26_SimpleStockPredictionTensorFlow.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Simple Stock Prediction Tensorflow"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AAPL'\n",
        "start = '2018-01-01'\n",
        "end = '2019-01-31'\n",
        "\n",
        "# Read data \n",
        "df = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-02</th>\n",
              "      <td>170.160004</td>\n",
              "      <td>172.300003</td>\n",
              "      <td>169.259995</td>\n",
              "      <td>172.259995</td>\n",
              "      <td>168.987320</td>\n",
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              "      <th>2018-01-03</th>\n",
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              "      <td>172.229996</td>\n",
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              "    <tr>\n",
              "      <th>2018-01-04</th>\n",
              "      <td>172.539993</td>\n",
              "      <td>173.470001</td>\n",
              "      <td>172.080002</td>\n",
              "      <td>173.029999</td>\n",
              "      <td>169.742706</td>\n",
              "      <td>22434600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05</th>\n",
              "      <td>173.440002</td>\n",
              "      <td>175.369995</td>\n",
              "      <td>173.050003</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>171.675278</td>\n",
              "      <td>23660000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08</th>\n",
              "      <td>174.350006</td>\n",
              "      <td>175.610001</td>\n",
              "      <td>173.929993</td>\n",
              "      <td>174.350006</td>\n",
              "      <td>171.037628</td>\n",
              "      <td>20567800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2018-01-02  170.160004  172.300003  169.259995  172.259995  168.987320   \n",
              "2018-01-03  172.529999  174.550003  171.960007  172.229996  168.957886   \n",
              "2018-01-04  172.539993  173.470001  172.080002  173.029999  169.742706   \n",
              "2018-01-05  173.440002  175.369995  173.050003  175.000000  171.675278   \n",
              "2018-01-08  174.350006  175.610001  173.929993  174.350006  171.037628   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2018-01-02  25555900  \n",
              "2018-01-03  29517900  \n",
              "2018-01-04  22434600  \n",
              "2018-01-05  23660000  \n",
              "2018-01-08  20567800  "
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
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              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>2019-01-25</th>\n",
              "      <td>155.479996</td>\n",
              "      <td>158.130005</td>\n",
              "      <td>154.320007</td>\n",
              "      <td>157.759995</td>\n",
              "      <td>157.086288</td>\n",
              "      <td>33535500</td>\n",
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              "    <tr>\n",
              "      <th>2019-01-28</th>\n",
              "      <td>155.789993</td>\n",
              "      <td>156.330002</td>\n",
              "      <td>153.660004</td>\n",
              "      <td>156.300003</td>\n",
              "      <td>155.632523</td>\n",
              "      <td>26192100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-29</th>\n",
              "      <td>156.250000</td>\n",
              "      <td>158.130005</td>\n",
              "      <td>154.110001</td>\n",
              "      <td>154.679993</td>\n",
              "      <td>154.019440</td>\n",
              "      <td>41587200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-30</th>\n",
              "      <td>163.250000</td>\n",
              "      <td>166.149994</td>\n",
              "      <td>160.229996</td>\n",
              "      <td>165.250000</td>\n",
              "      <td>164.544296</td>\n",
              "      <td>61109800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-31</th>\n",
              "      <td>166.110001</td>\n",
              "      <td>169.000000</td>\n",
              "      <td>164.559998</td>\n",
              "      <td>166.440002</td>\n",
              "      <td>165.729218</td>\n",
              "      <td>40739600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2019-01-25  155.479996  158.130005  154.320007  157.759995  157.086288   \n",
              "2019-01-28  155.789993  156.330002  153.660004  156.300003  155.632523   \n",
              "2019-01-29  156.250000  158.130005  154.110001  154.679993  154.019440   \n",
              "2019-01-30  163.250000  166.149994  160.229996  165.250000  164.544296   \n",
              "2019-01-31  166.110001  169.000000  164.559998  166.440002  165.729218   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2019-01-25  33535500  \n",
              "2019-01-28  26192100  \n",
              "2019-01-29  41587200  \n",
              "2019-01-30  61109800  \n",
              "2019-01-31  40739600  "
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      "execution_count": 3,
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    },
    {
      "cell_type": "code",
      "source": [
        "# Create Data\n",
        "df['Open_Close'] = (df['Open'] - df['Adj Close'])/df['Open']\n",
        "df['High_Low'] = (df['High'] - df['Low'])/df['Low']\n",
        "df['Increase_Decrease'] = np.where(df['Volume'].shift(-1) > df['Volume'],1,0)\n",
        "df['Buy_Sell_on_Open'] = np.where(df['Open'].shift(-1) > df['Open'],1,0)\n",
        "df['Buy_Sell'] = np.where(df['Adj Close'].shift(-1) > df['Adj Close'],1,0)\n",
        "df['Returns'] = df['Adj Close'].pct_change()\n",
        "df = df.dropna()\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
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              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Open_Close</th>\n",
              "      <th>High_Low</th>\n",
              "      <th>Increase_Decrease</th>\n",
              "      <th>Buy_Sell_on_Open</th>\n",
              "      <th>Buy_Sell</th>\n",
              "      <th>Returns</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",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-03</th>\n",
              "      <td>172.529999</td>\n",
              "      <td>174.550003</td>\n",
              "      <td>171.960007</td>\n",
              "      <td>172.229996</td>\n",
              "      <td>168.957886</td>\n",
              "      <td>29517900</td>\n",
              "      <td>0.020704</td>\n",
              "      <td>0.015062</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.000174</td>\n",
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              "    <tr>\n",
              "      <th>2018-01-04</th>\n",
              "      <td>172.539993</td>\n",
              "      <td>173.470001</td>\n",
              "      <td>172.080002</td>\n",
              "      <td>173.029999</td>\n",
              "      <td>169.742706</td>\n",
              "      <td>22434600</td>\n",
              "      <td>0.016212</td>\n",
              "      <td>0.008078</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.004645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05</th>\n",
              "      <td>173.440002</td>\n",
              "      <td>175.369995</td>\n",
              "      <td>173.050003</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>171.675278</td>\n",
              "      <td>23660000</td>\n",
              "      <td>0.010175</td>\n",
              "      <td>0.013406</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.011385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08</th>\n",
              "      <td>174.350006</td>\n",
              "      <td>175.610001</td>\n",
              "      <td>173.929993</td>\n",
              "      <td>174.350006</td>\n",
              "      <td>171.037628</td>\n",
              "      <td>20567800</td>\n",
              "      <td>0.018998</td>\n",
              "      <td>0.009659</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.003714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-09</th>\n",
              "      <td>174.550003</td>\n",
              "      <td>175.059998</td>\n",
              "      <td>173.410004</td>\n",
              "      <td>174.330002</td>\n",
              "      <td>171.018005</td>\n",
              "      <td>21584000</td>\n",
              "      <td>0.020235</td>\n",
              "      <td>0.009515</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.000115</td>\n",
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            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2018-01-03  172.529999  174.550003  171.960007  172.229996  168.957886   \n",
              "2018-01-04  172.539993  173.470001  172.080002  173.029999  169.742706   \n",
              "2018-01-05  173.440002  175.369995  173.050003  175.000000  171.675278   \n",
              "2018-01-08  174.350006  175.610001  173.929993  174.350006  171.037628   \n",
              "2018-01-09  174.550003  175.059998  173.410004  174.330002  171.018005   \n",
              "\n",
              "              Volume  Open_Close  High_Low  Increase_Decrease  \\\n",
              "Date                                                            \n",
              "2018-01-03  29517900    0.020704  0.015062                  0   \n",
              "2018-01-04  22434600    0.016212  0.008078                  1   \n",
              "2018-01-05  23660000    0.010175  0.013406                  0   \n",
              "2018-01-08  20567800    0.018998  0.009659                  1   \n",
              "2018-01-09  21584000    0.020235  0.009515                  1   \n",
              "\n",
              "            Buy_Sell_on_Open  Buy_Sell   Returns  \n",
              "Date                                              \n",
              "2018-01-03                 1         1 -0.000174  \n",
              "2018-01-04                 1         1  0.004645  \n",
              "2018-01-05                 1         0  0.011385  \n",
              "2018-01-08                 1         0 -0.003714  \n",
              "2018-01-09                 0         0 -0.000115  "
            ]
          },
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      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": [
              "(271, 12)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dense is a layer of connected neurons.\n",
        "\n",
        "Loss Function is used to optimize the parameter values in a neural network model. Therefore, it measures squared or absolute error between a network's output and some target or desired output.\n",
        "\nOptimizer generates a new and improved values."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])\n",
        "model.compile(optimizer='sgd', loss='mean_squared_error')"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array(df['Returns'], dtype = float) # Feature\n",
        "Y = np.array(df['Adj Close'], dtype = float) # Target"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X, Y, epochs=100)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "271/271 [==============================] - 0s 384us/step - loss: 29862.6151\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 2/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 20882.1033\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 3/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 14649.7054\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 4/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 10321.6915\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 5/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 7311.5090\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 6/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 5232.4851\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 7/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 3778.5427\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 8/100\n",
            "271/271 [==============================] - 0s 15us/step - loss: 2781.6663\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 9/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 2081.9986\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 10/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 1600.5304\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 11/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 1257.2099\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 12/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 1016.9080\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 13/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 852.8637\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 14/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 740.1994\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 15/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 660.6490\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 16/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 606.1884\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 17/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 567.5818\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 18/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 541.5006\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 19/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 521.9415\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 20/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 509.4774\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 21/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 500.3909\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 22/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 494.6873\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 23/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 490.1271\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 24/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 486.9333\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 25/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 485.0335\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 26/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 483.2784\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 27/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 482.5049\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 28/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 482.2680\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 29/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 481.4616\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 30/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 481.1853\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 31/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.9711\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 32/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.9908\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 33/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.7881\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 34/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.8242\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 35/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.9485\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 36/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.4828\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 37/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.5443\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 38/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.7324\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 39/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.4960\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 40/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.3549\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 41/100\n",
            "271/271 [==============================] - 0s 30us/step - loss: 480.6947\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 42/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.3781\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 43/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.4134\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 44/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.2967\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 45/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.3472\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 46/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.3613\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 47/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.5523\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 48/100\n",
            "271/271 [==============================] - 0s 30us/step - loss: 480.8593\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 49/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.5211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 50/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.4001\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 51/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.4453\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 52/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.3956\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 53/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.3641\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 54/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.5318\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 55/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.7172\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 56/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.2990\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 57/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.2599\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 58/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.8522\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 59/100\n",
            "271/271 [==============================] - 0s 26us/step - loss: 480.4668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 60/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.4831\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 61/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.5162\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 62/100\n",
            "271/271 [==============================] - 0s 15us/step - loss: 480.5690\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 63/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.5871\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 64/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.3003\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 65/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.3529\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 66/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.7579\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 67/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.3303\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 68/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.3165\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 69/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.5297\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 70/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.3234\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 71/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.2816\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 72/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.2696\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 73/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.5137\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 74/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.2702\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 75/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.3494\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 76/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.3009\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 77/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.3443\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 78/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.5458\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 79/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.3715\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 80/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.5095\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 81/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.5246\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 82/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.3587\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 83/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.3945\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 84/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.5273\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 85/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.7397\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 86/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.4097\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 87/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.3180\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 88/100\n",
            "271/271 [==============================] - 0s 15us/step - loss: 480.4787\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 89/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.5264\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 90/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.2346\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 91/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.4207\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 92/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.3832\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 93/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.4108\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 94/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.2546\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 95/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.2427\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 96/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.3454\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 97/100\n",
            "271/271 [==============================] - 0s 22us/step - loss: 480.6622\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 98/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.4723\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 99/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.3588\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 100/100\n",
            "271/271 [==============================] - 0s 18us/step - loss: 480.4675\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x233328291d0>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.predict([0])) # Predict on open at 18.15, What the closing price prediction?"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 184.52786255]]\n"
          ]
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "If the values return nan.  Therefore, we need to normalize the data."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "normalized_X = scaler.fit_transform(np.array(df['Returns']).reshape(271,-1))\n",
        "X = np.array(normalized_X, dtype = float)\n",
        "normalized_Y = scaler.fit_transform(np.array(df['Adj Close']).reshape(271,-1))\n",
        "Y = np.array(normalized_Y, dtype = float)"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape)\n",
        "print(Y.shape)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(271, 1)\n",
            "(271, 1)\n"
          ]
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model():\n",
        "  model = keras.Sequential([\n",
        "    layers.Dense(64, activation=tf.nn.relu, input_shape=[len(train_dataset.keys())]),\n",
        "    layers.Dense(64, activation=tf.nn.relu),\n",
        "    layers.Dense(1)\n",
        "  ])\n",
        "\n",
        "  optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
        "\n",
        "  model.compile(loss='mean_squared_error',\n",
        "                optimizer=optimizer,\n",
        "                metrics=['mean_absolute_error', 'mean_squared_error'])\n",
        "  return model"
      ],
      "outputs": [],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Still have nan\n",
        "print(model.predict([0])) # Predict on open at 18.15, What the closing price prediction?"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 184.52786255]]\n"
          ]
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      },
      "mimetype": "text/x-python",
      "name": "python",
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py",
      "version": "3.5.5"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}