djia_support_nikkei_sync_period_V2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sprkzoff/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/sprkzoff/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/sprkzoff/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/sprkzoff/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/sprkzoff/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/sprkzoff/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/home/sprkzoff/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/sprkzoff/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/sprkzoff/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/sprkzoff/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/sprkzoff/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/sprkzoff/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.preprocessing\n",
    "import sklearn\n",
    "import os\n",
    "from datetime import datetime, timedelta\n",
    "from tqdm import tqdm\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.layers import LSTM, GRU, Bidirectional, Dense, Dropout, Input, TimeDistributed, LeakyReLU, BatchNormalization\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau,TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DJI_ema.csv',\n",
       " 'DJI_STO_20period_sma10.csv',\n",
       " 'DJI_modified_sma.csv',\n",
       " '.DS_Store',\n",
       " 'DJI_modified_features - DJI.csv',\n",
       " 'nk_fe.csv',\n",
       " 'DJI_fe.csv',\n",
       " 'N225withoutNULL.csv',\n",
       " 'dowjone1985_2019.csv',\n",
       " 'N225fillVOL.csv',\n",
       " 'test_djia.csv',\n",
       " 'DJI_denoise.csv',\n",
       " 'N225.csv',\n",
       " 'stocknews',\n",
       " 'preprocessed',\n",
       " 'downjone5y.csv']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('./data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nikkei = pd.read_csv('./data/N225withoutNULL.csv')\n",
    "df_djia = pd.read_csv('./data/DJI_modified_sma.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985-01-29</td>\n",
       "      <td>11843.070313</td>\n",
       "      <td>11843.070313</td>\n",
       "      <td>11843.070313</td>\n",
       "      <td>11843.070313</td>\n",
       "      <td>11843.070313</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985-01-30</td>\n",
       "      <td>11960.629883</td>\n",
       "      <td>11960.629883</td>\n",
       "      <td>11960.629883</td>\n",
       "      <td>11960.629883</td>\n",
       "      <td>11960.629883</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985-01-31</td>\n",
       "      <td>11992.309570</td>\n",
       "      <td>11992.309570</td>\n",
       "      <td>11992.309570</td>\n",
       "      <td>11992.309570</td>\n",
       "      <td>11992.309570</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985-02-01</td>\n",
       "      <td>11946.959961</td>\n",
       "      <td>11946.959961</td>\n",
       "      <td>11946.959961</td>\n",
       "      <td>11946.959961</td>\n",
       "      <td>11946.959961</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985-02-04</td>\n",
       "      <td>11891.080078</td>\n",
       "      <td>11891.080078</td>\n",
       "      <td>11891.080078</td>\n",
       "      <td>11891.080078</td>\n",
       "      <td>11891.080078</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date          Open          High           Low         Close  \\\n",
       "0  1985-01-29  11843.070313  11843.070313  11843.070313  11843.070313   \n",
       "1  1985-01-30  11960.629883  11960.629883  11960.629883  11960.629883   \n",
       "2  1985-01-31  11992.309570  11992.309570  11992.309570  11992.309570   \n",
       "3  1985-02-01  11946.959961  11946.959961  11946.959961  11946.959961   \n",
       "4  1985-02-04  11891.080078  11891.080078  11891.080078  11891.080078   \n",
       "\n",
       "      Adj Close  Volume  \n",
       "0  11843.070313       0  \n",
       "1  11960.629883       0  \n",
       "2  11992.309570       0  \n",
       "3  11946.959961       0  \n",
       "4  11891.080078       0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nikkei.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1985-01-29'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_djia['Date'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/8762 [00:00<?, ?it/s]/usr/lib/python3/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "100%|██████████| 8762/8762 [00:21<00:00, 409.20it/s]\n"
     ]
    }
   ],
   "source": [
    "# postpone 1 day and concatenate both table\n",
    "\n",
    "# decrement datetime of djia first\n",
    "df_djia_dc = df_djia.copy()\n",
    "# df_djia_dc['Date'] = df_djia['Date'] + timedelta(days=-1)\n",
    "for i in tqdm(range(len(df_djia))):\n",
    "    df_djia_dc['Date'][i] = str((datetime.strptime(df_djia.iloc[i]['Date'], '%Y-%m-%d') + timedelta(days=-1)).date())\n",
    "\n",
    "# df_djia_dc.head()\n",
    "df = pd.merge(df_nikkei, df_djia_dc, on='Date', how='inner')\n",
    "df = df.rename({'Open_x': 'Open(Nikkei)', 'High_x': 'High(Nikkei)', 'Low_x': 'Low(Nikkei)', 'Close_x': 'Close(Nikkei)',\n",
    "                'Open_y': 'Open(DJIA)', 'High_y': 'High(DJIA)', 'Low_y': 'Low(DJIA)', 'Close_y': 'Close(DJIA)'\n",
    "               }, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open(Nikkei)</th>\n",
       "      <th>High(Nikkei)</th>\n",
       "      <th>Low(Nikkei)</th>\n",
       "      <th>Close(Nikkei)</th>\n",
       "      <th>Adj Close_x</th>\n",
       "      <th>Volume_x</th>\n",
       "      <th>Open(DJIA)</th>\n",
       "      <th>High(DJIA)</th>\n",
       "      <th>Low(DJIA)</th>\n",
       "      <th>Close(DJIA)</th>\n",
       "      <th>Adj Close_y</th>\n",
       "      <th>Volume_y</th>\n",
       "      <th>sma5</th>\n",
       "      <th>sma10</th>\n",
       "      <th>sma25</th>\n",
       "      <th>sma75</th>\n",
       "      <th>sma125</th>\n",
       "      <th>sma200</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985-01-29</td>\n",
       "      <td>11843.070313</td>\n",
       "      <td>11843.070313</td>\n",
       "      <td>11843.070313</td>\n",
       "      <td>11843.070313</td>\n",
       "      <td>11843.070313</td>\n",
       "      <td>0</td>\n",
       "      <td>1297.369995</td>\n",
       "      <td>1305.099976</td>\n",
       "      <td>1278.930054</td>\n",
       "      <td>1287.880005</td>\n",
       "      <td>1287.880005</td>\n",
       "      <td>16820000</td>\n",
       "      <td>1287.880005</td>\n",
       "      <td>1287.880005</td>\n",
       "      <td>1287.880005</td>\n",
       "      <td>1287.880005</td>\n",
       "      <td>1287.880005</td>\n",
       "      <td>1287.880005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985-01-30</td>\n",
       "      <td>11960.629883</td>\n",
       "      <td>11960.629883</td>\n",
       "      <td>11960.629883</td>\n",
       "      <td>11960.629883</td>\n",
       "      <td>11960.629883</td>\n",
       "      <td>0</td>\n",
       "      <td>1283.239990</td>\n",
       "      <td>1293.400024</td>\n",
       "      <td>1272.640015</td>\n",
       "      <td>1286.770020</td>\n",
       "      <td>1286.770020</td>\n",
       "      <td>14070000</td>\n",
       "      <td>1286.770020</td>\n",
       "      <td>1286.770020</td>\n",
       "      <td>1286.770020</td>\n",
       "      <td>1286.770020</td>\n",
       "      <td>1286.770020</td>\n",
       "      <td>1286.770020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985-01-31</td>\n",
       "      <td>11992.309570</td>\n",
       "      <td>11992.309570</td>\n",
       "      <td>11992.309570</td>\n",
       "      <td>11992.309570</td>\n",
       "      <td>11992.309570</td>\n",
       "      <td>0</td>\n",
       "      <td>1276.939941</td>\n",
       "      <td>1286.109985</td>\n",
       "      <td>1269.770020</td>\n",
       "      <td>1277.719971</td>\n",
       "      <td>1277.719971</td>\n",
       "      <td>10980000</td>\n",
       "      <td>1277.719971</td>\n",
       "      <td>1277.719971</td>\n",
       "      <td>1277.719971</td>\n",
       "      <td>1277.719971</td>\n",
       "      <td>1277.719971</td>\n",
       "      <td>1277.719971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985-02-04</td>\n",
       "      <td>11891.080078</td>\n",
       "      <td>11891.080078</td>\n",
       "      <td>11891.080078</td>\n",
       "      <td>11891.080078</td>\n",
       "      <td>11891.080078</td>\n",
       "      <td>0</td>\n",
       "      <td>1294.060059</td>\n",
       "      <td>1301.130005</td>\n",
       "      <td>1278.599976</td>\n",
       "      <td>1285.229980</td>\n",
       "      <td>1285.229980</td>\n",
       "      <td>13800000</td>\n",
       "      <td>1285.535986</td>\n",
       "      <td>1285.229980</td>\n",
       "      <td>1285.229980</td>\n",
       "      <td>1285.229980</td>\n",
       "      <td>1285.229980</td>\n",
       "      <td>1285.229980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985-02-05</td>\n",
       "      <td>11823.429688</td>\n",
       "      <td>11823.429688</td>\n",
       "      <td>11823.429688</td>\n",
       "      <td>11823.429688</td>\n",
       "      <td>11823.429688</td>\n",
       "      <td>0</td>\n",
       "      <td>1284.890015</td>\n",
       "      <td>1294.500000</td>\n",
       "      <td>1275.069946</td>\n",
       "      <td>1280.589966</td>\n",
       "      <td>1280.589966</td>\n",
       "      <td>14610000</td>\n",
       "      <td>1284.077979</td>\n",
       "      <td>1280.589966</td>\n",
       "      <td>1280.589966</td>\n",
       "      <td>1280.589966</td>\n",
       "      <td>1280.589966</td>\n",
       "      <td>1280.589966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Open(Nikkei)  High(Nikkei)   Low(Nikkei)  Close(Nikkei)  \\\n",
       "0  1985-01-29  11843.070313  11843.070313  11843.070313   11843.070313   \n",
       "1  1985-01-30  11960.629883  11960.629883  11960.629883   11960.629883   \n",
       "2  1985-01-31  11992.309570  11992.309570  11992.309570   11992.309570   \n",
       "3  1985-02-04  11891.080078  11891.080078  11891.080078   11891.080078   \n",
       "4  1985-02-05  11823.429688  11823.429688  11823.429688   11823.429688   \n",
       "\n",
       "    Adj Close_x  Volume_x   Open(DJIA)   High(DJIA)    Low(DJIA)  Close(DJIA)  \\\n",
       "0  11843.070313         0  1297.369995  1305.099976  1278.930054  1287.880005   \n",
       "1  11960.629883         0  1283.239990  1293.400024  1272.640015  1286.770020   \n",
       "2  11992.309570         0  1276.939941  1286.109985  1269.770020  1277.719971   \n",
       "3  11891.080078         0  1294.060059  1301.130005  1278.599976  1285.229980   \n",
       "4  11823.429688         0  1284.890015  1294.500000  1275.069946  1280.589966   \n",
       "\n",
       "   Adj Close_y  Volume_y         sma5        sma10        sma25        sma75  \\\n",
       "0  1287.880005  16820000  1287.880005  1287.880005  1287.880005  1287.880005   \n",
       "1  1286.770020  14070000  1286.770020  1286.770020  1286.770020  1286.770020   \n",
       "2  1277.719971  10980000  1277.719971  1277.719971  1277.719971  1277.719971   \n",
       "3  1285.229980  13800000  1285.535986  1285.229980  1285.229980  1285.229980   \n",
       "4  1280.589966  14610000  1284.077979  1280.589966  1280.589966  1280.589966   \n",
       "\n",
       "        sma125       sma200  \n",
       "0  1287.880005  1287.880005  \n",
       "1  1286.770020  1286.770020  \n",
       "2  1277.719971  1277.719971  \n",
       "3  1285.229980  1285.229980  \n",
       "4  1280.589966  1280.589966  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_col = ['Open(Nikkei)', 'High(Nikkei)', 'Low(Nikkei)', 'Close(Nikkei)',\n",
    "            'Open(DJIA)', 'High(DJIA)', 'Low(DJIA)', 'Close(DJIA)'\n",
    "            ]\n",
    "total_col = df.columns\n",
    "for col in total_col:\n",
    "    if col not in event_col:\n",
    "        df = df.drop(col, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open(Nikkei)</th>\n",
       "      <th>High(Nikkei)</th>\n",
       "      <th>Low(Nikkei)</th>\n",
       "      <th>Close(Nikkei)</th>\n",
       "      <th>Open(DJIA)</th>\n",
       "      <th>High(DJIA)</th>\n",
       "      <th>Low(DJIA)</th>\n",
       "      <th>Close(DJIA)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>26999.64063</td>\n",
       "      <td>27186.68945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6910</th>\n",
       "      <td>22953.169922</td>\n",
       "      <td>22961.230469</td>\n",
       "      <td>22827.929688</td>\n",
       "      <td>22843.119141</td>\n",
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       "      <td>26918.28906</td>\n",
       "      <td>27046.23047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Open(Nikkei)  High(Nikkei)   Low(Nikkei)  Close(Nikkei)   Open(DJIA)  \\\n",
       "6906  22619.769531  22648.810547  22457.890625   22625.380859  26893.92969   \n",
       "6907  22725.439453  22780.990234  22704.330078   22750.599609  26789.60938   \n",
       "6908  22854.439453  22896.220703  22830.570313   22867.269531  27061.07031   \n",
       "6909  22950.789063  23008.429688  22935.349609   22974.130859  27110.71094   \n",
       "6910  22953.169922  22961.230469  22827.929688   22843.119141  27188.36914   \n",
       "\n",
       "       High(DJIA)    Low(DJIA)  Close(DJIA)  \n",
       "6906  26931.77930  26714.33984  26805.52930  \n",
       "6907  27015.36914  26765.67969  26958.06055  \n",
       "6908  27165.93945  27039.75977  27071.46094  \n",
       "6909  27204.35938  26999.64063  27186.68945  \n",
       "6910  27188.36914  26918.28906  27046.23047  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_data(df, event_col):\n",
    "    min_max_scaler = sklearn.preprocessing.MinMaxScaler()\n",
    "    for col in event_col:\n",
    "        if col != 'Date':\n",
    "            df[col] = min_max_scaler.fit_transform(df[col].to_numpy().reshape(-1, 1))\n",
    "    return df\n",
    "\n",
    "def load_data(stock, seq_len):\n",
    "    data_raw = stock.as_matrix()\n",
    "    data = list()\n",
    "    for index in range(len(data_raw) - seq_len):\n",
    "        data.append(data_raw[index: index+seq_len])\n",
    "    data = np.array(data)\n",
    "    val_set_size = int(np.round(val_set_size_percentage/100*data.shape[0]))\n",
    "    test_set_size = int(np.round(test_set_size_percentage/100*data.shape[0]))\n",
    "    train_set_size = data.shape[0] - (val_set_size + test_set_size)\n",
    "    \n",
    "    x_train = data[:train_set_size, :-1, :]\n",
    "    y_train = data[:train_set_size, -1, :]\n",
    "    \n",
    "    x_val = data[train_set_size:train_set_size + val_set_size, :-1, :]\n",
    "    y_val = data[train_set_size:train_set_size + val_set_size, -1, :]\n",
    "    \n",
    "    x_test = data[train_set_size+val_set_size:,:-1,:]\n",
    "    y_test = data[train_set_size+val_set_size:,-1,:]\n",
    "    \n",
    "    return [x_train, y_train, x_val, y_val, x_test, y_test]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train.shape =  (5513, 19, 8)\n",
      "y_train.shape =  (5513, 8)\n",
      "x_val.shape =  (689, 19, 8)\n",
      "y_val.shape =  (689, 8)\n",
      "x_test.shape =  (689, 19, 8)\n",
      "y_test.shape =  (689, 8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:9: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "# parameter for preprocessing #\n",
    "\n",
    "val_set_size_percentage = 10\n",
    "test_set_size_percentage = 10\n",
    "seq_len = 20\n",
    "\n",
    "#*****************************#\n",
    "\n",
    "# normalize index\n",
    "df_norm = normalize_data(df, event_col)\n",
    "\n",
    "# split train, val, test data\n",
    "\n",
    "x_train, y_train, x_val, y_val, x_test, y_test = load_data(df_norm, seq_len)\n",
    "\n",
    "print('x_train.shape = ', x_train.shape)\n",
    "print('y_train.shape = ', y_train.shape)\n",
    "print('x_val.shape = ', x_val.shape)\n",
    "print('y_val.shape = ', y_val.shape)\n",
    "print('x_test.shape = ', x_test.shape)\n",
    "print('y_test.shape = ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot graph before train\n",
    "\n",
    "# !set color in graph\n",
    "# color_palette = ...\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.grid()\n",
    "color_palette = plt.get_cmap('Set1')\n",
    "for i, col in enumerate(event_col):\n",
    "    plt.plot(df_norm[col], color=color_palette(i), label=col)\n",
    "plt.title('Stock price')\n",
    "plt.xlabel('Time [days]')\n",
    "plt.ylabel('Normalized price / volume')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2638\n"
     ]
    },
    {
     "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>Open(Nikkei)</th>\n",
       "      <th>High(Nikkei)</th>\n",
       "      <th>Low(Nikkei)</th>\n",
       "      <th>Close(Nikkei)</th>\n",
       "      <th>Open(DJIA)</th>\n",
       "      <th>High(DJIA)</th>\n",
       "      <th>Low(DJIA)</th>\n",
       "      <th>Close(DJIA)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2638</th>\n",
       "      <td>0.258003</td>\n",
       "      <td>0.256758</td>\n",
       "      <td>0.237947</td>\n",
       "      <td>0.243070</td>\n",
       "      <td>0.252139</td>\n",
       "      <td>0.251625</td>\n",
       "      <td>0.248385</td>\n",
       "      <td>0.247049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2639</th>\n",
       "      <td>0.258003</td>\n",
       "      <td>0.256758</td>\n",
       "      <td>0.237947</td>\n",
       "      <td>0.243070</td>\n",
       "      <td>0.247315</td>\n",
       "      <td>0.248459</td>\n",
       "      <td>0.247263</td>\n",
       "      <td>0.245836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2640</th>\n",
       "      <td>0.247327</td>\n",
       "      <td>0.270863</td>\n",
       "      <td>0.249590</td>\n",
       "      <td>0.258785</td>\n",
       "      <td>0.246054</td>\n",
       "      <td>0.247600</td>\n",
       "      <td>0.247225</td>\n",
       "      <td>0.246570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2641</th>\n",
       "      <td>0.243875</td>\n",
       "      <td>0.243346</td>\n",
       "      <td>0.235396</td>\n",
       "      <td>0.242311</td>\n",
       "      <td>0.251280</td>\n",
       "      <td>0.255440</td>\n",
       "      <td>0.252319</td>\n",
       "      <td>0.255637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2642</th>\n",
       "      <td>0.244138</td>\n",
       "      <td>0.256102</td>\n",
       "      <td>0.246399</td>\n",
       "      <td>0.257487</td>\n",
       "      <td>0.255874</td>\n",
       "      <td>0.256852</td>\n",
       "      <td>0.255978</td>\n",
       "      <td>0.255342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6906</th>\n",
       "      <td>0.488358</td>\n",
       "      <td>0.488062</td>\n",
       "      <td>0.485756</td>\n",
       "      <td>0.488699</td>\n",
       "      <td>0.983626</td>\n",
       "      <td>0.983267</td>\n",
       "      <td>0.980601</td>\n",
       "      <td>0.979176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6907</th>\n",
       "      <td>0.491674</td>\n",
       "      <td>0.492212</td>\n",
       "      <td>0.493498</td>\n",
       "      <td>0.492630</td>\n",
       "      <td>0.979626</td>\n",
       "      <td>0.986467</td>\n",
       "      <td>0.982577</td>\n",
       "      <td>0.985018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6908</th>\n",
       "      <td>0.495723</td>\n",
       "      <td>0.495829</td>\n",
       "      <td>0.497464</td>\n",
       "      <td>0.496292</td>\n",
       "      <td>0.990036</td>\n",
       "      <td>0.992232</td>\n",
       "      <td>0.993125</td>\n",
       "      <td>0.989362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6909</th>\n",
       "      <td>0.498747</td>\n",
       "      <td>0.499351</td>\n",
       "      <td>0.500755</td>\n",
       "      <td>0.499646</td>\n",
       "      <td>0.991939</td>\n",
       "      <td>0.993703</td>\n",
       "      <td>0.991581</td>\n",
       "      <td>0.993776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6910</th>\n",
       "      <td>0.498822</td>\n",
       "      <td>0.497869</td>\n",
       "      <td>0.497381</td>\n",
       "      <td>0.495534</td>\n",
       "      <td>0.994917</td>\n",
       "      <td>0.993091</td>\n",
       "      <td>0.988450</td>\n",
       "      <td>0.988396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4273 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Open(Nikkei)  High(Nikkei)  Low(Nikkei)  Close(Nikkei)  Open(DJIA)  \\\n",
       "2638      0.258003      0.256758     0.237947       0.243070    0.252139   \n",
       "2639      0.258003      0.256758     0.237947       0.243070    0.247315   \n",
       "2640      0.247327      0.270863     0.249590       0.258785    0.246054   \n",
       "2641      0.243875      0.243346     0.235396       0.242311    0.251280   \n",
       "2642      0.244138      0.256102     0.246399       0.257487    0.255874   \n",
       "...            ...           ...          ...            ...         ...   \n",
       "6906      0.488358      0.488062     0.485756       0.488699    0.983626   \n",
       "6907      0.491674      0.492212     0.493498       0.492630    0.979626   \n",
       "6908      0.495723      0.495829     0.497464       0.496292    0.990036   \n",
       "6909      0.498747      0.499351     0.500755       0.499646    0.991939   \n",
       "6910      0.498822      0.497869     0.497381       0.495534    0.994917   \n",
       "\n",
       "      High(DJIA)  Low(DJIA)  Close(DJIA)  \n",
       "2638    0.251625   0.248385     0.247049  \n",
       "2639    0.248459   0.247263     0.245836  \n",
       "2640    0.247600   0.247225     0.246570  \n",
       "2641    0.255440   0.252319     0.255637  \n",
       "2642    0.256852   0.255978     0.255342  \n",
       "...          ...        ...          ...  \n",
       "6906    0.983267   0.980601     0.979176  \n",
       "6907    0.986467   0.982577     0.985018  \n",
       "6908    0.992232   0.993125     0.989362  \n",
       "6909    0.993703   0.991581     0.993776  \n",
       "6910    0.993091   0.988450     0.988396  \n",
       "\n",
       "[4273 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = 0\n",
    "for i in range(df.shape[0]) :\n",
    "    if df.loc[i]['Close(DJIA)'] >= df.loc[i]['Close(Nikkei)'] :\n",
    "        start = i\n",
    "        break\n",
    "print(start)\n",
    "df_2 = df[start:]\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9c93b40ac8>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot graph after 2 graph sync\n",
    "\n",
    "# !set color in graph\n",
    "# color_palette = ...\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.grid()\n",
    "color_palette = plt.get_cmap('Set1')\n",
    "plt.plot(df_2['Close(DJIA)'],color='magenta')\n",
    "plt.plot(df_2['Close(Nikkei)'],color='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train.shape =  (3403, 19, 8)\n",
      "y_train.shape =  (3403, 8)\n",
      "x_val.shape =  (425, 19, 8)\n",
      "y_val.shape =  (425, 8)\n",
      "x_test.shape =  (425, 19, 8)\n",
      "y_test.shape =  (425, 8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:9: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "# parameter for preprocessing #\n",
    "\n",
    "val_set_size_percentage = 10\n",
    "test_set_size_percentage = 10\n",
    "seq_len = 20\n",
    "\n",
    "#*****************************#\n",
    "\n",
    "# normalize index\n",
    "df_2_norm = normalize_data(df_2, event_col)\n",
    "\n",
    "# split train, val, test data\n",
    "\n",
    "x2_train, y2_train, x2_val, y2_val, x2_test, y2_test = load_data(df_2_norm, seq_len)\n",
    "\n",
    "print('x_train.shape = ', x2_train.shape)\n",
    "print('y_train.shape = ', y2_train.shape)\n",
    "print('x_val.shape = ', x2_val.shape)\n",
    "print('y_val.shape = ', y2_val.shape)\n",
    "print('x_test.shape = ', x2_test.shape)\n",
    "print('y_test.shape = ', y2_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open(Nikkei)</th>\n",
       "      <th>High(Nikkei)</th>\n",
       "      <th>Low(Nikkei)</th>\n",
       "      <th>Close(Nikkei)</th>\n",
       "      <th>Open(DJIA)</th>\n",
       "      <th>High(DJIA)</th>\n",
       "      <th>Low(DJIA)</th>\n",
       "      <th>Close(DJIA)</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2638</th>\n",
       "      <td>0.474720</td>\n",
       "      <td>0.471511</td>\n",
       "      <td>0.439808</td>\n",
       "      <td>0.449848</td>\n",
       "      <td>0.061221</td>\n",
       "      <td>0.051805</td>\n",
       "      <td>0.058767</td>\n",
       "      <td>0.052873</td>\n",
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       "      <th>2639</th>\n",
       "      <td>0.474720</td>\n",
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       "      <td>0.439808</td>\n",
       "      <td>0.449848</td>\n",
       "      <td>0.055165</td>\n",
       "      <td>0.047793</td>\n",
       "      <td>0.057362</td>\n",
       "      <td>0.051347</td>\n",
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       "    <tr>\n",
       "      <th>2640</th>\n",
       "      <td>0.455077</td>\n",
       "      <td>0.497415</td>\n",
       "      <td>0.461328</td>\n",
       "      <td>0.478932</td>\n",
       "      <td>0.053581</td>\n",
       "      <td>0.046705</td>\n",
       "      <td>0.057315</td>\n",
       "      <td>0.052270</td>\n",
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       "    <tr>\n",
       "      <th>2641</th>\n",
       "      <td>0.448725</td>\n",
       "      <td>0.446881</td>\n",
       "      <td>0.435092</td>\n",
       "      <td>0.448443</td>\n",
       "      <td>0.060142</td>\n",
       "      <td>0.056639</td>\n",
       "      <td>0.063694</td>\n",
       "      <td>0.063675</td>\n",
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       "    <tr>\n",
       "      <th>2642</th>\n",
       "      <td>0.449209</td>\n",
       "      <td>0.470308</td>\n",
       "      <td>0.455429</td>\n",
       "      <td>0.476530</td>\n",
       "      <td>0.065909</td>\n",
       "      <td>0.058427</td>\n",
       "      <td>0.068276</td>\n",
       "      <td>0.063304</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>6906</th>\n",
       "      <td>0.898570</td>\n",
       "      <td>0.896280</td>\n",
       "      <td>0.897844</td>\n",
       "      <td>0.904434</td>\n",
       "      <td>0.979446</td>\n",
       "      <td>0.978799</td>\n",
       "      <td>0.975707</td>\n",
       "      <td>0.973806</td>\n",
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       "    <tr>\n",
       "      <th>6907</th>\n",
       "      <td>0.904672</td>\n",
       "      <td>0.903900</td>\n",
       "      <td>0.912153</td>\n",
       "      <td>0.911707</td>\n",
       "      <td>0.974425</td>\n",
       "      <td>0.982854</td>\n",
       "      <td>0.978181</td>\n",
       "      <td>0.981155</td>\n",
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       "    <tr>\n",
       "      <th>6908</th>\n",
       "      <td>0.912122</td>\n",
       "      <td>0.910542</td>\n",
       "      <td>0.919483</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.987492</td>\n",
       "      <td>0.990158</td>\n",
       "      <td>0.991391</td>\n",
       "      <td>0.986619</td>\n",
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       "    <tr>\n",
       "      <th>6909</th>\n",
       "      <td>0.917686</td>\n",
       "      <td>0.917011</td>\n",
       "      <td>0.925567</td>\n",
       "      <td>0.924691</td>\n",
       "      <td>0.989882</td>\n",
       "      <td>0.992022</td>\n",
       "      <td>0.989458</td>\n",
       "      <td>0.992170</td>\n",
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       "    <tr>\n",
       "      <th>6910</th>\n",
       "      <td>0.917824</td>\n",
       "      <td>0.914290</td>\n",
       "      <td>0.919330</td>\n",
       "      <td>0.917081</td>\n",
       "      <td>0.993620</td>\n",
       "      <td>0.991247</td>\n",
       "      <td>0.985537</td>\n",
       "      <td>0.985403</td>\n",
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       "<p>4273 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Open(Nikkei)  High(Nikkei)  Low(Nikkei)  Close(Nikkei)  Open(DJIA)  \\\n",
       "2638      0.474720      0.471511     0.439808       0.449848    0.061221   \n",
       "2639      0.474720      0.471511     0.439808       0.449848    0.055165   \n",
       "2640      0.455077      0.497415     0.461328       0.478932    0.053581   \n",
       "2641      0.448725      0.446881     0.435092       0.448443    0.060142   \n",
       "2642      0.449209      0.470308     0.455429       0.476530    0.065909   \n",
       "...            ...           ...          ...            ...         ...   \n",
       "6906      0.898570      0.896280     0.897844       0.904434    0.979446   \n",
       "6907      0.904672      0.903900     0.912153       0.911707    0.974425   \n",
       "6908      0.912122      0.910542     0.919483       0.918484    0.987492   \n",
       "6909      0.917686      0.917011     0.925567       0.924691    0.989882   \n",
       "6910      0.917824      0.914290     0.919330       0.917081    0.993620   \n",
       "\n",
       "      High(DJIA)  Low(DJIA)  Close(DJIA)  \n",
       "2638    0.051805   0.058767     0.052873  \n",
       "2639    0.047793   0.057362     0.051347  \n",
       "2640    0.046705   0.057315     0.052270  \n",
       "2641    0.056639   0.063694     0.063675  \n",
       "2642    0.058427   0.068276     0.063304  \n",
       "...          ...        ...          ...  \n",
       "6906    0.978799   0.975707     0.973806  \n",
       "6907    0.982854   0.978181     0.981155  \n",
       "6908    0.990158   0.991391     0.986619  \n",
       "6909    0.992022   0.989458     0.992170  \n",
       "6910    0.991247   0.985537     0.985403  \n",
       "\n",
       "[4273 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyperparameter \n",
    "batch_size = 1000\n",
    "epochs = 500\n",
    "\n",
    "# set up callback_list\n",
    "weight_path_model = \"saved_model/model_djia_support_nikkei_sync_period_V2.h5\"\n",
    "tensorboard_path = 'Graph/djia_support_nikkei_sync_period_V2'\n",
    "callbacks_list_model = [\n",
    "    TensorBoard(log_dir=tensorboard_path),\n",
    "    ModelCheckpoint(\n",
    "            weight_path_model,\n",
    "            save_best_only=True,\n",
    "            save_weights_only=True,\n",
    "            monitor='val_loss',\n",
    "            mode='min',\n",
    "            verbose=2\n",
    "        ),\n",
    "    ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=3, min_lr=1e-9)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 19, 8)]           0         \n",
      "_________________________________________________________________\n",
      "gru (GRU)                    (None, 19, 256)           203520    \n",
      "_________________________________________________________________\n",
      "time_distributed (TimeDistri (None, 19, 256)           0         \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, 19, 256)           1024      \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, 128)               147840    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 8)                 1032      \n",
      "=================================================================\n",
      "Total params: 353,928\n",
      "Trainable params: 353,160\n",
      "Non-trainable params: 768\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "K.clear_session()\n",
    "\n",
    "def get_bi_gru():\n",
    "    \n",
    "    input1 = Input(shape=(19,8,))\n",
    "    x = GRU(256,return_sequences=True,kernel_initializer='Orthogonal')(input1)\n",
    "    x = TimeDistributed(LeakyReLU())(x)\n",
    "    x = TimeDistributed(BatchNormalization())(x)\n",
    "    x = GRU(128,kernel_initializer='Orthogonal')(x)\n",
    "    x =(LeakyReLU())(x)\n",
    "    x = (BatchNormalization())(x)\n",
    "    x = (Dropout(0.25))(x)\n",
    "    out = Dense(8,kernel_initializer='he_normal')(x)\n",
    "\n",
    "    \n",
    "    \n",
    "    model = Model(inputs=input1, outputs=out)\n",
    "    model.compile(optimizer=Adam(lr=1e-2, decay=1e-2),\n",
    "                loss='mse',\n",
    "                metrics=['mse'])   \n",
    "    \n",
    "    return model\n",
    "\n",
    "model = get_bi_gru()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment and Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5513 samples, validate on 689 samples\n",
      "WARNING:tensorflow:From /home/sprkzoff/.local/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "Epoch 1/500\n",
      "5000/5513 [==========================>...] - ETA: 1s - loss: 3.0930 - mean_squared_error: 3.0930\n",
      "Epoch 00001: val_loss improved from inf to 0.14129, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 14s 3ms/sample - loss: 2.8852 - mean_squared_error: 2.8851 - val_loss: 0.1413 - val_mean_squared_error: 0.1413\n",
      "Epoch 2/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.6921 - mean_squared_error: 0.6921\n",
      "Epoch 00002: val_loss did not improve from 0.14129\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.6829 - mean_squared_error: 0.6829 - val_loss: 0.2918 - val_mean_squared_error: 0.2918\n",
      "Epoch 3/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.6990 - mean_squared_error: 0.6990\n",
      "Epoch 00003: val_loss did not improve from 0.14129\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.6946 - mean_squared_error: 0.6946 - val_loss: 0.1908 - val_mean_squared_error: 0.1908\n",
      "Epoch 4/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.5784 - mean_squared_error: 0.5784\n",
      "Epoch 00004: val_loss improved from 0.14129 to 0.12748, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.5702 - mean_squared_error: 0.5702 - val_loss: 0.1275 - val_mean_squared_error: 0.1275\n",
      "Epoch 5/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.4519 - mean_squared_error: 0.4519\n",
      "Epoch 00005: val_loss improved from 0.12748 to 0.12426, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.4490 - mean_squared_error: 0.4490 - val_loss: 0.1243 - val_mean_squared_error: 0.1243\n",
      "Epoch 6/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.4091 - mean_squared_error: 0.4091\n",
      "Epoch 00006: val_loss improved from 0.12426 to 0.11470, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.4074 - mean_squared_error: 0.4074 - val_loss: 0.1147 - val_mean_squared_error: 0.1147\n",
      "Epoch 7/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.3569 - mean_squared_error: 0.3569\n",
      "Epoch 00007: val_loss improved from 0.11470 to 0.09082, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.3552 - mean_squared_error: 0.3552 - val_loss: 0.0908 - val_mean_squared_error: 0.0908\n",
      "Epoch 8/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.3139 - mean_squared_error: 0.3139\n",
      "Epoch 00008: val_loss improved from 0.09082 to 0.06873, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.3142 - mean_squared_error: 0.3142 - val_loss: 0.0687 - val_mean_squared_error: 0.0687\n",
      "Epoch 9/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.2825 - mean_squared_error: 0.2825\n",
      "Epoch 00009: val_loss improved from 0.06873 to 0.06482, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.2809 - mean_squared_error: 0.2809 - val_loss: 0.0648 - val_mean_squared_error: 0.0648\n",
      "Epoch 10/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.2572 - mean_squared_error: 0.2572\n",
      "Epoch 00010: val_loss improved from 0.06482 to 0.06029, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.2560 - mean_squared_error: 0.2560 - val_loss: 0.0603 - val_mean_squared_error: 0.0603\n",
      "Epoch 11/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.2316 - mean_squared_error: 0.2316\n",
      "Epoch 00011: val_loss improved from 0.06029 to 0.05555, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.2304 - mean_squared_error: 0.2304 - val_loss: 0.0555 - val_mean_squared_error: 0.0555\n",
      "Epoch 12/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.2139 - mean_squared_error: 0.2139\n",
      "Epoch 00012: val_loss improved from 0.05555 to 0.05433, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.2130 - mean_squared_error: 0.2130 - val_loss: 0.0543 - val_mean_squared_error: 0.0543\n",
      "Epoch 13/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.1963 - mean_squared_error: 0.1963\n",
      "Epoch 00013: val_loss improved from 0.05433 to 0.04927, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.1956 - mean_squared_error: 0.1956 - val_loss: 0.0493 - val_mean_squared_error: 0.0493\n",
      "Epoch 14/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.1793 - mean_squared_error: 0.1793\n",
      "Epoch 00014: val_loss improved from 0.04927 to 0.04852, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.1784 - mean_squared_error: 0.1784 - val_loss: 0.0485 - val_mean_squared_error: 0.0485\n",
      "Epoch 15/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.1660 - mean_squared_error: 0.1660\n",
      "Epoch 00015: val_loss improved from 0.04852 to 0.04599, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.1654 - mean_squared_error: 0.1654 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
      "Epoch 16/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.1541 - mean_squared_error: 0.1541\n",
      "Epoch 00016: val_loss did not improve from 0.04599\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.1536 - mean_squared_error: 0.1536 - val_loss: 0.0471 - val_mean_squared_error: 0.0471\n",
      "Epoch 17/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.1430 - mean_squared_error: 0.1430\n",
      "Epoch 00017: val_loss improved from 0.04599 to 0.04582, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.1422 - mean_squared_error: 0.1422 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
      "Epoch 18/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.1333 - mean_squared_error: 0.1333\n",
      "Epoch 00018: val_loss did not improve from 0.04582\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.1327 - mean_squared_error: 0.1327 - val_loss: 0.0484 - val_mean_squared_error: 0.0484\n",
      "Epoch 19/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.1244 - mean_squared_error: 0.1244\n",
      "Epoch 00019: val_loss did not improve from 0.04582\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.1237 - mean_squared_error: 0.1237 - val_loss: 0.0489 - val_mean_squared_error: 0.0489\n",
      "Epoch 20/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.1162 - mean_squared_error: 0.1162\n",
      "Epoch 00020: val_loss did not improve from 0.04582\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.1159 - mean_squared_error: 0.1159 - val_loss: 0.0483 - val_mean_squared_error: 0.0483\n",
      "Epoch 21/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.1074 - mean_squared_error: 0.1074\n",
      "Epoch 00021: val_loss improved from 0.04582 to 0.04579, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.1070 - mean_squared_error: 0.1070 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
      "Epoch 22/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.1019 - mean_squared_error: 0.1019\n",
      "Epoch 00022: val_loss improved from 0.04579 to 0.04422, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.1011 - mean_squared_error: 0.1011 - val_loss: 0.0442 - val_mean_squared_error: 0.0442\n",
      "Epoch 23/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0959 - mean_squared_error: 0.0959\n",
      "Epoch 00023: val_loss improved from 0.04422 to 0.04421, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0959 - mean_squared_error: 0.0959 - val_loss: 0.0442 - val_mean_squared_error: 0.0442\n",
      "Epoch 24/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0905 - mean_squared_error: 0.0905\n",
      "Epoch 00024: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0904 - mean_squared_error: 0.0904 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
      "Epoch 25/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0838 - mean_squared_error: 0.0838\n",
      "Epoch 00025: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0836 - mean_squared_error: 0.0836 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
      "Epoch 26/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0803 - mean_squared_error: 0.0803\n",
      "Epoch 00026: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0805 - mean_squared_error: 0.0805 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
      "Epoch 27/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0771 - mean_squared_error: 0.0771\n",
      "Epoch 00027: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0771 - mean_squared_error: 0.0771 - val_loss: 0.0447 - val_mean_squared_error: 0.0447\n",
      "Epoch 28/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0734 - mean_squared_error: 0.0734\n",
      "Epoch 00028: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0734 - mean_squared_error: 0.0734 - val_loss: 0.0450 - val_mean_squared_error: 0.0450\n",
      "Epoch 29/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0711 - mean_squared_error: 0.0711\n",
      "Epoch 00029: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0710 - mean_squared_error: 0.0710 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
      "Epoch 30/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0674 - mean_squared_error: 0.0674\n",
      "Epoch 00030: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0672 - mean_squared_error: 0.0672 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
      "Epoch 31/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0664 - mean_squared_error: 0.0664\n",
      "Epoch 00031: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0660 - mean_squared_error: 0.0660 - val_loss: 0.0465 - val_mean_squared_error: 0.0465\n",
      "Epoch 32/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0627 - mean_squared_error: 0.0627\n",
      "Epoch 00032: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0628 - mean_squared_error: 0.0628 - val_loss: 0.0484 - val_mean_squared_error: 0.0484\n",
      "Epoch 33/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0610 - mean_squared_error: 0.0610\n",
      "Epoch 00033: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0609 - mean_squared_error: 0.0609 - val_loss: 0.0479 - val_mean_squared_error: 0.0479\n",
      "Epoch 34/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0590 - mean_squared_error: 0.0590\n",
      "Epoch 00034: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0588 - mean_squared_error: 0.0588 - val_loss: 0.0470 - val_mean_squared_error: 0.0470\n",
      "Epoch 35/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0582 - mean_squared_error: 0.0582\n",
      "Epoch 00035: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0580 - mean_squared_error: 0.0580 - val_loss: 0.0485 - val_mean_squared_error: 0.0485\n",
      "Epoch 36/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0553 - mean_squared_error: 0.0553\n",
      "Epoch 00036: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0551 - mean_squared_error: 0.0551 - val_loss: 0.0494 - val_mean_squared_error: 0.0494\n",
      "Epoch 37/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0537 - mean_squared_error: 0.0537\n",
      "Epoch 00037: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 6s 1ms/sample - loss: 0.0538 - mean_squared_error: 0.0538 - val_loss: 0.0490 - val_mean_squared_error: 0.0490\n",
      "Epoch 38/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0527 - mean_squared_error: 0.0527\n",
      "Epoch 00038: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 6s 1ms/sample - loss: 0.0526 - mean_squared_error: 0.0526 - val_loss: 0.0485 - val_mean_squared_error: 0.0485\n",
      "Epoch 39/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0511 - mean_squared_error: 0.0511\n",
      "Epoch 00039: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 6s 1ms/sample - loss: 0.0510 - mean_squared_error: 0.0510 - val_loss: 0.0484 - val_mean_squared_error: 0.0484\n",
      "Epoch 40/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0498 - mean_squared_error: 0.0498\n",
      "Epoch 00040: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 6s 1ms/sample - loss: 0.0500 - mean_squared_error: 0.0500 - val_loss: 0.0477 - val_mean_squared_error: 0.0477\n",
      "Epoch 41/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0491 - mean_squared_error: 0.0491\n",
      "Epoch 00041: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 6s 1ms/sample - loss: 0.0488 - mean_squared_error: 0.0488 - val_loss: 0.0468 - val_mean_squared_error: 0.0468\n",
      "Epoch 42/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0472 - mean_squared_error: 0.0472\n",
      "Epoch 00042: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 6s 1ms/sample - loss: 0.0471 - mean_squared_error: 0.0471 - val_loss: 0.0464 - val_mean_squared_error: 0.0464\n",
      "Epoch 43/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0464 - mean_squared_error: 0.0464\n",
      "Epoch 00043: val_loss did not improve from 0.04421\n",
      "5513/5513 [==============================] - 6s 1ms/sample - loss: 0.0466 - mean_squared_error: 0.0466 - val_loss: 0.0448 - val_mean_squared_error: 0.0448\n",
      "Epoch 44/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0456 - mean_squared_error: 0.0456\n",
      "Epoch 00044: val_loss improved from 0.04421 to 0.04378, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 6s 1ms/sample - loss: 0.0456 - mean_squared_error: 0.0456 - val_loss: 0.0438 - val_mean_squared_error: 0.0438\n",
      "Epoch 45/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0455 - mean_squared_error: 0.0455\n",
      "Epoch 00045: val_loss improved from 0.04378 to 0.04300, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 5s 959us/sample - loss: 0.0454 - mean_squared_error: 0.0454 - val_loss: 0.0430 - val_mean_squared_error: 0.0430\n",
      "Epoch 46/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0438 - mean_squared_error: 0.0438\n",
      "Epoch 00046: val_loss improved from 0.04300 to 0.04205, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 5s 963us/sample - loss: 0.0440 - mean_squared_error: 0.0440 - val_loss: 0.0420 - val_mean_squared_error: 0.0420\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 47/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0437 - mean_squared_error: 0.0437\n",
      "Epoch 00047: val_loss improved from 0.04205 to 0.04104, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0439 - mean_squared_error: 0.0439 - val_loss: 0.0410 - val_mean_squared_error: 0.0410\n",
      "Epoch 48/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0424 - mean_squared_error: 0.0424\n",
      "Epoch 00048: val_loss improved from 0.04104 to 0.03973, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0424 - mean_squared_error: 0.0424 - val_loss: 0.0397 - val_mean_squared_error: 0.0397\n",
      "Epoch 49/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0411 - mean_squared_error: 0.0411\n",
      "Epoch 00049: val_loss improved from 0.03973 to 0.03869, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0410 - mean_squared_error: 0.0410 - val_loss: 0.0387 - val_mean_squared_error: 0.0387\n",
      "Epoch 50/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0411 - mean_squared_error: 0.0411\n",
      "Epoch 00050: val_loss did not improve from 0.03869\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0411 - mean_squared_error: 0.0411 - val_loss: 0.0389 - val_mean_squared_error: 0.0389\n",
      "Epoch 51/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0404 - mean_squared_error: 0.0404\n",
      "Epoch 00051: val_loss improved from 0.03869 to 0.03861, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0403 - mean_squared_error: 0.0403 - val_loss: 0.0386 - val_mean_squared_error: 0.0386\n",
      "Epoch 52/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0391 - mean_squared_error: 0.0391\n",
      "Epoch 00052: val_loss improved from 0.03861 to 0.03617, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0391 - mean_squared_error: 0.0391 - val_loss: 0.0362 - val_mean_squared_error: 0.0362\n",
      "Epoch 53/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0399 - mean_squared_error: 0.0399\n",
      "Epoch 00053: val_loss improved from 0.03617 to 0.03524, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0397 - mean_squared_error: 0.0397 - val_loss: 0.0352 - val_mean_squared_error: 0.0352\n",
      "Epoch 54/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0381 - mean_squared_error: 0.0381\n",
      "Epoch 00054: val_loss did not improve from 0.03524\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0382 - mean_squared_error: 0.0382 - val_loss: 0.0353 - val_mean_squared_error: 0.0353\n",
      "Epoch 55/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0378 - mean_squared_error: 0.0378\n",
      "Epoch 00055: val_loss did not improve from 0.03524\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0375 - mean_squared_error: 0.0375 - val_loss: 0.0356 - val_mean_squared_error: 0.0356\n",
      "Epoch 56/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0373 - mean_squared_error: 0.0373\n",
      "Epoch 00056: val_loss improved from 0.03524 to 0.03450, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0371 - mean_squared_error: 0.0371 - val_loss: 0.0345 - val_mean_squared_error: 0.0345\n",
      "Epoch 57/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0363 - mean_squared_error: 0.0363\n",
      "Epoch 00057: val_loss improved from 0.03450 to 0.03430, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0364 - mean_squared_error: 0.0364 - val_loss: 0.0343 - val_mean_squared_error: 0.0343\n",
      "Epoch 58/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0361 - mean_squared_error: 0.0361\n",
      "Epoch 00058: val_loss improved from 0.03430 to 0.03330, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0360 - mean_squared_error: 0.0360 - val_loss: 0.0333 - val_mean_squared_error: 0.0333\n",
      "Epoch 59/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0349 - mean_squared_error: 0.0349\n",
      "Epoch 00059: val_loss improved from 0.03330 to 0.03296, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0350 - mean_squared_error: 0.0350 - val_loss: 0.0330 - val_mean_squared_error: 0.0330\n",
      "Epoch 60/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0350 - mean_squared_error: 0.0350\n",
      "Epoch 00060: val_loss improved from 0.03296 to 0.03266, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0350 - mean_squared_error: 0.0350 - val_loss: 0.0327 - val_mean_squared_error: 0.0327\n",
      "Epoch 61/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0348 - mean_squared_error: 0.0348\n",
      "Epoch 00061: val_loss improved from 0.03266 to 0.03088, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0347 - mean_squared_error: 0.0347 - val_loss: 0.0309 - val_mean_squared_error: 0.0309\n",
      "Epoch 62/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0342 - mean_squared_error: 0.0342\n",
      "Epoch 00062: val_loss improved from 0.03088 to 0.03082, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0339 - mean_squared_error: 0.0339 - val_loss: 0.0308 - val_mean_squared_error: 0.0308\n",
      "Epoch 63/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0329 - mean_squared_error: 0.0329\n",
      "Epoch 00063: val_loss did not improve from 0.03082\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0330 - mean_squared_error: 0.0330 - val_loss: 0.0309 - val_mean_squared_error: 0.0309\n",
      "Epoch 64/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0324 - mean_squared_error: 0.0324\n",
      "Epoch 00064: val_loss improved from 0.03082 to 0.03033, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0324 - mean_squared_error: 0.0324 - val_loss: 0.0303 - val_mean_squared_error: 0.0303\n",
      "Epoch 65/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0322 - mean_squared_error: 0.0322\n",
      "Epoch 00065: val_loss improved from 0.03033 to 0.02919, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0321 - mean_squared_error: 0.0321 - val_loss: 0.0292 - val_mean_squared_error: 0.0292\n",
      "Epoch 66/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0319 - mean_squared_error: 0.0319\n",
      "Epoch 00066: val_loss improved from 0.02919 to 0.02889, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0320 - mean_squared_error: 0.0320 - val_loss: 0.0289 - val_mean_squared_error: 0.0289\n",
      "Epoch 67/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0312 - mean_squared_error: 0.0312\n",
      "Epoch 00067: val_loss improved from 0.02889 to 0.02838, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0311 - mean_squared_error: 0.0311 - val_loss: 0.0284 - val_mean_squared_error: 0.0284\n",
      "Epoch 68/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0309 - mean_squared_error: 0.0309\n",
      "Epoch 00068: val_loss improved from 0.02838 to 0.02766, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0308 - mean_squared_error: 0.0308 - val_loss: 0.0277 - val_mean_squared_error: 0.0277\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 69/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0302 - mean_squared_error: 0.0302\n",
      "Epoch 00069: val_loss improved from 0.02766 to 0.02757, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0301 - mean_squared_error: 0.0301 - val_loss: 0.0276 - val_mean_squared_error: 0.0276\n",
      "Epoch 70/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0298 - mean_squared_error: 0.0298\n",
      "Epoch 00070: val_loss improved from 0.02757 to 0.02636, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0297 - mean_squared_error: 0.0297 - val_loss: 0.0264 - val_mean_squared_error: 0.0264\n",
      "Epoch 71/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0290 - mean_squared_error: 0.0290\n",
      "Epoch 00071: val_loss improved from 0.02636 to 0.02570, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0291 - mean_squared_error: 0.0291 - val_loss: 0.0257 - val_mean_squared_error: 0.0257\n",
      "Epoch 72/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0289 - mean_squared_error: 0.0289\n",
      "Epoch 00072: val_loss improved from 0.02570 to 0.02526, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0288 - mean_squared_error: 0.0288 - val_loss: 0.0253 - val_mean_squared_error: 0.0253\n",
      "Epoch 73/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0282 - mean_squared_error: 0.0282\n",
      "Epoch 00073: val_loss improved from 0.02526 to 0.02459, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0282 - mean_squared_error: 0.0282 - val_loss: 0.0246 - val_mean_squared_error: 0.0246\n",
      "Epoch 74/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0283 - mean_squared_error: 0.0283\n",
      "Epoch 00074: val_loss improved from 0.02459 to 0.02415, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0283 - mean_squared_error: 0.0283 - val_loss: 0.0241 - val_mean_squared_error: 0.0241\n",
      "Epoch 75/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0275 - mean_squared_error: 0.0275\n",
      "Epoch 00075: val_loss improved from 0.02415 to 0.02329, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0273 - mean_squared_error: 0.0273 - val_loss: 0.0233 - val_mean_squared_error: 0.0233\n",
      "Epoch 76/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0277 - mean_squared_error: 0.0277\n",
      "Epoch 00076: val_loss improved from 0.02329 to 0.02298, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0276 - mean_squared_error: 0.0276 - val_loss: 0.0230 - val_mean_squared_error: 0.0230\n",
      "Epoch 77/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0274 - mean_squared_error: 0.0274\n",
      "Epoch 00077: val_loss improved from 0.02298 to 0.02248, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0274 - mean_squared_error: 0.0274 - val_loss: 0.0225 - val_mean_squared_error: 0.0225\n",
      "Epoch 78/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0269 - mean_squared_error: 0.0269\n",
      "Epoch 00078: val_loss improved from 0.02248 to 0.02152, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0270 - mean_squared_error: 0.0270 - val_loss: 0.0215 - val_mean_squared_error: 0.0215\n",
      "Epoch 79/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0262 - mean_squared_error: 0.0262\n",
      "Epoch 00079: val_loss improved from 0.02152 to 0.02144, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0262 - mean_squared_error: 0.0262 - val_loss: 0.0214 - val_mean_squared_error: 0.0214\n",
      "Epoch 80/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0263 - mean_squared_error: 0.0263\n",
      "Epoch 00080: val_loss improved from 0.02144 to 0.02067, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0262 - mean_squared_error: 0.0262 - val_loss: 0.0207 - val_mean_squared_error: 0.0207\n",
      "Epoch 81/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0258 - mean_squared_error: 0.0258\n",
      "Epoch 00081: val_loss improved from 0.02067 to 0.02032, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0257 - mean_squared_error: 0.0257 - val_loss: 0.0203 - val_mean_squared_error: 0.0203\n",
      "Epoch 82/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0254 - mean_squared_error: 0.0254\n",
      "Epoch 00082: val_loss improved from 0.02032 to 0.02024, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0254 - mean_squared_error: 0.0254 - val_loss: 0.0202 - val_mean_squared_error: 0.0202\n",
      "Epoch 83/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0253 - mean_squared_error: 0.0253\n",
      "Epoch 00083: val_loss improved from 0.02024 to 0.01969, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0253 - mean_squared_error: 0.0253 - val_loss: 0.0197 - val_mean_squared_error: 0.0197\n",
      "Epoch 84/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0247 - mean_squared_error: 0.0247\n",
      "Epoch 00084: val_loss improved from 0.01969 to 0.01867, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0247 - mean_squared_error: 0.0247 - val_loss: 0.0187 - val_mean_squared_error: 0.0187\n",
      "Epoch 85/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0238 - mean_squared_error: 0.0238\n",
      "Epoch 00085: val_loss improved from 0.01867 to 0.01832, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0240 - mean_squared_error: 0.0240 - val_loss: 0.0183 - val_mean_squared_error: 0.0183\n",
      "Epoch 86/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0243 - mean_squared_error: 0.0243\n",
      "Epoch 00086: val_loss improved from 0.01832 to 0.01800, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0244 - mean_squared_error: 0.0244 - val_loss: 0.0180 - val_mean_squared_error: 0.0180\n",
      "Epoch 87/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0236 - mean_squared_error: 0.0236\n",
      "Epoch 00087: val_loss improved from 0.01800 to 0.01745, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0237 - mean_squared_error: 0.0237 - val_loss: 0.0174 - val_mean_squared_error: 0.0174\n",
      "Epoch 88/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0231 - mean_squared_error: 0.0231\n",
      "Epoch 00088: val_loss improved from 0.01745 to 0.01708, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0231 - mean_squared_error: 0.0231 - val_loss: 0.0171 - val_mean_squared_error: 0.0171\n",
      "Epoch 89/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0231 - mean_squared_error: 0.0231\n",
      "Epoch 00089: val_loss did not improve from 0.01708\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0230 - mean_squared_error: 0.0230 - val_loss: 0.0171 - val_mean_squared_error: 0.0171\n",
      "Epoch 90/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0225 - mean_squared_error: 0.0225\n",
      "Epoch 00090: val_loss improved from 0.01708 to 0.01637, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0225 - mean_squared_error: 0.0225 - val_loss: 0.0164 - val_mean_squared_error: 0.0164\n",
      "Epoch 91/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0224 - mean_squared_error: 0.0224\n",
      "Epoch 00091: val_loss improved from 0.01637 to 0.01608, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 2ms/sample - loss: 0.0223 - mean_squared_error: 0.0223 - val_loss: 0.0161 - val_mean_squared_error: 0.0161\n",
      "Epoch 92/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0218 - mean_squared_error: 0.0218\n",
      "Epoch 00092: val_loss improved from 0.01608 to 0.01546, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0218 - mean_squared_error: 0.0218 - val_loss: 0.0155 - val_mean_squared_error: 0.0155\n",
      "Epoch 93/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0216 - mean_squared_error: 0.0216\n",
      "Epoch 00093: val_loss improved from 0.01546 to 0.01516, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0215 - mean_squared_error: 0.0215 - val_loss: 0.0152 - val_mean_squared_error: 0.0152\n",
      "Epoch 94/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0215 - mean_squared_error: 0.0215\n",
      "Epoch 00094: val_loss did not improve from 0.01516\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0215 - mean_squared_error: 0.0215 - val_loss: 0.0153 - val_mean_squared_error: 0.0153\n",
      "Epoch 95/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0212 - mean_squared_error: 0.0212\n",
      "Epoch 00095: val_loss improved from 0.01516 to 0.01461, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0211 - mean_squared_error: 0.0211 - val_loss: 0.0146 - val_mean_squared_error: 0.0146\n",
      "Epoch 96/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0212 - mean_squared_error: 0.0212\n",
      "Epoch 00096: val_loss improved from 0.01461 to 0.01461, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0212 - mean_squared_error: 0.0212 - val_loss: 0.0146 - val_mean_squared_error: 0.0146\n",
      "Epoch 97/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0209 - mean_squared_error: 0.0209\n",
      "Epoch 00097: val_loss did not improve from 0.01461\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0209 - mean_squared_error: 0.0209 - val_loss: 0.0147 - val_mean_squared_error: 0.0147\n",
      "Epoch 98/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0207 - mean_squared_error: 0.0207\n",
      "Epoch 00098: val_loss improved from 0.01461 to 0.01401, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0207 - mean_squared_error: 0.0207 - val_loss: 0.0140 - val_mean_squared_error: 0.0140\n",
      "Epoch 99/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0201 - mean_squared_error: 0.0201\n",
      "Epoch 00099: val_loss improved from 0.01401 to 0.01356, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0202 - mean_squared_error: 0.0202 - val_loss: 0.0136 - val_mean_squared_error: 0.0136\n",
      "Epoch 100/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0199 - mean_squared_error: 0.0199\n",
      "Epoch 00100: val_loss did not improve from 0.01356\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0200 - mean_squared_error: 0.0200 - val_loss: 0.0137 - val_mean_squared_error: 0.0137\n",
      "Epoch 101/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0199 - mean_squared_error: 0.0199\n",
      "Epoch 00101: val_loss did not improve from 0.01356\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0198 - mean_squared_error: 0.0198 - val_loss: 0.0136 - val_mean_squared_error: 0.0136\n",
      "Epoch 102/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0196 - mean_squared_error: 0.0196\n",
      "Epoch 00102: val_loss improved from 0.01356 to 0.01305, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0195 - mean_squared_error: 0.0195 - val_loss: 0.0131 - val_mean_squared_error: 0.0131\n",
      "Epoch 103/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0193 - mean_squared_error: 0.0193\n",
      "Epoch 00103: val_loss did not improve from 0.01305\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0194 - mean_squared_error: 0.0194 - val_loss: 0.0131 - val_mean_squared_error: 0.0131\n",
      "Epoch 104/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0191 - mean_squared_error: 0.0191\n",
      "Epoch 00104: val_loss improved from 0.01305 to 0.01289, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0191 - mean_squared_error: 0.0191 - val_loss: 0.0129 - val_mean_squared_error: 0.0129\n",
      "Epoch 105/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0187 - mean_squared_error: 0.0187\n",
      "Epoch 00105: val_loss improved from 0.01289 to 0.01253, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0187 - mean_squared_error: 0.0187 - val_loss: 0.0125 - val_mean_squared_error: 0.0125\n",
      "Epoch 106/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0185 - mean_squared_error: 0.0185\n",
      "Epoch 00106: val_loss improved from 0.01253 to 0.01248, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0185 - mean_squared_error: 0.0185 - val_loss: 0.0125 - val_mean_squared_error: 0.0125\n",
      "Epoch 107/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0183 - mean_squared_error: 0.0183\n",
      "Epoch 00107: val_loss improved from 0.01248 to 0.01235, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 2ms/sample - loss: 0.0184 - mean_squared_error: 0.0184 - val_loss: 0.0124 - val_mean_squared_error: 0.0124\n",
      "Epoch 108/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0178 - mean_squared_error: 0.0178\n",
      "Epoch 00108: val_loss did not improve from 0.01235\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0178 - mean_squared_error: 0.0178 - val_loss: 0.0124 - val_mean_squared_error: 0.0124\n",
      "Epoch 109/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0184 - mean_squared_error: 0.0184\n",
      "Epoch 00109: val_loss did not improve from 0.01235\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0184 - mean_squared_error: 0.0184 - val_loss: 0.0124 - val_mean_squared_error: 0.0124\n",
      "Epoch 110/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0178 - mean_squared_error: 0.0178\n",
      "Epoch 00110: val_loss did not improve from 0.01235\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0179 - mean_squared_error: 0.0179 - val_loss: 0.0124 - val_mean_squared_error: 0.0124\n",
      "Epoch 111/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0176 - mean_squared_error: 0.0176\n",
      "Epoch 00111: val_loss did not improve from 0.01235\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0175 - mean_squared_error: 0.0175 - val_loss: 0.0124 - val_mean_squared_error: 0.0124\n",
      "Epoch 112/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0174 - mean_squared_error: 0.0174\n",
      "Epoch 00112: val_loss improved from 0.01235 to 0.01213, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0174 - mean_squared_error: 0.0174 - val_loss: 0.0121 - val_mean_squared_error: 0.0121\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 113/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0176 - mean_squared_error: 0.0176\n",
      "Epoch 00113: val_loss improved from 0.01213 to 0.01194, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0176 - mean_squared_error: 0.0176 - val_loss: 0.0119 - val_mean_squared_error: 0.0119\n",
      "Epoch 114/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0174 - mean_squared_error: 0.0174\n",
      "Epoch 00114: val_loss did not improve from 0.01194\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0173 - mean_squared_error: 0.0173 - val_loss: 0.0123 - val_mean_squared_error: 0.0123\n",
      "Epoch 115/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0173 - mean_squared_error: 0.0173\n",
      "Epoch 00115: val_loss improved from 0.01194 to 0.01190, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0172 - mean_squared_error: 0.0172 - val_loss: 0.0119 - val_mean_squared_error: 0.0119\n",
      "Epoch 116/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0169 - mean_squared_error: 0.0169\n",
      "Epoch 00116: val_loss improved from 0.01190 to 0.01174, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0169 - mean_squared_error: 0.0169 - val_loss: 0.0117 - val_mean_squared_error: 0.0117\n",
      "Epoch 117/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0160 - mean_squared_error: 0.0160\n",
      "Epoch 00117: val_loss did not improve from 0.01174\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0160 - mean_squared_error: 0.0160 - val_loss: 0.0121 - val_mean_squared_error: 0.0121\n",
      "Epoch 118/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0165 - mean_squared_error: 0.0165\n",
      "Epoch 00118: val_loss did not improve from 0.01174\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0165 - mean_squared_error: 0.0165 - val_loss: 0.0120 - val_mean_squared_error: 0.0120\n",
      "Epoch 119/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0164 - mean_squared_error: 0.0164\n",
      "Epoch 00119: val_loss improved from 0.01174 to 0.01157, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0164 - mean_squared_error: 0.0164 - val_loss: 0.0116 - val_mean_squared_error: 0.0116\n",
      "Epoch 120/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0162 - mean_squared_error: 0.0162\n",
      "Epoch 00120: val_loss improved from 0.01157 to 0.01132, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0163 - mean_squared_error: 0.0163 - val_loss: 0.0113 - val_mean_squared_error: 0.0113\n",
      "Epoch 121/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0159 - mean_squared_error: 0.0159\n",
      "Epoch 00121: val_loss improved from 0.01132 to 0.01085, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0159 - mean_squared_error: 0.0159 - val_loss: 0.0109 - val_mean_squared_error: 0.0109\n",
      "Epoch 122/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0158 - mean_squared_error: 0.0158\n",
      "Epoch 00122: val_loss did not improve from 0.01085\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0157 - mean_squared_error: 0.0157 - val_loss: 0.0111 - val_mean_squared_error: 0.0111\n",
      "Epoch 123/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0157 - mean_squared_error: 0.0157\n",
      "Epoch 00123: val_loss did not improve from 0.01085\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0157 - mean_squared_error: 0.0157 - val_loss: 0.0110 - val_mean_squared_error: 0.0110\n",
      "Epoch 124/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0156 - mean_squared_error: 0.0156\n",
      "Epoch 00124: val_loss did not improve from 0.01085\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0156 - mean_squared_error: 0.0156 - val_loss: 0.0109 - val_mean_squared_error: 0.0109\n",
      "Epoch 125/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0155 - mean_squared_error: 0.0155\n",
      "Epoch 00125: val_loss did not improve from 0.01085\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0155 - mean_squared_error: 0.0155 - val_loss: 0.0111 - val_mean_squared_error: 0.0111\n",
      "Epoch 126/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0154 - mean_squared_error: 0.0154\n",
      "Epoch 00126: val_loss did not improve from 0.01085\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0154 - mean_squared_error: 0.0154 - val_loss: 0.0111 - val_mean_squared_error: 0.0111\n",
      "Epoch 127/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0149 - mean_squared_error: 0.0149\n",
      "Epoch 00127: val_loss did not improve from 0.01085\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0150 - mean_squared_error: 0.0150 - val_loss: 0.0111 - val_mean_squared_error: 0.0111\n",
      "Epoch 128/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0150 - mean_squared_error: 0.0150\n",
      "Epoch 00128: val_loss improved from 0.01085 to 0.01069, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0149 - mean_squared_error: 0.0149 - val_loss: 0.0107 - val_mean_squared_error: 0.0107\n",
      "Epoch 129/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0148 - mean_squared_error: 0.0148\n",
      "Epoch 00129: val_loss did not improve from 0.01069\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0148 - mean_squared_error: 0.0148 - val_loss: 0.0108 - val_mean_squared_error: 0.0108\n",
      "Epoch 130/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0148 - mean_squared_error: 0.0148\n",
      "Epoch 00130: val_loss did not improve from 0.01069\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0148 - mean_squared_error: 0.0148 - val_loss: 0.0109 - val_mean_squared_error: 0.0109\n",
      "Epoch 131/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0144 - mean_squared_error: 0.0144\n",
      "Epoch 00131: val_loss did not improve from 0.01069\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0144 - mean_squared_error: 0.0144 - val_loss: 0.0107 - val_mean_squared_error: 0.0107\n",
      "Epoch 132/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0145 - mean_squared_error: 0.0145\n",
      "Epoch 00132: val_loss did not improve from 0.01069\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0145 - mean_squared_error: 0.0145 - val_loss: 0.0109 - val_mean_squared_error: 0.0109\n",
      "Epoch 133/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0147 - mean_squared_error: 0.0147\n",
      "Epoch 00133: val_loss did not improve from 0.01069\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0146 - mean_squared_error: 0.0146 - val_loss: 0.0109 - val_mean_squared_error: 0.0109\n",
      "Epoch 134/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0143 - mean_squared_error: 0.0143\n",
      "Epoch 00134: val_loss did not improve from 0.01069\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0143 - mean_squared_error: 0.0143 - val_loss: 0.0109 - val_mean_squared_error: 0.0109\n",
      "Epoch 135/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0141 - mean_squared_error: 0.0141\n",
      "Epoch 00135: val_loss did not improve from 0.01069\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0141 - mean_squared_error: 0.0141 - val_loss: 0.0109 - val_mean_squared_error: 0.0109\n",
      "Epoch 136/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0140 - mean_squared_error: 0.0140\n",
      "Epoch 00136: val_loss did not improve from 0.01069\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0141 - mean_squared_error: 0.0141 - val_loss: 0.0110 - val_mean_squared_error: 0.0110\n",
      "Epoch 137/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0141 - mean_squared_error: 0.0141\n",
      "Epoch 00137: val_loss did not improve from 0.01069\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0141 - mean_squared_error: 0.0141 - val_loss: 0.0109 - val_mean_squared_error: 0.0109\n",
      "Epoch 138/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0139 - mean_squared_error: 0.0139\n",
      "Epoch 00138: val_loss did not improve from 0.01069\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0138 - mean_squared_error: 0.0138 - val_loss: 0.0110 - val_mean_squared_error: 0.0110\n",
      "Epoch 139/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0143 - mean_squared_error: 0.0143\n",
      "Epoch 00139: val_loss did not improve from 0.01069\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0143 - mean_squared_error: 0.0143 - val_loss: 0.0110 - val_mean_squared_error: 0.0110\n",
      "Epoch 140/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0137 - mean_squared_error: 0.0137\n",
      "Epoch 00140: val_loss did not improve from 0.01069\n",
      "5513/5513 [==============================] - 8s 2ms/sample - loss: 0.0137 - mean_squared_error: 0.0137 - val_loss: 0.0108 - val_mean_squared_error: 0.0108\n",
      "Epoch 141/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0139 - mean_squared_error: 0.0139\n",
      "Epoch 00141: val_loss improved from 0.01069 to 0.01064, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 2ms/sample - loss: 0.0139 - mean_squared_error: 0.0139 - val_loss: 0.0106 - val_mean_squared_error: 0.0106\n",
      "Epoch 142/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0136 - mean_squared_error: 0.0136\n",
      "Epoch 00142: val_loss improved from 0.01064 to 0.01060, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 9s 2ms/sample - loss: 0.0136 - mean_squared_error: 0.0136 - val_loss: 0.0106 - val_mean_squared_error: 0.0106\n",
      "Epoch 143/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0137 - mean_squared_error: 0.0137\n",
      "Epoch 00143: val_loss improved from 0.01060 to 0.01059, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 9s 2ms/sample - loss: 0.0137 - mean_squared_error: 0.0137 - val_loss: 0.0106 - val_mean_squared_error: 0.0106\n",
      "Epoch 144/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0134 - mean_squared_error: 0.0134\n",
      "Epoch 00144: val_loss did not improve from 0.01059\n",
      "5513/5513 [==============================] - 9s 2ms/sample - loss: 0.0133 - mean_squared_error: 0.0133 - val_loss: 0.0107 - val_mean_squared_error: 0.0107\n",
      "Epoch 145/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0136 - mean_squared_error: 0.0136\n",
      "Epoch 00145: val_loss did not improve from 0.01059\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0136 - mean_squared_error: 0.0136 - val_loss: 0.0107 - val_mean_squared_error: 0.0107\n",
      "Epoch 146/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0132 - mean_squared_error: 0.0132\n",
      "Epoch 00146: val_loss did not improve from 0.01059\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0132 - mean_squared_error: 0.0132 - val_loss: 0.0107 - val_mean_squared_error: 0.0107\n",
      "Epoch 147/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0131 - mean_squared_error: 0.0131\n",
      "Epoch 00147: val_loss did not improve from 0.01059\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0131 - mean_squared_error: 0.0131 - val_loss: 0.0106 - val_mean_squared_error: 0.0106\n",
      "Epoch 148/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0134 - mean_squared_error: 0.0134\n",
      "Epoch 00148: val_loss did not improve from 0.01059\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0134 - mean_squared_error: 0.0134 - val_loss: 0.0106 - val_mean_squared_error: 0.0106\n",
      "Epoch 149/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0131 - mean_squared_error: 0.0131\n",
      "Epoch 00149: val_loss improved from 0.01059 to 0.01056, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0131 - mean_squared_error: 0.0131 - val_loss: 0.0106 - val_mean_squared_error: 0.0106\n",
      "Epoch 150/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0131 - mean_squared_error: 0.0131\n",
      "Epoch 00150: val_loss improved from 0.01056 to 0.01050, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0132 - mean_squared_error: 0.0132 - val_loss: 0.0105 - val_mean_squared_error: 0.0105\n",
      "Epoch 151/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0133 - mean_squared_error: 0.0133\n",
      "Epoch 00151: val_loss improved from 0.01050 to 0.01039, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0132 - mean_squared_error: 0.0132 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 152/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0131 - mean_squared_error: 0.0131\n",
      "Epoch 00152: val_loss improved from 0.01039 to 0.01037, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0131 - mean_squared_error: 0.0131 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 153/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0132 - mean_squared_error: 0.0132\n",
      "Epoch 00153: val_loss did not improve from 0.01037\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0132 - mean_squared_error: 0.0132 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 154/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0130 - mean_squared_error: 0.0130\n",
      "Epoch 00154: val_loss did not improve from 0.01037\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0130 - mean_squared_error: 0.0130 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 155/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0132 - mean_squared_error: 0.0132\n",
      "Epoch 00155: val_loss did not improve from 0.01037\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0132 - mean_squared_error: 0.0132 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 156/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0132 - mean_squared_error: 0.0132\n",
      "Epoch 00156: val_loss did not improve from 0.01037\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0132 - mean_squared_error: 0.0132 - val_loss: 0.0105 - val_mean_squared_error: 0.0105\n",
      "Epoch 157/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0130 - mean_squared_error: 0.0130\n",
      "Epoch 00157: val_loss did not improve from 0.01037\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0130 - mean_squared_error: 0.0130 - val_loss: 0.0105 - val_mean_squared_error: 0.0105\n",
      "Epoch 158/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0130 - mean_squared_error: 0.0130\n",
      "Epoch 00158: val_loss did not improve from 0.01037\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0130 - mean_squared_error: 0.0130 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 159/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0129 - mean_squared_error: 0.0129\n",
      "Epoch 00159: val_loss did not improve from 0.01037\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0130 - mean_squared_error: 0.0130 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 160/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0127 - mean_squared_error: 0.0127\n",
      "Epoch 00160: val_loss did not improve from 0.01037\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0127 - mean_squared_error: 0.0127 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 161/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0127 - mean_squared_error: 0.0127\n",
      "Epoch 00161: val_loss did not improve from 0.01037\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0127 - mean_squared_error: 0.0127 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 162/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0128 - mean_squared_error: 0.0128\n",
      "Epoch 00162: val_loss did not improve from 0.01037\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0128 - mean_squared_error: 0.0128 - val_loss: 0.0105 - val_mean_squared_error: 0.0105\n",
      "Epoch 163/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0127 - mean_squared_error: 0.0127\n",
      "Epoch 00163: val_loss did not improve from 0.01037\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0127 - mean_squared_error: 0.0127 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 164/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0126 - mean_squared_error: 0.0126\n",
      "Epoch 00164: val_loss improved from 0.01037 to 0.01037, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0126 - mean_squared_error: 0.0126 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 165/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0127 - mean_squared_error: 0.0127\n",
      "Epoch 00165: val_loss improved from 0.01037 to 0.01034, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0126 - mean_squared_error: 0.0126 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 166/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0128 - mean_squared_error: 0.0128\n",
      "Epoch 00166: val_loss improved from 0.01034 to 0.01026, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0127 - mean_squared_error: 0.0127 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 167/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0123 - mean_squared_error: 0.0123\n",
      "Epoch 00167: val_loss improved from 0.01026 to 0.01024, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 2ms/sample - loss: 0.0124 - mean_squared_error: 0.0124 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 168/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0124 - mean_squared_error: 0.0124\n",
      "Epoch 00168: val_loss improved from 0.01024 to 0.01021, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0124 - mean_squared_error: 0.0124 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 169/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0125 - mean_squared_error: 0.0125\n",
      "Epoch 00169: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0125 - mean_squared_error: 0.0125 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 170/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0127 - mean_squared_error: 0.0127\n",
      "Epoch 00170: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0127 - mean_squared_error: 0.0127 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 171/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0125 - mean_squared_error: 0.0125\n",
      "Epoch 00171: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0124 - mean_squared_error: 0.0124 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 172/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0125 - mean_squared_error: 0.0125\n",
      "Epoch 00172: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0125 - mean_squared_error: 0.0125 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 173/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0122 - mean_squared_error: 0.0122\n",
      "Epoch 00173: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0122 - mean_squared_error: 0.0122 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 174/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0123 - mean_squared_error: 0.0123\n",
      "Epoch 00174: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0123 - mean_squared_error: 0.0123 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 175/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0123 - mean_squared_error: 0.0123\n",
      "Epoch 00175: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0123 - mean_squared_error: 0.0123 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 176/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0121 - mean_squared_error: 0.0121\n",
      "Epoch 00176: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0121 - mean_squared_error: 0.0121 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 177/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0123 - mean_squared_error: 0.0123\n",
      "Epoch 00177: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0123 - mean_squared_error: 0.0123 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 178/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0125 - mean_squared_error: 0.0125\n",
      "Epoch 00178: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0124 - mean_squared_error: 0.0124 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 179/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0122 - mean_squared_error: 0.0122\n",
      "Epoch 00179: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0121 - mean_squared_error: 0.0121 - val_loss: 0.0104 - val_mean_squared_error: 0.0104\n",
      "Epoch 180/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00180: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 181/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0123 - mean_squared_error: 0.0123\n",
      "Epoch 00181: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0123 - mean_squared_error: 0.0123 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 182/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0123 - mean_squared_error: 0.0123\n",
      "Epoch 00182: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0122 - mean_squared_error: 0.0122 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 183/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00183: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 184/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00184: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 185/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00185: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 186/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00186: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 187/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0121 - mean_squared_error: 0.0121\n",
      "Epoch 00187: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0121 - mean_squared_error: 0.0121 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 188/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00188: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0121 - mean_squared_error: 0.0121 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 189/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0121 - mean_squared_error: 0.0121\n",
      "Epoch 00189: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0121 - mean_squared_error: 0.0121 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 190/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0121 - mean_squared_error: 0.0121\n",
      "Epoch 00190: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 191/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0122 - mean_squared_error: 0.0122\n",
      "Epoch 00191: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0121 - mean_squared_error: 0.0121 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 192/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00192: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 193/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00193: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 194/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00194: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 195/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00195: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 196/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0121 - mean_squared_error: 0.0121\n",
      "Epoch 00196: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0121 - mean_squared_error: 0.0121 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 197/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00197: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 198/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00198: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 199/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00199: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 200/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0122 - mean_squared_error: 0.0122\n",
      "Epoch 00200: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0122 - mean_squared_error: 0.0122 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 201/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00201: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 202/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00202: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 203/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0121 - mean_squared_error: 0.0121\n",
      "Epoch 00203: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 204/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00204: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 205/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00205: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0121 - mean_squared_error: 0.0121 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 206/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00206: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 207/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00207: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 208/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00208: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 209/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00209: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 210/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00210: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 211/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00211: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 212/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0121 - mean_squared_error: 0.0121\n",
      "Epoch 00212: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0121 - mean_squared_error: 0.0121 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 213/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00213: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 214/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00214: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 215/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00215: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 216/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00216: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0103 - val_mean_squared_error: 0.0103\n",
      "Epoch 217/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00217: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 218/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00218: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 219/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00219: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 220/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00220: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 221/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00221: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 222/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00222: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 223/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00223: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 224/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00224: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 225/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00225: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 226/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00226: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 227/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00227: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 228/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00228: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 229/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00229: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 230/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00230: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 231/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00231: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 232/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00232: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 233/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00233: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 234/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00234: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0114 - mean_squared_error: 0.0114 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 235/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00235: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 236/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00236: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 237/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00237: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 238/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00238: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 239/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00239: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 240/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00240: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 241/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00241: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 242/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00242: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 243/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00243: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 244/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00244: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 245/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00245: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 246/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00246: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 247/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00247: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 248/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00248: val_loss improved from 0.01021 to 0.01021, saving model to saved_model/model_djia_support_nikkei_sync_period_V2.h5\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 249/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00249: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 250/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00250: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 251/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00251: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 252/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00252: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 253/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00253: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 254/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0121 - mean_squared_error: 0.0121\n",
      "Epoch 00254: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 255/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00255: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 256/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00256: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 257/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00257: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 258/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00258: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 259/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00259: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 260/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00260: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 261/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00261: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 262/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00262: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 263/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00263: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 264/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00264: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 265/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00265: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 266/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00266: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 267/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00267: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0114 - mean_squared_error: 0.0114 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 268/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00268: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 269/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00269: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 270/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00270: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 271/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00271: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 272/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00272: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 273/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00273: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 274/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00274: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 275/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00275: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 276/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00276: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 277/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00277: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 278/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00278: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 279/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00279: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 280/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00280: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 281/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00281: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 282/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00282: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 283/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00283: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 284/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00284: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 285/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00285: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 286/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00286: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 287/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00287: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 288/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00288: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 289/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00289: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 290/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00290: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 291/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00291: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 292/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00292: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 293/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00293: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 294/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00294: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 295/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00295: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 296/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00296: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 297/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00297: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 298/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00298: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 299/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00299: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 300/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00300: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 301/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00301: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 302/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00302: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 303/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00303: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 304/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00304: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 305/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00305: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 306/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00306: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 307/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00307: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 308/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00308: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 309/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00309: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 310/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00310: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 311/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00311: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 312/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00312: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 313/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00313: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 314/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00314: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 315/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00315: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 316/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00316: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 317/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00317: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 318/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00318: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 319/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00319: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 320/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00320: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 321/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00321: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 322/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00322: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 323/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00323: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 324/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0114 - mean_squared_error: 0.0114\n",
      "Epoch 00324: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 325/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00325: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 326/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00326: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 327/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00327: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 328/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00328: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 329/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00329: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 330/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00330: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 331/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00331: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 332/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00332: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 333/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00333: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 334/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00334: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 335/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00335: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 336/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0121 - mean_squared_error: 0.0121\n",
      "Epoch 00336: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 337/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00337: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 338/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00338: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 339/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00339: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 340/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00340: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 341/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00341: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 342/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00342: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 343/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0114 - mean_squared_error: 0.0114\n",
      "Epoch 00343: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 344/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00344: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 345/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00345: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 346/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00346: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 347/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00347: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 348/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00348: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 349/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00349: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 350/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0114 - mean_squared_error: 0.0114\n",
      "Epoch 00350: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0114 - mean_squared_error: 0.0114 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 351/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00351: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 352/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00352: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 353/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00353: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 354/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00354: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 355/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00355: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 356/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00356: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 357/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00357: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 358/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00358: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 359/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00359: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 360/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00360: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 361/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00361: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 362/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00362: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 363/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00363: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0114 - mean_squared_error: 0.0114 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 364/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00364: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 365/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00365: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 366/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00366: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 367/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00367: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 368/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00368: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 369/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00369: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 370/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00370: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 371/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00371: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 372/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00372: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 373/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00373: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 374/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00374: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 375/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00375: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 376/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00376: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 377/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00377: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 378/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00378: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 379/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00379: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 380/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00380: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 381/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00381: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 382/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00382: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 383/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00383: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 384/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00384: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 385/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00385: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 386/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00386: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 387/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00387: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 388/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00388: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 389/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0114 - mean_squared_error: 0.0114\n",
      "Epoch 00389: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0114 - mean_squared_error: 0.0114 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 390/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00390: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 391/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00391: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 392/500\n",
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      "Epoch 00392: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 393/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00393: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 394/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00394: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 395/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00395: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 396/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00396: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 397/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00397: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 398/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0121 - mean_squared_error: 0.0121\n",
      "Epoch 00398: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 399/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00399: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 400/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00400: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 401/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00401: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 402/500\n",
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      "Epoch 00402: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 403/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00403: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 404/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00404: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 405/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00405: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 406/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00406: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 2ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 407/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00407: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 9s 2ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 408/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00408: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 409/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00409: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 410/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0114 - mean_squared_error: 0.0114\n",
      "Epoch 00410: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0114 - mean_squared_error: 0.0114 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 411/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0114 - mean_squared_error: 0.0114\n",
      "Epoch 00411: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0114 - mean_squared_error: 0.0114 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 412/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00412: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 413/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00413: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 414/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00414: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 415/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00415: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 2ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 416/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00416: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 417/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00417: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 418/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00418: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 419/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00419: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 420/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00420: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 421/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00421: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 422/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00422: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 423/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00423: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 424/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00424: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 425/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00425: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 426/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00426: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 427/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00427: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 428/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00428: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 429/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00429: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 430/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0113 - mean_squared_error: 0.0113\n",
      "Epoch 00430: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0113 - mean_squared_error: 0.0113 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 431/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00431: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 432/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00432: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 433/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00433: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 434/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00434: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 435/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00435: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 436/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00436: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 437/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00437: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0120 - mean_squared_error: 0.0120 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 438/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00438: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 439/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00439: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 440/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00440: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 441/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00441: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 442/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00442: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 443/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00443: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 444/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00444: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 445/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00445: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 446/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00446: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 447/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00447: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 448/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00448: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 449/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00449: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 450/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00450: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 451/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0113 - mean_squared_error: 0.0113\n",
      "Epoch 00451: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0113 - mean_squared_error: 0.0113 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 452/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00452: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 453/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00453: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 454/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00454: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 455/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00455: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 456/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00456: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 457/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00457: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 458/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00458: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 459/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00459: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 460/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00460: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 461/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00461: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 462/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00462: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 463/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0114 - mean_squared_error: 0.0114\n",
      "Epoch 00463: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0114 - mean_squared_error: 0.0114 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 464/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00464: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 465/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0120\n",
      "Epoch 00465: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 466/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00466: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 467/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00467: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 468/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00468: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 469/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00469: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 470/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00470: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0114 - mean_squared_error: 0.0114 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 471/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00471: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 472/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00472: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 473/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00473: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 474/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00474: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 475/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00475: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 476/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00476: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 477/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00477: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 478/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0114 - mean_squared_error: 0.0114\n",
      "Epoch 00478: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 479/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00479: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 480/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00480: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 481/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00481: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 482/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00482: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 483/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00483: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 484/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00484: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 485/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00485: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 486/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00486: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 487/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 00487: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 488/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00488: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 489/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00489: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 490/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00490: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 491/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00491: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 492/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00492: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 493/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 00493: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 494/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00494: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 495/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 00495: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 7s 1ms/sample - loss: 0.0116 - mean_squared_error: 0.0116 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 496/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00496: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 497/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00497: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 498/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00498: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 499/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 00499: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0118 - mean_squared_error: 0.0118 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n",
      "Epoch 500/500\n",
      "5000/5513 [==========================>...] - ETA: 0s - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 00500: val_loss did not improve from 0.01021\n",
      "5513/5513 [==============================] - 8s 1ms/sample - loss: 0.0114 - mean_squared_error: 0.0114 - val_loss: 0.0102 - val_mean_squared_error: 0.0102\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, epochs=epochs , callbacks=callbacks_list_model,\n",
    "          validation_data=(x_val, y_val),batch_size=batch_size) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'mean_squared_error', 'val_loss', 'val_mean_squared_error', 'lr'])\n"
     ]
    }
   ],
   "source": [
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Loss (MSE)\")\n",
    "plt.plot(history.history['loss'],color = 'blue',label=\"train_loss\")\n",
    "plt.plot(history.history['val_loss'],color = 'red',label=\"val_loss\")\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend()\n",
    "plt.savefig('loss_support_sync_data2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = model.predict(x_train)\n",
    "y_val_pred = model.predict(x_val)\n",
    "y_test_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fix!\n",
    "def plot_compare(x, event_col):\n",
    "#     Col = {'Open':0, 'High':1, 'Low':2, 'Close':3}\n",
    "    Col = dict()\n",
    "    count = 0\n",
    "    for i, col in enumerate(event_col):\n",
    "        Col[col] = i\n",
    "    ft = Col[x]\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    plt.subplot(1, 2, 1)\n",
    "\n",
    "    plt.plot(np.arange(y_train.shape[0]), y_train[:, ft], color='blue', label='train target')\n",
    "\n",
    "    plt.plot(np.arange(y_train.shape[0], y_train.shape[0] + y_val.shape[0]), y_val[:, ft], \n",
    "             color='gray', label='val target')\n",
    "    plt.plot(np.arange(y_train.shape[0] + y_val.shape[0], y_train.shape[0] + y_val.shape[0] + y_test.shape[0]),\n",
    "            y_test[:, ft], color='black', label='test target')\n",
    "    plt.plot(np.arange(y_train_pred.shape[0]), y_train_pred[:, ft],\n",
    "            color='red', label='train prediction') \n",
    "    plt.plot(np.arange(y_train_pred.shape[0], y_train_pred.shape[0] + y_val_pred.shape[0]),\n",
    "            y_val_pred[:, ft], color='orange', label='val prediction')\n",
    "    plt.plot(np.arange(y_train_pred.shape[0] + y_val_pred.shape[0],\n",
    "            y_train_pred.shape[0] + y_val_pred.shape[0] + y_test_pred.shape[0]),\n",
    "            y_test_pred[:, ft], color ='green', label='test prediction')\n",
    "    plt.title('past and future stock price :' + x)\n",
    "    plt.xlabel('Time [Days]')\n",
    "    plt.ylabel('Normalized price')\n",
    "    plt.legend(loc='best')\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(np.arange(y_train.shape[0], y_train.shape[0] + y_test.shape[0]),\n",
    "            y_test[:, ft], color='black', label='test target')\n",
    "    plt.plot(np.arange(y_train.shape[0], y_train.shape[0] + y_test_pred.shape[0]),\n",
    "            y_test_pred[:, ft], color='green', label='test prediction')\n",
    "    plt.title('future stock prices :' + x)\n",
    "    plt.xlabel('Time [Days]')\n",
    "    plt.ylabel('Normalized price')\n",
    "    plt.legend(loc='best')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_compare('Close(Nikkei)', event_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "689/689 [==============================] - 1s 1ms/sample - loss: 0.0751 - mean_squared_error: 0.0751\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07512192406484801, 0.075121924]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "689/689 [==============================] - 0s 279us/sample - loss: 0.0102 - mean_squared_error: 0.0102\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.010223456000144146, 0.010223455]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_val,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5513/5513 [==============================] - 2s 275us/sample - loss: 2.9969e-04 - mean_squared_error: 2.9969e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.00029969300091389615, 0.00029969294]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_train,y_train)"
   ]
  }
 ],
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