absolute_error: 0.1198"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 911/1534 [================>.............] - ETA: 1s - loss: 0.0254 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 933/1534 [=================>............] - ETA: 1s - loss: 0.0253 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 955/1534 [=================>............] - ETA: 1s - loss: 0.0251 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 976/1534 [==================>...........] - ETA: 1s - loss: 0.0249 - mean_absolute_error: 0.1180"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 998/1534 [==================>...........] - ETA: 1s - loss: 0.0247 - mean_absolute_error: 0.1175"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1020/1534 [==================>...........] - ETA: 1s - loss: 0.0246 - mean_absolute_error: 0.1171"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1042/1534 [===================>..........] - ETA: 1s - loss: 0.0245 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1064/1534 [===================>..........] - ETA: 1s - loss: 0.0243 - mean_absolute_error: 0.1162"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1086/1534 [====================>.........] - ETA: 1s - loss: 0.0241 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1108/1534 [====================>.........] - ETA: 1s - loss: 0.0240 - mean_absolute_error: 0.1153"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1130/1534 [=====================>........] - ETA: 0s - loss: 0.0238 - mean_absolute_error: 0.1148"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1152/1534 [=====================>........] - ETA: 0s - loss: 0.0236 - mean_absolute_error: 0.1143"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1174/1534 [=====================>........] - ETA: 0s - loss: 0.0235 - mean_absolute_error: 0.1140"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1196/1534 [======================>.......] - ETA: 0s - loss: 0.0234 - mean_absolute_error: 0.1137"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1218/1534 [======================>.......] - ETA: 0s - loss: 0.0232 - mean_absolute_error: 0.1132"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1240/1534 [=======================>......] - ETA: 0s - loss: 0.0231 - mean_absolute_error: 0.1127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1261/1534 [=======================>......] - ETA: 0s - loss: 0.0229 - mean_absolute_error: 0.1124"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1282/1534 [========================>.....] - ETA: 0s - loss: 0.0228 - mean_absolute_error: 0.1120"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1304/1534 [========================>.....] - ETA: 0s - loss: 0.0226 - mean_absolute_error: 0.1116"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1325/1534 [========================>.....] - ETA: 0s - loss: 0.0225 - mean_absolute_error: 0.1112"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1346/1534 [=========================>....] - ETA: 0s - loss: 0.0224 - mean_absolute_error: 0.1109"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1367/1534 [=========================>....] - ETA: 0s - loss: 0.0223 - mean_absolute_error: 0.1105"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1389/1534 [==========================>...] - ETA: 0s - loss: 0.0222 - mean_absolute_error: 0.1102"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1410/1534 [==========================>...] - ETA: 0s - loss: 0.0221 - mean_absolute_error: 0.1099"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1431/1534 [==========================>...] - ETA: 0s - loss: 0.0220 - mean_absolute_error: 0.1096"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1452/1534 [===========================>..] - ETA: 0s - loss: 0.0219 - mean_absolute_error: 0.1093"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1473/1534 [===========================>..] - ETA: 0s - loss: 0.0218 - mean_absolute_error: 0.1090"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1494/1534 [============================>.] - ETA: 0s - loss: 0.0216 - mean_absolute_error: 0.1086"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1515/1534 [============================>.] - ETA: 0s - loss: 0.0215 - mean_absolute_error: 0.1083"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 4s 3ms/step - loss: 0.0214 - mean_absolute_error: 0.1080 - val_loss: 0.0116 - val_mean_absolute_error: 0.0802\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 59s - loss: 0.0102 - 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\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  21/1534 [..............................] - ETA: 3s - loss: 0.0113 - mean_absolute_error: 0.0786 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  42/1534 [..............................] - ETA: 3s - loss: 0.0121 - mean_absolute_error: 0.0818"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  63/1534 [>.............................] - ETA: 3s - loss: 0.0126 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  85/1534 [>.............................] - ETA: 3s - loss: 0.0130 - mean_absolute_error: 0.0846"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 107/1534 [=>............................] - ETA: 3s - loss: 0.0128 - mean_absolute_error: 0.0840"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 128/1534 [=>............................] - ETA: 3s - loss: 0.0125 - mean_absolute_error: 0.0832"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 150/1534 [=>............................] - ETA: 3s - loss: 0.0124 - mean_absolute_error: 0.0830"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 172/1534 [==>...........................] - ETA: 3s - loss: 0.0122 - mean_absolute_error: 0.0821"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 193/1534 [==>...........................] - ETA: 3s - loss: 0.0119 - mean_absolute_error: 0.0813"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 214/1534 [===>..........................] - ETA: 3s - loss: 0.0119 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 235/1534 [===>..........................] - ETA: 3s - loss: 0.0120 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 257/1534 [====>.........................] - ETA: 3s - loss: 0.0119 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 280/1534 [====>.........................] - ETA: 2s - loss: 0.0118 - mean_absolute_error: 0.0809"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 302/1534 [====>.........................] - ETA: 2s - loss: 0.0117 - mean_absolute_error: 0.0809"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 324/1534 [=====>........................] - ETA: 2s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 345/1534 [=====>........................] - ETA: 2s - loss: 0.0117 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 367/1534 [======>.......................] - ETA: 2s - loss: 0.0117 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 388/1534 [======>.......................] - ETA: 2s - loss: 0.0117 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 410/1534 [=======>......................] - ETA: 2s - loss: 0.0117 - 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\b\b\b\b\b\b\b\r",
      " 432/1534 [=======>......................] - ETA: 2s - loss: 0.0116 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 454/1534 [=======>......................] - ETA: 2s - loss: 0.0116 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 476/1534 [========>.....................] - ETA: 2s - loss: 0.0115 - mean_absolute_error: 0.0796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 497/1534 [========>.....................] - ETA: 2s - loss: 0.0115 - mean_absolute_error: 0.0796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 518/1534 [=========>....................] - ETA: 2s - loss: 0.0115 - mean_absolute_error: 0.0796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 539/1534 [=========>....................] - ETA: 2s - loss: 0.0115 - mean_absolute_error: 0.0795"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 561/1534 [=========>....................] - ETA: 2s - loss: 0.0116 - mean_absolute_error: 0.0795"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 582/1534 [==========>...................] - ETA: 2s - loss: 0.0115 - mean_absolute_error: 0.0793"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 603/1534 [==========>...................] - ETA: 2s - loss: 0.0115 - mean_absolute_error: 0.0791"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 624/1534 [===========>..................] - ETA: 2s - loss: 0.0114 - mean_absolute_error: 0.0790"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 645/1534 [===========>..................] - ETA: 2s - loss: 0.0113 - mean_absolute_error: 0.0788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 666/1534 [============>.................] - ETA: 2s - loss: 0.0114 - mean_absolute_error: 0.0788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 687/1534 [============>.................] - ETA: 2s - loss: 0.0113 - mean_absolute_error: 0.0786"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 708/1534 [============>.................] - ETA: 1s - loss: 0.0113 - mean_absolute_error: 0.0784"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 729/1534 [=============>................] - ETA: 1s - loss: 0.0112 - mean_absolute_error: 0.0781"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 751/1534 [=============>................] - ETA: 1s - loss: 0.0112 - mean_absolute_error: 0.0780"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 773/1534 [==============>...............] - ETA: 1s - loss: 0.0111 - mean_absolute_error: 0.0779"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 795/1534 [==============>...............] - ETA: 1s - loss: 0.0111 - mean_absolute_error: 0.0778"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 817/1534 [==============>...............] - ETA: 1s - loss: 0.0111 - mean_absolute_error: 0.0776"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 839/1534 [===============>..............] - ETA: 1s - loss: 0.0111 - mean_absolute_error: 0.0777"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 860/1534 [===============>..............] - ETA: 1s - loss: 0.0111 - mean_absolute_error: 0.0776"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 882/1534 [================>.............] - ETA: 1s - loss: 0.0111 - mean_absolute_error: 0.0776"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 904/1534 [================>.............] - ETA: 1s - loss: 0.0110 - mean_absolute_error: 0.0774"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 926/1534 [=================>............] - ETA: 1s - loss: 0.0110 - mean_absolute_error: 0.0773"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 947/1534 [=================>............] - ETA: 1s - loss: 0.0109 - mean_absolute_error: 0.0772"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 968/1534 [=================>............] - ETA: 1s - loss: 0.0109 - mean_absolute_error: 0.0771"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 989/1534 [==================>...........] - ETA: 1s - loss: 0.0109 - mean_absolute_error: 0.0770"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1011/1534 [==================>...........] - ETA: 1s - loss: 0.0108 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1032/1534 [===================>..........] - ETA: 1s - loss: 0.0108 - mean_absolute_error: 0.0768"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1053/1534 [===================>..........] - ETA: 1s - loss: 0.0108 - mean_absolute_error: 0.0768"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1074/1534 [====================>.........] - ETA: 1s - loss: 0.0108 - mean_absolute_error: 0.0768"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1095/1534 [====================>.........] - ETA: 1s - loss: 0.0108 - mean_absolute_error: 0.0766"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1116/1534 [====================>.........] - ETA: 0s - loss: 0.0107 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1138/1534 [=====================>........] - ETA: 0s - loss: 0.0107 - mean_absolute_error: 0.0764"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1160/1534 [=====================>........] - ETA: 0s - loss: 0.0107 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1182/1534 [======================>.......] - ETA: 0s - loss: 0.0107 - mean_absolute_error: 0.0764"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1204/1534 [======================>.......] - ETA: 0s - loss: 0.0107 - mean_absolute_error: 0.0764"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1226/1534 [======================>.......] - ETA: 0s - loss: 0.0107 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1247/1534 [=======================>......] - ETA: 0s - loss: 0.0107 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1269/1534 [=======================>......] - ETA: 0s - loss: 0.0106 - mean_absolute_error: 0.0761"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1290/1534 [========================>.....] - ETA: 0s - loss: 0.0106 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1312/1534 [========================>.....] - ETA: 0s - loss: 0.0106 - mean_absolute_error: 0.0759"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1334/1534 [=========================>....] - ETA: 0s - loss: 0.0106 - mean_absolute_error: 0.0759"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1356/1534 [=========================>....] - ETA: 0s - loss: 0.0106 - mean_absolute_error: 0.0759"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1378/1534 [=========================>....] - ETA: 0s - loss: 0.0106 - mean_absolute_error: 0.0758"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1400/1534 [==========================>...] - ETA: 0s - loss: 0.0106 - mean_absolute_error: 0.0757"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1422/1534 [==========================>...] - ETA: 0s - loss: 0.0106 - mean_absolute_error: 0.0758"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1443/1534 [===========================>..] - ETA: 0s - loss: 0.0106 - mean_absolute_error: 0.0757"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1464/1534 [===========================>..] - ETA: 0s - loss: 0.0106 - mean_absolute_error: 0.0757"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1485/1534 [============================>.] - ETA: 0s - loss: 0.0106 - mean_absolute_error: 0.0756"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1506/1534 [============================>.] - ETA: 0s - loss: 0.0106 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1527/1534 [============================>.] - ETA: 0s - loss: 0.0105 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 4s 3ms/step - loss: 0.0105 - mean_absolute_error: 0.0755 - val_loss: 0.0091 - val_mean_absolute_error: 0.0703\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 59s - loss: 0.0125 - mean_absolute_error: 0.0731"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  20/1534 [..............................] - ETA: 3s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  41/1534 [..............................] - ETA: 3s - loss: 0.0088 - mean_absolute_error: 0.0708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  62/1534 [>.............................] - ETA: 3s - loss: 0.0100 - mean_absolute_error: 0.0732"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  83/1534 [>.............................] - ETA: 3s - loss: 0.0099 - mean_absolute_error: 0.0732"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 104/1534 [=>............................] - ETA: 3s - loss: 0.0096 - mean_absolute_error: 0.0725"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 125/1534 [=>............................] - ETA: 3s - loss: 0.0094 - mean_absolute_error: 0.0723"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 146/1534 [=>............................] - ETA: 3s - loss: 0.0095 - mean_absolute_error: 0.0722"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 167/1534 [==>...........................] - ETA: 3s - loss: 0.0093 - mean_absolute_error: 0.0716"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 188/1534 [==>...........................] - ETA: 3s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 209/1534 [===>..........................] - ETA: 3s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 231/1534 [===>..........................] - ETA: 3s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 253/1534 [===>..........................] - ETA: 3s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 276/1534 [====>.........................] - ETA: 3s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 298/1534 [====>.........................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 319/1534 [=====>........................] - ETA: 2s - loss: 0.0091 - mean_absolute_error: 0.0708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 340/1534 [=====>........................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 361/1534 [======>.......................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 382/1534 [======>.......................] - ETA: 2s - loss: 0.0092 - mean_absolute_error: 0.0709"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 403/1534 [======>.......................] - ETA: 2s - loss: 0.0092 - mean_absolute_error: 0.0709"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 424/1534 [=======>......................] - ETA: 2s - loss: 0.0092 - mean_absolute_error: 0.0708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 445/1534 [=======>......................] - ETA: 2s - loss: 0.0092 - mean_absolute_error: 0.0709"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 466/1534 [========>.....................] - ETA: 2s - loss: 0.0092 - mean_absolute_error: 0.0708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 488/1534 [========>.....................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 510/1534 [========>.....................] - ETA: 2s - loss: 0.0092 - mean_absolute_error: 0.0708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 532/1534 [=========>....................] - ETA: 2s - loss: 0.0093 - mean_absolute_error: 0.0708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 554/1534 [=========>....................] - ETA: 2s - loss: 0.0093 - mean_absolute_error: 0.0708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 576/1534 [==========>...................] - ETA: 2s - loss: 0.0093 - mean_absolute_error: 0.0709"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 598/1534 [==========>...................] - ETA: 2s - loss: 0.0093 - mean_absolute_error: 0.0709"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 620/1534 [===========>..................] - ETA: 2s - loss: 0.0093 - mean_absolute_error: 0.0709"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 642/1534 [===========>..................] - ETA: 2s - loss: 0.0093 - mean_absolute_error: 0.0708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 663/1534 [===========>..................] - ETA: 2s - loss: 0.0092 - mean_absolute_error: 0.0707"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 685/1534 [============>.................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 707/1534 [============>.................] - ETA: 1s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 729/1534 [=============>................] - ETA: 1s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 751/1534 [=============>................] - ETA: 1s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 772/1534 [==============>...............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 793/1534 [==============>...............] - ETA: 1s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 815/1534 [==============>...............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 837/1534 [===============>..............] - ETA: 1s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 859/1534 [===============>..............] - ETA: 1s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 880/1534 [================>.............] - ETA: 1s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 902/1534 [================>.............] - ETA: 1s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 924/1534 [=================>............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 946/1534 [=================>............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 967/1534 [=================>............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 989/1534 [==================>...........] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1011/1534 [==================>...........] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1032/1534 [===================>..........] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1054/1534 [===================>..........] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1076/1534 [====================>.........] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1098/1534 [====================>.........] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1120/1534 [====================>.........] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1141/1534 [=====================>........] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1162/1534 [=====================>........] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1183/1534 [======================>.......] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1204/1534 [======================>.......] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      "1226/1534 [======================>.......] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      "1247/1534 [=======================>......] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      "1269/1534 [=======================>......] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      "1290/1534 [========================>.....] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1312/1534 [========================>.....] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1333/1534 [=========================>....] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      "1354/1534 [=========================>....] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1376/1534 [=========================>....] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      "1398/1534 [==========================>...] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      "1420/1534 [==========================>...] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      "1441/1534 [===========================>..] - ETA: 0s - loss: 0.0092 - 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\b\b\b\b\b\b\b\r",
      "1463/1534 [===========================>..] - ETA: 0s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1485/1534 [============================>.] - ETA: 0s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1507/1534 [============================>.] - ETA: 0s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1530/1534 [============================>.] - ETA: 0s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 4s 3ms/step - loss: 0.0092 - mean_absolute_error: 0.0702 - val_loss: 0.0087 - val_mean_absolute_error: 0.0687\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 1:00 - loss: 0.0103 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  22/1534 [..............................] - ETA: 3s - loss: 0.0087 - mean_absolute_error: 0.0682  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  43/1534 [..............................] - ETA: 3s - loss: 0.0089 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  64/1534 [>.............................] - ETA: 3s - loss: 0.0098 - mean_absolute_error: 0.0721"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  86/1534 [>.............................] - ETA: 3s - loss: 0.0097 - mean_absolute_error: 0.0722"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 108/1534 [=>............................] - ETA: 3s - loss: 0.0094 - mean_absolute_error: 0.0714"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 129/1534 [=>............................] - ETA: 3s - loss: 0.0093 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 150/1534 [=>............................] - ETA: 3s - loss: 0.0093 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 171/1534 [==>...........................] - ETA: 3s - loss: 0.0092 - mean_absolute_error: 0.0707"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 192/1534 [==>...........................] - ETA: 3s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 213/1534 [===>..........................] - ETA: 3s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 235/1534 [===>..........................] - ETA: 3s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 257/1534 [====>.........................] - ETA: 3s - loss: 0.0089 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 280/1534 [====>.........................] - ETA: 2s - loss: 0.0089 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 301/1534 [====>.........................] - ETA: 2s - loss: 0.0089 - 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\b\b\b\b\b\b\b\r",
      " 323/1534 [=====>........................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 345/1534 [=====>........................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 367/1534 [======>.......................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 389/1534 [======>.......................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 410/1534 [=======>......................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 432/1534 [=======>......................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 453/1534 [=======>......................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 474/1534 [========>.....................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 495/1534 [========>.....................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 516/1534 [=========>....................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 538/1534 [=========>....................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 560/1534 [=========>....................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\r",
      " 582/1534 [==========>...................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 604/1534 [==========>...................] - ETA: 2s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 625/1534 [===========>..................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 647/1534 [===========>..................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 669/1534 [============>.................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 691/1534 [============>.................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 713/1534 [============>.................] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 734/1534 [=============>................] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 755/1534 [=============>................] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 776/1534 [==============>...............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 797/1534 [==============>...............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 818/1534 [==============>...............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 839/1534 [===============>..............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 861/1534 [===============>..............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 883/1534 [================>.............] - ETA: 1s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 904/1534 [================>.............] - ETA: 1s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 925/1534 [=================>............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 946/1534 [=================>............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 967/1534 [=================>............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 988/1534 [==================>...........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1010/1534 [==================>...........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1031/1534 [===================>..........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1052/1534 [===================>..........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1074/1534 [====================>.........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1095/1534 [====================>.........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1117/1534 [====================>.........] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1138/1534 [=====================>........] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1159/1534 [=====================>........] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1181/1534 [======================>.......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1202/1534 [======================>.......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1224/1534 [======================>.......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1246/1534 [=======================>......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1268/1534 [=======================>......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1290/1534 [========================>.....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1312/1534 [========================>.....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1334/1534 [=========================>....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1356/1534 [=========================>....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1377/1534 [=========================>....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1399/1534 [==========================>...] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1421/1534 [==========================>...] - ETA: 0s - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1442/1534 [===========================>..] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1464/1534 [===========================>..] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1486/1534 [============================>.] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1508/1534 [============================>.] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1530/1534 [============================>.] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 4s 3ms/step - loss: 0.0091 - mean_absolute_error: 0.0698 - val_loss: 0.0087 - val_mean_absolute_error: 0.0689\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 58s - loss: 0.0103 - mean_absolute_error: 0.0727"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  22/1534 [..............................] - ETA: 3s - loss: 0.0087 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  44/1534 [..............................] - ETA: 3s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  66/1534 [>.............................] - ETA: 3s - loss: 0.0095 - mean_absolute_error: 0.0714"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  88/1534 [>.............................] - ETA: 3s - loss: 0.0095 - mean_absolute_error: 0.0719"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 110/1534 [=>............................] - ETA: 3s - loss: 0.0094 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 132/1534 [=>............................] - ETA: 3s - loss: 0.0093 - mean_absolute_error: 0.0712"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 153/1534 [=>............................] - ETA: 3s - loss: 0.0093 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 175/1534 [==>...........................] - ETA: 3s - loss: 0.0091 - mean_absolute_error: 0.0707"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 197/1534 [==>...........................] - ETA: 3s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 218/1534 [===>..........................] - ETA: 3s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 240/1534 [===>..........................] - ETA: 3s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 263/1534 [====>.........................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 286/1534 [====>.........................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 307/1534 [=====>........................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 329/1534 [=====>........................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 351/1534 [=====>........................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 372/1534 [======>.......................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 393/1534 [======>.......................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 415/1534 [=======>......................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 436/1534 [=======>......................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 457/1534 [=======>......................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 479/1534 [========>.....................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 500/1534 [========>.....................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 521/1534 [=========>....................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 542/1534 [=========>....................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 564/1534 [==========>...................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 586/1534 [==========>...................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 608/1534 [==========>...................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 630/1534 [===========>..................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 652/1534 [===========>..................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 674/1534 [============>.................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 696/1534 [============>.................] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 718/1534 [=============>................] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 740/1534 [=============>................] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 762/1534 [=============>................] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 783/1534 [==============>...............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 804/1534 [==============>...............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 824/1534 [===============>..............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 845/1534 [===============>..............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 866/1534 [===============>..............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 888/1534 [================>.............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 910/1534 [================>.............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 931/1534 [=================>............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 953/1534 [=================>............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 975/1534 [==================>...........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 997/1534 [==================>...........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1019/1534 [==================>...........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1041/1534 [===================>..........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1063/1534 [===================>..........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1085/1534 [====================>.........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1107/1534 [====================>.........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1128/1534 [=====================>........] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1150/1534 [=====================>........] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1171/1534 [=====================>........] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1192/1534 [======================>.......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1214/1534 [======================>.......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1235/1534 [=======================>......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1256/1534 [=======================>......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1277/1534 [=======================>......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1299/1534 [========================>.....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1320/1534 [========================>.....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1341/1534 [=========================>....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1363/1534 [=========================>....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1384/1534 [==========================>...] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1405/1534 [==========================>...] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1427/1534 [==========================>...] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1448/1534 [===========================>..] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1469/1534 [===========================>..] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1490/1534 [============================>.] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1511/1534 [============================>.] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1532/1534 [============================>.] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 4s 3ms/step - loss: 0.0091 - mean_absolute_error: 0.0698 - val_loss: 0.0087 - val_mean_absolute_error: 0.0684\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 59s - loss: 0.0062 - mean_absolute_error: 0.0573"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  21/1534 [..............................] - ETA: 3s - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  43/1534 [..............................] - ETA: 3s - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  64/1534 [>.............................] - ETA: 3s - loss: 0.0097 - mean_absolute_error: 0.0717"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  85/1534 [>.............................] - ETA: 3s - loss: 0.0097 - mean_absolute_error: 0.0722"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 107/1534 [=>............................] - ETA: 3s - loss: 0.0093 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 129/1534 [=>............................] - ETA: 3s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 151/1534 [=>............................] - ETA: 3s - loss: 0.0092 - mean_absolute_error: 0.0709"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 172/1534 [==>...........................] - ETA: 3s - loss: 0.0092 - mean_absolute_error: 0.0708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 193/1534 [==>...........................] - ETA: 3s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 214/1534 [===>..........................] - ETA: 3s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 236/1534 [===>..........................] - ETA: 3s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 258/1534 [====>.........................] - ETA: 3s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 282/1534 [====>.........................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 303/1534 [====>.........................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 325/1534 [=====>........................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 346/1534 [=====>........................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 367/1534 [======>.......................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 388/1534 [======>.......................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 410/1534 [=======>......................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 432/1534 [=======>......................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 453/1534 [=======>......................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 475/1534 [========>.....................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 496/1534 [========>.....................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 518/1534 [=========>....................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 539/1534 [=========>....................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 560/1534 [=========>....................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 581/1534 [==========>...................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 602/1534 [==========>...................] - ETA: 2s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 622/1534 [===========>..................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 643/1534 [===========>..................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 664/1534 [===========>..................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 685/1534 [============>.................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 706/1534 [============>.................] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 727/1534 [=============>................] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 749/1534 [=============>................] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 771/1534 [==============>...............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 793/1534 [==============>...............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 815/1534 [==============>...............] - ETA: 1s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 837/1534 [===============>..............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 858/1534 [===============>..............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 879/1534 [================>.............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 900/1534 [================>.............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 921/1534 [=================>............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 943/1534 [=================>............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 964/1534 [=================>............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 985/1534 [==================>...........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1007/1534 [==================>...........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1029/1534 [===================>..........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1051/1534 [===================>..........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1073/1534 [===================>..........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1095/1534 [====================>.........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1117/1534 [====================>.........] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1139/1534 [=====================>........] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1161/1534 [=====================>........] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1183/1534 [======================>.......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1205/1534 [======================>.......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1227/1534 [======================>.......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1248/1534 [=======================>......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1270/1534 [=======================>......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1292/1534 [========================>.....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1314/1534 [========================>.....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1336/1534 [=========================>....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1358/1534 [=========================>....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1380/1534 [=========================>....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1402/1534 [==========================>...] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1423/1534 [==========================>...] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1444/1534 [===========================>..] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1465/1534 [===========================>..] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1487/1534 [============================>.] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1508/1534 [============================>.] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1531/1534 [============================>.] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 4s 3ms/step - loss: 0.0091 - mean_absolute_error: 0.0699 - val_loss: 0.0087 - val_mean_absolute_error: 0.0687\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 59s - loss: 0.0081 - mean_absolute_error: 0.0683"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  22/1534 [..............................] - ETA: 3s - loss: 0.0092 - mean_absolute_error: 0.0689 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  43/1534 [..............................] - ETA: 3s - loss: 0.0093 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  64/1534 [>.............................] - ETA: 3s - loss: 0.0097 - mean_absolute_error: 0.0713"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  85/1534 [>.............................] - ETA: 3s - loss: 0.0096 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 105/1534 [=>............................] - ETA: 3s - loss: 0.0093 - mean_absolute_error: 0.0712"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 125/1534 [=>............................] - ETA: 3s - loss: 0.0092 - mean_absolute_error: 0.0708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 145/1534 [=>............................] - ETA: 3s - loss: 0.0093 - mean_absolute_error: 0.0713"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 165/1534 [==>...........................] - ETA: 3s - loss: 0.0092 - mean_absolute_error: 0.0709"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 185/1534 [==>...........................] - ETA: 3s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 206/1534 [===>..........................] - ETA: 3s - loss: 0.0089 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 227/1534 [===>..........................] - ETA: 3s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 248/1534 [===>..........................] - ETA: 3s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 270/1534 [====>.........................] - ETA: 3s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 292/1534 [====>.........................] - ETA: 3s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 312/1534 [=====>........................] - ETA: 3s - loss: 0.0089 - 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\b\b\b\b\b\b\b\r",
      " 332/1534 [=====>........................] - ETA: 2s - loss: 0.0089 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 352/1534 [=====>........................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 373/1534 [======>.......................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 395/1534 [======>.......................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\r",
      " 416/1534 [=======>......................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 437/1534 [=======>......................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 458/1534 [=======>......................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 479/1534 [========>.....................] - ETA: 2s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 500/1534 [========>.....................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 522/1534 [=========>....................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 544/1534 [=========>....................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 566/1534 [==========>...................] - ETA: 2s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 588/1534 [==========>...................] - ETA: 2s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 609/1534 [==========>...................] - ETA: 2s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 630/1534 [===========>..................] - ETA: 2s - loss: 0.0092 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 652/1534 [===========>..................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 674/1534 [============>.................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\r",
      " 696/1534 [============>.................] - ETA: 2s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 718/1534 [=============>................] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 740/1534 [=============>................] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 762/1534 [=============>................] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 784/1534 [==============>...............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 805/1534 [==============>...............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 827/1534 [===============>..............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 849/1534 [===============>..............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 871/1534 [================>.............] - ETA: 1s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 892/1534 [================>.............] - ETA: 1s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 914/1534 [================>.............] - ETA: 1s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 935/1534 [=================>............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 956/1534 [=================>............] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 978/1534 [==================>...........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1000/1534 [==================>...........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1022/1534 [==================>...........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1044/1534 [===================>..........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1066/1534 [===================>..........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1087/1534 [====================>.........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1109/1534 [====================>.........] - ETA: 1s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1130/1534 [=====================>........] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1151/1534 [=====================>........] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1173/1534 [=====================>........] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1194/1534 [======================>.......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1215/1534 [======================>.......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1236/1534 [=======================>......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1257/1534 [=======================>......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1278/1534 [=======================>......] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1300/1534 [========================>.....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1321/1534 [========================>.....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1343/1534 [=========================>....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1364/1534 [=========================>....] - ETA: 0s - loss: 0.0090 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1386/1534 [==========================>...] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1408/1534 [==========================>...] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1429/1534 [==========================>...] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1451/1534 [===========================>..] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1472/1534 [===========================>..] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1494/1534 [============================>.] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1516/1534 [============================>.] - ETA: 0s - loss: 0.0091 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 4s 3ms/step - loss: 0.0091 - mean_absolute_error: 0.0699 - val_loss: 0.0087 - val_mean_absolute_error: 0.0686\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/439 [..............................] - ETA: 37s - loss: 0.0096 - 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\b\b\b\b\b\b\b\b\b\r",
      " 26/439 [>.............................] - ETA: 0s - loss: 0.0086 - mean_absolute_error: 0.0682 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 53/439 [==>...........................] - ETA: 0s - loss: 0.0085 - 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\b\b\b\b\b\b\b\b\r",
      " 81/439 [====>.........................] - ETA: 0s - loss: 0.0087 - 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\b\b\b\b\b\b\b\b\r",
      "108/439 [======>.......................] - ETA: 0s - loss: 0.0087 - mean_absolute_error: 0.0688"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/439 [========>.....................] - ETA: 0s - loss: 0.0087 - mean_absolute_error: 0.0684"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/439 [=========>....................] - ETA: 0s - loss: 0.0086 - mean_absolute_error: 0.0684"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "186/439 [===========>..................] - ETA: 0s - loss: 0.0086 - mean_absolute_error: 0.0682"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "212/439 [=============>................] - ETA: 0s - loss: 0.0085 - mean_absolute_error: 0.0681"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/439 [===============>..............] - ETA: 0s - loss: 0.0085 - mean_absolute_error: 0.0681"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "263/439 [================>.............] - ETA: 0s - loss: 0.0086 - mean_absolute_error: 0.0682"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "289/439 [==================>...........] - ETA: 0s - loss: 0.0087 - 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\b\b\b\b\b\b\b\b\r",
      "316/439 [====================>.........] - ETA: 0s - loss: 0.0088 - mean_absolute_error: 0.0687"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "343/439 [======================>.......] - ETA: 0s - loss: 0.0087 - mean_absolute_error: 0.0686"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "370/439 [========================>.....] - ETA: 0s - loss: 0.0087 - mean_absolute_error: 0.0686"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "397/439 [==========================>...] - ETA: 0s - loss: 0.0087 - 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\b\b\b\b\b\b\b\b\r",
      "424/439 [===========================>..] - ETA: 0s - loss: 0.0086 - mean_absolute_error: 0.0683"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "439/439 [==============================] - 1s 2ms/step - loss: 0.0087 - mean_absolute_error: 0.0686\n"
     ]
    }
   ],
   "source": [
    "history = compile_and_fit(linear, single_step_window)\n",
    "\n",
    "val_performance['Linear'] = linear.evaluate(single_step_window.val)\n",
    "performance['Linear'] = linear.evaluate(single_step_window.test, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7U9XukYh8beN"
   },
   "source": [
    "`baseline` モデルと同様に、線形モデルは、ワイドウィンドウのバッチで呼び出すことができます。このように使用することで、モデルは連続した時間ステップに対して独立した一連の予測を立てます。`time` 軸は、別の `batch` 軸のように機能します。各時間ステップの予測間に相互作用はありません。\n",
    "\n",
    "![単一ステップの予測](images/wide_window.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:54.036815Z",
     "iopub.status.busy": "2024-01-11T20:37:54.036548Z",
     "iopub.status.idle": "2024-01-11T20:37:54.059690Z",
     "shell.execute_reply": "2024-01-11T20:37:54.059075Z"
    },
    "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": [
    "次は、`wide_widow` に対するサンプル予測の図です。多くの場合、入力気温のみを返すよりも予測が明らかに優れているのがわかりますが、いくつかのケースでは悪化しています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:54.063069Z",
     "iopub.status.busy": "2024-01-11T20:37:54.062816Z",
     "iopub.status.idle": "2024-01-11T20:37:54.499674Z",
     "shell.execute_reply": "2024-01-11T20:37:54.499018Z"
    },
    "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": [
    "線形モデルには、比較的解釈しやすいというメリットがあります。レイヤーの重みを引き出して、各入力に割り当てられた重みを確認することができます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:54.503705Z",
     "iopub.status.busy": "2024-01-11T20:37:54.503422Z",
     "iopub.status.idle": "2024-01-11T20:37:54.706983Z",
     "shell.execute_reply": "2024-01-11T20:37:54.706365Z"
    },
    "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": [
    "場合によっては、モデルには、入力 `T (degC)` のほとんどの重みを配置しないこともあります。これが、ランダム初期化のリスクの 1 つです。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W18e6da1cNbw"
   },
   "source": [
    "### Dense\n",
    "\n",
    "複数の時間ステップで実際に動作するモデルを適用する前に、より深く強力な単一入力ステップモデルのパフォーマンスを確認しておく価値があります。\n",
    "\n",
    "次は、`linear` モデルに似たモデルですが、入力と出力の間にいくつかの `Dense` レイヤーがスタックされています。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:37:54.710712Z",
     "iopub.status.busy": "2024-01-11T20:37:54.710125Z",
     "iopub.status.idle": "2024-01-11T20:38:40.995739Z",
     "shell.execute_reply": "2024-01-11T20:38:40.994781Z"
    },
    "id": "Z86WkYp7cNAD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 32:51 - loss: 1.4916 - mean_absolute_error: 0.9511"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  16/1534 [..............................] - ETA: 5s - loss: 0.3978 - mean_absolute_error: 0.4270   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  32/1534 [..............................] - ETA: 5s - loss: 0.2345 - mean_absolute_error: 0.3181"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  48/1534 [..............................] - ETA: 4s - loss: 0.1674 - mean_absolute_error: 0.2591"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  64/1534 [>.............................] - ETA: 4s - loss: 0.1310 - mean_absolute_error: 0.2227"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  80/1534 [>.............................] - ETA: 4s - loss: 0.1086 - mean_absolute_error: 0.1994"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  96/1534 [>.............................] - ETA: 4s - loss: 0.0933 - mean_absolute_error: 0.1831"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 112/1534 [=>............................] - ETA: 4s - loss: 0.0820 - mean_absolute_error: 0.1700"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 128/1534 [=>............................] - ETA: 4s - loss: 0.0735 - mean_absolute_error: 0.1603"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 144/1534 [=>............................] - ETA: 4s - loss: 0.0667 - mean_absolute_error: 0.1519"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 160/1534 [==>...........................] - ETA: 4s - loss: 0.0616 - mean_absolute_error: 0.1462"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 176/1534 [==>...........................] - ETA: 4s - loss: 0.0570 - mean_absolute_error: 0.1403"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 192/1534 [==>...........................] - ETA: 4s - loss: 0.0534 - mean_absolute_error: 0.1359"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 209/1534 [===>..........................] - ETA: 4s - loss: 0.0501 - mean_absolute_error: 0.1317"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 225/1534 [===>..........................] - ETA: 4s - loss: 0.0474 - mean_absolute_error: 0.1283"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 241/1534 [===>..........................] - ETA: 4s - loss: 0.0451 - mean_absolute_error: 0.1254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 257/1534 [====>.........................] - ETA: 4s - loss: 0.0430 - mean_absolute_error: 0.1227"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 273/1534 [====>.........................] - ETA: 4s - loss: 0.0411 - mean_absolute_error: 0.1201"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 290/1534 [====>.........................] - ETA: 4s - loss: 0.0393 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 306/1534 [====>.........................] - ETA: 3s - loss: 0.0378 - mean_absolute_error: 0.1156"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 322/1534 [=====>........................] - ETA: 3s - loss: 0.0364 - mean_absolute_error: 0.1136"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 338/1534 [=====>........................] - ETA: 3s - loss: 0.0352 - mean_absolute_error: 0.1120"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 354/1534 [=====>........................] - ETA: 3s - loss: 0.0340 - mean_absolute_error: 0.1101"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 370/1534 [======>.......................] - ETA: 3s - loss: 0.0330 - mean_absolute_error: 0.1087"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 386/1534 [======>.......................] - ETA: 3s - loss: 0.0321 - mean_absolute_error: 0.1076"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 402/1534 [======>.......................] - ETA: 3s - loss: 0.0313 - mean_absolute_error: 0.1064"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 418/1534 [=======>......................] - ETA: 3s - loss: 0.0305 - mean_absolute_error: 0.1052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 434/1534 [=======>......................] - ETA: 3s - loss: 0.0298 - mean_absolute_error: 0.1041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 450/1534 [=======>......................] - ETA: 3s - loss: 0.0291 - mean_absolute_error: 0.1030"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 466/1534 [========>.....................] - ETA: 3s - loss: 0.0284 - mean_absolute_error: 0.1020"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 483/1534 [========>.....................] - ETA: 3s - loss: 0.0277 - mean_absolute_error: 0.1008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 499/1534 [========>.....................] - ETA: 3s - loss: 0.0271 - mean_absolute_error: 0.0999"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 515/1534 [=========>....................] - ETA: 3s - loss: 0.0265 - mean_absolute_error: 0.0990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 531/1534 [=========>....................] - ETA: 3s - loss: 0.0260 - mean_absolute_error: 0.0983"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 547/1534 [=========>....................] - ETA: 3s - loss: 0.0255 - mean_absolute_error: 0.0975"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 563/1534 [==========>...................] - ETA: 3s - loss: 0.0251 - mean_absolute_error: 0.0969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 579/1534 [==========>...................] - ETA: 3s - loss: 0.0247 - mean_absolute_error: 0.0962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 595/1534 [==========>...................] - ETA: 3s - loss: 0.0243 - mean_absolute_error: 0.0957"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 611/1534 [==========>...................] - ETA: 2s - loss: 0.0239 - mean_absolute_error: 0.0950"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 627/1534 [===========>..................] - ETA: 2s - loss: 0.0235 - mean_absolute_error: 0.0943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 643/1534 [===========>..................] - ETA: 2s - loss: 0.0231 - mean_absolute_error: 0.0937"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 659/1534 [===========>..................] - ETA: 2s - loss: 0.0228 - mean_absolute_error: 0.0932"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 675/1534 [============>.................] - ETA: 2s - loss: 0.0225 - mean_absolute_error: 0.0928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 691/1534 [============>.................] - ETA: 2s - loss: 0.0222 - mean_absolute_error: 0.0923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 707/1534 [============>.................] - ETA: 2s - loss: 0.0219 - mean_absolute_error: 0.0918"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 723/1534 [=============>................] - ETA: 2s - loss: 0.0216 - mean_absolute_error: 0.0913"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 739/1534 [=============>................] - ETA: 2s - loss: 0.0213 - mean_absolute_error: 0.0908"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 755/1534 [=============>................] - ETA: 2s - loss: 0.0211 - mean_absolute_error: 0.0906"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 771/1534 [==============>...............] - ETA: 2s - loss: 0.0209 - mean_absolute_error: 0.0902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 787/1534 [==============>...............] - ETA: 2s - loss: 0.0206 - mean_absolute_error: 0.0898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 803/1534 [==============>...............] - ETA: 2s - loss: 0.0204 - mean_absolute_error: 0.0895"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 819/1534 [===============>..............] - ETA: 2s - loss: 0.0202 - mean_absolute_error: 0.0892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 835/1534 [===============>..............] - ETA: 2s - loss: 0.0200 - mean_absolute_error: 0.0887"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 851/1534 [===============>..............] - ETA: 2s - loss: 0.0198 - mean_absolute_error: 0.0884"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 867/1534 [===============>..............] - ETA: 2s - loss: 0.0196 - mean_absolute_error: 0.0880"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 883/1534 [================>.............] - ETA: 2s - loss: 0.0194 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 899/1534 [================>.............] - ETA: 2s - loss: 0.0192 - mean_absolute_error: 0.0874"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 915/1534 [================>.............] - ETA: 1s - loss: 0.0190 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 931/1534 [=================>............] - ETA: 1s - loss: 0.0188 - mean_absolute_error: 0.0867"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 947/1534 [=================>............] - ETA: 1s - loss: 0.0186 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 963/1534 [=================>............] - ETA: 1s - loss: 0.0185 - mean_absolute_error: 0.0860"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 979/1534 [==================>...........] - ETA: 1s - loss: 0.0183 - mean_absolute_error: 0.0858"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 994/1534 [==================>...........] - ETA: 1s - loss: 0.0182 - mean_absolute_error: 0.0856"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1009/1534 [==================>...........] - ETA: 1s - loss: 0.0181 - mean_absolute_error: 0.0854"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1024/1534 [===================>..........] - ETA: 1s - loss: 0.0179 - mean_absolute_error: 0.0852"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1040/1534 [===================>..........] - ETA: 1s - loss: 0.0178 - mean_absolute_error: 0.0848"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1056/1534 [===================>..........] - ETA: 1s - loss: 0.0176 - mean_absolute_error: 0.0846"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1072/1534 [===================>..........] - ETA: 1s - loss: 0.0175 - mean_absolute_error: 0.0843"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1088/1534 [====================>.........] - ETA: 1s - loss: 0.0174 - mean_absolute_error: 0.0840"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1104/1534 [====================>.........] - ETA: 1s - loss: 0.0172 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1120/1534 [====================>.........] - ETA: 1s - loss: 0.0171 - mean_absolute_error: 0.0837"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1136/1534 [=====================>........] - ETA: 1s - loss: 0.0170 - mean_absolute_error: 0.0835"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1152/1534 [=====================>........] - ETA: 1s - loss: 0.0169 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1168/1534 [=====================>........] - ETA: 1s - loss: 0.0168 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1184/1534 [======================>.......] - ETA: 1s - loss: 0.0167 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1200/1534 [======================>.......] - ETA: 1s - loss: 0.0166 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1216/1534 [======================>.......] - ETA: 1s - loss: 0.0165 - mean_absolute_error: 0.0825"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1232/1534 [=======================>......] - ETA: 0s - loss: 0.0164 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1248/1534 [=======================>......] - ETA: 0s - loss: 0.0163 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1264/1534 [=======================>......] - ETA: 0s - loss: 0.0162 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1280/1534 [========================>.....] - ETA: 0s - loss: 0.0161 - mean_absolute_error: 0.0816"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1296/1534 [========================>.....] - ETA: 0s - loss: 0.0160 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1312/1534 [========================>.....] - ETA: 0s - loss: 0.0159 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1328/1534 [========================>.....] - ETA: 0s - loss: 0.0158 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1344/1534 [=========================>....] - ETA: 0s - loss: 0.0157 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1360/1534 [=========================>....] - ETA: 0s - loss: 0.0156 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1376/1534 [=========================>....] - ETA: 0s - loss: 0.0155 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1392/1534 [==========================>...] - ETA: 0s - loss: 0.0155 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1408/1534 [==========================>...] - ETA: 0s - loss: 0.0154 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1424/1534 [==========================>...] - ETA: 0s - loss: 0.0153 - 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\b\b\b\b\b\b\b\r",
      "1440/1534 [===========================>..] - ETA: 0s - loss: 0.0152 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1456/1534 [===========================>..] - ETA: 0s - loss: 0.0151 - mean_absolute_error: 0.0798"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1472/1534 [===========================>..] - ETA: 0s - loss: 0.0151 - mean_absolute_error: 0.0797"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1488/1534 [============================>.] - ETA: 0s - loss: 0.0150 - mean_absolute_error: 0.0796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1504/1534 [============================>.] - ETA: 0s - loss: 0.0149 - mean_absolute_error: 0.0795"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1520/1534 [============================>.] - ETA: 0s - loss: 0.0149 - mean_absolute_error: 0.0793"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - ETA: 0s - loss: 0.0148 - mean_absolute_error: 0.0793"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 7s 4ms/step - loss: 0.0148 - mean_absolute_error: 0.0793 - val_loss: 0.0087 - val_mean_absolute_error: 0.0685\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 1:02 - loss: 0.0140 - mean_absolute_error: 0.0787"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  16/1534 [..............................] - ETA: 5s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  32/1534 [..............................] - ETA: 5s - loss: 0.0078 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  48/1534 [..............................] - ETA: 4s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  64/1534 [>.............................] - ETA: 4s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  80/1534 [>.............................] - ETA: 4s - loss: 0.0082 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  96/1534 [>.............................] - ETA: 4s - loss: 0.0082 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 113/1534 [=>............................] - ETA: 4s - loss: 0.0082 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 129/1534 [=>............................] - ETA: 4s - loss: 0.0081 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 145/1534 [=>............................] - ETA: 4s - loss: 0.0081 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 161/1534 [==>...........................] - ETA: 4s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 177/1534 [==>...........................] - ETA: 4s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 193/1534 [==>...........................] - ETA: 4s - loss: 0.0081 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 209/1534 [===>..........................] - ETA: 4s - loss: 0.0081 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 225/1534 [===>..........................] - ETA: 4s - loss: 0.0081 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 241/1534 [===>..........................] - ETA: 4s - loss: 0.0082 - mean_absolute_error: 0.0660"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 257/1534 [====>.........................] - ETA: 4s - loss: 0.0082 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 273/1534 [====>.........................] - ETA: 4s - loss: 0.0082 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 289/1534 [====>.........................] - ETA: 4s - loss: 0.0082 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 305/1534 [====>.........................] - ETA: 3s - loss: 0.0081 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 321/1534 [=====>........................] - ETA: 3s - loss: 0.0081 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 337/1534 [=====>........................] - ETA: 3s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 353/1534 [=====>........................] - ETA: 3s - loss: 0.0079 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 369/1534 [======>.......................] - ETA: 3s - loss: 0.0079 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 385/1534 [======>.......................] - ETA: 3s - loss: 0.0079 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 401/1534 [======>.......................] - ETA: 3s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 417/1534 [=======>......................] - ETA: 3s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 433/1534 [=======>......................] - ETA: 3s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 449/1534 [=======>......................] - ETA: 3s - loss: 0.0081 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 465/1534 [========>.....................] - ETA: 3s - loss: 0.0081 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 481/1534 [========>.....................] - ETA: 3s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 497/1534 [========>.....................] - ETA: 3s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 512/1534 [=========>....................] - ETA: 3s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 528/1534 [=========>....................] - ETA: 3s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 544/1534 [=========>....................] - ETA: 3s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 559/1534 [=========>....................] - ETA: 3s - loss: 0.0079 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 575/1534 [==========>...................] - ETA: 3s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 591/1534 [==========>...................] - ETA: 3s - loss: 0.0079 - mean_absolute_error: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 607/1534 [==========>...................] - ETA: 2s - loss: 0.0079 - mean_absolute_error: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 623/1534 [===========>..................] - ETA: 2s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 639/1534 [===========>..................] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 655/1534 [===========>..................] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 671/1534 [============>.................] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 687/1534 [============>.................] - ETA: 2s - loss: 0.0079 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 702/1534 [============>.................] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 718/1534 [=============>................] - ETA: 2s - loss: 0.0079 - mean_absolute_error: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 734/1534 [=============>................] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 750/1534 [=============>................] - ETA: 2s - loss: 0.0079 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 766/1534 [=============>................] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 782/1534 [==============>...............] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 798/1534 [==============>...............] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 814/1534 [==============>...............] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 830/1534 [===============>..............] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 846/1534 [===============>..............] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 862/1534 [===============>..............] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 878/1534 [================>.............] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 894/1534 [================>.............] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 910/1534 [================>.............] - ETA: 2s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 926/1534 [=================>............] - ETA: 1s - loss: 0.0079 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 942/1534 [=================>............] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 958/1534 [=================>............] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 974/1534 [==================>...........] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 990/1534 [==================>...........] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1006/1534 [==================>...........] - ETA: 1s - loss: 0.0080 - mean_absolute_error: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1021/1534 [==================>...........] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1037/1534 [===================>..........] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1053/1534 [===================>..........] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1068/1534 [===================>..........] - ETA: 1s - loss: 0.0079 - 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\b\b\b\b\b\b\b\r",
      "1084/1534 [====================>.........] - ETA: 1s - loss: 0.0079 - 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\b\b\b\b\b\b\b\r",
      "1100/1534 [====================>.........] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1116/1534 [====================>.........] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1132/1534 [=====================>........] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1147/1534 [=====================>........] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1162/1534 [=====================>........] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1177/1534 [======================>.......] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1192/1534 [======================>.......] - ETA: 1s - loss: 0.0080 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1208/1534 [======================>.......] - ETA: 1s - loss: 0.0080 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1224/1534 [======================>.......] - ETA: 1s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1240/1534 [=======================>......] - ETA: 0s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1256/1534 [=======================>......] - ETA: 0s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1272/1534 [=======================>......] - ETA: 0s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1288/1534 [========================>.....] - ETA: 0s - loss: 0.0079 - 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\b\b\b\b\b\b\b\r",
      "1304/1534 [========================>.....] - ETA: 0s - loss: 0.0079 - 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\b\b\b\b\b\b\b\r",
      "1319/1534 [========================>.....] - ETA: 0s - loss: 0.0079 - 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\b\b\b\b\b\b\b\r",
      "1335/1534 [=========================>....] - ETA: 0s - loss: 0.0079 - 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\b\b\b\b\b\b\b\r",
      "1351/1534 [=========================>....] - ETA: 0s - loss: 0.0079 - 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\b\b\b\b\b\b\b\r",
      "1367/1534 [=========================>....] - ETA: 0s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1383/1534 [==========================>...] - ETA: 0s - loss: 0.0079 - 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\b\b\b\b\b\b\b\r",
      "1399/1534 [==========================>...] - ETA: 0s - loss: 0.0079 - 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\b\b\b\b\b\b\b\r",
      "1414/1534 [==========================>...] - ETA: 0s - loss: 0.0079 - mean_absolute_error: 0.0646"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1429/1534 [==========================>...] - ETA: 0s - loss: 0.0079 - mean_absolute_error: 0.0646"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1445/1534 [===========================>..] - ETA: 0s - loss: 0.0079 - mean_absolute_error: 0.0646"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1461/1534 [===========================>..] - ETA: 0s - loss: 0.0079 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1477/1534 [===========================>..] - ETA: 0s - loss: 0.0079 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1492/1534 [============================>.] - ETA: 0s - loss: 0.0079 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1508/1534 [============================>.] - ETA: 0s - loss: 0.0079 - mean_absolute_error: 0.0646"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1523/1534 [============================>.] - ETA: 0s - loss: 0.0079 - mean_absolute_error: 0.0646"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 6s 4ms/step - loss: 0.0079 - mean_absolute_error: 0.0646 - val_loss: 0.0092 - val_mean_absolute_error: 0.0718\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 1:01 - loss: 0.0080 - mean_absolute_error: 0.0739"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  16/1534 [..............................] - ETA: 5s - loss: 0.0083 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  31/1534 [..............................] - ETA: 5s - loss: 0.0086 - mean_absolute_error: 0.0690"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  47/1534 [..............................] - ETA: 5s - loss: 0.0084 - mean_absolute_error: 0.0682"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  62/1534 [>.............................] - ETA: 4s - loss: 0.0082 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  78/1534 [>.............................] - ETA: 4s - loss: 0.0083 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  94/1534 [>.............................] - ETA: 4s - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 109/1534 [=>............................] - ETA: 4s - loss: 0.0081 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 125/1534 [=>............................] - ETA: 4s - loss: 0.0079 - mean_absolute_error: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 141/1534 [=>............................] - ETA: 4s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 156/1534 [==>...........................] - ETA: 4s - loss: 0.0080 - mean_absolute_error: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 172/1534 [==>...........................] - ETA: 4s - loss: 0.0078 - mean_absolute_error: 0.0644"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 187/1534 [==>...........................] - ETA: 4s - loss: 0.0079 - mean_absolute_error: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 202/1534 [==>...........................] - ETA: 4s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 217/1534 [===>..........................] - ETA: 4s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 232/1534 [===>..........................] - ETA: 4s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 248/1534 [===>..........................] - ETA: 4s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 264/1534 [====>.........................] - ETA: 4s - loss: 0.0080 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 279/1534 [====>.........................] - ETA: 4s - loss: 0.0079 - mean_absolute_error: 0.0648"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 295/1534 [====>.........................] - ETA: 4s - loss: 0.0078 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 311/1534 [=====>........................] - ETA: 4s - loss: 0.0078 - mean_absolute_error: 0.0644"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 326/1534 [=====>........................] - ETA: 4s - loss: 0.0077 - mean_absolute_error: 0.0642"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 342/1534 [=====>........................] - ETA: 3s - loss: 0.0077 - mean_absolute_error: 0.0639"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 357/1534 [=====>........................] - ETA: 3s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 373/1534 [======>.......................] - ETA: 3s - loss: 0.0076 - mean_absolute_error: 0.0635"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 389/1534 [======>.......................] - ETA: 3s - loss: 0.0076 - mean_absolute_error: 0.0635"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 406/1534 [======>.......................] - ETA: 3s - loss: 0.0076 - mean_absolute_error: 0.0635"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 422/1534 [=======>......................] - ETA: 3s - loss: 0.0078 - mean_absolute_error: 0.0638"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 438/1534 [=======>......................] - ETA: 3s - loss: 0.0078 - mean_absolute_error: 0.0639"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 453/1534 [=======>......................] - ETA: 3s - loss: 0.0078 - mean_absolute_error: 0.0639"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 468/1534 [========>.....................] - ETA: 3s - loss: 0.0078 - mean_absolute_error: 0.0639"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 483/1534 [========>.....................] - ETA: 3s - loss: 0.0077 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 498/1534 [========>.....................] - ETA: 3s - loss: 0.0077 - mean_absolute_error: 0.0636"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 514/1534 [=========>....................] - ETA: 3s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 530/1534 [=========>....................] - ETA: 3s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 546/1534 [=========>....................] - ETA: 3s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 561/1534 [=========>....................] - ETA: 3s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 577/1534 [==========>...................] - ETA: 3s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 592/1534 [==========>...................] - ETA: 3s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 608/1534 [==========>...................] - ETA: 3s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 624/1534 [===========>..................] - ETA: 3s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 640/1534 [===========>..................] - ETA: 2s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 656/1534 [===========>..................] - ETA: 2s - loss: 0.0076 - mean_absolute_error: 0.0631"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 672/1534 [============>.................] - ETA: 2s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 688/1534 [============>.................] - ETA: 2s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 704/1534 [============>.................] - ETA: 2s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 719/1534 [=============>................] - ETA: 2s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 734/1534 [=============>................] - ETA: 2s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 749/1534 [=============>................] - ETA: 2s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 765/1534 [=============>................] - ETA: 2s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 781/1534 [==============>...............] - ETA: 2s - loss: 0.0075 - mean_absolute_error: 0.0631"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 796/1534 [==============>...............] - ETA: 2s - loss: 0.0076 - mean_absolute_error: 0.0631"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 812/1534 [==============>...............] - ETA: 2s - loss: 0.0076 - mean_absolute_error: 0.0631"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 828/1534 [===============>..............] - ETA: 2s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 844/1534 [===============>..............] - ETA: 2s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 859/1534 [===============>..............] - ETA: 2s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 875/1534 [================>.............] - ETA: 2s - loss: 0.0076 - mean_absolute_error: 0.0634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 890/1534 [================>.............] - ETA: 2s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 906/1534 [================>.............] - ETA: 2s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 922/1534 [=================>............] - ETA: 2s - loss: 0.0076 - mean_absolute_error: 0.0634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 938/1534 [=================>............] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 954/1534 [=================>............] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 970/1534 [=================>............] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 986/1534 [==================>...........] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1002/1534 [==================>...........] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1018/1534 [==================>...........] - ETA: 1s - loss: 0.0077 - mean_absolute_error: 0.0635"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1034/1534 [===================>..........] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0635"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1049/1534 [===================>..........] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1065/1534 [===================>..........] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1080/1534 [====================>.........] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1095/1534 [====================>.........] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1110/1534 [====================>.........] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1126/1534 [=====================>........] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1142/1534 [=====================>........] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1157/1534 [=====================>........] - ETA: 1s - loss: 0.0076 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1172/1534 [=====================>........] - ETA: 1s - loss: 0.0077 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1188/1534 [======================>.......] - ETA: 1s - loss: 0.0077 - mean_absolute_error: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1204/1534 [======================>.......] - ETA: 1s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1220/1534 [======================>.......] - ETA: 1s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1235/1534 [=======================>......] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1251/1534 [=======================>......] - ETA: 0s - loss: 0.0076 - mean_absolute_error: 0.0631"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1267/1534 [=======================>......] - ETA: 0s - loss: 0.0076 - mean_absolute_error: 0.0631"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1283/1534 [========================>.....] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1299/1534 [========================>.....] - ETA: 0s - loss: 0.0076 - mean_absolute_error: 0.0631"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1315/1534 [========================>.....] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1330/1534 [=========================>....] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1345/1534 [=========================>....] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1360/1534 [=========================>....] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1376/1534 [=========================>....] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1392/1534 [==========================>...] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1407/1534 [==========================>...] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1422/1534 [==========================>...] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1438/1534 [===========================>..] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1454/1534 [===========================>..] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1469/1534 [===========================>..] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1485/1534 [============================>.] - ETA: 0s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1500/1534 [============================>.] - ETA: 0s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1515/1534 [============================>.] - ETA: 0s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1531/1534 [============================>.] - ETA: 0s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 6s 4ms/step - loss: 0.0076 - mean_absolute_error: 0.0628 - val_loss: 0.0071 - val_mean_absolute_error: 0.0604\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 1:02 - loss: 0.0038 - mean_absolute_error: 0.0484"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  15/1534 [..............................] - ETA: 5s - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  30/1534 [..............................] - ETA: 5s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  45/1534 [..............................] - ETA: 5s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  60/1534 [>.............................] - ETA: 5s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  75/1534 [>.............................] - ETA: 5s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  90/1534 [>.............................] - ETA: 4s - loss: 0.0076 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 105/1534 [=>............................] - ETA: 4s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 120/1534 [=>............................] - ETA: 4s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 135/1534 [=>............................] - ETA: 4s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 151/1534 [=>............................] - ETA: 4s - loss: 0.0074 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 166/1534 [==>...........................] - ETA: 4s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 182/1534 [==>...........................] - ETA: 4s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 198/1534 [==>...........................] - ETA: 4s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 214/1534 [===>..........................] - ETA: 4s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 229/1534 [===>..........................] - ETA: 4s - loss: 0.0074 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 245/1534 [===>..........................] - ETA: 4s - loss: 0.0074 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 261/1534 [====>.........................] - ETA: 4s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 276/1534 [====>.........................] - ETA: 4s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 291/1534 [====>.........................] - ETA: 4s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 307/1534 [=====>........................] - ETA: 4s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 323/1534 [=====>........................] - ETA: 4s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 338/1534 [=====>........................] - ETA: 4s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 354/1534 [=====>........................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 369/1534 [======>.......................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 386/1534 [======>.......................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 403/1534 [======>.......................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 419/1534 [=======>......................] - ETA: 3s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 434/1534 [=======>......................] - ETA: 3s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 450/1534 [=======>......................] - ETA: 3s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 466/1534 [========>.....................] - ETA: 3s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 482/1534 [========>.....................] - ETA: 3s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 498/1534 [========>.....................] - ETA: 3s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 514/1534 [=========>....................] - ETA: 3s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 530/1534 [=========>....................] - ETA: 3s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 546/1534 [=========>....................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 562/1534 [=========>....................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 578/1534 [==========>...................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 594/1534 [==========>...................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 609/1534 [==========>...................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 624/1534 [===========>..................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 639/1534 [===========>..................] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 655/1534 [===========>..................] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 671/1534 [============>.................] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 687/1534 [============>.................] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 702/1534 [============>.................] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 717/1534 [=============>................] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 732/1534 [=============>................] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 747/1534 [=============>................] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 762/1534 [=============>................] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 777/1534 [==============>...............] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 793/1534 [==============>...............] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 809/1534 [==============>...............] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 825/1534 [===============>..............] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 841/1534 [===============>..............] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 857/1534 [===============>..............] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 873/1534 [================>.............] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 888/1534 [================>.............] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 904/1534 [================>.............] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 919/1534 [================>.............] - ETA: 2s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 934/1534 [=================>............] - ETA: 1s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 950/1534 [=================>............] - ETA: 1s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 966/1534 [=================>............] - ETA: 1s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 982/1534 [==================>...........] - ETA: 1s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 998/1534 [==================>...........] - ETA: 1s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1013/1534 [==================>...........] - ETA: 1s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1028/1534 [===================>..........] - ETA: 1s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1043/1534 [===================>..........] - ETA: 1s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1058/1534 [===================>..........] - ETA: 1s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1073/1534 [===================>..........] - ETA: 1s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1088/1534 [====================>.........] - ETA: 1s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1103/1534 [====================>.........] - ETA: 1s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1118/1534 [====================>.........] - ETA: 1s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1133/1534 [=====================>........] - ETA: 1s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1149/1534 [=====================>........] - ETA: 1s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1164/1534 [=====================>........] - ETA: 1s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1179/1534 [======================>.......] - ETA: 1s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1194/1534 [======================>.......] - ETA: 1s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1209/1534 [======================>.......] - ETA: 1s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1225/1534 [======================>.......] - ETA: 1s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1241/1534 [=======================>......] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1257/1534 [=======================>......] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1273/1534 [=======================>......] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1289/1534 [========================>.....] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1304/1534 [========================>.....] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1319/1534 [========================>.....] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1335/1534 [=========================>....] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1350/1534 [=========================>....] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1365/1534 [=========================>....] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1380/1534 [=========================>....] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1395/1534 [==========================>...] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1410/1534 [==========================>...] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1425/1534 [==========================>...] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1441/1534 [===========================>..] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1457/1534 [===========================>..] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1473/1534 [===========================>..] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1488/1534 [============================>.] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1503/1534 [============================>.] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1519/1534 [============================>.] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 6s 4ms/step - loss: 0.0072 - mean_absolute_error: 0.0612 - val_loss: 0.0070 - val_mean_absolute_error: 0.0591\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 57s - loss: 0.0053 - mean_absolute_error: 0.0557"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  16/1534 [..............................] - ETA: 5s - loss: 0.0060 - 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\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  32/1534 [..............................] - ETA: 5s - loss: 0.0066 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  47/1534 [..............................] - ETA: 5s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  63/1534 [>.............................] - ETA: 4s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  79/1534 [>.............................] - ETA: 4s - loss: 0.0075 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  95/1534 [>.............................] - ETA: 4s - loss: 0.0073 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 111/1534 [=>............................] - ETA: 4s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 126/1534 [=>............................] - ETA: 4s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 142/1534 [=>............................] - ETA: 4s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 158/1534 [==>...........................] - ETA: 4s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 174/1534 [==>...........................] - ETA: 4s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 190/1534 [==>...........................] - ETA: 4s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 206/1534 [===>..........................] - ETA: 4s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 221/1534 [===>..........................] - ETA: 4s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 237/1534 [===>..........................] - ETA: 4s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 252/1534 [===>..........................] - ETA: 4s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 268/1534 [====>.........................] - ETA: 4s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 284/1534 [====>.........................] - ETA: 4s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 300/1534 [====>.........................] - ETA: 4s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 316/1534 [=====>........................] - ETA: 4s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 332/1534 [=====>........................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 348/1534 [=====>........................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 363/1534 [======>.......................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 379/1534 [======>.......................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 395/1534 [======>.......................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 411/1534 [=======>......................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 426/1534 [=======>......................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 441/1534 [=======>......................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 457/1534 [=======>......................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 473/1534 [========>.....................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 489/1534 [========>.....................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 505/1534 [========>.....................] - ETA: 3s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 521/1534 [=========>....................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 537/1534 [=========>....................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 552/1534 [=========>....................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 568/1534 [==========>...................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 584/1534 [==========>...................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 600/1534 [==========>...................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 615/1534 [===========>..................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 631/1534 [===========>..................] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 646/1534 [===========>..................] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 662/1534 [===========>..................] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 677/1534 [============>.................] - ETA: 2s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 692/1534 [============>.................] - ETA: 2s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 708/1534 [============>.................] - ETA: 2s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 724/1534 [=============>................] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 739/1534 [=============>................] - ETA: 2s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 755/1534 [=============>................] - ETA: 2s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 771/1534 [==============>...............] - ETA: 2s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 786/1534 [==============>...............] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 801/1534 [==============>...............] - ETA: 2s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 816/1534 [==============>...............] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 831/1534 [===============>..............] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 846/1534 [===============>..............] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 861/1534 [===============>..............] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 876/1534 [================>.............] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 891/1534 [================>.............] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 907/1534 [================>.............] - ETA: 2s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 922/1534 [=================>............] - ETA: 2s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 937/1534 [=================>............] - ETA: 1s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 953/1534 [=================>............] - ETA: 1s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 968/1534 [=================>............] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 983/1534 [==================>...........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 998/1534 [==================>...........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1013/1534 [==================>...........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1028/1534 [===================>..........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1043/1534 [===================>..........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1058/1534 [===================>..........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1073/1534 [===================>..........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1088/1534 [====================>.........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1103/1534 [====================>.........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1119/1534 [====================>.........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1135/1534 [=====================>........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1150/1534 [=====================>........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1165/1534 [=====================>........] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1180/1534 [======================>.......] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1196/1534 [======================>.......] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1212/1534 [======================>.......] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1228/1534 [=======================>......] - ETA: 1s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1244/1534 [=======================>......] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1260/1534 [=======================>......] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1276/1534 [=======================>......] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1291/1534 [========================>.....] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1306/1534 [========================>.....] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1321/1534 [========================>.....] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1337/1534 [=========================>....] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1352/1534 [=========================>....] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1368/1534 [=========================>....] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1383/1534 [==========================>...] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1399/1534 [==========================>...] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1415/1534 [==========================>...] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1431/1534 [==========================>...] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1446/1534 [===========================>..] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1461/1534 [===========================>..] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1476/1534 [===========================>..] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1491/1534 [============================>.] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1506/1534 [============================>.] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1522/1534 [============================>.] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 6s 4ms/step - loss: 0.0071 - mean_absolute_error: 0.0602 - val_loss: 0.0066 - val_mean_absolute_error: 0.0570\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 58s - loss: 0.0057 - mean_absolute_error: 0.0564"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  15/1534 [..............................] - ETA: 5s - loss: 0.0069 - mean_absolute_error: 0.0577 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  31/1534 [..............................] - ETA: 5s - loss: 0.0064 - mean_absolute_error: 0.0567"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  46/1534 [..............................] - ETA: 5s - loss: 0.0066 - mean_absolute_error: 0.0579"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  61/1534 [>.............................] - ETA: 5s - loss: 0.0065 - mean_absolute_error: 0.0576"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  77/1534 [>.............................] - ETA: 4s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  93/1534 [>.............................] - ETA: 4s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 108/1534 [=>............................] - ETA: 4s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 123/1534 [=>............................] - ETA: 4s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 138/1534 [=>............................] - ETA: 4s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 154/1534 [==>...........................] - ETA: 4s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 170/1534 [==>...........................] - ETA: 4s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 186/1534 [==>...........................] - ETA: 4s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 203/1534 [==>...........................] - ETA: 4s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 219/1534 [===>..........................] - ETA: 4s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 234/1534 [===>..........................] - ETA: 4s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 250/1534 [===>..........................] - ETA: 4s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 266/1534 [====>.........................] - ETA: 4s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 281/1534 [====>.........................] - ETA: 4s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 296/1534 [====>.........................] - ETA: 4s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 312/1534 [=====>........................] - ETA: 4s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 327/1534 [=====>........................] - ETA: 4s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 342/1534 [=====>........................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 358/1534 [======>.......................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 374/1534 [======>.......................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 389/1534 [======>.......................] - ETA: 3s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 405/1534 [======>.......................] - ETA: 3s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 420/1534 [=======>......................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 436/1534 [=======>......................] - ETA: 3s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 451/1534 [=======>......................] - ETA: 3s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 467/1534 [========>.....................] - ETA: 3s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 483/1534 [========>.....................] - ETA: 3s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 499/1534 [========>.....................] - ETA: 3s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 514/1534 [=========>....................] - ETA: 3s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 530/1534 [=========>....................] - ETA: 3s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 545/1534 [=========>....................] - ETA: 3s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 561/1534 [=========>....................] - ETA: 3s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 577/1534 [==========>...................] - ETA: 3s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 593/1534 [==========>...................] - ETA: 3s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 609/1534 [==========>...................] - ETA: 3s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 624/1534 [===========>..................] - ETA: 3s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 640/1534 [===========>..................] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 656/1534 [===========>..................] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 671/1534 [============>.................] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 687/1534 [============>.................] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 703/1534 [============>.................] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 718/1534 [=============>................] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 733/1534 [=============>................] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 749/1534 [=============>................] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 765/1534 [=============>................] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 780/1534 [==============>...............] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 795/1534 [==============>...............] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 810/1534 [==============>...............] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 825/1534 [===============>..............] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 840/1534 [===============>..............] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 856/1534 [===============>..............] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 871/1534 [================>.............] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 887/1534 [================>.............] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 902/1534 [================>.............] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 918/1534 [================>.............] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 933/1534 [=================>............] - ETA: 2s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 948/1534 [=================>............] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 963/1534 [=================>............] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 978/1534 [==================>...........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 993/1534 [==================>...........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1008/1534 [==================>...........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1023/1534 [===================>..........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1038/1534 [===================>..........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1054/1534 [===================>..........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1070/1534 [===================>..........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1086/1534 [====================>.........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1102/1534 [====================>.........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1117/1534 [====================>.........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1132/1534 [=====================>........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1148/1534 [=====================>........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1164/1534 [=====================>........] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1180/1534 [======================>.......] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1195/1534 [======================>.......] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1210/1534 [======================>.......] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1225/1534 [======================>.......] - ETA: 1s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1240/1534 [=======================>......] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1255/1534 [=======================>......] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1270/1534 [=======================>......] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1285/1534 [========================>.....] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1301/1534 [========================>.....] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1316/1534 [========================>.....] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1331/1534 [=========================>....] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1347/1534 [=========================>....] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1362/1534 [=========================>....] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1377/1534 [=========================>....] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1393/1534 [==========================>...] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1408/1534 [==========================>...] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1423/1534 [==========================>...] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1438/1534 [===========================>..] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1453/1534 [===========================>..] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1468/1534 [===========================>..] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1483/1534 [============================>.] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1498/1534 [============================>.] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1513/1534 [============================>.] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1528/1534 [============================>.] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 6s 4ms/step - loss: 0.0069 - mean_absolute_error: 0.0594 - val_loss: 0.0067 - val_mean_absolute_error: 0.0581\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1534 [..............................] - ETA: 1:02 - loss: 0.0040 - mean_absolute_error: 0.0493"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  16/1534 [..............................] - ETA: 5s - loss: 0.0064 - mean_absolute_error: 0.0566  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  32/1534 [..............................] - ETA: 5s - loss: 0.0060 - mean_absolute_error: 0.0565"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  47/1534 [..............................] - ETA: 5s - loss: 0.0065 - 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\b\b\b\b\b\b\b\r",
      "  63/1534 [>.............................] - ETA: 4s - loss: 0.0065 - mean_absolute_error: 0.0583"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  79/1534 [>.............................] - ETA: 4s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "  95/1534 [>.............................] - ETA: 4s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 111/1534 [=>............................] - ETA: 4s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 127/1534 [=>............................] - ETA: 4s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 142/1534 [=>............................] - ETA: 4s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 158/1534 [==>...........................] - ETA: 4s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 174/1534 [==>...........................] - ETA: 4s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 190/1534 [==>...........................] - ETA: 4s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 207/1534 [===>..........................] - ETA: 4s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 223/1534 [===>..........................] - ETA: 4s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 238/1534 [===>..........................] - ETA: 4s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 253/1534 [===>..........................] - ETA: 4s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 268/1534 [====>.........................] - ETA: 4s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 283/1534 [====>.........................] - ETA: 4s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 299/1534 [====>.........................] - ETA: 4s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 314/1534 [=====>........................] - ETA: 4s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 330/1534 [=====>........................] - ETA: 3s - loss: 0.0066 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 346/1534 [=====>........................] - ETA: 3s - loss: 0.0066 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 362/1534 [======>.......................] - ETA: 3s - loss: 0.0066 - 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\b\b\b\b\b\b\b\r",
      " 378/1534 [======>.......................] - ETA: 3s - loss: 0.0066 - mean_absolute_error: 0.0583"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 394/1534 [======>.......................] - ETA: 3s - loss: 0.0066 - 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\b\b\b\b\b\b\b\r",
      " 410/1534 [=======>......................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 426/1534 [=======>......................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 442/1534 [=======>......................] - ETA: 3s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 458/1534 [=======>......................] - ETA: 3s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 473/1534 [========>.....................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 489/1534 [========>.....................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 504/1534 [========>.....................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\r",
      " 519/1534 [=========>....................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 534/1534 [=========>....................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\r",
      " 549/1534 [=========>....................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\r",
      " 564/1534 [==========>...................] - ETA: 3s - loss: 0.0067 - mean_absolute_error: 0.0583"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 580/1534 [==========>...................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\r",
      " 595/1534 [==========>...................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 611/1534 [==========>...................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 626/1534 [===========>..................] - ETA: 3s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 642/1534 [===========>..................] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 658/1534 [===========>..................] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 674/1534 [============>.................] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 689/1534 [============>.................] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 705/1534 [============>.................] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 720/1534 [=============>................] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 735/1534 [=============>................] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 751/1534 [=============>................] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 767/1534 [==============>...............] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 783/1534 [==============>...............] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 799/1534 [==============>...............] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 815/1534 [==============>...............] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 830/1534 [===============>..............] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 846/1534 [===============>..............] - ETA: 2s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 861/1534 [===============>..............] - ETA: 2s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 876/1534 [================>.............] - ETA: 2s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 891/1534 [================>.............] - ETA: 2s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 907/1534 [================>.............] - ETA: 2s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 923/1534 [=================>............] - ETA: 2s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 938/1534 [=================>............] - ETA: 1s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 954/1534 [=================>............] - ETA: 1s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 970/1534 [=================>............] - ETA: 1s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 986/1534 [==================>...........] - ETA: 1s - loss: 0.0067 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1002/1534 [==================>...........] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1018/1534 [==================>...........] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1033/1534 [===================>..........] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1049/1534 [===================>..........] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1064/1534 [===================>..........] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1079/1534 [====================>.........] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1095/1534 [====================>.........] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1111/1534 [====================>.........] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1127/1534 [=====================>........] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1143/1534 [=====================>........] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1158/1534 [=====================>........] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1174/1534 [=====================>........] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1190/1534 [======================>.......] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1205/1534 [======================>.......] - ETA: 1s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1220/1534 [======================>.......] - ETA: 1s - loss: 0.0068 - mean_absolute_error: 0.0585"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1235/1534 [=======================>......] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1251/1534 [=======================>......] - ETA: 0s - loss: 0.0067 - 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\b\b\b\b\b\b\b\r",
      "1267/1534 [=======================>......] - ETA: 0s - loss: 0.0067 - 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\b\b\b\b\b\b\b\r",
      "1283/1534 [========================>.....] - ETA: 0s - loss: 0.0067 - 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\b\b\b\b\b\b\b\r",
      "1299/1534 [========================>.....] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1314/1534 [========================>.....] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1330/1534 [=========================>....] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\r",
      "1346/1534 [=========================>....] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1362/1534 [=========================>....] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1378/1534 [=========================>....] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1394/1534 [==========================>...] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1410/1534 [==========================>...] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1426/1534 [==========================>...] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1442/1534 [===========================>..] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1457/1534 [===========================>..] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\r",
      "1473/1534 [===========================>..] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\r",
      "1488/1534 [============================>.] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1503/1534 [============================>.] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1519/1534 [============================>.] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "1534/1534 [==============================] - 6s 4ms/step - loss: 0.0068 - mean_absolute_error: 0.0585 - val_loss: 0.0067 - val_mean_absolute_error: 0.0569\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/439 [..............................] - ETA: 37s - loss: 0.0075 - mean_absolute_error: 0.0644"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 20/439 [>.............................] - ETA: 1s - loss: 0.0081 - 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\b\b\b\b\b\b\b\b\r",
      " 41/439 [=>............................] - ETA: 1s - loss: 0.0065 - mean_absolute_error: 0.0565"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/439 [===>..........................] - ETA: 0s - loss: 0.0065 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 83/439 [====>.........................] - ETA: 0s - loss: 0.0065 - mean_absolute_error: 0.0574"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "418/439 [===========================>..] - ETA: 0s - loss: 0.0066 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "439/439 [==============================] - 1s 2ms/step - loss: 0.0067 - mean_absolute_error: 0.0569\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)\n",
    "performance['Dense'] = dense.evaluate(single_step_window.test, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j5dv_whJdswH"
   },
   "source": [
    "### 複数ステップの Dense\n",
    "\n",
    "単一時間ステップモデルには、入力の現在の値に関するコンテキストがありません。そのため、時間の経過とともに、入力特徴量が変化する様子を確認できません。この問題を解決するために、モデルは予測を立てる際に複数の時間ステップにアクセスする必要があります。\n",
    "\n",
    "![各予測に 3 つの時間ステップが使用されている。](images/conv_window.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zac-ti8agbJ7"
   },
   "source": [
    "`baseline`、`linear`、および `dense` モデルは、各時間ステップを個別に処理しました。ここでは、モデルは複数の時間ステップを入力として取り、単一の出力を生成します。\n",
    "\n",
    "3 時間分の入力のバッチと、1 時間のラベルを生成する `WindowGenerator` を作成します。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gtN4BwZ37niR"
   },
   "source": [
    "`Window` の `shift` パラメータが 2 つのウィンドウの最後に相対的であるところに注意してください。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:38:41.000464Z",
     "iopub.status.busy": "2024-01-11T20:38:40.999747Z",
     "iopub.status.idle": "2024-01-11T20:38:41.005611Z",
     "shell.execute_reply": "2024-01-11T20:38:41.004866Z"
    },
    "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": "2024-01-11T20:38:41.008823Z",
     "iopub.status.busy": "2024-01-11T20:38:41.008309Z",
     "iopub.status.idle": "2024-01-11T20:38:41.585049Z",
     "shell.execute_reply": "2024-01-11T20:38:41.584398Z"
    },
    "id": "dCQ5gvs68Xkd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '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.title(\"Given 3 hours of inputs, predict 1 hour into the future.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "We0HdMxKeqB_"
   },
   "source": [
    "モデルの最初のレイヤーとして `tf.keras.layers.Flatten` を追加することで、複数入力ステップウィンドウで `dense` モデルをトレーニングすることができます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:38:41.588881Z",
     "iopub.status.busy": "2024-01-11T20:38:41.588617Z",
     "iopub.status.idle": "2024-01-11T20:38:41.600748Z",
     "shell.execute_reply": "2024-01-11T20:38:41.600129Z"
    },
    "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": "2024-01-11T20:38:41.603888Z",
     "iopub.status.busy": "2024-01-11T20:38:41.603630Z",
     "iopub.status.idle": "2024-01-11T20:38:41.657242Z",
     "shell.execute_reply": "2024-01-11T20:38:41.656559Z"
    },
    "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": "2024-01-11T20:38:41.660258Z",
     "iopub.status.busy": "2024-01-11T20:38:41.660034Z",
     "iopub.status.idle": "2024-01-11T20:39:24.180271Z",
     "shell.execute_reply": "2024-01-11T20:39:24.179519Z"
    },
    "id": "fu91yEbRo9-J"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/438 [..............................] - ETA: 37s - loss: 0.0078 - 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\b\b\b\b\b\b\b\b\b\r",
      " 25/438 [>.............................] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\r",
      " 53/438 [==>...........................] - ETA: 0s - loss: 0.0068 - 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\b\b\b\b\b\b\b\b\r",
      " 80/438 [====>.........................] - ETA: 0s - loss: 0.0069 - 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\b\b\b\b\b\b\b\b\r",
      "108/438 [======>.......................] - ETA: 0s - loss: 0.0069 - mean_absolute_error: 0.0609"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "136/438 [========>.....................] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\r",
      "164/438 [==========>...................] - ETA: 0s - loss: 0.0070 - 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\b\b\b\b\b\b\b\b\r",
      "192/438 [============>.................] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\r",
      "219/438 [==============>...............] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\r",
      "246/438 [===============>..............] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\r",
      "274/438 [=================>............] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\r",
      "301/438 [===================>..........] - ETA: 0s - loss: 0.0072 - 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\b\b\b\b\b\b\b\b\r",
      "330/438 [=====================>........] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\r",
      "359/438 [=======================>......] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\r",
      "387/438 [=========================>....] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\r",
      "415/438 [===========================>..] - ETA: 0s - loss: 0.0071 - 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\b\b\b\b\b\b\b\b\r",
      "438/438 [==============================] - 1s 2ms/step - loss: 0.0071 - mean_absolute_error: 0.0611\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)\n",
    "performance['Multi step dense'] = multi_step_dense.evaluate(conv_window.test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:39:24.184388Z",
     "iopub.status.busy": "2024-01-11T20:39:24.183858Z",
     "iopub.status.idle": "2024-01-11T20:39:24.633751Z",
     "shell.execute_reply": "2024-01-11T20:39:24.633117Z"
    },
    "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": [
    "このアプローチの主な欠点は、結果モデルを、まったくこの形状の入力ウィンドウでしか実行できないことです。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:39:24.638043Z",
     "iopub.status.busy": "2024-01-11T20:39:24.637508Z",
     "iopub.status.idle": "2024-01-11T20:39:24.669389Z",
     "shell.execute_reply": "2024-01-11T20:39:24.668691Z"
    },
    "id": "j-q6tz5Yq8Jk"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (32, 24, 19)\n",
      "\n",
      "ValueError:Exception encountered when calling layer 'sequential_2' (type Sequential).\n",
      "\n",
      "Input 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)\n",
      "\n",
      "Call arguments received by layer 'sequential_2' (type Sequential):\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": [
    "この問題は、次のセクションの畳み込みモデルで解決することができます。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CrpU6gwSJome"
   },
   "source": [
    "### 畳み込みニューラルネットワーク\n",
    "\n",
    "畳み込みレイヤー（`tf.keras.layers.Conv1D`）も、複数の時間ステップを各予測への入力として取ります。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cdLBwoaHmsWb"
   },
   "source": [
    "以下に示すのは、`multi_step_dense` と**同じ**モデルを畳み込みで書き直したものです。\n",
    "\n",
    "次の変更箇所に注意してください。\n",
    "\n",
    "- `tf.keras.layers.Flatten` と最初の `tf.keras.layers.Dense` は `tf.keras.layers.Conv1D` に置き換えられています。\n",
    "- 畳み込みが出力に time 軸を維持するため、`tf.keras.layers.Reshape` は不要となっています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:39:24.673295Z",
     "iopub.status.busy": "2024-01-11T20:39:24.672627Z",
     "iopub.status.idle": "2024-01-11T20:39:24.682392Z",
     "shell.execute_reply": "2024-01-11T20:39:24.681808Z"
    },
    "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": [
    "これをサンプルバッチで実行し、モデルが期待される形状の出力を生成することを確認します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:39:24.686185Z",
     "iopub.status.busy": "2024-01-11T20:39:24.685593Z",
     "iopub.status.idle": "2024-01-11T20:39:24.731629Z",
     "shell.execute_reply": "2024-01-11T20:39:24.730942Z"
    },
    "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": [
    "`conv_window` でトレーニングして評価すると、`multi_step_dense` モデルと似たようなパフォーマンスが得られます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:39:24.735205Z",
     "iopub.status.busy": "2024-01-11T20:39:24.734498Z",
     "iopub.status.idle": "2024-01-11T20:40:23.620102Z",
     "shell.execute_reply": "2024-01-11T20:40:23.619353Z"
    },
    "id": "QDVWdm4paUW7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/438 [..............................] - ETA: 37s - loss: 0.0063 - mean_absolute_error: 0.0573"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 21/438 [>.............................] - ETA: 1s - loss: 0.0061 - 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\b\b\b\b\b\r",
      " 43/438 [=>............................] - ETA: 0s - loss: 0.0064 - 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\b\b\b\b\b\b\b\b\r",
      " 64/438 [===>..........................] - ETA: 0s - loss: 0.0065 - 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\b\b\b\b\b\b\b\b\r",
      " 85/438 [====>.........................] - ETA: 0s - loss: 0.0064 - mean_absolute_error: 0.0583"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "107/438 [======>.......................] - ETA: 0s - loss: 0.0063 - mean_absolute_error: 0.0577"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/438 [=======>......................] - ETA: 0s - loss: 0.0065 - 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\b\b\b\b\b\r",
      "151/438 [=========>....................] - ETA: 0s - loss: 0.0065 - mean_absolute_error: 0.0583"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "173/438 [==========>...................] - ETA: 0s - loss: 0.0065 - 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\b\b\b\b\b\r",
      "195/438 [============>.................] - ETA: 0s - loss: 0.0065 - 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\b\b\b\b\b\r",
      "217/438 [=============>................] - ETA: 0s - loss: 0.0065 - mean_absolute_error: 0.0581"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "239/438 [===============>..............] - ETA: 0s - loss: 0.0064 - mean_absolute_error: 0.0580"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "261/438 [================>.............] - ETA: 0s - loss: 0.0064 - mean_absolute_error: 0.0579"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "284/438 [==================>...........] - ETA: 0s - 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\b\b\b\b\b\r",
      "305/438 [===================>..........] - ETA: 0s - loss: 0.0065 - 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\b\b\b\b\b\r",
      "326/438 [=====================>........] - ETA: 0s - loss: 0.0066 - mean_absolute_error: 0.0583"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "347/438 [======================>.......] - ETA: 0s - loss: 0.0065 - mean_absolute_error: 0.0581"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "368/438 [========================>.....] - ETA: 0s - loss: 0.0066 - 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\b\b\b\b\b\r",
      "388/438 [=========================>....] - ETA: 0s - loss: 0.0065 - 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\b\b\b\b\b\r",
      "409/438 [===========================>..] - ETA: 0s - loss: 0.0066 - mean_absolute_error: 0.0583"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "430/438 [============================>.] - ETA: 0s - loss: 0.0065 - mean_absolute_error: 0.0581"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "438/438 [==============================] - 1s 2ms/step - loss: 0.0065 - mean_absolute_error: 0.0580\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)\n",
    "performance['Conv'] = conv_model.evaluate(conv_window.test, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sYRipDeXs0Kr"
   },
   "source": [
    "この `conv_model` と `multi_step_dense` モデルの違いは、`conv_model` はあらゆる長さの入力に対して実行できるところにあります。畳み込みレイヤーは入力のスライドウィンドウに適用されます。\n",
    "\n",
    "![シーケンスで畳み込みモデルを実行する](images/wide_conv_window.png)\n",
    "\n",
    "より幅広い入力に対してこれを実行する場合、生成される出力も幅広くなります。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:40:23.624701Z",
     "iopub.status.busy": "2024-01-11T20:40:23.624058Z",
     "iopub.status.idle": "2024-01-11T20:40:23.725174Z",
     "shell.execute_reply": "2024-01-11T20:40:23.724485Z"
    },
    "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"
     ]
    }
   ],
   "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": [
    "出力が入力よりも短いことに注意してください。トレーニングまたは描画がうまく機能するには、ラベルと、長さの同じ予測が必要です。そのため、いくつかの入力時間ステップを追加してワイドウィンドウを生成し、ラベルと予測の長さが一致するように、`WindowGenerator` を構築します。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:40:23.728569Z",
     "iopub.status.busy": "2024-01-11T20:40:23.728334Z",
     "iopub.status.idle": "2024-01-11T20:40:23.733453Z",
     "shell.execute_reply": "2024-01-11T20:40:23.732831Z"
    },
    "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": "2024-01-11T20:40:23.736415Z",
     "iopub.status.busy": "2024-01-11T20:40:23.736162Z",
     "iopub.status.idle": "2024-01-11T20:40:23.920907Z",
     "shell.execute_reply": "2024-01-11T20:40:23.920090Z"
    },
    "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"
     ]
    }
   ],
   "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": [
    "これで、幅広いウィンドウでモデルの予測を描画できるようになりました。最初の予測の前に、3 つの入力時間ステップがあることに注目してください。各予測は、前の 3 つの時間ステップに基づきます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:40:23.924478Z",
     "iopub.status.busy": "2024-01-11T20:40:23.924217Z",
     "iopub.status.idle": "2024-01-11T20:40:24.387241Z",
     "shell.execute_reply": "2024-01-11T20:40:24.386341Z"
    },
    "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": [
    "### 回帰ニューラルネットワーク\n",
    "\n",
    "回帰ニューラルネットワーク（RNN）は、時系列データに最適なニューラルネットワークの種類です。RNN は、ステップごとに時系列を処理し、時間ステップから時間ステップまでの内部状態を維持することができます。\n",
    "\n",
    "[RNN によるテキスト生成](https://www.tensorflow.org/text/tutorials/text_generation)チュートリアルと [Keras による回帰ニューラルネットワーク（RNN）](https://www.tensorflow.org/guide/keras/rnn)ガイドでさらに学習することができます。\n",
    "\n",
    "このチュートリアルでは、Long Short Term Memory（`tf.keras.layers.LSTM`）という RNN レイヤーを使用します。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vfQbHSMb1ATa"
   },
   "source": [
    "`tf.keras.layers.LSTM` などのすべての Keras RNN レイヤーの重要なコンストラクタ引数は、`return_sequences` 引数です。この設定は、次の 2 つのいずれかの方法でレイヤーを構成することができます。\n",
    "\n",
    "1. `False` である場合（デフォルト）、レイヤーは、最後の時間ステップの出力のみを返すため、単一の予測を立てる前に、内部状態をウォームアップする時間を得られます。\n",
    "\n",
    "![ウォームアップして単一の予測を立てる LSTM](images/lstm_1_window.png)\n",
    "\n",
    "1. `True` である場合、レイヤーは、各入力に対する出力を返し、次の項目に役立てることができます。\n",
    "    - RNN レイヤーをスタックする。\n",
    "    - 複数の時間ステップで同時にモデルをトレーニングする。\n",
    "\n",
    "![時間ステップの後に予測を立てる LSTM](images/lstm_many_window.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:40:24.392271Z",
     "iopub.status.busy": "2024-01-11T20:40:24.392015Z",
     "iopub.status.idle": "2024-01-11T20:40:24.405409Z",
     "shell.execute_reply": "2024-01-11T20:40:24.404775Z"
    },
    "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": [
    "`return_sequences=True` の場合、24 時間のデータで一度にモデルをトレーニングすることができます。\n",
    "\n",
    "注意: モデルのパフォーマンスとしては悲観的な見解になります。最初の時間ステップは前のステップにアクセスできないため、上記に示した単純な `linear` モデルと `dense` モデルとあまり変わりません。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:40:24.408846Z",
     "iopub.status.busy": "2024-01-11T20:40:24.408617Z",
     "iopub.status.idle": "2024-01-11T20:40:24.850721Z",
     "shell.execute_reply": "2024-01-11T20:40:24.849828Z"
    },
    "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": "2024-01-11T20:40:24.854403Z",
     "iopub.status.busy": "2024-01-11T20:40:24.854143Z",
     "iopub.status.idle": "2024-01-11T20:41:46.474087Z",
     "shell.execute_reply": "2024-01-11T20:41:46.473173Z"
    },
    "id": "uvdWRl1e9WJl"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/438 [..............................] - ETA: 38s - loss: 0.0061 - mean_absolute_error: 0.0529"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 18/438 [>.............................] - ETA: 1s - loss: 0.0055 - mean_absolute_error: 0.0522 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 36/438 [=>............................] - ETA: 1s - loss: 0.0053 - mean_absolute_error: 0.0515"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 54/438 [==>...........................] - ETA: 1s - loss: 0.0054 - mean_absolute_error: 0.0518"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 72/438 [===>..........................] - ETA: 1s - loss: 0.0054 - mean_absolute_error: 0.0517"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 91/438 [=====>........................] - ETA: 0s - loss: 0.0055 - mean_absolute_error: 0.0518"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "110/438 [======>.......................] - ETA: 0s - loss: 0.0054 - mean_absolute_error: 0.0517"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "164/438 [==========>...................] - ETA: 0s - loss: 0.0055 - mean_absolute_error: 0.0516"
     ]
    },
    {
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     "output_type": "stream",
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "182/438 [===========>..................] - ETA: 0s - loss: 0.0055 - mean_absolute_error: 0.0514"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "290/438 [==================>...........] - ETA: 0s - loss: 0.0055 - mean_absolute_error: 0.0516"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "326/438 [=====================>........] - ETA: 0s - loss: 0.0055 - mean_absolute_error: 0.0517"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "345/438 [======================>.......] - ETA: 0s - loss: 0.0055 - mean_absolute_error: 0.0517"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "363/438 [=======================>......] - ETA: 0s - loss: 0.0055 - mean_absolute_error: 0.0517"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "399/438 [==========================>...] - ETA: 0s - loss: 0.0056 - mean_absolute_error: 0.0517"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "417/438 [===========================>..] - ETA: 0s - loss: 0.0056 - mean_absolute_error: 0.0518"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "435/438 [============================>.] - ETA: 0s - loss: 0.0056 - mean_absolute_error: 0.0518"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0056 - mean_absolute_error: 0.0518\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)\n",
    "performance['LSTM'] = lstm_model.evaluate(wide_window.test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:41:46.478532Z",
     "iopub.status.busy": "2024-01-11T20:41:46.477877Z",
     "iopub.status.idle": "2024-01-11T20:41:46.931196Z",
     "shell.execute_reply": "2024-01-11T20:41:46.930479Z"
    },
    "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": [
    "### パフォーマンス"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2pCk0_rwhi8H"
   },
   "source": [
    "このデータセットでは、通常、各モデルは前のモデルよりわずかな改善が見られます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:41:46.936737Z",
     "iopub.status.busy": "2024-01-11T20:41:46.936071Z",
     "iopub.status.idle": "2024-01-11T20:41:47.116505Z",
     "shell.execute_reply": "2024-01-11T20:41:47.115883Z"
    },
    "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",
    "metric_index = lstm_model.metrics_names.index('mean_absolute_error')\n",
    "val_mae = [v[metric_index] for v in val_performance.values()]\n",
    "test_mae = [v[metric_index] 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": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:41:47.119602Z",
     "iopub.status.busy": "2024-01-11T20:41:47.119355Z",
     "iopub.status.idle": "2024-01-11T20:41:47.123005Z",
     "shell.execute_reply": "2024-01-11T20:41:47.122407Z"
    },
    "id": "cBMCpsdphi8L"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline    : 0.0852\n",
      "Linear      : 0.0670\n",
      "Dense       : 0.0575\n",
      "Multi step dense: 0.0616\n",
      "Conv        : 0.0601\n",
      "LSTM        : 0.0529\n"
     ]
    }
   ],
   "source": [
    "for name, value in performance.items():\n",
    "  print(f'{name:12s}: {value[1]:0.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b5rUJ_2YMWzG"
   },
   "source": [
    "### 複数出力モデル\n",
    "\n",
    "モデルはこれまで、単一時間ステップに対して単一出力特徴量 `T (degC)` をすべて予測しました。\n",
    "\n",
    "これらのモデルはすべて、出力レイヤーのユニット数を変更し、`labels`（`example_labels`）のすべての特徴量を含めるようにトレーニングウィンドウを調整するだけで、複数の特徴量を予測するように変換することができます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:41:47.126592Z",
     "iopub.status.busy": "2024-01-11T20:41:47.126047Z",
     "iopub.status.idle": "2024-01-11T20:41:47.262855Z",
     "shell.execute_reply": "2024-01-11T20:41:47.262130Z"
    },
    "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": [
    "上記では、ラベルの `features` 軸に `1` ではなく、入力と同じ深度があることに注意してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9k7S5IHNhSNF"
   },
   "source": [
    "#### 基準\n",
    "\n",
    "ここでは同じ基準モデル（`Baseline`）を使用できますが、今回は、特定の `label_index` を選択する代わりにすべての特徴量を繰り返します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:41:47.267014Z",
     "iopub.status.busy": "2024-01-11T20:41:47.266337Z",
     "iopub.status.idle": "2024-01-11T20:41:47.280296Z",
     "shell.execute_reply": "2024-01-11T20:41:47.279702Z"
    },
    "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": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:41:47.283805Z",
     "iopub.status.busy": "2024-01-11T20:41:47.283113Z",
     "iopub.status.idle": "2024-01-11T20:41:48.672798Z",
     "shell.execute_reply": "2024-01-11T20:41:48.672006Z"
    },
    "id": "ltQdgaqQjQWu"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/438 [..............................] - ETA: 1:04 - loss: 0.1117 - mean_absolute_error: 0.1729"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 28/438 [>.............................] - ETA: 0s - loss: 0.0878 - mean_absolute_error: 0.1574  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 58/438 [==>...........................] - ETA: 0s - loss: 0.0882 - mean_absolute_error: 0.1584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 88/438 [=====>........................] - ETA: 0s - loss: 0.0888 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "119/438 [=======>......................] - ETA: 0s - loss: 0.0884 - mean_absolute_error: 0.1588"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "150/438 [=========>....................] - ETA: 0s - loss: 0.0885 - mean_absolute_error: 0.1588"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "181/438 [===========>..................] - ETA: 0s - loss: 0.0886 - mean_absolute_error: 0.1588"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "211/438 [=============>................] - ETA: 0s - loss: 0.0885 - mean_absolute_error: 0.1588"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/438 [===============>..............] - ETA: 0s - loss: 0.0886 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "270/438 [=================>............] - ETA: 0s - loss: 0.0887 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "299/438 [===================>..........] - ETA: 0s - loss: 0.0886 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "328/438 [=====================>........] - ETA: 0s - loss: 0.0884 - mean_absolute_error: 0.1587"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "358/438 [=======================>......] - ETA: 0s - loss: 0.0885 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "388/438 [=========================>....] - ETA: 0s - loss: 0.0886 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "418/438 [===========================>..] - ETA: 0s - loss: 0.0886 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "438/438 [==============================] - 1s 2ms/step - loss: 0.0886 - mean_absolute_error: 0.1589\n"
     ]
    }
   ],
   "source": [
    "val_performance = {}\n",
    "performance = {}\n",
    "val_performance['Baseline'] = baseline.evaluate(wide_window.val)\n",
    "performance['Baseline'] = baseline.evaluate(wide_window.test, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dfbCrf5q3P6n"
   },
   "source": [
    "#### Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:41:48.677140Z",
     "iopub.status.busy": "2024-01-11T20:41:48.676480Z",
     "iopub.status.idle": "2024-01-11T20:41:48.685715Z",
     "shell.execute_reply": "2024-01-11T20:41:48.684964Z"
    },
    "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": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:41:48.688705Z",
     "iopub.status.busy": "2024-01-11T20:41:48.688479Z",
     "iopub.status.idle": "2024-01-11T20:42:29.124549Z",
     "shell.execute_reply": "2024-01-11T20:42:29.123575Z"
    },
    "id": "6uHuU9Cd3PTo"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/439 [..............................] - ETA: 36s - loss: 0.0797 - mean_absolute_error: 0.1553"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 21/439 [>.............................] - ETA: 1s - loss: 0.0653 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 41/439 [=>............................] - ETA: 1s - loss: 0.0669 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 62/439 [===>..........................] - ETA: 0s - loss: 0.0669 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 82/439 [====>.........................] - ETA: 0s - loss: 0.0667 - mean_absolute_error: 0.1330"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "102/439 [=====>........................] - ETA: 0s - loss: 0.0676 - mean_absolute_error: 0.1332"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "123/439 [=======>......................] - ETA: 0s - loss: 0.0681 - mean_absolute_error: 0.1330"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/439 [========>.....................] - ETA: 0s - loss: 0.0680 - mean_absolute_error: 0.1333"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "164/439 [==========>...................] - ETA: 0s - loss: 0.0680 - mean_absolute_error: 0.1333"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "185/439 [===========>..................] - ETA: 0s - loss: 0.0689 - mean_absolute_error: 0.1339"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "206/439 [=============>................] - ETA: 0s - loss: 0.0691 - mean_absolute_error: 0.1341"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "227/439 [==============>...............] - ETA: 0s - loss: 0.0694 - mean_absolute_error: 0.1342"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "248/439 [===============>..............] - ETA: 0s - loss: 0.0694 - mean_absolute_error: 0.1343"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "271/439 [=================>............] - ETA: 0s - loss: 0.0691 - mean_absolute_error: 0.1339"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "291/439 [==================>...........] - ETA: 0s - loss: 0.0691 - mean_absolute_error: 0.1340"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "312/439 [====================>.........] - ETA: 0s - loss: 0.0689 - mean_absolute_error: 0.1338"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "333/439 [=====================>........] - ETA: 0s - loss: 0.0685 - 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\b\b\b\b\b\b\b\b\r",
      "354/439 [=======================>......] - ETA: 0s - loss: 0.0685 - mean_absolute_error: 0.1337"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "375/439 [========================>.....] - ETA: 0s - loss: 0.0689 - mean_absolute_error: 0.1340"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "395/439 [=========================>....] - ETA: 0s - loss: 0.0693 - mean_absolute_error: 0.1343"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "415/439 [===========================>..] - ETA: 0s - loss: 0.0693 - mean_absolute_error: 0.1344"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "436/439 [============================>.] - ETA: 0s - loss: 0.0692 - mean_absolute_error: 0.1344"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "439/439 [==============================] - 1s 2ms/step - loss: 0.0692 - mean_absolute_error: 0.1344\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)\n",
    "performance['Dense'] = dense.evaluate(single_step_window.test, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dsc9pur_mHsx"
   },
   "source": [
    "#### RNN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:42:29.129262Z",
     "iopub.status.busy": "2024-01-11T20:42:29.128464Z",
     "iopub.status.idle": "2024-01-11T20:45:08.382388Z",
     "shell.execute_reply": "2024-01-11T20:45:08.381446Z"
    },
    "id": "4QbGLMyomXaz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/438 [..............................] - ETA: 37s - loss: 0.0702 - mean_absolute_error: 0.1272"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 19/438 [>.............................] - ETA: 1s - loss: 0.0627 - mean_absolute_error: 0.1214 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 37/438 [=>............................] - ETA: 1s - loss: 0.0622 - mean_absolute_error: 0.1210"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 55/438 [==>...........................] - ETA: 1s - loss: 0.0616 - mean_absolute_error: 0.1203"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 74/438 [====>.........................] - ETA: 1s - loss: 0.0614 - mean_absolute_error: 0.1201"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 92/438 [=====>........................] - ETA: 0s - loss: 0.0612 - mean_absolute_error: 0.1199"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/438 [======>.......................] - ETA: 0s - loss: 0.0616 - mean_absolute_error: 0.1203"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/438 [=======>......................] - ETA: 0s - loss: 0.0620 - mean_absolute_error: 0.1207"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "149/438 [=========>....................] - ETA: 0s - loss: 0.0619 - mean_absolute_error: 0.1205"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "167/438 [==========>...................] - ETA: 0s - loss: 0.0616 - mean_absolute_error: 0.1201"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "185/438 [===========>..................] - ETA: 0s - loss: 0.0615 - mean_absolute_error: 0.1200"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "203/438 [============>.................] - ETA: 0s - loss: 0.0615 - mean_absolute_error: 0.1201"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "221/438 [==============>...............] - ETA: 0s - loss: 0.0615 - mean_absolute_error: 0.1201"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "240/438 [===============>..............] - ETA: 0s - loss: 0.0615 - mean_absolute_error: 0.1201"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "276/438 [=================>............] - ETA: 0s - loss: 0.0614 - mean_absolute_error: 0.1200"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "294/438 [===================>..........] - ETA: 0s - loss: 0.0616 - mean_absolute_error: 0.1201"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "313/438 [====================>.........] - ETA: 0s - loss: 0.0615 - mean_absolute_error: 0.1201"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "331/438 [=====================>........] - ETA: 0s - loss: 0.0616 - mean_absolute_error: 0.1201"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "349/438 [======================>.......] - ETA: 0s - loss: 0.0615 - mean_absolute_error: 0.1201"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "387/438 [=========================>....] - ETA: 0s - loss: 0.0613 - mean_absolute_error: 0.1198"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "406/438 [==========================>...] - ETA: 0s - loss: 0.0611 - 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\b\b\b\b\b\b\b\b\r",
      "424/438 [============================>.] - ETA: 0s - loss: 0.0611 - 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\b\b\b\b\b\b\b\b\r",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0611 - mean_absolute_error: 0.1197\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 5min 52s, sys: 1min 14s, total: 7min 7s\n",
      "Wall time: 2min 39s\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)\n",
    "performance['LSTM'] = lstm_model.evaluate( wide_window.test, verbose=0)\n",
    "\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UwhY2f_Nn0_K"
   },
   "source": [
    "<a id=\"residual\"></a>\n",
    "\n",
    "#### 高度: 残差接続\n",
    "\n",
    "上述の `Baseline` モデルは、時間ステップ間でシーケンスが大幅に変化しない事実を利用しました。このチュートリアルでトレーニングされたモデルはこれまで、ランダムに初期化されてから、出力が前の時間ステップからわずかに変化することを学習する必要がありました。\n",
    "\n",
    "初期化に注意を払うことで、この問題を回避することはできますが、これをモデル構造に構築する方がより単純です。\n",
    "\n",
    "時系列の分析では、次の値を予測する代わりに、次の時間ステップで値がどのように変化するかを予測するモデルを構築するのが一般的です。同様に、ディープラーニングの「<a href=\"https://arxiv.org/abs/1512.03385\" class=\"external\">残差ネットワーク</a>」または「ResNet」は、各レイヤーがモデルの累積結果に追加されるアーキテクチャを指しています。\n",
    "\n",
    "これが、変化は小さいものだという理解を活用する方法です。\n",
    "\n",
    "![残差接続のあるモデル](images/residual.png)\n",
    "\n",
    "基本的に、これによってモデルは `Baseline` に一致するように初期化されます。このタスクでは、モデルの収束を高速化することができ、わずかにパフォーマンスが向上されます。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yP58A_ORx0kM"
   },
   "source": [
    "このアプローチは、このチュートリアルで触れたあらゆるモデルと併用することができます。\n",
    "\n",
    "ここでは、LSTM モデルに適用されています。最初の予測変化が小さく、残差接続より上回らないように、`tf.initializers.zeros` が使用されているところに注意してください。`zeros` は最後のレイヤーにだけ使用されているため、ここでは勾配の対称性が壊される懸念はありません。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:45:08.385931Z",
     "iopub.status.busy": "2024-01-11T20:45:08.385650Z",
     "iopub.status.idle": "2024-01-11T20:45:08.390156Z",
     "shell.execute_reply": "2024-01-11T20:45:08.389402Z"
    },
    "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": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:45:08.393680Z",
     "iopub.status.busy": "2024-01-11T20:45:08.392988Z",
     "iopub.status.idle": "2024-01-11T20:45:51.819013Z",
     "shell.execute_reply": "2024-01-11T20:45:51.818239Z"
    },
    "id": "NNeH02pspc9B"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/438 [..............................] - ETA: 37s - loss: 0.0649 - mean_absolute_error: 0.1218"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 18/438 [>.............................] - ETA: 1s - loss: 0.0635 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 36/438 [=>............................] - ETA: 1s - loss: 0.0631 - 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\b\b\b\b\b\b\b\b\r",
      " 54/438 [==>...........................] - ETA: 1s - loss: 0.0637 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 72/438 [===>..........................] - ETA: 1s - loss: 0.0631 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 90/438 [=====>........................] - ETA: 1s - loss: 0.0630 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "108/438 [======>.......................] - ETA: 0s - loss: 0.0625 - mean_absolute_error: 0.1182"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "126/438 [=======>......................] - ETA: 0s - loss: 0.0623 - mean_absolute_error: 0.1181"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/438 [========>.....................] - ETA: 0s - loss: 0.0623 - mean_absolute_error: 0.1180"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "162/438 [==========>...................] - ETA: 0s - loss: 0.0625 - mean_absolute_error: 0.1180"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "180/438 [===========>..................] - ETA: 0s - loss: 0.0624 - mean_absolute_error: 0.1180"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "198/438 [============>.................] - ETA: 0s - loss: 0.0622 - mean_absolute_error: 0.1179"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "216/438 [=============>................] - ETA: 0s - loss: 0.0620 - mean_absolute_error: 0.1177"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "235/438 [===============>..............] - ETA: 0s - loss: 0.0620 - 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\b\b\b\b\b\b\b\b\r",
      "254/438 [================>.............] - ETA: 0s - loss: 0.0619 - mean_absolute_error: 0.1175"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "272/438 [=================>............] - ETA: 0s - loss: 0.0620 - 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\b\b\b\b\b\b\b\b\r",
      "290/438 [==================>...........] - ETA: 0s - loss: 0.0621 - mean_absolute_error: 0.1177"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "308/438 [====================>.........] - ETA: 0s - loss: 0.0620 - mean_absolute_error: 0.1177"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "326/438 [=====================>........] - ETA: 0s - loss: 0.0620 - 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\b\b\b\b\b\b\b\b\r",
      "344/438 [======================>.......] - ETA: 0s - loss: 0.0621 - mean_absolute_error: 0.1177"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "362/438 [=======================>......] - ETA: 0s - loss: 0.0621 - mean_absolute_error: 0.1177"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "381/438 [=========================>....] - ETA: 0s - loss: 0.0621 - mean_absolute_error: 0.1178"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "399/438 [==========================>...] - ETA: 0s - loss: 0.0621 - mean_absolute_error: 0.1178"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "417/438 [===========================>..] - ETA: 0s - loss: 0.0621 - mean_absolute_error: 0.1178"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "436/438 [============================>.] - ETA: 0s - loss: 0.0621 - mean_absolute_error: 0.1178"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0621 - mean_absolute_error: 0.1177\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 1min 34s, sys: 19.1 s, total: 1min 53s\n",
      "Wall time: 43.4 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)\n",
    "performance['Residual LSTM'] = residual_lstm.evaluate(wide_window.test, verbose=0)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I42Er9Du6co1"
   },
   "source": [
    "#### パフォーマンス"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LZxR38P_6pUi"
   },
   "source": [
    "これらの複数出力モデルの全体的なパフォーマンスは、次のようになります。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:45:51.823137Z",
     "iopub.status.busy": "2024-01-11T20:45:51.822502Z",
     "iopub.status.idle": "2024-01-11T20:45:51.989569Z",
     "shell.execute_reply": "2024-01-11T20:45:51.988884Z"
    },
    "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",
    "metric_index = lstm_model.metrics_names.index('mean_absolute_error')\n",
    "val_mae = [v[metric_index] for v in val_performance.values()]\n",
    "test_mae = [v[metric_index] 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": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:45:51.993104Z",
     "iopub.status.busy": "2024-01-11T20:45:51.992492Z",
     "iopub.status.idle": "2024-01-11T20:45:51.996410Z",
     "shell.execute_reply": "2024-01-11T20:45:51.995643Z"
    },
    "id": "URz3ajCc6kBj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline       : 0.1638\n",
      "Dense          : 0.1356\n",
      "LSTM           : 0.1210\n",
      "Residual LSTM  : 0.1192\n"
     ]
    }
   ],
   "source": [
    "for name, value in performance.items():\n",
    "  print(f'{name:15s}: {value[1]:0.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Vt2MJhNxwPU"
   },
   "source": [
    "上記のパフォーマンスは、すべてのモデル出力の平均です。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eYokb7Om2YbK"
   },
   "source": [
    "## 複数ステップのモデル\n",
    "\n",
    "前のセクションの単一出力と複数出力はともに、1 時間先までの**単一時間ステップ予測**を行いました。\n",
    "\n",
    "このセクションでは、これらのモデルを拡張し、**複数時間ステップ予測**を行います。\n",
    "\n",
    "複数ステップ予測では、モデルは将来の値の範囲を予測できるように学習する必要があります。したがって、1 つの未来点を予測するだけの単一ステップモデルとは異なり、複数ステップモデルは、一連の未来の値を予測します。\n",
    "\n",
    "これには大まかなアプローチが 2 つあります。\n",
    "\n",
    "1. 時系列全体を一度に予測するシングルショット予測\n",
    "2. モデルは単一ステップ予測を行い、その出力が入力としてフィードされる、自動回帰予測\n",
    "\n",
    "このセクションでは、すべてのモデルは、**全出力時間ステップのすべての特徴量**を予測します。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WFsDAwVt4_rq"
   },
   "source": [
    "複数ステップモデルでは、トレーニングデータは時間ごとのサンプルで構成されますが、ここでは、過去 24 時間のデータがある場合に、モデルは 24 時間先を予測するように学習します。\n",
    "\n",
    "次は、データセットからこれらのスライスを生成する `Window` オブジェクトです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:45:51.999926Z",
     "iopub.status.busy": "2024-01-11T20:45:51.999534Z",
     "iopub.status.idle": "2024-01-11T20:45:52.516379Z",
     "shell.execute_reply": "2024-01-11T20:45:52.515632Z"
    },
    "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": 77,
     "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": [
    "### 基準"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "axwpoWYOApJL"
   },
   "source": [
    "このタスクの単純な基準は、最後の入力時間ステップを必要な出力時間ステップ数、繰り返すことです。\n",
    "\n",
    "![各出力ステップごとに最後の入力を繰り返す](images/multistep_last.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:45:52.520568Z",
     "iopub.status.busy": "2024-01-11T20:45:52.520068Z",
     "iopub.status.idle": "2024-01-11T20:45:54.398827Z",
     "shell.execute_reply": "2024-01-11T20:45:54.398110Z"
    },
    "id": "_5iaHSaJ9Rxv"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/437 [..............................] - ETA: 1:15 - loss: 0.6322 - mean_absolute_error: 0.4858"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/437 [>.............................] - ETA: 0s - loss: 0.6220 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 58/437 [==>...........................] - ETA: 0s - loss: 0.6168 - mean_absolute_error: 0.4967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 86/437 [====>.........................] - ETA: 0s - loss: 0.6156 - mean_absolute_error: 0.4962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "114/437 [======>.......................] - ETA: 0s - loss: 0.6190 - mean_absolute_error: 0.4977"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "142/437 [========>.....................] - ETA: 0s - loss: 0.6193 - mean_absolute_error: 0.4970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "170/437 [==========>...................] - ETA: 0s - loss: 0.6221 - mean_absolute_error: 0.4977"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "199/437 [============>.................] - ETA: 0s - loss: 0.6238 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "228/437 [==============>...............] - ETA: 0s - loss: 0.6244 - mean_absolute_error: 0.4988"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/437 [================>.............] - ETA: 0s - loss: 0.6262 - mean_absolute_error: 0.4998"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "286/437 [==================>...........] - ETA: 0s - loss: 0.6273 - mean_absolute_error: 0.5003"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "315/437 [====================>.........] - ETA: 0s - loss: 0.6266 - mean_absolute_error: 0.5000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "346/437 [======================>.......] - ETA: 0s - loss: 0.6279 - mean_absolute_error: 0.5006"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "376/437 [========================>.....] - ETA: 0s - loss: 0.6282 - mean_absolute_error: 0.5007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "406/437 [==========================>...] - ETA: 0s - loss: 0.6289 - mean_absolute_error: 0.5009"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "436/437 [============================>.] - ETA: 0s - loss: 0.6286 - mean_absolute_error: 0.5007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "437/437 [==============================] - 1s 2ms/step - loss: 0.6285 - mean_absolute_error: 0.5007\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)\n",
    "multi_performance['Last'] = last_baseline.evaluate(multi_window.test, verbose=0)\n",
    "multi_window.plot(last_baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AvHZ93ObAfMA"
   },
   "source": [
    "このタスクは、24 時間の履歴がある場合に 24 時間を予測するため、もう 1 つの単純なアプローチとして、翌日が同様であることを仮定し、前日を繰り返すことができます。\n",
    "\n",
    "![前日を繰り返す](images/multistep_repeat.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:45:54.402850Z",
     "iopub.status.busy": "2024-01-11T20:45:54.402605Z",
     "iopub.status.idle": "2024-01-11T20:45:56.248617Z",
     "shell.execute_reply": "2024-01-11T20:45:56.247940Z"
    },
    "id": "L8Y1uMhGwIRs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/437 [..............................] - ETA: 1:09 - loss: 0.3822 - mean_absolute_error: 0.3827"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 30/437 [=>............................] - ETA: 0s - loss: 0.4185 - mean_absolute_error: 0.3926  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/437 [===>..........................] - ETA: 0s - loss: 0.4308 - mean_absolute_error: 0.3979"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 88/437 [=====>........................] - ETA: 0s - loss: 0.4360 - mean_absolute_error: 0.3991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "118/437 [=======>......................] - ETA: 0s - loss: 0.4356 - mean_absolute_error: 0.3997"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "147/437 [=========>....................] - ETA: 0s - loss: 0.4323 - mean_absolute_error: 0.3980"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "175/437 [===========>..................] - ETA: 0s - loss: 0.4280 - mean_absolute_error: 0.3963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "205/437 [=============>................] - ETA: 0s - loss: 0.4276 - mean_absolute_error: 0.3964"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "233/437 [==============>...............] - ETA: 0s - loss: 0.4257 - mean_absolute_error: 0.3956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "262/437 [================>.............] - ETA: 0s - loss: 0.4260 - mean_absolute_error: 0.3956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "292/437 [===================>..........] - ETA: 0s - loss: 0.4253 - mean_absolute_error: 0.3952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "323/437 [=====================>........] - ETA: 0s - loss: 0.4252 - mean_absolute_error: 0.3951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "353/437 [=======================>......] - ETA: 0s - loss: 0.4266 - mean_absolute_error: 0.3956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "383/437 [=========================>....] - ETA: 0s - loss: 0.4277 - mean_absolute_error: 0.3961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "413/437 [===========================>..] - ETA: 0s - loss: 0.4269 - mean_absolute_error: 0.3958"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "437/437 [==============================] - 1s 2ms/step - loss: 0.4270 - mean_absolute_error: 0.3959\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)\n",
    "multi_performance['Repeat'] = repeat_baseline.evaluate(multi_window.test, verbose=0)\n",
    "multi_window.plot(repeat_baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tbndS-ct9C2Q"
   },
   "source": [
    "### シングルショットモデル\n",
    "\n",
    "この問題の高レベルなアプローチには、モデルがシーケンス全体の予測を単一のステップで行う「シングルショット」モデルがあります。\n",
    "\n",
    "これは、`OUT_STEPS*features` 出力ユニットを使って `tf.keras.layers.Dense` として効率的に実装できます。このモデルには、出力の形状を必要な `(OUTPUT_STEPS, features)` に設定し直すことだけが必要です。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NCKS4m1VKrDQ"
   },
   "source": [
    "#### 線形\n",
    "\n",
    "最後の時間ステップに基づく単純な線形モデルは、いずれの基準よりも優れていますが、パワーに劣ります。モデルは、線形投影の単一入力時間ステップから、`OUTPUT_STEPS` 時間ステップを予測する必要があります。おそらく主に時間帯と時期に基づいて、低次元スライスの行動のみをキャプチャできます。\n",
    "\n",
    "![最後の時間ステップからのすべての時間ステップを予測する](images/multistep_dense.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:45:56.252963Z",
     "iopub.status.busy": "2024-01-11T20:45:56.252693Z",
     "iopub.status.idle": "2024-01-11T20:46:26.854043Z",
     "shell.execute_reply": "2024-01-11T20:46:26.853325Z"
    },
    "id": "kfRz_WVhIQcd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/437 [..............................] - ETA: 36s - loss: 0.2190 - mean_absolute_error: 0.2912"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 25/437 [>.............................] - ETA: 0s - loss: 0.2521 - mean_absolute_error: 0.3029 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/437 [==>...........................] - ETA: 0s - loss: 0.2562 - mean_absolute_error: 0.3055"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 75/437 [====>.........................] - ETA: 0s - loss: 0.2548 - mean_absolute_error: 0.3047"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "100/437 [=====>........................] - ETA: 0s - loss: 0.2533 - mean_absolute_error: 0.3041"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "175/437 [===========>..................] - ETA: 0s - loss: 0.2563 - mean_absolute_error: 0.3054"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "300/437 [===================>..........] - ETA: 0s - loss: 0.2564 - mean_absolute_error: 0.3049"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "326/437 [=====================>........] - ETA: 0s - loss: 0.2556 - mean_absolute_error: 0.3044"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "437/437 [==============================] - 1s 2ms/step - loss: 0.2559 - mean_absolute_error: 0.3048\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)\n",
    "multi_performance['Linear'] = multi_linear_model.evaluate(multi_window.test, verbose=0)\n",
    "multi_window.plot(multi_linear_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zi2TMHk2IRrh"
   },
   "source": [
    "#### Dense\n",
    "\n",
    "入力と出力の間に `tf.keras.layers.Dense` を追加すると、線形モデルにパワーが追加されますが、依然として、単一入力時間ステップのみに基づいたままとなります。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:46:26.858737Z",
     "iopub.status.busy": "2024-01-11T20:46:26.858321Z",
     "iopub.status.idle": "2024-01-11T20:47:32.148334Z",
     "shell.execute_reply": "2024-01-11T20:47:32.147513Z"
    },
    "id": "jezm-BKaGj91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/437 [..............................] - ETA: 36s - loss: 0.1966 - mean_absolute_error: 0.2754"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/437 [>.............................] - ETA: 0s - loss: 0.2102 - mean_absolute_error: 0.2785 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 46/437 [==>...........................] - ETA: 0s - loss: 0.2138 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 69/437 [===>..........................] - ETA: 0s - loss: 0.2168 - mean_absolute_error: 0.2808"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 90/437 [=====>........................] - ETA: 0s - loss: 0.2165 - mean_absolute_error: 0.2800"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/437 [======>.......................] - ETA: 0s - loss: 0.2179 - mean_absolute_error: 0.2813"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/437 [========>.....................] - ETA: 0s - loss: 0.2171 - mean_absolute_error: 0.2804"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "156/437 [=========>....................] - ETA: 0s - loss: 0.2184 - mean_absolute_error: 0.2813"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/437 [===========>..................] - ETA: 0s - loss: 0.2199 - mean_absolute_error: 0.2821"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "201/437 [============>.................] - ETA: 0s - loss: 0.2193 - mean_absolute_error: 0.2819"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "223/437 [==============>...............] - ETA: 0s - loss: 0.2198 - mean_absolute_error: 0.2822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245/437 [===============>..............] - ETA: 0s - loss: 0.2201 - mean_absolute_error: 0.2823"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "267/437 [=================>............] - ETA: 0s - loss: 0.2192 - mean_absolute_error: 0.2818"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "289/437 [==================>...........] - ETA: 0s - loss: 0.2193 - mean_absolute_error: 0.2819"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "311/437 [====================>.........] - ETA: 0s - loss: 0.2197 - mean_absolute_error: 0.2822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "333/437 [=====================>........] - ETA: 0s - loss: 0.2199 - mean_absolute_error: 0.2822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "355/437 [=======================>......] - ETA: 0s - loss: 0.2201 - mean_absolute_error: 0.2823"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "377/437 [========================>.....] - ETA: 0s - loss: 0.2201 - mean_absolute_error: 0.2823"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "399/437 [==========================>...] - ETA: 0s - loss: 0.2204 - mean_absolute_error: 0.2826"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "421/437 [===========================>..] - ETA: 0s - loss: 0.2205 - mean_absolute_error: 0.2826"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "437/437 [==============================] - 1s 2ms/step - loss: 0.2201 - mean_absolute_error: 0.2824\n"
     ]
    },
    {
     "data": {
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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)\n",
    "multi_performance['Dense'] = multi_dense_model.evaluate(multi_window.test, verbose=0)\n",
    "multi_window.plot(multi_dense_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "icsBAjCzMaMl"
   },
   "source": [
    "#### CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "34lCZrWYNBwd"
   },
   "source": [
    "畳み込みモデルは、固定幅の履歴に基づく予測を行います。このため、時間の経過とともに変化する様子を確認できるため、密なモデルよりも優れたパフォーマンスが得られる可能性があります。\n",
    "\n",
    "![畳み込みモデルは、時間の経過とともに変化する様子を確認する](images/multistep_conv.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:47:32.152461Z",
     "iopub.status.busy": "2024-01-11T20:47:32.152180Z",
     "iopub.status.idle": "2024-01-11T20:48:29.976413Z",
     "shell.execute_reply": "2024-01-11T20:48:29.975676Z"
    },
    "id": "0xJoIP6PMWMI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/437 [..............................] - ETA: 37s - loss: 0.2326 - mean_absolute_error: 0.2773"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 24/437 [>.............................] - ETA: 0s - loss: 0.2157 - mean_absolute_error: 0.2820 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/437 [==>...........................] - ETA: 0s - loss: 0.2143 - mean_absolute_error: 0.2814"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 70/437 [===>..........................] - ETA: 0s - loss: 0.2143 - mean_absolute_error: 0.2822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 93/437 [=====>........................] - ETA: 0s - loss: 0.2171 - mean_absolute_error: 0.2832"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "116/437 [======>.......................] - ETA: 0s - loss: 0.2156 - mean_absolute_error: 0.2824"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "140/437 [========>.....................] - ETA: 0s - loss: 0.2165 - mean_absolute_error: 0.2827"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "164/437 [==========>...................] - ETA: 0s - loss: 0.2155 - mean_absolute_error: 0.2819"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "188/437 [===========>..................] - ETA: 0s - loss: 0.2161 - mean_absolute_error: 0.2823"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "212/437 [=============>................] - ETA: 0s - loss: 0.2166 - mean_absolute_error: 0.2825"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "236/437 [===============>..............] - ETA: 0s - loss: 0.2172 - mean_absolute_error: 0.2826"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "259/437 [================>.............] - ETA: 0s - loss: 0.2175 - mean_absolute_error: 0.2827"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "283/437 [==================>...........] - ETA: 0s - loss: 0.2174 - mean_absolute_error: 0.2826"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "307/437 [====================>.........] - ETA: 0s - loss: 0.2164 - mean_absolute_error: 0.2823"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "330/437 [=====================>........] - ETA: 0s - loss: 0.2164 - mean_absolute_error: 0.2822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "354/437 [=======================>......] - ETA: 0s - loss: 0.2165 - mean_absolute_error: 0.2825"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "377/437 [========================>.....] - ETA: 0s - loss: 0.2168 - mean_absolute_error: 0.2826"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "401/437 [==========================>...] - ETA: 0s - loss: 0.2166 - mean_absolute_error: 0.2823"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "425/437 [============================>.] - ETA: 0s - loss: 0.2163 - mean_absolute_error: 0.2822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "437/437 [==============================] - 1s 2ms/step - loss: 0.2160 - mean_absolute_error: 0.2820\n"
     ]
    },
    {
     "data": {
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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)\n",
    "multi_performance['Conv'] = multi_conv_model.evaluate(multi_window.test, verbose=0)\n",
    "multi_window.plot(multi_conv_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "weBjeZAFJOP4"
   },
   "source": [
    "#### RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8022xOKxOO92"
   },
   "source": [
    "回帰モデルは、入力の長い履歴がモデルが行おうとしている予測に関連している場合に、それを使用して学習できます。ここでは、内部状態を 24 時間累積した上で、次の 24 時間の単一の予測が行われます。\n",
    "\n",
    "このシングルショット形式では、LSTM は、最後の時間ステップの出力のみを生成する必要があるため、`tf.keras.layers.LSTM` で `return_sequences=False` に設定します。\n",
    "\n",
    "![LSTM は入力ウィンドウ全体の状態を蓄積し、次の 24 時間について 1 つの予測を行う](images/multistep_lstm.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:48:29.981542Z",
     "iopub.status.busy": "2024-01-11T20:48:29.980914Z",
     "iopub.status.idle": "2024-01-11T20:49:38.384634Z",
     "shell.execute_reply": "2024-01-11T20:49:38.383940Z"
    },
    "id": "Bf1ks6RTzF64"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/437 [..............................] - ETA: 36s - loss: 0.2466 - mean_absolute_error: 0.3059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 19/437 [>.............................] - ETA: 1s - loss: 0.2214 - mean_absolute_error: 0.2872 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 39/437 [=>............................] - ETA: 1s - loss: 0.2151 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 60/437 [===>..........................] - ETA: 0s - loss: 0.2151 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 80/437 [====>.........................] - ETA: 0s - loss: 0.2146 - mean_absolute_error: 0.2845"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/437 [=====>........................] - ETA: 0s - loss: 0.2130 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "121/437 [=======>......................] - ETA: 0s - loss: 0.2127 - mean_absolute_error: 0.2839"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "141/437 [========>.....................] - ETA: 0s - loss: 0.2125 - mean_absolute_error: 0.2839"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/437 [==========>...................] - ETA: 0s - loss: 0.2124 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "181/437 [===========>..................] - ETA: 0s - loss: 0.2131 - mean_absolute_error: 0.2839"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "201/437 [============>.................] - ETA: 0s - loss: 0.2129 - mean_absolute_error: 0.2839"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "221/437 [==============>...............] - ETA: 0s - loss: 0.2126 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "241/437 [===============>..............] - ETA: 0s - loss: 0.2123 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "261/437 [================>.............] - ETA: 0s - loss: 0.2122 - mean_absolute_error: 0.2835"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "281/437 [==================>...........] - ETA: 0s - loss: 0.2124 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "301/437 [===================>..........] - ETA: 0s - loss: 0.2124 - mean_absolute_error: 0.2835"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "321/437 [=====================>........] - ETA: 0s - loss: 0.2125 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "341/437 [======================>.......] - ETA: 0s - loss: 0.2133 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "361/437 [=======================>......] - ETA: 0s - loss: 0.2134 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "381/437 [=========================>....] - ETA: 0s - loss: 0.2133 - mean_absolute_error: 0.2841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "402/437 [==========================>...] - ETA: 0s - loss: 0.2135 - mean_absolute_error: 0.2841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "423/437 [============================>.] - ETA: 0s - 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\b\b\b\b\b\b\b\b\b\b\r",
      "437/437 [==============================] - 1s 3ms/step - loss: 0.2139 - mean_absolute_error: 0.2842\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)\n",
    "multi_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.test, verbose=0)\n",
    "multi_window.plot(multi_lstm_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d5n-1cDW12Vo"
   },
   "source": [
    "### 高度: 自動回帰モデル\n",
    "\n",
    "上記のモデルはすべて、単一のステップで、出力シーケンス全体を予測します。\n",
    "\n",
    "一部のケースでは、モデルがこの予測を個別の時間ステップに分解することが役立つ可能性があります。その上で、各モデルの出力を各ステップでそれ自体にフィードし、従来の「<a href=\"https://arxiv.org/abs/1308.0850\" class=\"external\">Generating Sequences With Recurrent Neural Networks</a>」のように、前の予測で条件づけられた予測を立てることができます。\n",
    "\n",
    "このスタイルのモデルには、長さの異なる出力を生成するようにセットアップできるという明確なメリットがあります。\n",
    "\n",
    "このチュートリアルの前半でトレーニングされた単一ステップ複数出力モデルを使って、自動回帰フィードバックループで実行することもできますが、ここでは、それを行うように明示的にトレーニングされたモデルを構築することに焦点を当てることにします。\n",
    "\n",
    "![モデルの出力を入力にフィードバックする](images/multistep_autoregressive.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PKRreBbULRXY"
   },
   "source": [
    "#### RNN\n",
    "\n",
    "このチュートリアルでは自動回帰 RNN モデルのみを構築しますが、このパターンは、単一時間ステップを出力するために設計されたモデルに適用することができます。\n",
    "\n",
    "モデルには、前の単一ステップの LSTM モデルと同じ基本形式があります。`LSTM` レイヤーの出力をモデルの予測に変換する、`tf.keras.layers.Dense` が続く `tf.keras.layers.LSTM` レイヤーです。\n",
    "\n",
    "`tf.keras.layers.LSTM` は、状態とシーケンス結果を管理するより高位の `tf.keras.layers.RNN` にラッピングされた `tf.keras.layers.LSTMCell` です（詳細は、[Keras による回帰ニューラルネットワーク（RNN）](https://www.tensorflow.org/guide/keras/rnn)ガイドをご覧ください。\n",
    "\n",
    "この場合、モデルは、より低レベルの単一時間ステップインターフェースに直接 `tf.keras.layers.LSTMCell` を使用するように、各ステップの入力を手動で管理する必要があります。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:49:38.389283Z",
     "iopub.status.busy": "2024-01-11T20:49:38.388832Z",
     "iopub.status.idle": "2024-01-11T20:49:38.393518Z",
     "shell.execute_reply": "2024-01-11T20:49:38.392833Z"
    },
    "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": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:49:38.396452Z",
     "iopub.status.busy": "2024-01-11T20:49:38.396185Z",
     "iopub.status.idle": "2024-01-11T20:49:38.406843Z",
     "shell.execute_reply": "2024-01-11T20:49:38.406186Z"
    },
    "id": "2OXVM9G1U7xR"
   },
   "outputs": [],
   "source": [
    "feedback_model = FeedBack(units=32, out_steps=OUT_STEPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ph5uFSfTUNho"
   },
   "source": [
    "このモデルが必要とする最初のメソッドは、入力に応じて内部状態を初期化する `warmup` メソッドです。トレーニングされると、この状態は入力履歴の関連する部分をキャプチャするようになります。これは、上記の単一ステップ `LSTM` モデルと同等です。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:49:38.410179Z",
     "iopub.status.busy": "2024-01-11T20:49:38.409737Z",
     "iopub.status.idle": "2024-01-11T20:49:38.413408Z",
     "shell.execute_reply": "2024-01-11T20:49:38.412709Z"
    },
    "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": [
    "このメソッドは、単一の時間ステップ予測と `LSTM` の内部状態を返します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:49:38.416551Z",
     "iopub.status.busy": "2024-01-11T20:49:38.416098Z",
     "iopub.status.idle": "2024-01-11T20:49:38.585634Z",
     "shell.execute_reply": "2024-01-11T20:49:38.584846Z"
    },
    "id": "w9Fz6NTKXXwU"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([32, 19])"
      ]
     },
     "execution_count": 87,
     "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": [
    "`RNN` の状態と初期の予測によって、各ステップの予測を入力としてフィードし直すモデルのイテレーションを続行できるようになりました。\n",
    "\n",
    "出力予測を収集する最も単純なアプローチは、Python リストと、ループ後に `tf.stack` を使用する方法です。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yotTad3nZXQU"
   },
   "source": [
    "注意: このような Python リストのスタックは、Eager execution、トレーニングの `Model.compile(..., run_eagerly=True)` の使用、または固定長出力によってのみ機能します。動的出力長については、Python リストの代わりに `tf.TensorArray`、Python `range` の代わりに `tf.range` を使用する必要があります。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:49:38.589599Z",
     "iopub.status.busy": "2024-01-11T20:49:38.588987Z",
     "iopub.status.idle": "2024-01-11T20:49:38.594128Z",
     "shell.execute_reply": "2024-01-11T20:49:38.593477Z"
    },
    "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": [
    "サンプル入力にこのモデルをテスト実行します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:49:38.597662Z",
     "iopub.status.busy": "2024-01-11T20:49:38.597023Z",
     "iopub.status.idle": "2024-01-11T20:49:38.703739Z",
     "shell.execute_reply": "2024-01-11T20:49:38.703087Z"
    },
    "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": [
    "次に、モデルをトレーニングします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:49:38.706716Z",
     "iopub.status.busy": "2024-01-11T20:49:38.706470Z",
     "iopub.status.idle": "2024-01-11T20:57:24.288688Z",
     "shell.execute_reply": "2024-01-11T20:57:24.287943Z"
    },
    "id": "VBRVG2hnNyrO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/437 [..............................] - ETA: 39s - loss: 0.2162 - mean_absolute_error: 0.3019"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  7/437 [..............................] - ETA: 3s - loss: 0.2202 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 14/437 [..............................] - ETA: 3s - loss: 0.2177 - mean_absolute_error: 0.2981"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 21/437 [>.............................] - ETA: 3s - loss: 0.2274 - mean_absolute_error: 0.3030"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 28/437 [>.............................] - ETA: 3s - loss: 0.2223 - mean_absolute_error: 0.2991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/437 [=>............................] - ETA: 3s - loss: 0.2229 - mean_absolute_error: 0.2995"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 42/437 [=>............................] - ETA: 3s - loss: 0.2257 - mean_absolute_error: 0.3014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/437 [==>...........................] - ETA: 2s - loss: 0.2233 - mean_absolute_error: 0.3000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 56/437 [==>...........................] - ETA: 2s - loss: 0.2204 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 63/437 [===>..........................] - ETA: 2s - loss: 0.2221 - mean_absolute_error: 0.2988"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 70/437 [===>..........................] - ETA: 2s - loss: 0.2226 - mean_absolute_error: 0.2990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/437 [====>.........................] - ETA: 2s - loss: 0.2203 - mean_absolute_error: 0.2981"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 84/437 [====>.........................] - ETA: 2s - loss: 0.2199 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 91/437 [=====>........................] - ETA: 2s - loss: 0.2195 - 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\b\b\b\b\b\b\b\b\b\b\r",
      " 98/437 [=====>........................] - ETA: 2s - loss: 0.2189 - mean_absolute_error: 0.2975"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "105/437 [======>.......................] - ETA: 2s - loss: 0.2206 - mean_absolute_error: 0.2985"
     ]
    },
    {
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     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/437 [======>.......................] - ETA: 2s - loss: 0.2191 - mean_absolute_error: 0.2973"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/437 [=======>......................] - ETA: 2s - loss: 0.2188 - mean_absolute_error: 0.2970"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "126/437 [=======>......................] - ETA: 2s - loss: 0.2195 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "133/437 [========>.....................] - ETA: 2s - loss: 0.2195 - mean_absolute_error: 0.2977"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "140/437 [========>.....................] - ETA: 2s - loss: 0.2199 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "147/437 [=========>....................] - ETA: 2s - loss: 0.2203 - mean_absolute_error: 0.2980"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "154/437 [=========>....................] - ETA: 2s - loss: 0.2208 - mean_absolute_error: 0.2984"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/437 [==========>...................] - ETA: 2s - loss: 0.2202 - mean_absolute_error: 0.2980"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "168/437 [==========>...................] - ETA: 2s - loss: 0.2199 - mean_absolute_error: 0.2978"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "175/437 [===========>..................] - ETA: 2s - loss: 0.2202 - mean_absolute_error: 0.2981"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "182/437 [===========>..................] - ETA: 1s - loss: 0.2205 - mean_absolute_error: 0.2983"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "189/437 [===========>..................] - ETA: 1s - loss: 0.2210 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "196/437 [============>.................] - ETA: 1s - loss: 0.2215 - mean_absolute_error: 0.2988"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "203/437 [============>.................] - ETA: 1s - loss: 0.2214 - mean_absolute_error: 0.2988"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "210/437 [=============>................] - ETA: 1s - loss: 0.2214 - mean_absolute_error: 0.2987"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "217/437 [=============>................] - ETA: 1s - loss: 0.2212 - mean_absolute_error: 0.2985"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/437 [==============>...............] - ETA: 1s - loss: 0.2210 - mean_absolute_error: 0.2983"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "231/437 [==============>...............] - ETA: 1s - loss: 0.2212 - mean_absolute_error: 0.2985"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/437 [===============>..............] - ETA: 1s - loss: 0.2216 - mean_absolute_error: 0.2988"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245/437 [===============>..............] - ETA: 1s - loss: 0.2215 - mean_absolute_error: 0.2988"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "252/437 [================>.............] - ETA: 1s - loss: 0.2213 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "259/437 [================>.............] - ETA: 1s - loss: 0.2213 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "266/437 [=================>............] - ETA: 1s - loss: 0.2213 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "273/437 [=================>............] - ETA: 1s - loss: 0.2210 - mean_absolute_error: 0.2985"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "280/437 [==================>...........] - ETA: 1s - loss: 0.2213 - mean_absolute_error: 0.2987"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "287/437 [==================>...........] - ETA: 1s - loss: 0.2212 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "294/437 [===================>..........] - ETA: 1s - loss: 0.2213 - 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\b\b\b\b\b\b\b\b\b\b\r",
      "301/437 [===================>..........] - ETA: 1s - loss: 0.2215 - mean_absolute_error: 0.2987"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "308/437 [====================>.........] - ETA: 0s - loss: 0.2215 - mean_absolute_error: 0.2989"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "315/437 [====================>.........] - ETA: 0s - loss: 0.2217 - mean_absolute_error: 0.2990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "322/437 [=====================>........] - ETA: 0s - loss: 0.2220 - mean_absolute_error: 0.2992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "329/437 [=====================>........] - ETA: 0s - loss: 0.2216 - mean_absolute_error: 0.2990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "336/437 [======================>.......] - ETA: 0s - loss: 0.2213 - mean_absolute_error: 0.2988"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "343/437 [======================>.......] - ETA: 0s - loss: 0.2214 - mean_absolute_error: 0.2989"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "350/437 [=======================>......] - ETA: 0s - loss: 0.2213 - mean_absolute_error: 0.2989"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "357/437 [=======================>......] - ETA: 0s - loss: 0.2210 - mean_absolute_error: 0.2988"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "364/437 [=======================>......] - ETA: 0s - loss: 0.2214 - mean_absolute_error: 0.2990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "371/437 [========================>.....] - ETA: 0s - loss: 0.2214 - mean_absolute_error: 0.2990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "378/437 [========================>.....] - ETA: 0s - loss: 0.2212 - mean_absolute_error: 0.2990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "385/437 [=========================>....] - ETA: 0s - loss: 0.2213 - mean_absolute_error: 0.2990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "392/437 [=========================>....] - ETA: 0s - loss: 0.2215 - mean_absolute_error: 0.2990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "399/437 [==========================>...] - ETA: 0s - loss: 0.2220 - mean_absolute_error: 0.2992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "406/437 [==========================>...] - ETA: 0s - loss: 0.2221 - mean_absolute_error: 0.2992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "413/437 [===========================>..] - ETA: 0s - loss: 0.2223 - mean_absolute_error: 0.2993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "420/437 [===========================>..] - ETA: 0s - loss: 0.2221 - mean_absolute_error: 0.2992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "427/437 [============================>.] - ETA: 0s - loss: 0.2222 - mean_absolute_error: 0.2993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "434/437 [============================>.] - ETA: 0s - loss: 0.2222 - mean_absolute_error: 0.2993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "437/437 [==============================] - 3s 8ms/step - loss: 0.2224 - mean_absolute_error: 0.2994\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)\n",
    "multi_performance['AR LSTM'] = feedback_model.evaluate(multi_window.test, verbose=0)\n",
    "multi_window.plot(feedback_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hGjcJsAQJUkI"
   },
   "source": [
    "### パフォーマンス"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sODAwr2ndtDB"
   },
   "source": [
    "この問題では、モデルの複雑さの関数として、戻り値が明確に小さくなっています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:57:24.293004Z",
     "iopub.status.busy": "2024-01-11T20:57:24.292748Z",
     "iopub.status.idle": "2024-01-11T20:57:24.464880Z",
     "shell.execute_reply": "2024-01-11T20:57:24.464252Z"
    },
    "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",
    "metric_index = lstm_model.metrics_names.index('mean_absolute_error')\n",
    "val_mae = [v[metric_index] for v in multi_val_performance.values()]\n",
    "test_mae = [v[metric_index] 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": [
    "このチュートリアルの前半で説明した複数出力モデルのメトリックから、すべての出力特徴量全体で平均化されていることがわかります。これらのパフォーマンスは似ていますが、出力時間ステップ間でも平均化されています。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T20:57:24.468700Z",
     "iopub.status.busy": "2024-01-11T20:57:24.468074Z",
     "iopub.status.idle": "2024-01-11T20:57:24.472085Z",
     "shell.execute_reply": "2024-01-11T20:57:24.471432Z"
    },
    "id": "jKq3eAIvH4Db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last    : 0.5157\n",
      "Repeat  : 0.3774\n",
      "Linear  : 0.2978\n",
      "Dense   : 0.2765\n",
      "Conv    : 0.2752\n",
      "LSTM    : 0.2775\n",
      "AR LSTM : 0.2884\n"
     ]
    }
   ],
   "source": [
    "for name, value in multi_performance.items():\n",
    "  print(f'{name:8s}: {value[1]:0.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MpBFwfnaHP23"
   },
   "source": [
    "密なモデルから畳み込みと回帰モデルに移行したことで得られたのは、あったとしてもわずか数パーセント程度で、自動回帰モデルのパフォーマンスは明らかに低いものでした。そのため、こういったより複雑なアプローチは**この**問題に使用するほどの価値はありませんでしたが、試さなければ、このような結果も知るすべはありません。これらのモデルは、**他の**問題には役立つものかもしれません。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pOzaIRYBhqwg"
   },
   "source": [
    "## 次のステップ\n",
    "\n",
    "このチュートリアルでは、TensorFlow を使った時系列予測を簡単に紹介しました。\n",
    "\n",
    "さらに学習するには、以下をご覧ください。\n",
    "\n",
    "- 「<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>」（第 2 版）の第 15 章\n",
    "- 「<a href=\"https://www.manning.com/books/deep-learning-with-python\" class=\"external\">Deep Learning with Python</a>」の第 6 章\n",
    "- 「<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>」のレッスン 8、および<a href=\"https://github.com/tensorflow/examples/tree/master/courses/udacity_intro_to_tensorflow_for_deep_learning\" class=\"external\">実践ノートブック</a>\n",
    "\n",
    "また、TensorFlow では、<a href=\"https://otexts.com/fpp2/index.html\" class=\"external\">古典的な時系列モデル</a>を実装することもできます。このチュートリアルは、TensorFlow の組み込み機能に焦点が当てられています。\n"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "time_series.ipynb",
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}