SingleNet.ipynb

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    "colab": {
      "name": "ModAugNet.ipynb",
      "version": "0.3.2",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ShreshthSaxena/Algorithmic-Trading/blob/master/SingleNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "spJuHmJrWc2x",
        "colab_type": "code",
        "outputId": "9addecd2-b016-4408-f939-4f2461da5889",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install quandl"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: quandl in /usr/local/lib/python3.6/dist-packages (3.4.6)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from quandl) (2.5.3)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.6/dist-packages (from quandl) (7.0.0)\n",
            "Requirement already satisfied: pandas>=0.14 in /usr/local/lib/python3.6/dist-packages (from quandl) (0.23.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from quandl) (1.11.0)\n",
            "Requirement already satisfied: numpy>=1.8 in /usr/local/lib/python3.6/dist-packages (from quandl) (1.16.2)\n",
            "Requirement already satisfied: inflection>=0.3.1 in /usr/local/lib/python3.6/dist-packages (from quandl) (0.3.1)\n",
            "Requirement already satisfied: requests>=2.7.0 in /usr/local/lib/python3.6/dist-packages (from quandl) (2.18.4)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.14->quandl) (2018.9)\n",
            "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (2.6)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (2019.3.9)\n",
            "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (1.22)\n"
          ],
          "name": "stdout"
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    {
      "metadata": {
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        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense, Dropout, Activation\n",
        "from keras.layers.recurrent import LSTM\n",
        "from keras.models import load_model\n",
        "import keras\n",
        "from sklearn import preprocessing\n",
        "import matplotlib.pyplot as plt\n",
        "import math, time\n",
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6b2my-sHh1tW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**DATA GATHERING**"
      ]
    },
    {
      "metadata": {
        "id": "NnDryACpTI1c",
        "colab_type": "code",
        "outputId": "4d0eede2-d1c8-4c88-d6d7-9b5ec9d658f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "from pandas_datareader import data as pdr\n",
        "import fix_yahoo_finance\n",
        "\n",
        "snp_data = pdr.get_data_yahoo('^GSPC', start=datetime.datetime(2000, 1, 4), end=datetime.datetime(2017, 7, 27))\n",
        "snp_data = snp_data.drop(['High','Close','Low', 'Open', 'Volume'],1)\n",
        "snp_data.head()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>1399.420044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>1402.109985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>1403.449951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>1441.469971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-10</th>\n",
              "      <td>1457.599976</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "              Adj Close\n",
              "Date                   \n",
              "2000-01-04  1399.420044\n",
              "2000-01-05  1402.109985\n",
              "2000-01-06  1403.449951\n",
              "2000-01-07  1441.469971\n",
              "2000-01-10  1457.599976"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "v8d6VGoBGEqN",
        "colab_type": "code",
        "outputId": "3a804886-45f5-4f68-efcc-95acea7f2bae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "import pandas_datareader\n",
        "nasdaq = pandas_datareader.nasdaq_trader.get_nasdaq_symbols(retry_count=3, timeout=30, pause=None)\n",
        "nasdaq.head()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Nasdaq Traded</th>\n",
              "      <th>Security Name</th>\n",
              "      <th>Listing Exchange</th>\n",
              "      <th>Market Category</th>\n",
              "      <th>ETF</th>\n",
              "      <th>Round Lot Size</th>\n",
              "      <th>Test Issue</th>\n",
              "      <th>Financial Status</th>\n",
              "      <th>CQS Symbol</th>\n",
              "      <th>NASDAQ Symbol</th>\n",
              "      <th>NextShares</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symbol</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>True</td>\n",
              "      <td>Agilent Technologies, Inc. Common Stock</td>\n",
              "      <td>N</td>\n",
              "      <td></td>\n",
              "      <td>False</td>\n",
              "      <td>100.0</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AA</th>\n",
              "      <td>True</td>\n",
              "      <td>Alcoa Corporation Common Stock</td>\n",
              "      <td>N</td>\n",
              "      <td></td>\n",
              "      <td>False</td>\n",
              "      <td>100.0</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>AA</td>\n",
              "      <td>AA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAAU</th>\n",
              "      <td>True</td>\n",
              "      <td>Perth Mint Physical Gold ETF</td>\n",
              "      <td>P</td>\n",
              "      <td></td>\n",
              "      <td>True</td>\n",
              "      <td>100.0</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>AAAU</td>\n",
              "      <td>AAAU</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AABA</th>\n",
              "      <td>True</td>\n",
              "      <td>Altaba Inc. - Common Stock</td>\n",
              "      <td>Q</td>\n",
              "      <td>Q</td>\n",
              "      <td>False</td>\n",
              "      <td>100.0</td>\n",
              "      <td>False</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>AABA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAC</th>\n",
              "      <td>True</td>\n",
              "      <td>AAC Holdings, Inc. Common Stock</td>\n",
              "      <td>N</td>\n",
              "      <td></td>\n",
              "      <td>False</td>\n",
              "      <td>100.0</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>AAC</td>\n",
              "      <td>AAC</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Nasdaq Traded                            Security Name  \\\n",
              "Symbol                                                           \n",
              "A                True  Agilent Technologies, Inc. Common Stock   \n",
              "AA               True          Alcoa Corporation Common Stock    \n",
              "AAAU             True             Perth Mint Physical Gold ETF   \n",
              "AABA             True               Altaba Inc. - Common Stock   \n",
              "AAC              True          AAC Holdings, Inc. Common Stock   \n",
              "\n",
              "       Listing Exchange Market Category    ETF  Round Lot Size  Test Issue  \\\n",
              "Symbol                                                                       \n",
              "A                     N                  False           100.0       False   \n",
              "AA                    N                  False           100.0       False   \n",
              "AAAU                  P                   True           100.0       False   \n",
              "AABA                  Q               Q  False           100.0       False   \n",
              "AAC                   N                  False           100.0       False   \n",
              "\n",
              "       Financial Status CQS Symbol NASDAQ Symbol  NextShares  \n",
              "Symbol                                                        \n",
              "A                   NaN          A             A       False  \n",
              "AA                  NaN         AA            AA       False  \n",
              "AAAU                NaN       AAAU          AAAU       False  \n",
              "AABA                  N        NaN          AABA       False  \n",
              "AAC                 NaN        AAC           AAC       False  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "metadata": {
        "id": "G9d8FhheR7gb",
        "colab_type": "code",
        "outputId": "65d344a1-3cc6-44fd-f4a2-f8f660b81291",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "def get(tickers, startdate, enddate):\n",
        "  def data(ticker):\n",
        "    return (pdr.get_data_yahoo(ticker, start=startdate, end=enddate))\n",
        "  datas = map (data, tickers)\n",
        "  return(pd.concat(datas, keys=tickers, names=['Ticker', 'Date']))\n",
        "\n",
        "tickers = ['AAPL', 'MSFT', 'AMZN', 'CSCO', 'ADBE', 'AMGN', 'NVDA', 'SBUX', 'ASML', 'GILD']\n",
        "all_data = get(tickers, datetime.datetime(2000, 1, 4), datetime.datetime(2017, 7, 27))\n",
        "\n",
        "all_data.describe()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>44190.000000</td>\n",
              "      <td>44190.000000</td>\n",
              "      <td>44190.000000</td>\n",
              "      <td>44190.000000</td>\n",
              "      <td>4.419000e+04</td>\n",
              "      <td>44190.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>53.682041</td>\n",
              "      <td>52.416926</td>\n",
              "      <td>53.062888</td>\n",
              "      <td>53.067752</td>\n",
              "      <td>3.115848e+07</td>\n",
              "      <td>48.951830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>91.943761</td>\n",
              "      <td>90.322117</td>\n",
              "      <td>91.188382</td>\n",
              "      <td>91.184721</td>\n",
              "      <td>5.088124e+07</td>\n",
              "      <td>91.266430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.942143</td>\n",
              "      <td>0.908571</td>\n",
              "      <td>0.927857</td>\n",
              "      <td>0.937143</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.624890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.170000</td>\n",
              "      <td>16.530001</td>\n",
              "      <td>16.870001</td>\n",
              "      <td>16.876667</td>\n",
              "      <td>5.508250e+06</td>\n",
              "      <td>13.808631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>28.250000</td>\n",
              "      <td>27.566250</td>\n",
              "      <td>27.910000</td>\n",
              "      <td>27.914532</td>\n",
              "      <td>1.260745e+07</td>\n",
              "      <td>22.690001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>58.299999</td>\n",
              "      <td>57.049999</td>\n",
              "      <td>57.619999</td>\n",
              "      <td>57.669998</td>\n",
              "      <td>3.763510e+07</td>\n",
              "      <td>50.179255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1083.310059</td>\n",
              "      <td>1043.199951</td>\n",
              "      <td>1069.550049</td>\n",
              "      <td>1052.800049</td>\n",
              "      <td>1.855410e+09</td>\n",
              "      <td>1052.800049</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               High           Low          Open         Close        Volume  \\\n",
              "count  44190.000000  44190.000000  44190.000000  44190.000000  4.419000e+04   \n",
              "mean      53.682041     52.416926     53.062888     53.067752  3.115848e+07   \n",
              "std       91.943761     90.322117     91.188382     91.184721  5.088124e+07   \n",
              "min        0.942143      0.908571      0.927857      0.937143  0.000000e+00   \n",
              "25%       17.170000     16.530001     16.870001     16.876667  5.508250e+06   \n",
              "50%       28.250000     27.566250     27.910000     27.914532  1.260745e+07   \n",
              "75%       58.299999     57.049999     57.619999     57.669998  3.763510e+07   \n",
              "max     1083.310059   1043.199951   1069.550049   1052.800049  1.855410e+09   \n",
              "\n",
              "          Adj Close  \n",
              "count  44190.000000  \n",
              "mean      48.951830  \n",
              "std       91.266430  \n",
              "min        0.624890  \n",
              "25%       13.808631  \n",
              "50%       22.690001  \n",
              "75%       50.179255  \n",
              "max     1052.800049  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "mq4H8WNnVn-R",
        "colab_type": "code",
        "outputId": "2c7a4d1b-69f9-4b3e-cd57-2a82b39cc219",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2000
        }
      },
      "cell_type": "code",
      "source": [
        "d=all_data[['Adj Close']].reset_index().pivot('Date', 'Ticker', 'Adj Close')\n",
        "d"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Ticker</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>ADBE</th>\n",
              "      <th>AMGN</th>\n",
              "      <th>AMZN</th>\n",
              "      <th>ASML</th>\n",
              "      <th>CSCO</th>\n",
              "      <th>GILD</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NVDA</th>\n",
              "      <th>SBUX</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>2.440975</td>\n",
              "      <td>14.791295</td>\n",
              "      <td>48.689911</td>\n",
              "      <td>81.937500</td>\n",
              "      <td>26.826624</td>\n",
              "      <td>40.324173</td>\n",
              "      <td>1.513320</td>\n",
              "      <td>40.692646</td>\n",
              "      <td>3.506938</td>\n",
              "      <td>2.393081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>2.476697</td>\n",
              "      <td>15.083735</td>\n",
              "      <td>50.365246</td>\n",
              "      <td>69.750000</td>\n",
              "      <td>26.085730</td>\n",
              "      <td>40.200638</td>\n",
              "      <td>1.501003</td>\n",
              "      <td>41.121727</td>\n",
              "      <td>3.391485</td>\n",
              "      <td>2.424404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>2.262367</td>\n",
              "      <td>15.206868</td>\n",
              "      <td>51.202930</td>\n",
              "      <td>65.562500</td>\n",
              "      <td>24.480459</td>\n",
              "      <td>39.533516</td>\n",
              "      <td>1.603064</td>\n",
              "      <td>39.744217</td>\n",
              "      <td>3.170195</td>\n",
              "      <td>2.512110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>2.369532</td>\n",
              "      <td>15.945663</td>\n",
              "      <td>56.961960</td>\n",
              "      <td>69.562500</td>\n",
              "      <td>25.036133</td>\n",
              "      <td>41.856102</td>\n",
              "      <td>1.847659</td>\n",
              "      <td>40.263588</td>\n",
              "      <td>3.223112</td>\n",
              "      <td>2.499581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-10</th>\n",
              "      <td>2.327857</td>\n",
              "      <td>16.561329</td>\n",
              "      <td>60.417358</td>\n",
              "      <td>69.187500</td>\n",
              "      <td>27.413177</td>\n",
              "      <td>43.412739</td>\n",
              "      <td>2.088733</td>\n",
              "      <td>40.557178</td>\n",
              "      <td>3.328946</td>\n",
              "      <td>2.606078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-11</th>\n",
              "      <td>2.208785</td>\n",
              "      <td>15.422350</td>\n",
              "      <td>55.653084</td>\n",
              "      <td>66.750000</td>\n",
              "      <td>26.517927</td>\n",
              "      <td>42.103184</td>\n",
              "      <td>1.897808</td>\n",
              "      <td>39.518398</td>\n",
              "      <td>3.194248</td>\n",
              "      <td>2.530903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-12</th>\n",
              "      <td>2.076318</td>\n",
              "      <td>15.345394</td>\n",
              "      <td>53.244766</td>\n",
              "      <td>63.562500</td>\n",
              "      <td>27.011856</td>\n",
              "      <td>41.040722</td>\n",
              "      <td>1.881092</td>\n",
              "      <td>38.231228</td>\n",
              "      <td>3.112468</td>\n",
              "      <td>2.537168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-13</th>\n",
              "      <td>2.304043</td>\n",
              "      <td>16.161148</td>\n",
              "      <td>55.967213</td>\n",
              "      <td>65.937500</td>\n",
              "      <td>28.200378</td>\n",
              "      <td>41.979649</td>\n",
              "      <td>1.984913</td>\n",
              "      <td>38.953846</td>\n",
              "      <td>3.242355</td>\n",
              "      <td>2.718842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-14</th>\n",
              "      <td>2.391858</td>\n",
              "      <td>16.330452</td>\n",
              "      <td>57.223717</td>\n",
              "      <td>64.250000</td>\n",
              "      <td>30.901564</td>\n",
              "      <td>42.523228</td>\n",
              "      <td>1.939162</td>\n",
              "      <td>40.557178</td>\n",
              "      <td>3.381863</td>\n",
              "      <td>2.649931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-18</th>\n",
              "      <td>2.475209</td>\n",
              "      <td>15.760963</td>\n",
              "      <td>57.851990</td>\n",
              "      <td>64.125000</td>\n",
              "      <td>31.318316</td>\n",
              "      <td>44.277538</td>\n",
              "      <td>1.926844</td>\n",
              "      <td>41.663681</td>\n",
              "      <td>3.526181</td>\n",
              "      <td>2.612343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-19</th>\n",
              "      <td>2.537721</td>\n",
              "      <td>15.545485</td>\n",
              "      <td>60.312637</td>\n",
              "      <td>66.812500</td>\n",
              "      <td>31.858557</td>\n",
              "      <td>44.401077</td>\n",
              "      <td>1.770233</td>\n",
              "      <td>38.660282</td>\n",
              "      <td>3.492506</td>\n",
              "      <td>2.555961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-20</th>\n",
              "      <td>2.702933</td>\n",
              "      <td>15.714791</td>\n",
              "      <td>60.155582</td>\n",
              "      <td>64.750000</td>\n",
              "      <td>34.945625</td>\n",
              "      <td>44.425785</td>\n",
              "      <td>1.761435</td>\n",
              "      <td>38.298973</td>\n",
              "      <td>3.482885</td>\n",
              "      <td>2.512110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-21</th>\n",
              "      <td>2.650840</td>\n",
              "      <td>15.483920</td>\n",
              "      <td>62.563885</td>\n",
              "      <td>62.062500</td>\n",
              "      <td>34.328209</td>\n",
              "      <td>45.562378</td>\n",
              "      <td>1.756156</td>\n",
              "      <td>37.486023</td>\n",
              "      <td>3.444401</td>\n",
              "      <td>2.502707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-24</th>\n",
              "      <td>2.530279</td>\n",
              "      <td>14.914429</td>\n",
              "      <td>58.009041</td>\n",
              "      <td>70.125000</td>\n",
              "      <td>32.229004</td>\n",
              "      <td>43.116241</td>\n",
              "      <td>1.560832</td>\n",
              "      <td>36.582752</td>\n",
              "      <td>3.290461</td>\n",
              "      <td>2.461992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-25</th>\n",
              "      <td>2.673166</td>\n",
              "      <td>15.514700</td>\n",
              "      <td>58.113758</td>\n",
              "      <td>69.250000</td>\n",
              "      <td>32.908154</td>\n",
              "      <td>44.326954</td>\n",
              "      <td>1.594265</td>\n",
              "      <td>37.147285</td>\n",
              "      <td>3.261598</td>\n",
              "      <td>2.606078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-26</th>\n",
              "      <td>2.624048</td>\n",
              "      <td>15.068344</td>\n",
              "      <td>53.715950</td>\n",
              "      <td>64.812500</td>\n",
              "      <td>30.345892</td>\n",
              "      <td>42.374985</td>\n",
              "      <td>1.519478</td>\n",
              "      <td>35.905289</td>\n",
              "      <td>3.126900</td>\n",
              "      <td>2.906780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-27</th>\n",
              "      <td>2.619583</td>\n",
              "      <td>15.437745</td>\n",
              "      <td>51.412342</td>\n",
              "      <td>66.937500</td>\n",
              "      <td>30.191536</td>\n",
              "      <td>42.152611</td>\n",
              "      <td>1.397182</td>\n",
              "      <td>35.679459</td>\n",
              "      <td>2.963339</td>\n",
              "      <td>2.894251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-28</th>\n",
              "      <td>2.420138</td>\n",
              "      <td>14.175632</td>\n",
              "      <td>49.998779</td>\n",
              "      <td>61.687500</td>\n",
              "      <td>28.987577</td>\n",
              "      <td>41.065434</td>\n",
              "      <td>1.378704</td>\n",
              "      <td>35.498810</td>\n",
              "      <td>2.876748</td>\n",
              "      <td>2.988219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-31</th>\n",
              "      <td>2.470743</td>\n",
              "      <td>13.559971</td>\n",
              "      <td>53.349468</td>\n",
              "      <td>64.562500</td>\n",
              "      <td>30.361322</td>\n",
              "      <td>43.289185</td>\n",
              "      <td>1.317997</td>\n",
              "      <td>35.363297</td>\n",
              "      <td>2.852695</td>\n",
              "      <td>3.207481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-01</th>\n",
              "      <td>2.387393</td>\n",
              "      <td>13.760060</td>\n",
              "      <td>54.710693</td>\n",
              "      <td>67.437500</td>\n",
              "      <td>31.642458</td>\n",
              "      <td>46.575420</td>\n",
              "      <td>1.300400</td>\n",
              "      <td>37.192444</td>\n",
              "      <td>2.920043</td>\n",
              "      <td>3.238804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-02</th>\n",
              "      <td>2.353160</td>\n",
              "      <td>15.099129</td>\n",
              "      <td>53.349468</td>\n",
              "      <td>69.437500</td>\n",
              "      <td>32.846420</td>\n",
              "      <td>45.018784</td>\n",
              "      <td>1.293361</td>\n",
              "      <td>36.424679</td>\n",
              "      <td>3.001823</td>\n",
              "      <td>3.188687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-03</th>\n",
              "      <td>2.460325</td>\n",
              "      <td>17.007681</td>\n",
              "      <td>53.611248</td>\n",
              "      <td>84.187500</td>\n",
              "      <td>34.575172</td>\n",
              "      <td>46.575420</td>\n",
              "      <td>1.266966</td>\n",
              "      <td>37.440842</td>\n",
              "      <td>3.049930</td>\n",
              "      <td>3.151099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-04</th>\n",
              "      <td>2.571954</td>\n",
              "      <td>18.223619</td>\n",
              "      <td>54.134804</td>\n",
              "      <td>78.562500</td>\n",
              "      <td>33.834270</td>\n",
              "      <td>47.884968</td>\n",
              "      <td>1.316237</td>\n",
              "      <td>38.502216</td>\n",
              "      <td>3.122089</td>\n",
              "      <td>3.445537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-07</th>\n",
              "      <td>2.716329</td>\n",
              "      <td>18.977798</td>\n",
              "      <td>57.956669</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>33.772541</td>\n",
              "      <td>49.491013</td>\n",
              "      <td>1.529157</td>\n",
              "      <td>38.524776</td>\n",
              "      <td>2.929665</td>\n",
              "      <td>3.433008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-08</th>\n",
              "      <td>2.735678</td>\n",
              "      <td>20.624699</td>\n",
              "      <td>59.056133</td>\n",
              "      <td>83.125000</td>\n",
              "      <td>36.056969</td>\n",
              "      <td>49.738094</td>\n",
              "      <td>1.669931</td>\n",
              "      <td>39.721622</td>\n",
              "      <td>3.069173</td>\n",
              "      <td>3.508182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-09</th>\n",
              "      <td>2.682096</td>\n",
              "      <td>21.563587</td>\n",
              "      <td>55.967213</td>\n",
              "      <td>80.250000</td>\n",
              "      <td>35.177158</td>\n",
              "      <td>50.924095</td>\n",
              "      <td>1.546754</td>\n",
              "      <td>37.576332</td>\n",
              "      <td>3.146142</td>\n",
              "      <td>3.458066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-10</th>\n",
              "      <td>2.702933</td>\n",
              "      <td>22.471691</td>\n",
              "      <td>54.605988</td>\n",
              "      <td>76.187500</td>\n",
              "      <td>35.563038</td>\n",
              "      <td>53.740860</td>\n",
              "      <td>1.558630</td>\n",
              "      <td>38.298973</td>\n",
              "      <td>3.790765</td>\n",
              "      <td>3.207481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-11</th>\n",
              "      <td>2.589815</td>\n",
              "      <td>22.271597</td>\n",
              "      <td>52.459435</td>\n",
              "      <td>76.187500</td>\n",
              "      <td>33.155125</td>\n",
              "      <td>51.764202</td>\n",
              "      <td>1.574909</td>\n",
              "      <td>36.108521</td>\n",
              "      <td>3.848492</td>\n",
              "      <td>3.295185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-14</th>\n",
              "      <td>2.758004</td>\n",
              "      <td>22.071505</td>\n",
              "      <td>54.501274</td>\n",
              "      <td>74.437500</td>\n",
              "      <td>33.031635</td>\n",
              "      <td>51.591228</td>\n",
              "      <td>1.691047</td>\n",
              "      <td>35.995617</td>\n",
              "      <td>4.464251</td>\n",
              "      <td>3.401683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-15</th>\n",
              "      <td>2.833913</td>\n",
              "      <td>21.886814</td>\n",
              "      <td>54.501274</td>\n",
              "      <td>73.812500</td>\n",
              "      <td>32.769234</td>\n",
              "      <td>50.602886</td>\n",
              "      <td>1.787830</td>\n",
              "      <td>35.611725</td>\n",
              "      <td>4.291070</td>\n",
              "      <td>3.470594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-15</th>\n",
              "      <td>140.489960</td>\n",
              "      <td>137.520004</td>\n",
              "      <td>156.075424</td>\n",
              "      <td>964.169983</td>\n",
              "      <td>126.459839</td>\n",
              "      <td>29.685419</td>\n",
              "      <td>61.409721</td>\n",
              "      <td>67.715675</td>\n",
              "      <td>151.550781</td>\n",
              "      <td>57.854607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-16</th>\n",
              "      <td>138.523209</td>\n",
              "      <td>137.839996</td>\n",
              "      <td>154.468170</td>\n",
              "      <td>987.710022</td>\n",
              "      <td>128.491852</td>\n",
              "      <td>29.732418</td>\n",
              "      <td>60.755924</td>\n",
              "      <td>67.812546</td>\n",
              "      <td>150.804825</td>\n",
              "      <td>57.902744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-19</th>\n",
              "      <td>142.486008</td>\n",
              "      <td>140.350006</td>\n",
              "      <td>157.007446</td>\n",
              "      <td>995.169983</td>\n",
              "      <td>130.295853</td>\n",
              "      <td>30.070824</td>\n",
              "      <td>61.409721</td>\n",
              "      <td>68.655357</td>\n",
              "      <td>156.474167</td>\n",
              "      <td>58.634472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-20</th>\n",
              "      <td>141.191010</td>\n",
              "      <td>140.910004</td>\n",
              "      <td>158.148682</td>\n",
              "      <td>992.590027</td>\n",
              "      <td>127.361855</td>\n",
              "      <td>29.939224</td>\n",
              "      <td>62.148800</td>\n",
              "      <td>67.725357</td>\n",
              "      <td>156.245392</td>\n",
              "      <td>57.633160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-21</th>\n",
              "      <td>142.028366</td>\n",
              "      <td>144.240005</td>\n",
              "      <td>162.960968</td>\n",
              "      <td>1002.229980</td>\n",
              "      <td>129.284805</td>\n",
              "      <td>29.929821</td>\n",
              "      <td>63.958603</td>\n",
              "      <td>68.074112</td>\n",
              "      <td>158.612595</td>\n",
              "      <td>57.729435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-22</th>\n",
              "      <td>141.794708</td>\n",
              "      <td>143.690002</td>\n",
              "      <td>165.310043</td>\n",
              "      <td>1001.299988</td>\n",
              "      <td>128.878418</td>\n",
              "      <td>29.939224</td>\n",
              "      <td>66.782249</td>\n",
              "      <td>68.064423</td>\n",
              "      <td>157.518509</td>\n",
              "      <td>57.296177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-23</th>\n",
              "      <td>142.427597</td>\n",
              "      <td>145.410004</td>\n",
              "      <td>164.054672</td>\n",
              "      <td>1003.739990</td>\n",
              "      <td>133.457855</td>\n",
              "      <td>30.164822</td>\n",
              "      <td>66.820160</td>\n",
              "      <td>68.984726</td>\n",
              "      <td>153.002945</td>\n",
              "      <td>57.585018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-26</th>\n",
              "      <td>141.979691</td>\n",
              "      <td>144.960007</td>\n",
              "      <td>165.167389</td>\n",
              "      <td>993.979980</td>\n",
              "      <td>132.605408</td>\n",
              "      <td>30.305826</td>\n",
              "      <td>67.502373</td>\n",
              "      <td>68.325989</td>\n",
              "      <td>151.331940</td>\n",
              "      <td>57.421345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-27</th>\n",
              "      <td>139.944733</td>\n",
              "      <td>142.539993</td>\n",
              "      <td>162.485443</td>\n",
              "      <td>976.780029</td>\n",
              "      <td>131.207779</td>\n",
              "      <td>29.854622</td>\n",
              "      <td>66.943329</td>\n",
              "      <td>67.047234</td>\n",
              "      <td>145.791916</td>\n",
              "      <td>56.766640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-28</th>\n",
              "      <td>141.989441</td>\n",
              "      <td>143.809998</td>\n",
              "      <td>165.547791</td>\n",
              "      <td>990.330017</td>\n",
              "      <td>133.804779</td>\n",
              "      <td>30.155428</td>\n",
              "      <td>68.146690</td>\n",
              "      <td>67.618805</td>\n",
              "      <td>150.934097</td>\n",
              "      <td>56.978462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-29</th>\n",
              "      <td>139.896042</td>\n",
              "      <td>141.240005</td>\n",
              "      <td>164.140244</td>\n",
              "      <td>975.929993</td>\n",
              "      <td>128.462112</td>\n",
              "      <td>29.525620</td>\n",
              "      <td>67.142311</td>\n",
              "      <td>66.349731</td>\n",
              "      <td>145.891342</td>\n",
              "      <td>56.188961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-30</th>\n",
              "      <td>140.227097</td>\n",
              "      <td>141.440002</td>\n",
              "      <td>163.797897</td>\n",
              "      <td>968.000000</td>\n",
              "      <td>129.165863</td>\n",
              "      <td>29.422216</td>\n",
              "      <td>67.066505</td>\n",
              "      <td>66.775978</td>\n",
              "      <td>143.782761</td>\n",
              "      <td>56.140823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-03</th>\n",
              "      <td>139.720795</td>\n",
              "      <td>138.410004</td>\n",
              "      <td>164.339966</td>\n",
              "      <td>953.659973</td>\n",
              "      <td>128.531494</td>\n",
              "      <td>29.450420</td>\n",
              "      <td>66.990700</td>\n",
              "      <td>66.039726</td>\n",
              "      <td>138.580887</td>\n",
              "      <td>56.083050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-05</th>\n",
              "      <td>140.295242</td>\n",
              "      <td>141.210007</td>\n",
              "      <td>165.728485</td>\n",
              "      <td>971.400024</td>\n",
              "      <td>130.325592</td>\n",
              "      <td>29.516836</td>\n",
              "      <td>67.142311</td>\n",
              "      <td>66.921303</td>\n",
              "      <td>142.280899</td>\n",
              "      <td>55.784580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-06</th>\n",
              "      <td>138.971069</td>\n",
              "      <td>140.750000</td>\n",
              "      <td>163.312851</td>\n",
              "      <td>965.140015</td>\n",
              "      <td>130.067886</td>\n",
              "      <td>29.146803</td>\n",
              "      <td>65.834709</td>\n",
              "      <td>66.427231</td>\n",
              "      <td>142.708572</td>\n",
              "      <td>55.457230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-07</th>\n",
              "      <td>140.382874</td>\n",
              "      <td>142.220001</td>\n",
              "      <td>163.037048</td>\n",
              "      <td>978.760010</td>\n",
              "      <td>131.723221</td>\n",
              "      <td>29.317587</td>\n",
              "      <td>65.616783</td>\n",
              "      <td>67.289413</td>\n",
              "      <td>145.970963</td>\n",
              "      <td>55.880867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-10</th>\n",
              "      <td>141.239700</td>\n",
              "      <td>143.339996</td>\n",
              "      <td>163.769363</td>\n",
              "      <td>996.469971</td>\n",
              "      <td>133.626358</td>\n",
              "      <td>29.393492</td>\n",
              "      <td>65.654678</td>\n",
              "      <td>67.793182</td>\n",
              "      <td>152.873611</td>\n",
              "      <td>55.659424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-11</th>\n",
              "      <td>141.697327</td>\n",
              "      <td>142.929993</td>\n",
              "      <td>164.834534</td>\n",
              "      <td>994.130005</td>\n",
              "      <td>134.627502</td>\n",
              "      <td>29.497856</td>\n",
              "      <td>65.711533</td>\n",
              "      <td>67.802856</td>\n",
              "      <td>155.041916</td>\n",
              "      <td>55.746071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-12</th>\n",
              "      <td>141.901810</td>\n",
              "      <td>145.910004</td>\n",
              "      <td>164.786972</td>\n",
              "      <td>1006.510010</td>\n",
              "      <td>135.836777</td>\n",
              "      <td>29.564274</td>\n",
              "      <td>66.137932</td>\n",
              "      <td>68.926613</td>\n",
              "      <td>161.636246</td>\n",
              "      <td>56.362259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-13</th>\n",
              "      <td>143.878342</td>\n",
              "      <td>145.050003</td>\n",
              "      <td>166.470276</td>\n",
              "      <td>1000.630005</td>\n",
              "      <td>135.648453</td>\n",
              "      <td>29.668640</td>\n",
              "      <td>66.877007</td>\n",
              "      <td>69.527222</td>\n",
              "      <td>159.766373</td>\n",
              "      <td>56.208221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-14</th>\n",
              "      <td>145.114883</td>\n",
              "      <td>146.160004</td>\n",
              "      <td>168.457962</td>\n",
              "      <td>1001.809998</td>\n",
              "      <td>138.691498</td>\n",
              "      <td>29.810959</td>\n",
              "      <td>66.867523</td>\n",
              "      <td>70.505669</td>\n",
              "      <td>164.063141</td>\n",
              "      <td>56.574081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-17</th>\n",
              "      <td>145.621170</td>\n",
              "      <td>146.399994</td>\n",
              "      <td>168.714752</td>\n",
              "      <td>1010.039978</td>\n",
              "      <td>138.988861</td>\n",
              "      <td>29.886860</td>\n",
              "      <td>67.417091</td>\n",
              "      <td>71.057854</td>\n",
              "      <td>163.366898</td>\n",
              "      <td>56.160084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-18</th>\n",
              "      <td>146.127518</td>\n",
              "      <td>147.889999</td>\n",
              "      <td>168.790848</td>\n",
              "      <td>1024.449951</td>\n",
              "      <td>141.992249</td>\n",
              "      <td>29.896349</td>\n",
              "      <td>67.928772</td>\n",
              "      <td>71.009422</td>\n",
              "      <td>165.067703</td>\n",
              "      <td>56.044540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-19</th>\n",
              "      <td>147.042740</td>\n",
              "      <td>148.919998</td>\n",
              "      <td>170.407608</td>\n",
              "      <td>1026.869995</td>\n",
              "      <td>149.307465</td>\n",
              "      <td>30.266376</td>\n",
              "      <td>68.904732</td>\n",
              "      <td>71.551918</td>\n",
              "      <td>164.212341</td>\n",
              "      <td>55.948265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-20</th>\n",
              "      <td>146.380661</td>\n",
              "      <td>149.940002</td>\n",
              "      <td>170.540771</td>\n",
              "      <td>1028.699951</td>\n",
              "      <td>152.469452</td>\n",
              "      <td>30.228426</td>\n",
              "      <td>69.567993</td>\n",
              "      <td>71.900673</td>\n",
              "      <td>166.599426</td>\n",
              "      <td>55.871243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-21</th>\n",
              "      <td>146.312531</td>\n",
              "      <td>149.520004</td>\n",
              "      <td>171.387192</td>\n",
              "      <td>1025.670044</td>\n",
              "      <td>152.935318</td>\n",
              "      <td>30.209452</td>\n",
              "      <td>69.890160</td>\n",
              "      <td>71.484108</td>\n",
              "      <td>167.196213</td>\n",
              "      <td>55.823101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-24</th>\n",
              "      <td>148.084564</td>\n",
              "      <td>149.529999</td>\n",
              "      <td>172.195572</td>\n",
              "      <td>1038.949951</td>\n",
              "      <td>152.390152</td>\n",
              "      <td>30.228426</td>\n",
              "      <td>70.487106</td>\n",
              "      <td>71.300049</td>\n",
              "      <td>165.256683</td>\n",
              "      <td>55.861610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-25</th>\n",
              "      <td>148.717453</td>\n",
              "      <td>148.210007</td>\n",
              "      <td>172.033890</td>\n",
              "      <td>1039.869995</td>\n",
              "      <td>151.389023</td>\n",
              "      <td>30.475107</td>\n",
              "      <td>69.871208</td>\n",
              "      <td>71.871605</td>\n",
              "      <td>164.461014</td>\n",
              "      <td>56.371891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-26</th>\n",
              "      <td>149.418518</td>\n",
              "      <td>149.779999</td>\n",
              "      <td>167.278687</td>\n",
              "      <td>1052.800049</td>\n",
              "      <td>153.173218</td>\n",
              "      <td>30.038666</td>\n",
              "      <td>70.297607</td>\n",
              "      <td>71.735985</td>\n",
              "      <td>166.360703</td>\n",
              "      <td>55.784580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-27</th>\n",
              "      <td>146.594864</td>\n",
              "      <td>146.199997</td>\n",
              "      <td>163.721786</td>\n",
              "      <td>1046.000000</td>\n",
              "      <td>151.894547</td>\n",
              "      <td>29.953278</td>\n",
              "      <td>71.027199</td>\n",
              "      <td>70.873810</td>\n",
              "      <td>160.870407</td>\n",
              "      <td>57.286552</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4419 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Ticker            AAPL        ADBE        AMGN         AMZN        ASML  \\\n",
              "Date                                                                      \n",
              "2000-01-04    2.440975   14.791295   48.689911    81.937500   26.826624   \n",
              "2000-01-05    2.476697   15.083735   50.365246    69.750000   26.085730   \n",
              "2000-01-06    2.262367   15.206868   51.202930    65.562500   24.480459   \n",
              "2000-01-07    2.369532   15.945663   56.961960    69.562500   25.036133   \n",
              "2000-01-10    2.327857   16.561329   60.417358    69.187500   27.413177   \n",
              "2000-01-11    2.208785   15.422350   55.653084    66.750000   26.517927   \n",
              "2000-01-12    2.076318   15.345394   53.244766    63.562500   27.011856   \n",
              "2000-01-13    2.304043   16.161148   55.967213    65.937500   28.200378   \n",
              "2000-01-14    2.391858   16.330452   57.223717    64.250000   30.901564   \n",
              "2000-01-18    2.475209   15.760963   57.851990    64.125000   31.318316   \n",
              "2000-01-19    2.537721   15.545485   60.312637    66.812500   31.858557   \n",
              "2000-01-20    2.702933   15.714791   60.155582    64.750000   34.945625   \n",
              "2000-01-21    2.650840   15.483920   62.563885    62.062500   34.328209   \n",
              "2000-01-24    2.530279   14.914429   58.009041    70.125000   32.229004   \n",
              "2000-01-25    2.673166   15.514700   58.113758    69.250000   32.908154   \n",
              "2000-01-26    2.624048   15.068344   53.715950    64.812500   30.345892   \n",
              "2000-01-27    2.619583   15.437745   51.412342    66.937500   30.191536   \n",
              "2000-01-28    2.420138   14.175632   49.998779    61.687500   28.987577   \n",
              "2000-01-31    2.470743   13.559971   53.349468    64.562500   30.361322   \n",
              "2000-02-01    2.387393   13.760060   54.710693    67.437500   31.642458   \n",
              "2000-02-02    2.353160   15.099129   53.349468    69.437500   32.846420   \n",
              "2000-02-03    2.460325   17.007681   53.611248    84.187500   34.575172   \n",
              "2000-02-04    2.571954   18.223619   54.134804    78.562500   33.834270   \n",
              "2000-02-07    2.716329   18.977798   57.956669    75.000000   33.772541   \n",
              "2000-02-08    2.735678   20.624699   59.056133    83.125000   36.056969   \n",
              "2000-02-09    2.682096   21.563587   55.967213    80.250000   35.177158   \n",
              "2000-02-10    2.702933   22.471691   54.605988    76.187500   35.563038   \n",
              "2000-02-11    2.589815   22.271597   52.459435    76.187500   33.155125   \n",
              "2000-02-14    2.758004   22.071505   54.501274    74.437500   33.031635   \n",
              "2000-02-15    2.833913   21.886814   54.501274    73.812500   32.769234   \n",
              "...                ...         ...         ...          ...         ...   \n",
              "2017-06-15  140.489960  137.520004  156.075424   964.169983  126.459839   \n",
              "2017-06-16  138.523209  137.839996  154.468170   987.710022  128.491852   \n",
              "2017-06-19  142.486008  140.350006  157.007446   995.169983  130.295853   \n",
              "2017-06-20  141.191010  140.910004  158.148682   992.590027  127.361855   \n",
              "2017-06-21  142.028366  144.240005  162.960968  1002.229980  129.284805   \n",
              "2017-06-22  141.794708  143.690002  165.310043  1001.299988  128.878418   \n",
              "2017-06-23  142.427597  145.410004  164.054672  1003.739990  133.457855   \n",
              "2017-06-26  141.979691  144.960007  165.167389   993.979980  132.605408   \n",
              "2017-06-27  139.944733  142.539993  162.485443   976.780029  131.207779   \n",
              "2017-06-28  141.989441  143.809998  165.547791   990.330017  133.804779   \n",
              "2017-06-29  139.896042  141.240005  164.140244   975.929993  128.462112   \n",
              "2017-06-30  140.227097  141.440002  163.797897   968.000000  129.165863   \n",
              "2017-07-03  139.720795  138.410004  164.339966   953.659973  128.531494   \n",
              "2017-07-05  140.295242  141.210007  165.728485   971.400024  130.325592   \n",
              "2017-07-06  138.971069  140.750000  163.312851   965.140015  130.067886   \n",
              "2017-07-07  140.382874  142.220001  163.037048   978.760010  131.723221   \n",
              "2017-07-10  141.239700  143.339996  163.769363   996.469971  133.626358   \n",
              "2017-07-11  141.697327  142.929993  164.834534   994.130005  134.627502   \n",
              "2017-07-12  141.901810  145.910004  164.786972  1006.510010  135.836777   \n",
              "2017-07-13  143.878342  145.050003  166.470276  1000.630005  135.648453   \n",
              "2017-07-14  145.114883  146.160004  168.457962  1001.809998  138.691498   \n",
              "2017-07-17  145.621170  146.399994  168.714752  1010.039978  138.988861   \n",
              "2017-07-18  146.127518  147.889999  168.790848  1024.449951  141.992249   \n",
              "2017-07-19  147.042740  148.919998  170.407608  1026.869995  149.307465   \n",
              "2017-07-20  146.380661  149.940002  170.540771  1028.699951  152.469452   \n",
              "2017-07-21  146.312531  149.520004  171.387192  1025.670044  152.935318   \n",
              "2017-07-24  148.084564  149.529999  172.195572  1038.949951  152.390152   \n",
              "2017-07-25  148.717453  148.210007  172.033890  1039.869995  151.389023   \n",
              "2017-07-26  149.418518  149.779999  167.278687  1052.800049  153.173218   \n",
              "2017-07-27  146.594864  146.199997  163.721786  1046.000000  151.894547   \n",
              "\n",
              "Ticker           CSCO       GILD       MSFT        NVDA       SBUX  \n",
              "Date                                                                \n",
              "2000-01-04  40.324173   1.513320  40.692646    3.506938   2.393081  \n",
              "2000-01-05  40.200638   1.501003  41.121727    3.391485   2.424404  \n",
              "2000-01-06  39.533516   1.603064  39.744217    3.170195   2.512110  \n",
              "2000-01-07  41.856102   1.847659  40.263588    3.223112   2.499581  \n",
              "2000-01-10  43.412739   2.088733  40.557178    3.328946   2.606078  \n",
              "2000-01-11  42.103184   1.897808  39.518398    3.194248   2.530903  \n",
              "2000-01-12  41.040722   1.881092  38.231228    3.112468   2.537168  \n",
              "2000-01-13  41.979649   1.984913  38.953846    3.242355   2.718842  \n",
              "2000-01-14  42.523228   1.939162  40.557178    3.381863   2.649931  \n",
              "2000-01-18  44.277538   1.926844  41.663681    3.526181   2.612343  \n",
              "2000-01-19  44.401077   1.770233  38.660282    3.492506   2.555961  \n",
              "2000-01-20  44.425785   1.761435  38.298973    3.482885   2.512110  \n",
              "2000-01-21  45.562378   1.756156  37.486023    3.444401   2.502707  \n",
              "2000-01-24  43.116241   1.560832  36.582752    3.290461   2.461992  \n",
              "2000-01-25  44.326954   1.594265  37.147285    3.261598   2.606078  \n",
              "2000-01-26  42.374985   1.519478  35.905289    3.126900   2.906780  \n",
              "2000-01-27  42.152611   1.397182  35.679459    2.963339   2.894251  \n",
              "2000-01-28  41.065434   1.378704  35.498810    2.876748   2.988219  \n",
              "2000-01-31  43.289185   1.317997  35.363297    2.852695   3.207481  \n",
              "2000-02-01  46.575420   1.300400  37.192444    2.920043   3.238804  \n",
              "2000-02-02  45.018784   1.293361  36.424679    3.001823   3.188687  \n",
              "2000-02-03  46.575420   1.266966  37.440842    3.049930   3.151099  \n",
              "2000-02-04  47.884968   1.316237  38.502216    3.122089   3.445537  \n",
              "2000-02-07  49.491013   1.529157  38.524776    2.929665   3.433008  \n",
              "2000-02-08  49.738094   1.669931  39.721622    3.069173   3.508182  \n",
              "2000-02-09  50.924095   1.546754  37.576332    3.146142   3.458066  \n",
              "2000-02-10  53.740860   1.558630  38.298973    3.790765   3.207481  \n",
              "2000-02-11  51.764202   1.574909  36.108521    3.848492   3.295185  \n",
              "2000-02-14  51.591228   1.691047  35.995617    4.464251   3.401683  \n",
              "2000-02-15  50.602886   1.787830  35.611725    4.291070   3.470594  \n",
              "...               ...        ...        ...         ...        ...  \n",
              "2017-06-15  29.685419  61.409721  67.715675  151.550781  57.854607  \n",
              "2017-06-16  29.732418  60.755924  67.812546  150.804825  57.902744  \n",
              "2017-06-19  30.070824  61.409721  68.655357  156.474167  58.634472  \n",
              "2017-06-20  29.939224  62.148800  67.725357  156.245392  57.633160  \n",
              "2017-06-21  29.929821  63.958603  68.074112  158.612595  57.729435  \n",
              "2017-06-22  29.939224  66.782249  68.064423  157.518509  57.296177  \n",
              "2017-06-23  30.164822  66.820160  68.984726  153.002945  57.585018  \n",
              "2017-06-26  30.305826  67.502373  68.325989  151.331940  57.421345  \n",
              "2017-06-27  29.854622  66.943329  67.047234  145.791916  56.766640  \n",
              "2017-06-28  30.155428  68.146690  67.618805  150.934097  56.978462  \n",
              "2017-06-29  29.525620  67.142311  66.349731  145.891342  56.188961  \n",
              "2017-06-30  29.422216  67.066505  66.775978  143.782761  56.140823  \n",
              "2017-07-03  29.450420  66.990700  66.039726  138.580887  56.083050  \n",
              "2017-07-05  29.516836  67.142311  66.921303  142.280899  55.784580  \n",
              "2017-07-06  29.146803  65.834709  66.427231  142.708572  55.457230  \n",
              "2017-07-07  29.317587  65.616783  67.289413  145.970963  55.880867  \n",
              "2017-07-10  29.393492  65.654678  67.793182  152.873611  55.659424  \n",
              "2017-07-11  29.497856  65.711533  67.802856  155.041916  55.746071  \n",
              "2017-07-12  29.564274  66.137932  68.926613  161.636246  56.362259  \n",
              "2017-07-13  29.668640  66.877007  69.527222  159.766373  56.208221  \n",
              "2017-07-14  29.810959  66.867523  70.505669  164.063141  56.574081  \n",
              "2017-07-17  29.886860  67.417091  71.057854  163.366898  56.160084  \n",
              "2017-07-18  29.896349  67.928772  71.009422  165.067703  56.044540  \n",
              "2017-07-19  30.266376  68.904732  71.551918  164.212341  55.948265  \n",
              "2017-07-20  30.228426  69.567993  71.900673  166.599426  55.871243  \n",
              "2017-07-21  30.209452  69.890160  71.484108  167.196213  55.823101  \n",
              "2017-07-24  30.228426  70.487106  71.300049  165.256683  55.861610  \n",
              "2017-07-25  30.475107  69.871208  71.871605  164.461014  56.371891  \n",
              "2017-07-26  30.038666  70.297607  71.735985  166.360703  55.784580  \n",
              "2017-07-27  29.953278  71.027199  70.873810  160.870407  57.286552  \n",
              "\n",
              "[4419 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "e39uGWpZqL64",
        "colab_type": "code",
        "outputId": "da0909c2-5829-4d4c-91bb-48f9800de150",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "cell_type": "code",
      "source": [
        "d.corrwith(snp_data['Adj Close'], axis=0, drop=False).sort_values(ascending = False)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ticker\n",
              "ASML    0.939346\n",
              "ADBE    0.931655\n",
              "AMGN    0.920196\n",
              "MSFT    0.920161\n",
              "SBUX    0.913389\n",
              "AAPL    0.899090\n",
              "AMZN    0.870971\n",
              "GILD    0.861092\n",
              "NVDA    0.694488\n",
              "CSCO    0.489332\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "metadata": {
        "id": "EvSXd0SQhtkL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**NORMALIZATION**"
      ]
    },
    {
      "metadata": {
        "id": "O9i-mQbWcpQ0",
        "colab_type": "code",
        "outputId": "6415ce67-5506-461f-903e-f6f7bdfd638d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "norm_d=(d-d.min())/(d.max()-d.min())\n",
        "snp_data = (snp_data-snp_data.min())/(snp_data.max()-snp_data.min())\n",
        "print(snp_data.shape, norm_d.shape)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4419, 1) (4419, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9sZgvsd7hgY8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**TRAIN - TEST SPLIT**"
      ]
    },
    {
      "metadata": {
        "id": "TPccEUHiWPy6",
        "colab_type": "code",
        "outputId": "839e9906-d79a-4c24-c54e-08c31d53b70e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "train_snp = snp_data[:'2007-12-31']\n",
        "test_snp = snp_data['2008-1-2':]\n",
        "train_d = d[:'2007-12-31']\n",
        "test_d = d['2008-1-2':]\n",
        "print(train_d.shape, test_d.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2009, 10) (2410, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sHeS9zuYiPNX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**SINGLENET**"
      ]
    },
    {
      "metadata": {
        "id": "8EjekXGr3FP-",
        "colab_type": "code",
        "outputId": "4c873c46-4c70-4fe4-e399-78526617114b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "def create_dataset(dataset, look_back = 20):\n",
        "  dataX,dataY = [],[]\n",
        "  print(dataset.shape)\n",
        "  for i in range(len(dataset) - look_back -1):\n",
        "    a = dataset.iloc[i:(i+look_back),0]\n",
        "    dataX.append(a)\n",
        "    dataY.append(dataset.iloc[i + look_back,0])\n",
        "  return np.array(dataX),np.array(dataY)\n",
        "\n",
        "look_back = 20\n",
        "trainX, trainY = create_dataset(train_snp, look_back)\n",
        "testX, testY = create_dataset(test_snp, look_back)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2009, 1)\n",
            "(2410, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tLK8HYj4gD1q",
        "colab_type": "code",
        "outputId": "190120ce-eb0b-4008-d0b2-26d40f0f1b26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "testX.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2389, 20)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "metadata": {
        "id": "Dp1pTxWyfGG3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
        "testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tSd2BvnFhWPo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def build_model(layers):\n",
        "    d = 0.3\n",
        "    model = Sequential()\n",
        "    \n",
        "    model.add(LSTM(4, activation='relu', input_shape=(1, look_back), return_sequences=False))\n",
        "    model.add(Dropout(d))\n",
        "        \n",
        "    model.add(Dense(1,kernel_initializer=\"uniform\",activation='linear'))        \n",
        "    \n",
        "    #adam = keras.optimizers.Adam(decay=0.2)\n",
        "        \n",
        "    start = time.time()\n",
        "    model.compile(loss='mse', optimizer='adam', metrics=['mse'])\n",
        "    print(\"Compilation Time : \", time.time() - start)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5UebCnD-EpCE",
        "colab_type": "code",
        "outputId": "aa2dec9e-4c39-4297-8b23-5f5ab6b475be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 13702
        }
      },
      "cell_type": "code",
      "source": [
        "model = build_model(look_back)\n",
        "\n",
        "history = model.fit(trainX,trainY,batch_size=500,epochs=400,validation_split=0.2,verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Compilation Time :  0.03136563301086426\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Train on 1590 samples, validate on 398 samples\n",
            "Epoch 1/400\n",
            "1590/1590 [==============================] - 1s 923us/step - loss: 0.0797 - mean_squared_error: 0.0797 - val_loss: 0.1703 - val_mean_squared_error: 0.1703\n",
            "Epoch 2/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0774 - mean_squared_error: 0.0774 - val_loss: 0.1661 - val_mean_squared_error: 0.1661\n",
            "Epoch 3/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0748 - mean_squared_error: 0.0748 - val_loss: 0.1618 - val_mean_squared_error: 0.1618\n",
            "Epoch 4/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0722 - mean_squared_error: 0.0722 - val_loss: 0.1573 - val_mean_squared_error: 0.1573\n",
            "Epoch 5/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0696 - mean_squared_error: 0.0696 - val_loss: 0.1527 - val_mean_squared_error: 0.1527\n",
            "Epoch 6/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0669 - mean_squared_error: 0.0669 - val_loss: 0.1479 - val_mean_squared_error: 0.1479\n",
            "Epoch 7/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0643 - mean_squared_error: 0.0643 - val_loss: 0.1429 - val_mean_squared_error: 0.1429\n",
            "Epoch 8/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0615 - mean_squared_error: 0.0615 - val_loss: 0.1376 - val_mean_squared_error: 0.1376\n",
            "Epoch 9/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0586 - mean_squared_error: 0.0586 - val_loss: 0.1319 - val_mean_squared_error: 0.1319\n",
            "Epoch 10/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0556 - mean_squared_error: 0.0556 - val_loss: 0.1258 - val_mean_squared_error: 0.1258\n",
            "Epoch 11/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0523 - mean_squared_error: 0.0523 - val_loss: 0.1193 - val_mean_squared_error: 0.1193\n",
            "Epoch 12/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0490 - mean_squared_error: 0.0490 - val_loss: 0.1122 - val_mean_squared_error: 0.1122\n",
            "Epoch 13/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0458 - mean_squared_error: 0.0458 - val_loss: 0.1047 - val_mean_squared_error: 0.1047\n",
            "Epoch 14/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0424 - mean_squared_error: 0.0424 - val_loss: 0.0968 - val_mean_squared_error: 0.0968\n",
            "Epoch 15/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0387 - mean_squared_error: 0.0387 - val_loss: 0.0885 - val_mean_squared_error: 0.0885\n",
            "Epoch 16/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0349 - mean_squared_error: 0.0349 - val_loss: 0.0798 - val_mean_squared_error: 0.0798\n",
            "Epoch 17/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0313 - mean_squared_error: 0.0313 - val_loss: 0.0709 - val_mean_squared_error: 0.0709\n",
            "Epoch 18/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0273 - mean_squared_error: 0.0273 - val_loss: 0.0617 - val_mean_squared_error: 0.0617\n",
            "Epoch 19/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0237 - mean_squared_error: 0.0237 - val_loss: 0.0525 - val_mean_squared_error: 0.0525\n",
            "Epoch 20/400\n",
            "1590/1590 [==============================] - 0s 17us/step - loss: 0.0201 - mean_squared_error: 0.0201 - val_loss: 0.0435 - val_mean_squared_error: 0.0435\n",
            "Epoch 21/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0168 - mean_squared_error: 0.0168 - val_loss: 0.0349 - val_mean_squared_error: 0.0349\n",
            "Epoch 22/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0143 - mean_squared_error: 0.0143 - val_loss: 0.0270 - val_mean_squared_error: 0.0270\n",
            "Epoch 23/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0115 - mean_squared_error: 0.0115 - val_loss: 0.0200 - val_mean_squared_error: 0.0200\n",
            "Epoch 24/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0095 - mean_squared_error: 0.0095 - val_loss: 0.0141 - val_mean_squared_error: 0.0141\n",
            "Epoch 25/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0074 - mean_squared_error: 0.0074 - val_loss: 0.0095 - val_mean_squared_error: 0.0095\n",
            "Epoch 26/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0067 - mean_squared_error: 0.0067 - val_loss: 0.0062 - val_mean_squared_error: 0.0062\n",
            "Epoch 27/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0063 - mean_squared_error: 0.0063 - val_loss: 0.0040 - val_mean_squared_error: 0.0040\n",
            "Epoch 28/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0061 - mean_squared_error: 0.0061 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 29/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0062 - mean_squared_error: 0.0062 - val_loss: 0.0019 - val_mean_squared_error: 0.0019\n",
            "Epoch 30/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0065 - mean_squared_error: 0.0065 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 31/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0061 - mean_squared_error: 0.0061 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 32/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0061 - mean_squared_error: 0.0061 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 33/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0060 - mean_squared_error: 0.0060 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 34/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0057 - mean_squared_error: 0.0057 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 35/400\n",
            "1590/1590 [==============================] - 0s 17us/step - loss: 0.0057 - mean_squared_error: 0.0057 - val_loss: 0.0019 - val_mean_squared_error: 0.0019\n",
            "Epoch 36/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0058 - mean_squared_error: 0.0058 - val_loss: 0.0022 - val_mean_squared_error: 0.0022\n",
            "Epoch 37/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0054 - mean_squared_error: 0.0054 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 38/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0058 - mean_squared_error: 0.0058 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 39/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0058 - mean_squared_error: 0.0058 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 40/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0057 - mean_squared_error: 0.0057 - val_loss: 0.0030 - val_mean_squared_error: 0.0030\n",
            "Epoch 41/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0055 - mean_squared_error: 0.0055 - val_loss: 0.0030 - val_mean_squared_error: 0.0030\n",
            "Epoch 42/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0056 - mean_squared_error: 0.0056 - val_loss: 0.0030 - val_mean_squared_error: 0.0030\n",
            "Epoch 43/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0055 - mean_squared_error: 0.0055 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 44/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0058 - mean_squared_error: 0.0058 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 45/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0059 - mean_squared_error: 0.0059 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 46/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0058 - mean_squared_error: 0.0058 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 47/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0053 - mean_squared_error: 0.0053 - val_loss: 0.0024 - val_mean_squared_error: 0.0024\n",
            "Epoch 48/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0055 - mean_squared_error: 0.0055 - val_loss: 0.0024 - val_mean_squared_error: 0.0024\n",
            "Epoch 49/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0048 - mean_squared_error: 0.0048 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 50/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0053 - mean_squared_error: 0.0053 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 51/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0057 - mean_squared_error: 0.0057 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 52/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0052 - mean_squared_error: 0.0052 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 53/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0060 - mean_squared_error: 0.0060 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 54/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0051 - mean_squared_error: 0.0051 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 55/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0051 - mean_squared_error: 0.0051 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 56/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0053 - mean_squared_error: 0.0053 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 57/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0051 - mean_squared_error: 0.0051 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 58/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0053 - mean_squared_error: 0.0053 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 59/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0053 - mean_squared_error: 0.0053 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 60/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0051 - mean_squared_error: 0.0051 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 61/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0053 - mean_squared_error: 0.0053 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 62/400\n",
            "1590/1590 [==============================] - 0s 10us/step - loss: 0.0052 - mean_squared_error: 0.0052 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 63/400\n",
            "1590/1590 [==============================] - 0s 17us/step - loss: 0.0047 - mean_squared_error: 0.0047 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 64/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0052 - mean_squared_error: 0.0052 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 65/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0052 - mean_squared_error: 0.0052 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 66/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0050 - mean_squared_error: 0.0050 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 67/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0053 - mean_squared_error: 0.0053 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 68/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0054 - mean_squared_error: 0.0054 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 69/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0053 - mean_squared_error: 0.0053 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 70/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0053 - mean_squared_error: 0.0053 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 71/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0052 - mean_squared_error: 0.0052 - val_loss: 0.0024 - val_mean_squared_error: 0.0024\n",
            "Epoch 72/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0049 - mean_squared_error: 0.0049 - val_loss: 0.0024 - val_mean_squared_error: 0.0024\n",
            "Epoch 73/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0051 - mean_squared_error: 0.0051 - val_loss: 0.0023 - val_mean_squared_error: 0.0023\n",
            "Epoch 74/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0048 - mean_squared_error: 0.0048 - val_loss: 0.0023 - val_mean_squared_error: 0.0023\n",
            "Epoch 75/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0051 - mean_squared_error: 0.0051 - val_loss: 0.0024 - val_mean_squared_error: 0.0024\n",
            "Epoch 76/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 77/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0050 - mean_squared_error: 0.0050 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 78/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0049 - mean_squared_error: 0.0049 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 79/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0052 - mean_squared_error: 0.0052 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 80/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0049 - mean_squared_error: 0.0049 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 81/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0047 - mean_squared_error: 0.0047 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 82/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0050 - mean_squared_error: 0.0050 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 83/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0051 - mean_squared_error: 0.0051 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 84/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0049 - mean_squared_error: 0.0049 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 85/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0049 - mean_squared_error: 0.0049 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 86/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0049 - mean_squared_error: 0.0049 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 87/400\n",
            "1590/1590 [==============================] - 0s 17us/step - loss: 0.0048 - mean_squared_error: 0.0048 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 88/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0052 - mean_squared_error: 0.0052 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 89/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0049 - mean_squared_error: 0.0049 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 90/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0052 - mean_squared_error: 0.0052 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 91/400\n",
            "1590/1590 [==============================] - 0s 17us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 92/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0047 - mean_squared_error: 0.0047 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 93/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0047 - mean_squared_error: 0.0047 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 94/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0047 - mean_squared_error: 0.0047 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 95/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0045 - mean_squared_error: 0.0045 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 96/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0046 - mean_squared_error: 0.0046 - val_loss: 0.0030 - val_mean_squared_error: 0.0030\n",
            "Epoch 97/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0049 - mean_squared_error: 0.0049 - val_loss: 0.0031 - val_mean_squared_error: 0.0031\n",
            "Epoch 98/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0046 - mean_squared_error: 0.0046 - val_loss: 0.0031 - val_mean_squared_error: 0.0031\n",
            "Epoch 99/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0049 - mean_squared_error: 0.0049 - val_loss: 0.0031 - val_mean_squared_error: 0.0031\n",
            "Epoch 100/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0045 - mean_squared_error: 0.0045 - val_loss: 0.0030 - val_mean_squared_error: 0.0030\n",
            "Epoch 101/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0047 - mean_squared_error: 0.0047 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 102/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0046 - mean_squared_error: 0.0046 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 103/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0046 - mean_squared_error: 0.0046 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 104/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 105/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0049 - mean_squared_error: 0.0049 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 106/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 107/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0047 - mean_squared_error: 0.0047 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 108/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 109/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0040 - mean_squared_error: 0.0040 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 110/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0046 - mean_squared_error: 0.0046 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 111/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0047 - mean_squared_error: 0.0047 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 112/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 113/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 114/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0045 - mean_squared_error: 0.0045 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 115/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0045 - mean_squared_error: 0.0045 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 116/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0047 - mean_squared_error: 0.0047 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 117/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 118/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0047 - mean_squared_error: 0.0047 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 119/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0045 - mean_squared_error: 0.0045 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 120/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 121/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0040 - mean_squared_error: 0.0040 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 122/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 123/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 124/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 125/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 126/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 127/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 128/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 129/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 130/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 131/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 132/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 133/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0046 - mean_squared_error: 0.0046 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 134/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 135/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0030 - val_mean_squared_error: 0.0030\n",
            "Epoch 136/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0045 - mean_squared_error: 0.0045 - val_loss: 0.0032 - val_mean_squared_error: 0.0032\n",
            "Epoch 137/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0032 - val_mean_squared_error: 0.0032\n",
            "Epoch 138/400\n",
            "1590/1590 [==============================] - 0s 17us/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0032 - val_mean_squared_error: 0.0032\n",
            "Epoch 139/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0031 - val_mean_squared_error: 0.0031\n",
            "Epoch 140/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0046 - mean_squared_error: 0.0046 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 141/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0045 - mean_squared_error: 0.0045 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 142/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 143/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 144/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 145/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0040 - mean_squared_error: 0.0040 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 146/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0038 - mean_squared_error: 0.0038 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 147/400\n",
            "1590/1590 [==============================] - 0s 19us/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 148/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 149/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 150/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 151/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 152/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 153/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0039 - mean_squared_error: 0.0039 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 154/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0039 - mean_squared_error: 0.0039 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 155/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 156/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0040 - mean_squared_error: 0.0040 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 157/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0039 - mean_squared_error: 0.0039 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 158/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0040 - mean_squared_error: 0.0040 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 159/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0040 - mean_squared_error: 0.0040 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 160/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 161/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 162/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 163/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0040 - mean_squared_error: 0.0040 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 164/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0023 - val_mean_squared_error: 0.0023\n",
            "Epoch 165/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0039 - mean_squared_error: 0.0039 - val_loss: 0.0023 - val_mean_squared_error: 0.0023\n",
            "Epoch 166/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0038 - mean_squared_error: 0.0038 - val_loss: 0.0024 - val_mean_squared_error: 0.0024\n",
            "Epoch 167/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 168/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 169/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 170/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0039 - mean_squared_error: 0.0039 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 171/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0038 - mean_squared_error: 0.0038 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 172/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0037 - mean_squared_error: 0.0037 - val_loss: 0.0029 - val_mean_squared_error: 0.0029\n",
            "Epoch 173/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 174/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0038 - mean_squared_error: 0.0038 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 175/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0038 - mean_squared_error: 0.0038 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 176/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0038 - mean_squared_error: 0.0038 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 177/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 178/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0039 - mean_squared_error: 0.0039 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 179/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 180/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 181/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0038 - mean_squared_error: 0.0038 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 182/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0039 - mean_squared_error: 0.0039 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 183/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 184/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0037 - mean_squared_error: 0.0037 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 185/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 186/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 187/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 188/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 189/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 190/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0038 - mean_squared_error: 0.0038 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 191/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0039 - mean_squared_error: 0.0039 - val_loss: 0.0028 - val_mean_squared_error: 0.0028\n",
            "Epoch 192/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0038 - mean_squared_error: 0.0038 - val_loss: 0.0027 - val_mean_squared_error: 0.0027\n",
            "Epoch 193/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0037 - mean_squared_error: 0.0037 - val_loss: 0.0026 - val_mean_squared_error: 0.0026\n",
            "Epoch 194/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0037 - mean_squared_error: 0.0037 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 195/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0024 - val_mean_squared_error: 0.0024\n",
            "Epoch 196/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0024 - val_mean_squared_error: 0.0024\n",
            "Epoch 197/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 198/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0024 - val_mean_squared_error: 0.0024\n",
            "Epoch 199/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0024 - val_mean_squared_error: 0.0024\n",
            "Epoch 200/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0037 - mean_squared_error: 0.0037 - val_loss: 0.0023 - val_mean_squared_error: 0.0023\n",
            "Epoch 201/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0022 - val_mean_squared_error: 0.0022\n",
            "Epoch 202/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0022 - val_mean_squared_error: 0.0022\n",
            "Epoch 203/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0023 - val_mean_squared_error: 0.0023\n",
            "Epoch 204/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0024 - val_mean_squared_error: 0.0024\n",
            "Epoch 205/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0025 - val_mean_squared_error: 0.0025\n",
            "Epoch 206/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0037 - mean_squared_error: 0.0037 - val_loss: 0.0024 - val_mean_squared_error: 0.0024\n",
            "Epoch 207/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0037 - mean_squared_error: 0.0037 - val_loss: 0.0023 - val_mean_squared_error: 0.0023\n",
            "Epoch 208/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0038 - mean_squared_error: 0.0038 - val_loss: 0.0023 - val_mean_squared_error: 0.0023\n",
            "Epoch 209/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0034 - mean_squared_error: 0.0034 - val_loss: 0.0023 - val_mean_squared_error: 0.0023\n",
            "Epoch 210/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0023 - val_mean_squared_error: 0.0023\n",
            "Epoch 211/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0036 - mean_squared_error: 0.0036 - val_loss: 0.0023 - val_mean_squared_error: 0.0023\n",
            "Epoch 212/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0022 - val_mean_squared_error: 0.0022\n",
            "Epoch 213/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0022 - val_mean_squared_error: 0.0022\n",
            "Epoch 214/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0034 - mean_squared_error: 0.0034 - val_loss: 0.0021 - val_mean_squared_error: 0.0021\n",
            "Epoch 215/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0039 - mean_squared_error: 0.0039 - val_loss: 0.0020 - val_mean_squared_error: 0.0020\n",
            "Epoch 216/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0037 - mean_squared_error: 0.0037 - val_loss: 0.0020 - val_mean_squared_error: 0.0020\n",
            "Epoch 217/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0020 - val_mean_squared_error: 0.0020\n",
            "Epoch 218/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0020 - val_mean_squared_error: 0.0020\n",
            "Epoch 219/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0039 - mean_squared_error: 0.0039 - val_loss: 0.0019 - val_mean_squared_error: 0.0019\n",
            "Epoch 220/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0019 - val_mean_squared_error: 0.0019\n",
            "Epoch 221/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0020 - val_mean_squared_error: 0.0020\n",
            "Epoch 222/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0034 - mean_squared_error: 0.0034 - val_loss: 0.0022 - val_mean_squared_error: 0.0022\n",
            "Epoch 223/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0022 - val_mean_squared_error: 0.0022\n",
            "Epoch 224/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0022 - val_mean_squared_error: 0.0022\n",
            "Epoch 225/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0034 - mean_squared_error: 0.0034 - val_loss: 0.0021 - val_mean_squared_error: 0.0021\n",
            "Epoch 226/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0021 - val_mean_squared_error: 0.0021\n",
            "Epoch 227/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0034 - mean_squared_error: 0.0034 - val_loss: 0.0020 - val_mean_squared_error: 0.0020\n",
            "Epoch 228/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0019 - val_mean_squared_error: 0.0019\n",
            "Epoch 229/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0020 - val_mean_squared_error: 0.0020\n",
            "Epoch 230/400\n",
            "1590/1590 [==============================] - 0s 17us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0019 - val_mean_squared_error: 0.0019\n",
            "Epoch 231/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0020 - val_mean_squared_error: 0.0020\n",
            "Epoch 232/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0020 - val_mean_squared_error: 0.0020\n",
            "Epoch 233/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0021 - val_mean_squared_error: 0.0021\n",
            "Epoch 234/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0034 - mean_squared_error: 0.0034 - val_loss: 0.0022 - val_mean_squared_error: 0.0022\n",
            "Epoch 235/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0022 - val_mean_squared_error: 0.0022\n",
            "Epoch 236/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0021 - val_mean_squared_error: 0.0021\n",
            "Epoch 237/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0035 - mean_squared_error: 0.0035 - val_loss: 0.0020 - val_mean_squared_error: 0.0020\n",
            "Epoch 238/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0019 - val_mean_squared_error: 0.0019\n",
            "Epoch 239/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0018 - val_mean_squared_error: 0.0018\n",
            "Epoch 240/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0034 - mean_squared_error: 0.0034 - val_loss: 0.0018 - val_mean_squared_error: 0.0018\n",
            "Epoch 241/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0017 - val_mean_squared_error: 0.0017\n",
            "Epoch 242/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0017 - val_mean_squared_error: 0.0017\n",
            "Epoch 243/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0018 - val_mean_squared_error: 0.0018\n",
            "Epoch 244/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0019 - val_mean_squared_error: 0.0019\n",
            "Epoch 245/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0019 - val_mean_squared_error: 0.0019\n",
            "Epoch 246/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0018 - val_mean_squared_error: 0.0018\n",
            "Epoch 247/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0017 - val_mean_squared_error: 0.0017\n",
            "Epoch 248/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0017 - val_mean_squared_error: 0.0017\n",
            "Epoch 249/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0034 - mean_squared_error: 0.0034 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 250/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0034 - mean_squared_error: 0.0034 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 251/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 252/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 253/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 254/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0034 - mean_squared_error: 0.0034 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 255/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0018 - val_mean_squared_error: 0.0018\n",
            "Epoch 256/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0020 - val_mean_squared_error: 0.0020\n",
            "Epoch 257/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0020 - val_mean_squared_error: 0.0020\n",
            "Epoch 258/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0019 - val_mean_squared_error: 0.0019\n",
            "Epoch 259/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0017 - val_mean_squared_error: 0.0017\n",
            "Epoch 260/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 261/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 262/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 263/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 264/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0017 - val_mean_squared_error: 0.0017\n",
            "Epoch 265/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0033 - mean_squared_error: 0.0033 - val_loss: 0.0018 - val_mean_squared_error: 0.0018\n",
            "Epoch 266/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0018 - val_mean_squared_error: 0.0018\n",
            "Epoch 267/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0017 - val_mean_squared_error: 0.0017\n",
            "Epoch 268/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 269/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 270/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 271/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 272/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 273/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0034 - mean_squared_error: 0.0034 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 274/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 275/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 276/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 277/400\n",
            "1590/1590 [==============================] - 0s 17us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 278/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0017 - val_mean_squared_error: 0.0017\n",
            "Epoch 279/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 280/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 281/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 282/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 283/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 284/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0034 - mean_squared_error: 0.0034 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 285/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0017 - val_mean_squared_error: 0.0017\n",
            "Epoch 286/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0017 - val_mean_squared_error: 0.0017\n",
            "Epoch 287/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 288/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 289/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 290/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 291/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 292/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 293/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 294/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 295/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 296/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 297/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 298/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 299/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 300/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 301/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 302/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 303/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 304/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 305/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 306/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 307/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 308/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0016 - val_mean_squared_error: 0.0016\n",
            "Epoch 309/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 310/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 311/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 312/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 313/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 314/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 315/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 316/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 317/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 318/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 319/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 320/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 321/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 322/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 323/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 324/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 325/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 326/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 327/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 328/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 329/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 330/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 331/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 332/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 333/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 334/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 335/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 336/400\n",
            "1590/1590 [==============================] - 0s 18us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 337/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 338/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 339/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 340/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 341/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 342/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 343/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 344/400\n",
            "1590/1590 [==============================] - 0s 20us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0010 - val_mean_squared_error: 0.0010\n",
            "Epoch 345/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0010 - val_mean_squared_error: 0.0010\n",
            "Epoch 346/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 347/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 348/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 349/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 350/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 351/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 352/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 353/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 354/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 355/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0010 - val_mean_squared_error: 0.0010\n",
            "Epoch 356/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 9.6981e-04 - val_mean_squared_error: 9.6981e-04\n",
            "Epoch 357/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 9.7550e-04 - val_mean_squared_error: 9.7550e-04\n",
            "Epoch 358/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0010 - val_mean_squared_error: 0.0010\n",
            "Epoch 359/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 360/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 361/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 362/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 363/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 364/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0026 - mean_squared_error: 0.0026 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 365/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 366/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 367/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 368/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 369/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 370/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 371/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 372/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0010 - val_mean_squared_error: 0.0010\n",
            "Epoch 373/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0010 - val_mean_squared_error: 0.0010\n",
            "Epoch 374/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0010 - val_mean_squared_error: 0.0010\n",
            "Epoch 375/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 9.7248e-04 - val_mean_squared_error: 9.7248e-04\n",
            "Epoch 376/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0010 - val_mean_squared_error: 0.0010\n",
            "Epoch 377/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0010 - val_mean_squared_error: 0.0010\n",
            "Epoch 378/400\n",
            "1590/1590 [==============================] - 0s 16us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 379/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0031 - mean_squared_error: 0.0031 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 380/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 381/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0032 - mean_squared_error: 0.0032 - val_loss: 0.0015 - val_mean_squared_error: 0.0015\n",
            "Epoch 382/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 383/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 384/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 385/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0010 - val_mean_squared_error: 0.0010\n",
            "Epoch 386/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 9.9956e-04 - val_mean_squared_error: 9.9956e-04\n",
            "Epoch 387/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 9.8333e-04 - val_mean_squared_error: 9.8333e-04\n",
            "Epoch 388/400\n",
            "1590/1590 [==============================] - 0s 14us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 9.6172e-04 - val_mean_squared_error: 9.6172e-04\n",
            "Epoch 389/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0011 - val_mean_squared_error: 0.0011\n",
            "Epoch 390/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 391/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0029 - mean_squared_error: 0.0029 - val_loss: 0.0013 - val_mean_squared_error: 0.0013\n",
            "Epoch 392/400\n",
            "1590/1590 [==============================] - 0s 15us/step - loss: 0.0030 - mean_squared_error: 0.0030 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
            "Epoch 393/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 394/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0010 - val_mean_squared_error: 0.0010\n",
            "Epoch 395/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 8.9187e-04 - val_mean_squared_error: 8.9187e-04\n",
            "Epoch 396/400\n",
            "1590/1590 [==============================] - 0s 13us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 8.9968e-04 - val_mean_squared_error: 8.9968e-04\n",
            "Epoch 397/400\n",
            "1590/1590 [==============================] - 0s 21us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0010 - val_mean_squared_error: 0.0010\n",
            "Epoch 398/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 399/400\n",
            "1590/1590 [==============================] - 0s 11us/step - loss: 0.0027 - mean_squared_error: 0.0027 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n",
            "Epoch 400/400\n",
            "1590/1590 [==============================] - 0s 12us/step - loss: 0.0028 - mean_squared_error: 0.0028 - val_loss: 0.0012 - val_mean_squared_error: 0.0012\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nESiRvx7jIsS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_history(history):\n",
        "    loss_list = [s for s in history.history.keys() if 'loss' in s and 'val' not in s]\n",
        "    val_loss_list = [s for s in history.history.keys() if 'loss' in s and 'val' in s]\n",
        "    acc_list = [s for s in history.history.keys() if 'acc' in s and 'val' not in s]\n",
        "    val_acc_list = [s for s in history.history.keys() if 'acc' in s and 'val' in s]\n",
        "    \n",
        "    if len(loss_list) == 0:\n",
        "        print('Loss is missing in history')\n",
        "        return \n",
        "    \n",
        "    ## As loss always exists\n",
        "    epochs = range(1,len(history.history[loss_list[0]]) + 1)\n",
        "    \n",
        "    ## Loss\n",
        "    plt.figure(1)\n",
        "    for l in loss_list:\n",
        "        plt.plot(epochs, history.history[l], 'b', label='Training loss (' + str(str(format(history.history[l][-1],'.5f'))+')'))\n",
        "    for l in val_loss_list:\n",
        "        plt.plot(epochs, history.history[l], 'g', label='Validation loss (' + str(str(format(history.history[l][-1],'.5f'))+')'))\n",
        "    \n",
        "    plt.title('Loss')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "    \n",
        "    ## Accuracy\n",
        "    plt.figure(2)\n",
        "    for l in acc_list:\n",
        "        plt.plot(epochs, history.history[l], 'b', label='Training accuracy (' + str(format(history.history[l][-1],'.5f'))+')')\n",
        "    for l in val_acc_list:    \n",
        "        plt.plot(epochs, history.history[l], 'g', label='Validation accuracy (' + str(format(history.history[l][-1],'.5f'))+')')\n",
        "\n",
        "    plt.title('Accuracy')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "43S8M9E7YryH",
        "colab_type": "code",
        "outputId": "0e5f74d2-649b-40dc-8d50-d737cf096854",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        }
      },
      "cell_type": "code",
      "source": [
        "plot_history(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ISmpLAhmGMcYEjD9bJAuAfap6QFWbgJXAEt8NVDVfVfOA1g77Xgr8Q1XLVLUc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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2JTl3D4+/JcktSW5K8qUkR3RZjyRp5joLiiSLgAuBE4G1wIYkayd1ux5YV1UvBD4H/HFX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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2KfB_x_UY6Kz",
        "colab_type": "code",
        "outputId": "229eb2d6-b60f-422a-b227-25911667e3cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt2\n",
        "\n",
        "plt2.plot(model.predict(trainX),color='red', label='Prediction')\n",
        "plt2.plot(trainY,color='blue', label='Actual')\n",
        "plt2.legend(loc='best')\n",
        "plt2.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UHUFT3y4+zcpBZP2LAAAlx085HLO/iU6f7jRjQXGxd9nhmcpJ/fpGN6An6I24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\nq/dZtthBAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "KBlOjJBGY7EV",
        "colab_type": "code",
        "outputId": "09db0d11-dc2c-4fdc-f048-fc37db6f0a73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt2\n",
        "\n",
        "plt2.plot(model.predict(testX),color='red', label='Prediction')\n",
        "plt2.plot(testY,color='blue', label='Actual')\n",
        "plt2.legend(loc='best')\n",
        "plt2.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FSq9alP0BACEKgQR/IfysXz+zbN8++77LNy+AzEyiHriHR7ttAGDY0ZfsFyQk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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}