Stock_Price_Prediction_with_various_regressions.ipynb

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  "metadata": {
    "colab": {
      "name": "Stock Price Prediction with various regressions.ipynb",
      "version": "0.3.2",
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      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
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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/davearch/stock-price-prediction-machine-learning/blob/master/Stock_Price_Prediction_with_various_regressions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V93SybBqgIxm",
        "colab_type": "code",
        "outputId": "282ea095-0030-4fbd-9a46-30a6f3510ec6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# import packages\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import sklearn\n",
        "from sklearn.linear_model import Ridge, Lasso\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv1D, LSTM, Dense\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "from tensorflow.keras.losses import Huber\n",
        "from keras.optimizers import SGD"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xvxcEWwg0r5",
        "colab_type": "code",
        "outputId": "51c564be-1c59-47b3-fa2d-3f5fda6c365e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "!pip install yfinance --upgrade --no-cache-dir"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting yfinance\n",
            "  Downloading https://files.pythonhosted.org/packages/53/0e/40387099824c98be22cd7e33a620e9d38b61998b031f0b33f0b9959717d2/yfinance-0.1.45.tar.gz\n",
            "Requirement already satisfied, skipping upgrade: pandas>=0.24 in /usr/local/lib/python3.6/dist-packages (from yfinance) (0.24.2)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.16.5)\n",
            "Requirement already satisfied, skipping upgrade: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from yfinance) (2.21.0)\n",
            "Requirement already satisfied, skipping upgrade: multitasking>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from yfinance) (0.0.9)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2.5.3)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2.8)\n",
            "Requirement already satisfied, skipping upgrade: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2019.6.16)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.5.0->pandas>=0.24->yfinance) (1.12.0)\n",
            "Building wheels for collected packages: yfinance\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.45-cp36-none-any.whl size=14652 sha256=f7384eb7c414d052ca39f5fa67d378bfce732450da196a41b53931787c4df851\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-23tedf87/wheels/0c/d1/df/aa9a7744a4ac353cc9a1f2c3aaea7c1f457fc49de4286f2d88\n",
            "Successfully built yfinance\n",
            "Installing collected packages: yfinance\n",
            "Successfully installed yfinance-0.1.45\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64qDPpoehYRF",
        "colab_type": "code",
        "outputId": "1f2078c9-b523-456a-fe11-66efed13b768",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "# download stock data to my google drive\n",
        "from google.colab import drive\n",
        "drive.mount('drive', force_remount=True)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQ4iIYXZhnAp",
        "colab_type": "code",
        "outputId": "35a9cdb9-0257-48eb-e34d-34c088fcc7e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "# import stock data from yahoo\n",
        "# Looking at Bitcoin prices\n",
        "from pandas_datareader import data as pdr\n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()\n",
        "df = pdr.get_data_yahoo(\"BTC-USD\", start=\"2010-07-16\").reset_index()\n",
        "df.to_csv('BTC_USD.csv', index=False)\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ],
          "name": "stdout"
        },
        {
          "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-07-17</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-07-18</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.08585</td>\n",
              "      <td>0.05941</td>\n",
              "      <td>0.08584</td>\n",
              "      <td>0.08584</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-07-19</td>\n",
              "      <td>0.08584</td>\n",
              "      <td>0.09307</td>\n",
              "      <td>0.07723</td>\n",
              "      <td>0.08080</td>\n",
              "      <td>0.08080</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-07-20</td>\n",
              "      <td>0.08080</td>\n",
              "      <td>0.08181</td>\n",
              "      <td>0.07426</td>\n",
              "      <td>0.07474</td>\n",
              "      <td>0.07474</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-07-21</td>\n",
              "      <td>0.07474</td>\n",
              "      <td>0.07921</td>\n",
              "      <td>0.06634</td>\n",
              "      <td>0.07921</td>\n",
              "      <td>0.07921</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "        Date     Open     High      Low    Close  Adj Close  Volume\n",
              "0 2010-07-17  0.04951  0.04951  0.04951  0.04951    0.04951       0\n",
              "1 2010-07-18  0.04951  0.08585  0.05941  0.08584    0.08584       5\n",
              "2 2010-07-19  0.08584  0.09307  0.07723  0.08080    0.08080      49\n",
              "3 2010-07-20  0.08080  0.08181  0.07426  0.07474    0.07474      20\n",
              "4 2010-07-21  0.07474  0.07921  0.06634  0.07921    0.07921      42"
            ]
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          "metadata": {
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    {
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      "metadata": {
        "id": "Dw9zhMKMi1mJ",
        "colab_type": "code",
        "outputId": "4fbd07bb-a037-4f76-c8fd-8f79d95dc00a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "# replace numerical index with Date field\n",
        "df.set_index('Date', inplace=True)\n",
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-07-17</th>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.04951</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-18</th>\n",
              "      <td>0.04951</td>\n",
              "      <td>0.08585</td>\n",
              "      <td>0.05941</td>\n",
              "      <td>0.08584</td>\n",
              "      <td>0.08584</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-19</th>\n",
              "      <td>0.08584</td>\n",
              "      <td>0.09307</td>\n",
              "      <td>0.07723</td>\n",
              "      <td>0.08080</td>\n",
              "      <td>0.08080</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-20</th>\n",
              "      <td>0.08080</td>\n",
              "      <td>0.08181</td>\n",
              "      <td>0.07426</td>\n",
              "      <td>0.07474</td>\n",
              "      <td>0.07474</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-21</th>\n",
              "      <td>0.07474</td>\n",
              "      <td>0.07921</td>\n",
              "      <td>0.06634</td>\n",
              "      <td>0.07921</td>\n",
              "      <td>0.07921</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Open     High      Low    Close  Adj Close  Volume\n",
              "Date                                                             \n",
              "2010-07-17  0.04951  0.04951  0.04951  0.04951    0.04951       0\n",
              "2010-07-18  0.04951  0.08585  0.05941  0.08584    0.08584       5\n",
              "2010-07-19  0.08584  0.09307  0.07723  0.08080    0.08080      49\n",
              "2010-07-20  0.08080  0.08181  0.07426  0.07474    0.07474      20\n",
              "2010-07-21  0.07474  0.07921  0.06634  0.07921    0.07921      42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
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      "metadata": {
        "id": "O1DcGomEjRRd",
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        "outputId": "05c2bd4a-38d9-42f9-c335-35dc382cad4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "df['Adj Close'].plot(label='BTC-USD', figsize=(16,8), title='Adjusted Closing Price', grid=True)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9e43ef0b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
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/H8Zaf6XjcyV93sySkjKSPuScCxaZ+rC8lZQ75c3BDebhXi3pJjO7QtILki6p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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-nbmkKRLjvi-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "window_size = 32 # look at 32 days in the past\n",
        "# we will have 32 day data windows and predict the 33rd day\n",
        "\n",
        "# get indices of access for the data\n",
        "num_samples = len(df) - window_size\n",
        "indices = np.arange(num_samples).astype(np.int)[:,None] + np.arange(window_size + 1).astype(np.int)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oO4nw8-ik4Ic",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = df['Adj Close'].values[indices] # create 2D matrix of training samples"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3M1nBlllFRI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = data[:,:-1] # each row represents 32 days in the past\n",
        "Y = data[:,-1] # each output value represents the 33rd day"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3r46fCq2lWgd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train and test split - 80% training 20% testing\n",
        "split_fraction = 0.8\n",
        "ind_split = int(split_fraction * num_samples)\n",
        "X_train = X[:ind_split]\n",
        "Y_train = Y[:ind_split]\n",
        "X_test = X[ind_split:]\n",
        "Y_test = Y[ind_split:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NkScAPdyl5_z",
        "colab_type": "text"
      },
      "source": [
        "# Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xHLZRPcvl-WB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train\n",
        "ridge_model = Ridge()\n",
        "ridge_model.fit(X_train, Y_train)\n",
        "\n",
        "# Infer\n",
        "y_pred_train_ridge = ridge_model.predict(X_train)\n",
        "y_pred_ridge = ridge_model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yG_GlkiXmSfX",
        "colab_type": "code",
        "outputId": "eb79a5c6-ddf5-45c7-8c76-c4fd75a3b99d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "# plot\n",
        "df_ridge = df.copy()\n",
        "df_ridge.drop(['Open','High','Low','Close','Volume'], axis=1, inplace=True)\n",
        "df_ridge = df_ridge.iloc[window_size:ind_split] # past 32 days we don't know yet\n",
        "df_ridge['Adj Close Train'] = y_pred_train_ridge[:-window_size]\n",
        "df_ridge.plot(label=\"BTC-USD\", figsize=(16, 8), title='Adjusted Closing Price', grid=True)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9e3cf6ac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
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SJEnSZOreAUAx10xpUKxMo2zOtKN9x5DzdPwFNX2cwVWSJEmStFe33P8sO7sq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\nJEkaNQtXSZIkSdKo/QcdiCn9FrJ7HgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCHuh1DXntol",
        "colab_type": "code",
        "outputId": "ae273cac-7507-47a7-803a-7e59f8380a98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "# test\n",
        "df_ridge = df.copy()\n",
        "df_ridge.drop(['Open','High','Low','Close','Volume'], axis=1, inplace=True)\n",
        "df_ridge = df_ridge.iloc[ind_split+window_size:] # past 32 days we don't know\n",
        "df_ridge['Adj Close Test'] = y_pred_ridge\n",
        "df_ridge.plot(label='BTC-USD', figsize=(16,8), title='Adjusted Closing Price', grid=True)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9e14b2828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kwSjUHqgHb4BXn6CrEtKSTkKtl+853ZKLd00KvOYxqdR+8afrqHTuiV/43XW/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8GHYt9ifziz5/SbVsxEUZSxHvVZcVgMtMe0z9B7j8DuP0M0SDSRpNEfEW7JES9YXP3Ou3FxITcu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\nsoRaAAAAliXUAgAAsCyhFgAAgGX9AJQ7vgu/C1NPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qF1KQMw7sw-h",
        "colab_type": "text"
      },
      "source": [
        "# Lasso Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_XUxE_DZs15U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train\n",
        "lasso_model = Lasso(alpha=0.1, tol=0.01)\n",
        "lasso_model.fit(X_train, Y_train)\n",
        "\n",
        "# infer\n",
        "y_pred_train_lasso = lasso_model.predict(X_train)\n",
        "y_pred_lasso = lasso_model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jQOPAVVt45a",
        "colab_type": "code",
        "outputId": "588ab9ae-584f-43da-b4a1-87bf03965365",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "# plot lasso training\n",
        "df_lasso = df.copy()\n",
        "df_lasso.drop(['Open', 'High','Low','Close','Volume'], axis=1, inplace=True)\n",
        "df_lasso = df_lasso.iloc[window_size:ind_split]\n",
        "df_lasso['Adj Close Train'] = y_pred_train_lasso[:-window_size]\n",
        "df_lasso.plot(label='BTC-USD', figsize=(16,8), title='Adjusted Closing Price', grid=True)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9e12fbf98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YatKNzn4P6+wJri7OJEmSJEkaST3BtYFijD3NxUx3cO1/KnB3xbXW4CpJkiRJ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\nRKSiqXAVERERERGRivb/5sqskFt7IMQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OC3I1L4LvRMF",
        "colab_type": "code",
        "outputId": "598b30b6-bba0-483f-ae88-724fa0266036",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "# plot lasso testing\n",
        "df_lasso = df.copy()\n",
        "df_lasso.drop(['Open','High','Low','Close','Volume'], axis=1, inplace=True)\n",
        "df_lasso = df_lasso.iloc[ind_split+window_size:]\n",
        "df_lasso['Adj Close Test'] = y_pred_lasso\n",
        "df_lasso.plot(label='BTC-USD', figsize=(16,8), title='Adjusted Closing Price', grid=True)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9df9dbcc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GmobvHiIiIiIiIiPUULb0uQx431rbXVZsjKk3xnjd9yfjNITa7pYXtxtjznPX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\nVwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3atU-f2vojaL",
        "colab_type": "text"
      },
      "source": [
        "# Gradient Boosting Trees"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0gOKPy5Uom88",
        "colab_type": "code",
        "outputId": "956dd198-aa8b-4119-aa8a-68c59b8d9197",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "# Gradient Boosting Trees\n",
        "\n",
        "# tune hyperparameters first via grid search\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "param_grid = {\n",
        "    'learning_rate': [0.1, 0.05, 0.02, 0.01],\n",
        "    'max_depth': [4,6],\n",
        "    'min_samples_leaf': [3, 5, 9, 17],\n",
        "    'max_features': [1.0, 0.3, 0.1]\n",
        "}\n",
        "\n",
        "gb_model = GradientBoostingRegressor(n_estimators=300)\n",
        "gs_cv = GridSearchCV(gb_model, param_grid).fit(X_train, Y_train)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
            "  warnings.warn(CV_WARNING, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
            "  DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ULoc__ah1LM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# optimized hyper parameters\n",
        "lr = gs_cv.best_params_['learning_rate']\n",
        "md = gs_cv.best_params_['max_depth']\n",
        "mf = gs_cv.best_params_['max_features']\n",
        "msl = gs_cv.best_params_['min_samples_leaf']\n",
        "optimized_gb_model = GradientBoostingRegressor(n_estimators=300, learning_rate=lr, max_depth=md, max_features=mf, min_samples_leaf=msl)\n",
        "optimized_gb_model.fit(X_train, Y_train)\n",
        "\n",
        "# infer\n",
        "y_pred_train_gb = optimized_gb_model.predict(X_train)\n",
        "y_pred_gb = optimized_gb_model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1OoKpX2o4zb",
        "colab_type": "code",
        "outputId": "502c7fbb-032f-42ac-c265-2abcd91b5ef1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "# plot training data\n",
        "df_gb = df.copy()\n",
        "df_gb.drop(['Open', 'High', 'Low', 'Close', 'Volume'], axis=1, inplace=True)\n",
        "df_gb = df_gb.iloc[window_size:ind_split] # past 32 days we don't know yet\n",
        "df_gb['Adj Close Train'] = y_pred_train_gb[:-window_size]\n",
        "df_gb.plot(label='BTC_USD', figsize=(16,8), title='Adjusted Closing Price', grid=True)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9df8bbac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D/0OMMqcKS5IkSVKN+htF/cQnPtGz/c///M/9nrdu3Tr23HPPHfY3Nzfzj//4j/zjP/7j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\nkiRJktQ0g6skSZIkqWm/AUgAuVXVsCESAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SyV4NVGppdR1",
        "colab_type": "code",
        "outputId": "4cc43476-d29d-42d6-974c-ec5c55f39ab8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "# test\n",
        "df_gb = df.copy()\n",
        "df_gb.drop(['Open', 'High', 'Low', 'Close', 'Volume'], axis=1, inplace=True)\n",
        "df_gb = df_gb.iloc[ind_split+window_size:] # past 32 days\n",
        "df_gb['Adj Close Test'] = y_pred_gb\n",
        "df_gb.plot(label='BTC-USD', figsize=(16,8), title='Adjusted Closing Price', grid=True)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9df7c0b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GzjzzTJYvX85vfvOb0n8RZab2YxERERERkTIoVH299tprc4/vuOOOgq87fPgwM2fOHHQ8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\nElhKakVERERERCSw/h/nEaQU7iMtVwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AUpBPYx4qKbq",
        "colab_type": "text"
      },
      "source": [
        "# Neural Networks with 1D convolutional and LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01nIFJw3qVZN",
        "colab_type": "code",
        "outputId": "a6168136-5e7a-472c-fafc-34009b81aebd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        }
      },
      "source": [
        "# RNNs\n",
        "keras_model = Sequential([\n",
        "    Conv1D(filters=128, kernel_size=5, input_shape=(window_size, 1), strides=1, padding=\"causal\", activation=\"tanh\"),\n",
        "    Conv1D(filters=128, kernel_size=5, strides=1, padding=\"causal\", activation=\"tanh\"),\n",
        "    LSTM(128, return_sequences=True),\n",
        "    LSTM(128),\n",
        "    Dense(64, activation=\"tanh\"),\n",
        "    Dense(64, activation=\"tanh\"),\n",
        "    Dense(1)\n",
        "])\n",
        "keras_model.summary()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d_1 (Conv1D)            (None, 32, 128)           768       \n",
            "_________________________________________________________________\n",
            "conv1d_2 (Conv1D)            (None, 32, 128)           82048     \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 32, 128)           131584    \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 128)               131584    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 358,465\n",
            "Trainable params: 358,465\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1siLRd5crB2y",
        "colab_type": "code",
        "outputId": "5b89d638-4fa7-4e02-aa94-1dfc9ccac4d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# find a learning rate\n",
        "lr_schedule = LearningRateScheduler(lambda epoch: 1e-8 * 10**(epoch / 20))\n",
        "optimizer = SGD(lr=1e-8, momentum=0.9)\n",
        "keras_model.compile(loss=Huber(),\n",
        "                   optimizer=optimizer,\n",
        "                   metrics=['mae'])\n",
        "# scale date due to LSTM units\n",
        "scaler = MinMaxScaler(feature_range=(-1,1))\n",
        "data_transform = scaler.fit_transform(df['Adj Close'].values[:,None])\n",
        "data_transform = data_transform[indices]\n",
        "X2 = data_transform[:,:-1]\n",
        "Y2 = data_transform[:,-1]\n",
        "X_train_reshape = X2[:ind_split]\n",
        "Y_train_reshape = Y2[:ind_split]\n",
        "X_test_reshape = X2[ind_split:]\n",
        "Y_test_reshape = Y2[ind_split:]"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9LF_c2ItT2I",
        "colab_type": "code",
        "outputId": "6624af8d-c31f-489f-e2d0-577cc43205bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = keras_model.fit(X_train_reshape, Y_train_reshape, batch_size=256, epochs=250, callbacks=[lr_schedule], validation_data=(X_test_reshape, Y_test_reshape))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "Train on 2648 samples, validate on 663 samples\n",
            "Epoch 1/250\n",
            "2648/2648 [==============================] - 11s 4ms/step - loss: 0.4314 - mean_absolute_error: 0.9229 - val_loss: 0.0694 - val_mean_absolute_error: 0.3127\n",
            "Epoch 2/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 0.4314 - mean_absolute_error: 0.9229 - val_loss: 0.0694 - val_mean_absolute_error: 0.3127\n",
            "Epoch 3/250\n",
            "2648/2648 [==============================] - 2s 705us/step - loss: 0.4314 - mean_absolute_error: 0.9229 - val_loss: 0.0694 - val_mean_absolute_error: 0.3127\n",
            "Epoch 4/250\n",
            "2648/2648 [==============================] - 2s 707us/step - loss: 0.4314 - mean_absolute_error: 0.9229 - val_loss: 0.0694 - val_mean_absolute_error: 0.3127\n",
            "Epoch 5/250\n",
            "2648/2648 [==============================] - 2s 712us/step - loss: 0.4314 - mean_absolute_error: 0.9229 - val_loss: 0.0694 - val_mean_absolute_error: 0.3127\n",
            "Epoch 6/250\n",
            "2648/2648 [==============================] - 2s 716us/step - loss: 0.4313 - mean_absolute_error: 0.9228 - val_loss: 0.0694 - val_mean_absolute_error: 0.3127\n",
            "Epoch 7/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 0.4313 - mean_absolute_error: 0.9228 - val_loss: 0.0694 - val_mean_absolute_error: 0.3127\n",
            "Epoch 8/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 0.4313 - mean_absolute_error: 0.9228 - val_loss: 0.0694 - val_mean_absolute_error: 0.3126\n",
            "Epoch 9/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 0.4313 - mean_absolute_error: 0.9228 - val_loss: 0.0694 - val_mean_absolute_error: 0.3126\n",
            "Epoch 10/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 0.4313 - mean_absolute_error: 0.9228 - val_loss: 0.0694 - val_mean_absolute_error: 0.3126\n",
            "Epoch 11/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 0.4312 - mean_absolute_error: 0.9227 - val_loss: 0.0694 - val_mean_absolute_error: 0.3126\n",
            "Epoch 12/250\n",
            "2648/2648 [==============================] - 2s 763us/step - loss: 0.4312 - mean_absolute_error: 0.9227 - val_loss: 0.0694 - val_mean_absolute_error: 0.3126\n",
            "Epoch 13/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 0.4312 - mean_absolute_error: 0.9227 - val_loss: 0.0694 - val_mean_absolute_error: 0.3126\n",
            "Epoch 14/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 0.4311 - mean_absolute_error: 0.9226 - val_loss: 0.0694 - val_mean_absolute_error: 0.3126\n",
            "Epoch 15/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 0.4311 - mean_absolute_error: 0.9226 - val_loss: 0.0693 - val_mean_absolute_error: 0.3125\n",
            "Epoch 16/250\n",
            "2648/2648 [==============================] - 2s 770us/step - loss: 0.4310 - mean_absolute_error: 0.9225 - val_loss: 0.0693 - val_mean_absolute_error: 0.3125\n",
            "Epoch 17/250\n",
            "2648/2648 [==============================] - 2s 746us/step - loss: 0.4310 - mean_absolute_error: 0.9224 - val_loss: 0.0693 - val_mean_absolute_error: 0.3125\n",
            "Epoch 18/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 0.4309 - mean_absolute_error: 0.9224 - val_loss: 0.0693 - val_mean_absolute_error: 0.3124\n",
            "Epoch 19/250\n",
            "2648/2648 [==============================] - 2s 735us/step - loss: 0.4308 - mean_absolute_error: 0.9223 - val_loss: 0.0693 - val_mean_absolute_error: 0.3124\n",
            "Epoch 20/250\n",
            "2648/2648 [==============================] - 2s 744us/step - loss: 0.4308 - mean_absolute_error: 0.9222 - val_loss: 0.0693 - val_mean_absolute_error: 0.3124\n",
            "Epoch 21/250\n",
            "2648/2648 [==============================] - 2s 749us/step - loss: 0.4307 - mean_absolute_error: 0.9221 - val_loss: 0.0693 - val_mean_absolute_error: 0.3123\n",
            "Epoch 22/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 0.4306 - mean_absolute_error: 0.9220 - val_loss: 0.0692 - val_mean_absolute_error: 0.3123\n",
            "Epoch 23/250\n",
            "2648/2648 [==============================] - 2s 750us/step - loss: 0.4305 - mean_absolute_error: 0.9219 - val_loss: 0.0692 - val_mean_absolute_error: 0.3122\n",
            "Epoch 24/250\n",
            "2648/2648 [==============================] - 2s 723us/step - loss: 0.4303 - mean_absolute_error: 0.9217 - val_loss: 0.0692 - val_mean_absolute_error: 0.3121\n",
            "Epoch 25/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 0.4302 - mean_absolute_error: 0.9216 - val_loss: 0.0692 - val_mean_absolute_error: 0.3121\n",
            "Epoch 26/250\n",
            "2648/2648 [==============================] - 2s 764us/step - loss: 0.4300 - mean_absolute_error: 0.9214 - val_loss: 0.0691 - val_mean_absolute_error: 0.3120\n",
            "Epoch 27/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 0.4298 - mean_absolute_error: 0.9212 - val_loss: 0.0691 - val_mean_absolute_error: 0.3119\n",
            "Epoch 28/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 0.4296 - mean_absolute_error: 0.9210 - val_loss: 0.0690 - val_mean_absolute_error: 0.3118\n",
            "Epoch 29/250\n",
            "2648/2648 [==============================] - 2s 730us/step - loss: 0.4294 - mean_absolute_error: 0.9207 - val_loss: 0.0690 - val_mean_absolute_error: 0.3117\n",
            "Epoch 30/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 0.4291 - mean_absolute_error: 0.9204 - val_loss: 0.0689 - val_mean_absolute_error: 0.3116\n",
            "Epoch 31/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 0.4288 - mean_absolute_error: 0.9201 - val_loss: 0.0689 - val_mean_absolute_error: 0.3114\n",
            "Epoch 32/250\n",
            "2648/2648 [==============================] - 2s 767us/step - loss: 0.4285 - mean_absolute_error: 0.9198 - val_loss: 0.0688 - val_mean_absolute_error: 0.3112\n",
            "Epoch 33/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 0.4281 - mean_absolute_error: 0.9194 - val_loss: 0.0688 - val_mean_absolute_error: 0.3111\n",
            "Epoch 34/250\n",
            "2648/2648 [==============================] - 2s 718us/step - loss: 0.4277 - mean_absolute_error: 0.9189 - val_loss: 0.0687 - val_mean_absolute_error: 0.3109\n",
            "Epoch 35/250\n",
            "2648/2648 [==============================] - 2s 725us/step - loss: 0.4273 - mean_absolute_error: 0.9184 - val_loss: 0.0686 - val_mean_absolute_error: 0.3106\n",
            "Epoch 36/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 0.4268 - mean_absolute_error: 0.9179 - val_loss: 0.0685 - val_mean_absolute_error: 0.3104\n",
            "Epoch 37/250\n",
            "2648/2648 [==============================] - 2s 757us/step - loss: 0.4262 - mean_absolute_error: 0.9172 - val_loss: 0.0684 - val_mean_absolute_error: 0.3101\n",
            "Epoch 38/250\n",
            "2648/2648 [==============================] - 2s 759us/step - loss: 0.4255 - mean_absolute_error: 0.9165 - val_loss: 0.0682 - val_mean_absolute_error: 0.3098\n",
            "Epoch 39/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 0.4248 - mean_absolute_error: 0.9158 - val_loss: 0.0681 - val_mean_absolute_error: 0.3094\n",
            "Epoch 40/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 0.4240 - mean_absolute_error: 0.9149 - val_loss: 0.0679 - val_mean_absolute_error: 0.3090\n",
            "Epoch 41/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 0.4231 - mean_absolute_error: 0.9139 - val_loss: 0.0677 - val_mean_absolute_error: 0.3085\n",
            "Epoch 42/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 0.4221 - mean_absolute_error: 0.9128 - val_loss: 0.0675 - val_mean_absolute_error: 0.3080\n",
            "Epoch 43/250\n",
            "2648/2648 [==============================] - 2s 796us/step - loss: 0.4209 - mean_absolute_error: 0.9115 - val_loss: 0.0673 - val_mean_absolute_error: 0.3075\n",
            "Epoch 44/250\n",
            "2648/2648 [==============================] - 2s 744us/step - loss: 0.4196 - mean_absolute_error: 0.9101 - val_loss: 0.0671 - val_mean_absolute_error: 0.3068\n",
            "Epoch 45/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 0.4182 - mean_absolute_error: 0.9086 - val_loss: 0.0668 - val_mean_absolute_error: 0.3061\n",
            "Epoch 46/250\n",
            "2648/2648 [==============================] - 2s 724us/step - loss: 0.4166 - mean_absolute_error: 0.9068 - val_loss: 0.0665 - val_mean_absolute_error: 0.3053\n",
            "Epoch 47/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 0.4148 - mean_absolute_error: 0.9049 - val_loss: 0.0661 - val_mean_absolute_error: 0.3044\n",
            "Epoch 48/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 0.4128 - mean_absolute_error: 0.9027 - val_loss: 0.0657 - val_mean_absolute_error: 0.3034\n",
            "Epoch 49/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 0.4106 - mean_absolute_error: 0.9002 - val_loss: 0.0653 - val_mean_absolute_error: 0.3023\n",
            "Epoch 50/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 0.4081 - mean_absolute_error: 0.8974 - val_loss: 0.0648 - val_mean_absolute_error: 0.3011\n",
            "Epoch 51/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 0.4054 - mean_absolute_error: 0.8944 - val_loss: 0.0643 - val_mean_absolute_error: 0.2997\n",
            "Epoch 52/250\n",
            "2648/2648 [==============================] - 2s 746us/step - loss: 0.4023 - mean_absolute_error: 0.8909 - val_loss: 0.0637 - val_mean_absolute_error: 0.2982\n",
            "Epoch 53/250\n",
            "2648/2648 [==============================] - 2s 725us/step - loss: 0.3988 - mean_absolute_error: 0.8871 - val_loss: 0.0631 - val_mean_absolute_error: 0.2965\n",
            "Epoch 54/250\n",
            "2648/2648 [==============================] - 2s 747us/step - loss: 0.3950 - mean_absolute_error: 0.8827 - val_loss: 0.0624 - val_mean_absolute_error: 0.2946\n",
            "Epoch 55/250\n",
            "2648/2648 [==============================] - 2s 758us/step - loss: 0.3907 - mean_absolute_error: 0.8779 - val_loss: 0.0616 - val_mean_absolute_error: 0.2925\n",
            "Epoch 56/250\n",
            "2648/2648 [==============================] - 2s 758us/step - loss: 0.3860 - mean_absolute_error: 0.8726 - val_loss: 0.0607 - val_mean_absolute_error: 0.2902\n",
            "Epoch 57/250\n",
            "2648/2648 [==============================] - 2s 782us/step - loss: 0.3808 - mean_absolute_error: 0.8666 - val_loss: 0.0598 - val_mean_absolute_error: 0.2877\n",
            "Epoch 58/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 0.3749 - mean_absolute_error: 0.8599 - val_loss: 0.0587 - val_mean_absolute_error: 0.2849\n",
            "Epoch 59/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 0.3685 - mean_absolute_error: 0.8524 - val_loss: 0.0576 - val_mean_absolute_error: 0.2818\n",
            "Epoch 60/250\n",
            "2648/2648 [==============================] - 2s 773us/step - loss: 0.3614 - mean_absolute_error: 0.8441 - val_loss: 0.0564 - val_mean_absolute_error: 0.2784\n",
            "Epoch 61/250\n",
            "2648/2648 [==============================] - 2s 755us/step - loss: 0.3536 - mean_absolute_error: 0.8348 - val_loss: 0.0551 - val_mean_absolute_error: 0.2747\n",
            "Epoch 62/250\n",
            "2648/2648 [==============================] - 2s 719us/step - loss: 0.3451 - mean_absolute_error: 0.8245 - val_loss: 0.0538 - val_mean_absolute_error: 0.2706\n",
            "Epoch 63/250\n",
            "2648/2648 [==============================] - 2s 743us/step - loss: 0.3357 - mean_absolute_error: 0.8131 - val_loss: 0.0523 - val_mean_absolute_error: 0.2661\n",
            "Epoch 64/250\n",
            "2648/2648 [==============================] - 2s 744us/step - loss: 0.3254 - mean_absolute_error: 0.8004 - val_loss: 0.0508 - val_mean_absolute_error: 0.2614\n",
            "Epoch 65/250\n",
            "2648/2648 [==============================] - 2s 763us/step - loss: 0.3142 - mean_absolute_error: 0.7864 - val_loss: 0.0492 - val_mean_absolute_error: 0.2563\n",
            "Epoch 66/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 0.3020 - mean_absolute_error: 0.7708 - val_loss: 0.0475 - val_mean_absolute_error: 0.2509\n",
            "Epoch 67/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 0.2889 - mean_absolute_error: 0.7537 - val_loss: 0.0459 - val_mean_absolute_error: 0.2450\n",
            "Epoch 68/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 0.2747 - mean_absolute_error: 0.7347 - val_loss: 0.0442 - val_mean_absolute_error: 0.2389\n",
            "Epoch 69/250\n",
            "2648/2648 [==============================] - 2s 761us/step - loss: 0.2595 - mean_absolute_error: 0.7138 - val_loss: 0.0426 - val_mean_absolute_error: 0.2329\n",
            "Epoch 70/250\n",
            "2648/2648 [==============================] - 2s 761us/step - loss: 0.2433 - mean_absolute_error: 0.6909 - val_loss: 0.0411 - val_mean_absolute_error: 0.2270\n",
            "Epoch 71/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 0.2261 - mean_absolute_error: 0.6657 - val_loss: 0.0398 - val_mean_absolute_error: 0.2214\n",
            "Epoch 72/250\n",
            "2648/2648 [==============================] - 2s 721us/step - loss: 0.2080 - mean_absolute_error: 0.6382 - val_loss: 0.0388 - val_mean_absolute_error: 0.2162\n",
            "Epoch 73/250\n",
            "2648/2648 [==============================] - 2s 725us/step - loss: 0.1893 - mean_absolute_error: 0.6083 - val_loss: 0.0381 - val_mean_absolute_error: 0.2117\n",
            "Epoch 74/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 0.1699 - mean_absolute_error: 0.5758 - val_loss: 0.0379 - val_mean_absolute_error: 0.2080\n",
            "Epoch 75/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 0.1501 - mean_absolute_error: 0.5408 - val_loss: 0.0382 - val_mean_absolute_error: 0.2048\n",
            "Epoch 76/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 0.1303 - mean_absolute_error: 0.5032 - val_loss: 0.0392 - val_mean_absolute_error: 0.2045\n",
            "Epoch 77/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 0.1106 - mean_absolute_error: 0.4632 - val_loss: 0.0410 - val_mean_absolute_error: 0.2104\n",
            "Epoch 78/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 0.0916 - mean_absolute_error: 0.4210 - val_loss: 0.0438 - val_mean_absolute_error: 0.2198\n",
            "Epoch 79/250\n",
            "2648/2648 [==============================] - 2s 754us/step - loss: 0.0736 - mean_absolute_error: 0.3770 - val_loss: 0.0475 - val_mean_absolute_error: 0.2320\n",
            "Epoch 80/250\n",
            "2648/2648 [==============================] - 2s 782us/step - loss: 0.0572 - mean_absolute_error: 0.3316 - val_loss: 0.0523 - val_mean_absolute_error: 0.2466\n",
            "Epoch 81/250\n",
            "2648/2648 [==============================] - 2s 743us/step - loss: 0.0426 - mean_absolute_error: 0.2857 - val_loss: 0.0580 - val_mean_absolute_error: 0.2616\n",
            "Epoch 82/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 0.0303 - mean_absolute_error: 0.2407 - val_loss: 0.0645 - val_mean_absolute_error: 0.2830\n",
            "Epoch 83/250\n",
            "2648/2648 [==============================] - 2s 726us/step - loss: 0.0205 - mean_absolute_error: 0.1975 - val_loss: 0.0715 - val_mean_absolute_error: 0.3097\n",
            "Epoch 84/250\n",
            "2648/2648 [==============================] - 2s 726us/step - loss: 0.0131 - mean_absolute_error: 0.1573 - val_loss: 0.0787 - val_mean_absolute_error: 0.3356\n",
            "Epoch 85/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 0.0081 - mean_absolute_error: 0.1220 - val_loss: 0.0855 - val_mean_absolute_error: 0.3585\n",
            "Epoch 86/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 0.0050 - mean_absolute_error: 0.0923 - val_loss: 0.0916 - val_mean_absolute_error: 0.3773\n",
            "Epoch 87/250\n",
            "2648/2648 [==============================] - 2s 725us/step - loss: 0.0033 - mean_absolute_error: 0.0686 - val_loss: 0.0964 - val_mean_absolute_error: 0.3918\n",
            "Epoch 88/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 0.0026 - mean_absolute_error: 0.0521 - val_loss: 0.1000 - val_mean_absolute_error: 0.4020\n",
            "Epoch 89/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 0.0023 - mean_absolute_error: 0.0420 - val_loss: 0.1022 - val_mean_absolute_error: 0.4080\n",
            "Epoch 90/250\n",
            "2648/2648 [==============================] - 2s 743us/step - loss: 0.0022 - mean_absolute_error: 0.0372 - val_loss: 0.1032 - val_mean_absolute_error: 0.4111\n",
            "Epoch 91/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 0.0022 - mean_absolute_error: 0.0352 - val_loss: 0.1034 - val_mean_absolute_error: 0.4118\n",
            "Epoch 92/250\n",
            "2648/2648 [==============================] - 2s 742us/step - loss: 0.0022 - mean_absolute_error: 0.0346 - val_loss: 0.1032 - val_mean_absolute_error: 0.4113\n",
            "Epoch 93/250\n",
            "2648/2648 [==============================] - 2s 735us/step - loss: 0.0022 - mean_absolute_error: 0.0347 - val_loss: 0.1026 - val_mean_absolute_error: 0.4100\n",
            "Epoch 94/250\n",
            "2648/2648 [==============================] - 2s 758us/step - loss: 0.0022 - mean_absolute_error: 0.0351 - val_loss: 0.1021 - val_mean_absolute_error: 0.4087\n",
            "Epoch 95/250\n",
            "2648/2648 [==============================] - 2s 747us/step - loss: 0.0022 - mean_absolute_error: 0.0353 - val_loss: 0.1016 - val_mean_absolute_error: 0.4076\n",
            "Epoch 96/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 0.0022 - mean_absolute_error: 0.0353 - val_loss: 0.1011 - val_mean_absolute_error: 0.4066\n",
            "Epoch 97/250\n",
            "2648/2648 [==============================] - 2s 746us/step - loss: 0.0022 - mean_absolute_error: 0.0351 - val_loss: 0.1006 - val_mean_absolute_error: 0.4056\n",
            "Epoch 98/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 0.0022 - mean_absolute_error: 0.0350 - val_loss: 0.0999 - val_mean_absolute_error: 0.4041\n",
            "Epoch 99/250\n",
            "2648/2648 [==============================] - 2s 744us/step - loss: 0.0022 - mean_absolute_error: 0.0350 - val_loss: 0.0992 - val_mean_absolute_error: 0.4024\n",
            "Epoch 100/250\n",
            "2648/2648 [==============================] - 2s 760us/step - loss: 0.0021 - mean_absolute_error: 0.0351 - val_loss: 0.0983 - val_mean_absolute_error: 0.4005\n",
            "Epoch 101/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 0.0021 - mean_absolute_error: 0.0350 - val_loss: 0.0974 - val_mean_absolute_error: 0.3985\n",
            "Epoch 102/250\n",
            "2648/2648 [==============================] - 2s 726us/step - loss: 0.0021 - mean_absolute_error: 0.0347 - val_loss: 0.0966 - val_mean_absolute_error: 0.3967\n",
            "Epoch 103/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 0.0021 - mean_absolute_error: 0.0344 - val_loss: 0.0955 - val_mean_absolute_error: 0.3943\n",
            "Epoch 104/250\n",
            "2648/2648 [==============================] - 2s 730us/step - loss: 0.0021 - mean_absolute_error: 0.0341 - val_loss: 0.0944 - val_mean_absolute_error: 0.3919\n",
            "Epoch 105/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 0.0020 - mean_absolute_error: 0.0340 - val_loss: 0.0929 - val_mean_absolute_error: 0.3884\n",
            "Epoch 106/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 0.0020 - mean_absolute_error: 0.0342 - val_loss: 0.0914 - val_mean_absolute_error: 0.3851\n",
            "Epoch 107/250\n",
            "2648/2648 [==============================] - 2s 774us/step - loss: 0.0020 - mean_absolute_error: 0.0337 - val_loss: 0.0897 - val_mean_absolute_error: 0.3813\n",
            "Epoch 108/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 0.0020 - mean_absolute_error: 0.0331 - val_loss: 0.0881 - val_mean_absolute_error: 0.3774\n",
            "Epoch 109/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 0.0019 - mean_absolute_error: 0.0335 - val_loss: 0.0856 - val_mean_absolute_error: 0.3713\n",
            "Epoch 110/250\n",
            "2648/2648 [==============================] - 2s 746us/step - loss: 0.0019 - mean_absolute_error: 0.0337 - val_loss: 0.0837 - val_mean_absolute_error: 0.3672\n",
            "Epoch 111/250\n",
            "2648/2648 [==============================] - 2s 748us/step - loss: 0.0019 - mean_absolute_error: 0.0310 - val_loss: 0.0814 - val_mean_absolute_error: 0.3616\n",
            "Epoch 112/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 0.0018 - mean_absolute_error: 0.0329 - val_loss: 0.0782 - val_mean_absolute_error: 0.3533\n",
            "Epoch 113/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 0.0018 - mean_absolute_error: 0.0313 - val_loss: 0.0756 - val_mean_absolute_error: 0.3473\n",
            "Epoch 114/250\n",
            "2648/2648 [==============================] - 2s 744us/step - loss: 0.0017 - mean_absolute_error: 0.0308 - val_loss: 0.0723 - val_mean_absolute_error: 0.3387\n",
            "Epoch 115/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 0.0016 - mean_absolute_error: 0.0309 - val_loss: 0.0688 - val_mean_absolute_error: 0.3293\n",
            "Epoch 116/250\n",
            "2648/2648 [==============================] - 2s 756us/step - loss: 0.0016 - mean_absolute_error: 0.0296 - val_loss: 0.0651 - val_mean_absolute_error: 0.3195\n",
            "Epoch 117/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 0.0015 - mean_absolute_error: 0.0298 - val_loss: 0.0613 - val_mean_absolute_error: 0.3091\n",
            "Epoch 118/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 0.0014 - mean_absolute_error: 0.0282 - val_loss: 0.0558 - val_mean_absolute_error: 0.2920\n",
            "Epoch 119/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 0.0013 - mean_absolute_error: 0.0271 - val_loss: 0.0515 - val_mean_absolute_error: 0.2791\n",
            "Epoch 120/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 0.0012 - mean_absolute_error: 0.0273 - val_loss: 0.0469 - val_mean_absolute_error: 0.2649\n",
            "Epoch 121/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 0.0011 - mean_absolute_error: 0.0259 - val_loss: 0.0418 - val_mean_absolute_error: 0.2471\n",
            "Epoch 122/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 0.0010 - mean_absolute_error: 0.0242 - val_loss: 0.0361 - val_mean_absolute_error: 0.2253\n",
            "Epoch 123/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 9.2538e-04 - mean_absolute_error: 0.0232 - val_loss: 0.0309 - val_mean_absolute_error: 0.2047\n",
            "Epoch 124/250\n",
            "2648/2648 [==============================] - 2s 748us/step - loss: 8.0904e-04 - mean_absolute_error: 0.0214 - val_loss: 0.0261 - val_mean_absolute_error: 0.1840\n",
            "Epoch 125/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 6.9616e-04 - mean_absolute_error: 0.0198 - val_loss: 0.0214 - val_mean_absolute_error: 0.1608\n",
            "Epoch 126/250\n",
            "2648/2648 [==============================] - 2s 730us/step - loss: 5.9509e-04 - mean_absolute_error: 0.0183 - val_loss: 0.0172 - val_mean_absolute_error: 0.1383\n",
            "Epoch 127/250\n",
            "2648/2648 [==============================] - 2s 747us/step - loss: 4.8502e-04 - mean_absolute_error: 0.0167 - val_loss: 0.0138 - val_mean_absolute_error: 0.1178\n",
            "Epoch 128/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 3.9289e-04 - mean_absolute_error: 0.0148 - val_loss: 0.0110 - val_mean_absolute_error: 0.1011\n",
            "Epoch 129/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 3.1015e-04 - mean_absolute_error: 0.0134 - val_loss: 0.0095 - val_mean_absolute_error: 0.0935\n",
            "Epoch 130/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 2.3908e-04 - mean_absolute_error: 0.0111 - val_loss: 0.0089 - val_mean_absolute_error: 0.0892\n",
            "Epoch 131/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 1.8970e-04 - mean_absolute_error: 0.0100 - val_loss: 0.0092 - val_mean_absolute_error: 0.0927\n",
            "Epoch 132/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 1.5536e-04 - mean_absolute_error: 0.0089 - val_loss: 0.0101 - val_mean_absolute_error: 0.0997\n",
            "Epoch 133/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 1.3202e-04 - mean_absolute_error: 0.0077 - val_loss: 0.0113 - val_mean_absolute_error: 0.1080\n",
            "Epoch 134/250\n",
            "2648/2648 [==============================] - 2s 730us/step - loss: 1.2320e-04 - mean_absolute_error: 0.0073 - val_loss: 0.0124 - val_mean_absolute_error: 0.1144\n",
            "Epoch 135/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 1.1691e-04 - mean_absolute_error: 0.0066 - val_loss: 0.0133 - val_mean_absolute_error: 0.1199\n",
            "Epoch 136/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 1.1626e-04 - mean_absolute_error: 0.0064 - val_loss: 0.0140 - val_mean_absolute_error: 0.1248\n",
            "Epoch 137/250\n",
            "2648/2648 [==============================] - 2s 749us/step - loss: 1.1682e-04 - mean_absolute_error: 0.0067 - val_loss: 0.0138 - val_mean_absolute_error: 0.1229\n",
            "Epoch 138/250\n",
            "2648/2648 [==============================] - 2s 751us/step - loss: 1.1663e-04 - mean_absolute_error: 0.0068 - val_loss: 0.0136 - val_mean_absolute_error: 0.1217\n",
            "Epoch 139/250\n",
            "2648/2648 [==============================] - 2s 770us/step - loss: 1.2413e-04 - mean_absolute_error: 0.0079 - val_loss: 0.0139 - val_mean_absolute_error: 0.1243\n",
            "Epoch 140/250\n",
            "2648/2648 [==============================] - 2s 754us/step - loss: 1.3322e-04 - mean_absolute_error: 0.0094 - val_loss: 0.0134 - val_mean_absolute_error: 0.1207\n",
            "Epoch 141/250\n",
            "2648/2648 [==============================] - 2s 766us/step - loss: 1.1853e-04 - mean_absolute_error: 0.0071 - val_loss: 0.0132 - val_mean_absolute_error: 0.1199\n",
            "Epoch 142/250\n",
            "2648/2648 [==============================] - 2s 726us/step - loss: 1.2300e-04 - mean_absolute_error: 0.0081 - val_loss: 0.0136 - val_mean_absolute_error: 0.1226\n",
            "Epoch 143/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 0.0070 - mean_absolute_error: 0.0872 - val_loss: 0.0096 - val_mean_absolute_error: 0.0984\n",
            "Epoch 144/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 0.0078 - mean_absolute_error: 0.1093 - val_loss: 0.0703 - val_mean_absolute_error: 0.3435\n",
            "Epoch 145/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 0.0035 - mean_absolute_error: 0.0644 - val_loss: 0.0587 - val_mean_absolute_error: 0.3008\n",
            "Epoch 146/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 0.0020 - mean_absolute_error: 0.0459 - val_loss: 0.0355 - val_mean_absolute_error: 0.2325\n",
            "Epoch 147/250\n",
            "2648/2648 [==============================] - 2s 713us/step - loss: 0.0011 - mean_absolute_error: 0.0358 - val_loss: 0.0092 - val_mean_absolute_error: 0.0973\n",
            "Epoch 148/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 6.2970e-04 - mean_absolute_error: 0.0298 - val_loss: 0.0121 - val_mean_absolute_error: 0.1137\n",
            "Epoch 149/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 0.0019 - mean_absolute_error: 0.0549 - val_loss: 0.0086 - val_mean_absolute_error: 0.0929\n",
            "Epoch 150/250\n",
            "2648/2648 [==============================] - 2s 735us/step - loss: 7.8922e-04 - mean_absolute_error: 0.0324 - val_loss: 0.0082 - val_mean_absolute_error: 0.0917\n",
            "Epoch 151/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 0.0013 - mean_absolute_error: 0.0482 - val_loss: 0.0093 - val_mean_absolute_error: 0.0972\n",
            "Epoch 152/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 0.0123 - mean_absolute_error: 0.1139 - val_loss: 0.0449 - val_mean_absolute_error: 0.2474\n",
            "Epoch 153/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 0.0070 - mean_absolute_error: 0.0920 - val_loss: 0.2212 - val_mean_absolute_error: 0.6166\n",
            "Epoch 154/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 0.0052 - mean_absolute_error: 0.0656 - val_loss: 0.1696 - val_mean_absolute_error: 0.5298\n",
            "Epoch 155/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 0.0031 - mean_absolute_error: 0.0496 - val_loss: 0.0466 - val_mean_absolute_error: 0.2606\n",
            "Epoch 156/250\n",
            "2648/2648 [==============================] - 2s 721us/step - loss: 0.0123 - mean_absolute_error: 0.1268 - val_loss: 0.1953 - val_mean_absolute_error: 0.6000\n",
            "Epoch 157/250\n",
            "2648/2648 [==============================] - 2s 725us/step - loss: 0.0101 - mean_absolute_error: 0.1109 - val_loss: 0.2260 - val_mean_absolute_error: 0.6125\n",
            "Epoch 158/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 0.0049 - mean_absolute_error: 0.0683 - val_loss: 0.1081 - val_mean_absolute_error: 0.3982\n",
            "Epoch 159/250\n",
            "2648/2648 [==============================] - 2s 748us/step - loss: 0.0129 - mean_absolute_error: 0.1236 - val_loss: 0.3473 - val_mean_absolute_error: 0.8121\n",
            "Epoch 160/250\n",
            "2648/2648 [==============================] - 2s 735us/step - loss: 0.7136 - mean_absolute_error: 1.0647 - val_loss: 50.8207 - val_mean_absolute_error: 51.3207\n",
            "Epoch 161/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 57.2068 - mean_absolute_error: 57.7068 - val_loss: 94.8894 - val_mean_absolute_error: 95.3894\n",
            "Epoch 162/250\n",
            "2648/2648 [==============================] - 2s 726us/step - loss: 74.7437 - mean_absolute_error: 75.2437 - val_loss: 72.6966 - val_mean_absolute_error: 73.1966\n",
            "Epoch 163/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 64.4522 - mean_absolute_error: 64.9522 - val_loss: 28.6682 - val_mean_absolute_error: 29.1682\n",
            "Epoch 164/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 27.6679 - mean_absolute_error: 28.1327 - val_loss: 140.5697 - val_mean_absolute_error: 141.0697\n",
            "Epoch 165/250\n",
            "2648/2648 [==============================] - 2s 723us/step - loss: 75.0934 - mean_absolute_error: 75.5806 - val_loss: 47.6156 - val_mean_absolute_error: 48.1156\n",
            "Epoch 166/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 66.7202 - mean_absolute_error: 67.2202 - val_loss: 151.1693 - val_mean_absolute_error: 151.6693\n",
            "Epoch 167/250\n",
            "2648/2648 [==============================] - 2s 723us/step - loss: 123.9225 - mean_absolute_error: 124.4225 - val_loss: 131.9369 - val_mean_absolute_error: 132.4369\n",
            "Epoch 168/250\n",
            "2648/2648 [==============================] - 2s 755us/step - loss: 113.9011 - mean_absolute_error: 114.4011 - val_loss: 1.9079 - val_mean_absolute_error: 2.4079\n",
            "Epoch 169/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 132.8358 - mean_absolute_error: 133.3358 - val_loss: 86.5554 - val_mean_absolute_error: 87.0554\n",
            "Epoch 170/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 160.5386 - mean_absolute_error: 161.0386 - val_loss: 248.9582 - val_mean_absolute_error: 249.4582\n",
            "Epoch 171/250\n",
            "2648/2648 [==============================] - 2s 775us/step - loss: 207.9922 - mean_absolute_error: 208.4922 - val_loss: 214.0141 - val_mean_absolute_error: 214.5141\n",
            "Epoch 172/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 182.2146 - mean_absolute_error: 182.7146 - val_loss: 86.4208 - val_mean_absolute_error: 86.9208\n",
            "Epoch 173/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 138.2490 - mean_absolute_error: 138.7490 - val_loss: 163.7230 - val_mean_absolute_error: 164.2230\n",
            "Epoch 174/250\n",
            "2648/2648 [==============================] - 2s 743us/step - loss: 212.7966 - mean_absolute_error: 213.2966 - val_loss: 5.5090 - val_mean_absolute_error: 6.0090\n",
            "Epoch 175/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 265.8124 - mean_absolute_error: 266.3124 - val_loss: 47.1407 - val_mean_absolute_error: 47.6407\n",
            "Epoch 176/250\n",
            "2648/2648 [==============================] - 2s 743us/step - loss: 230.7918 - mean_absolute_error: 231.2918 - val_loss: 272.5483 - val_mean_absolute_error: 273.0483\n",
            "Epoch 177/250\n",
            "2648/2648 [==============================] - 2s 752us/step - loss: 327.3481 - mean_absolute_error: 327.8481 - val_loss: 172.1614 - val_mean_absolute_error: 172.6614\n",
            "Epoch 178/250\n",
            "2648/2648 [==============================] - 2s 730us/step - loss: 297.7061 - mean_absolute_error: 298.2061 - val_loss: 301.9381 - val_mean_absolute_error: 302.4381\n",
            "Epoch 179/250\n",
            "2648/2648 [==============================] - 2s 720us/step - loss: 436.3551 - mean_absolute_error: 436.8551 - val_loss: 1056.5965 - val_mean_absolute_error: 1057.0965\n",
            "Epoch 180/250\n",
            "2648/2648 [==============================] - 2s 742us/step - loss: 560.5148 - mean_absolute_error: 561.0148 - val_loss: 793.6140 - val_mean_absolute_error: 794.1140\n",
            "Epoch 181/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 660.2906 - mean_absolute_error: 660.7906 - val_loss: 916.2834 - val_mean_absolute_error: 916.7834\n",
            "Epoch 182/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 468.0374 - mean_absolute_error: 468.5374 - val_loss: 1204.3768 - val_mean_absolute_error: 1204.8768\n",
            "Epoch 183/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 756.0385 - mean_absolute_error: 756.5385 - val_loss: 1286.4499 - val_mean_absolute_error: 1286.9499\n",
            "Epoch 184/250\n",
            "2648/2648 [==============================] - 2s 724us/step - loss: 959.4149 - mean_absolute_error: 959.9149 - val_loss: 986.9304 - val_mean_absolute_error: 987.4304\n",
            "Epoch 185/250\n",
            "2648/2648 [==============================] - 2s 742us/step - loss: 814.4449 - mean_absolute_error: 814.9449 - val_loss: 480.3496 - val_mean_absolute_error: 480.8496\n",
            "Epoch 186/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 879.2757 - mean_absolute_error: 879.7757 - val_loss: 1449.5098 - val_mean_absolute_error: 1450.0098\n",
            "Epoch 187/250\n",
            "2648/2648 [==============================] - 2s 730us/step - loss: 1359.0054 - mean_absolute_error: 1359.5054 - val_loss: 130.4555 - val_mean_absolute_error: 130.9555\n",
            "Epoch 188/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 1253.4978 - mean_absolute_error: 1253.9978 - val_loss: 2974.8813 - val_mean_absolute_error: 2975.3815\n",
            "Epoch 189/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 2790.5517 - mean_absolute_error: 2791.0517 - val_loss: 1486.4891 - val_mean_absolute_error: 1486.9891\n",
            "Epoch 190/250\n",
            "2648/2648 [==============================] - 2s 765us/step - loss: 2740.9669 - mean_absolute_error: 2741.4669 - val_loss: 2225.1244 - val_mean_absolute_error: 2225.6243\n",
            "Epoch 191/250\n",
            "2648/2648 [==============================] - 2s 779us/step - loss: 1127.6202 - mean_absolute_error: 1128.1202 - val_loss: 170.9197 - val_mean_absolute_error: 171.4197\n",
            "Epoch 192/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 2107.5824 - mean_absolute_error: 2108.0823 - val_loss: 3836.9981 - val_mean_absolute_error: 3837.4982\n",
            "Epoch 193/250\n",
            "2648/2648 [==============================] - 2s 722us/step - loss: 4219.0096 - mean_absolute_error: 4219.5097 - val_loss: 1321.4071 - val_mean_absolute_error: 1321.9071\n",
            "Epoch 194/250\n",
            "2648/2648 [==============================] - 2s 742us/step - loss: 4377.2599 - mean_absolute_error: 4377.7600 - val_loss: 4610.8477 - val_mean_absolute_error: 4611.3477\n",
            "Epoch 195/250\n",
            "2648/2648 [==============================] - 2s 730us/step - loss: 4614.9520 - mean_absolute_error: 4615.4519 - val_loss: 9026.7684 - val_mean_absolute_error: 9027.2684\n",
            "Epoch 196/250\n",
            "2648/2648 [==============================] - 2s 744us/step - loss: 5977.9248 - mean_absolute_error: 5978.4247 - val_loss: 5818.7590 - val_mean_absolute_error: 5819.2588\n",
            "Epoch 197/250\n",
            "2648/2648 [==============================] - 2s 715us/step - loss: 6522.7277 - mean_absolute_error: 6523.2276 - val_loss: 1481.1733 - val_mean_absolute_error: 1481.6733\n",
            "Epoch 198/250\n",
            "2648/2648 [==============================] - 2s 723us/step - loss: 2353.5591 - mean_absolute_error: 2354.0590 - val_loss: 1293.5350 - val_mean_absolute_error: 1294.0350\n",
            "Epoch 199/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 2766.0828 - mean_absolute_error: 2766.5827 - val_loss: 1125.5657 - val_mean_absolute_error: 1126.0657\n",
            "Epoch 200/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 6629.3564 - mean_absolute_error: 6629.8564 - val_loss: 14013.2571 - val_mean_absolute_error: 14013.7575\n",
            "Epoch 201/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 10576.0190 - mean_absolute_error: 10576.5187 - val_loss: 8122.4530 - val_mean_absolute_error: 8122.9528\n",
            "Epoch 202/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 4464.3318 - mean_absolute_error: 4464.8319 - val_loss: 12258.9984 - val_mean_absolute_error: 12259.4984\n",
            "Epoch 203/250\n",
            "2648/2648 [==============================] - 2s 757us/step - loss: 5436.1083 - mean_absolute_error: 5436.6081 - val_loss: 4401.3554 - val_mean_absolute_error: 4401.8554\n",
            "Epoch 204/250\n",
            "2648/2648 [==============================] - 2s 724us/step - loss: 2481.9457 - mean_absolute_error: 2482.4458 - val_loss: 447.7390 - val_mean_absolute_error: 448.2390\n",
            "Epoch 205/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 8438.3146 - mean_absolute_error: 8438.8148 - val_loss: 4781.7479 - val_mean_absolute_error: 4782.2479\n",
            "Epoch 206/250\n",
            "2648/2648 [==============================] - 2s 744us/step - loss: 11066.7435 - mean_absolute_error: 11067.2435 - val_loss: 13255.6157 - val_mean_absolute_error: 13256.1157\n",
            "Epoch 207/250\n",
            "2648/2648 [==============================] - 2s 747us/step - loss: 13169.1877 - mean_absolute_error: 13169.6879 - val_loss: 4725.6404 - val_mean_absolute_error: 4726.1404\n",
            "Epoch 208/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 9527.4396 - mean_absolute_error: 9527.9395 - val_loss: 21876.7902 - val_mean_absolute_error: 21877.2902\n",
            "Epoch 209/250\n",
            "2648/2648 [==============================] - 2s 762us/step - loss: 14251.0015 - mean_absolute_error: 14251.5008 - val_loss: 3015.4761 - val_mean_absolute_error: 3015.9759\n",
            "Epoch 210/250\n",
            "2648/2648 [==============================] - 2s 746us/step - loss: 22635.5488 - mean_absolute_error: 22636.0486 - val_loss: 9004.4937 - val_mean_absolute_error: 9004.9933\n",
            "Epoch 211/250\n",
            "2648/2648 [==============================] - 2s 755us/step - loss: 18893.5363 - mean_absolute_error: 18894.0362 - val_loss: 18879.7464 - val_mean_absolute_error: 18880.2480\n",
            "Epoch 212/250\n",
            "2648/2648 [==============================] - 2s 748us/step - loss: 34056.4970 - mean_absolute_error: 34056.9965 - val_loss: 41904.1168 - val_mean_absolute_error: 41904.6168\n",
            "Epoch 213/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 38010.6606 - mean_absolute_error: 38011.1602 - val_loss: 62145.0661 - val_mean_absolute_error: 62145.5698\n",
            "Epoch 214/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 45182.6845 - mean_absolute_error: 45183.1847 - val_loss: 57128.8473 - val_mean_absolute_error: 57129.3473\n",
            "Epoch 215/250\n",
            "2648/2648 [==============================] - 2s 735us/step - loss: 54301.1379 - mean_absolute_error: 54301.6377 - val_loss: 441.4408 - val_mean_absolute_error: 441.9408\n",
            "Epoch 216/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 16876.9252 - mean_absolute_error: 16877.4247 - val_loss: 21738.2567 - val_mean_absolute_error: 21738.7567\n",
            "Epoch 217/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 21592.4567 - mean_absolute_error: 21592.9567 - val_loss: 20445.6251 - val_mean_absolute_error: 20446.1251\n",
            "Epoch 218/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 23805.4426 - mean_absolute_error: 23805.9423 - val_loss: 8665.0751 - val_mean_absolute_error: 8665.5743\n",
            "Epoch 219/250\n",
            "2648/2648 [==============================] - 2s 743us/step - loss: 26044.7449 - mean_absolute_error: 26045.2453 - val_loss: 46277.0845 - val_mean_absolute_error: 46277.5830\n",
            "Epoch 220/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 31928.7927 - mean_absolute_error: 31929.2933 - val_loss: 30582.8641 - val_mean_absolute_error: 30583.3634\n",
            "Epoch 221/250\n",
            "2648/2648 [==============================] - 2s 721us/step - loss: 33800.4205 - mean_absolute_error: 33800.9207 - val_loss: 41622.2223 - val_mean_absolute_error: 41622.7223\n",
            "Epoch 222/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 36430.4148 - mean_absolute_error: 36430.9153 - val_loss: 47261.8401 - val_mean_absolute_error: 47262.3380\n",
            "Epoch 223/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 44870.0892 - mean_absolute_error: 44870.5900 - val_loss: 893.4484 - val_mean_absolute_error: 893.9483\n",
            "Epoch 224/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 143056.4963 - mean_absolute_error: 143056.9937 - val_loss: 85224.1574 - val_mean_absolute_error: 85224.6574\n",
            "Epoch 225/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 92632.0621 - mean_absolute_error: 92632.5616 - val_loss: 61957.4660 - val_mean_absolute_error: 61957.9621\n",
            "Epoch 226/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 171041.0583 - mean_absolute_error: 171041.5591 - val_loss: 265520.5427 - val_mean_absolute_error: 265521.0356\n",
            "Epoch 227/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 200234.6437 - mean_absolute_error: 200235.1392 - val_loss: 248799.4765 - val_mean_absolute_error: 248799.9825\n",
            "Epoch 228/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 214974.0963 - mean_absolute_error: 214974.5997 - val_loss: 60204.8988 - val_mean_absolute_error: 60205.3979\n",
            "Epoch 229/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 83801.5563 - mean_absolute_error: 83802.0606 - val_loss: 53652.5357 - val_mean_absolute_error: 53653.0348\n",
            "Epoch 230/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 205948.9856 - mean_absolute_error: 205949.4822 - val_loss: 534544.5186 - val_mean_absolute_error: 534545.0186\n",
            "Epoch 231/250\n",
            "2648/2648 [==============================] - 2s 723us/step - loss: 188917.1865 - mean_absolute_error: 188917.6836 - val_loss: 59368.1872 - val_mean_absolute_error: 59368.6879\n",
            "Epoch 232/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 386544.2253 - mean_absolute_error: 386544.7226 - val_loss: 917807.1306 - val_mean_absolute_error: 917807.5965\n",
            "Epoch 233/250\n",
            "2648/2648 [==============================] - 2s 744us/step - loss: 610496.1526 - mean_absolute_error: 610496.6422 - val_loss: 948203.1974 - val_mean_absolute_error: 948203.6974\n",
            "Epoch 234/250\n",
            "2648/2648 [==============================] - 2s 782us/step - loss: 681598.8667 - mean_absolute_error: 681599.3990 - val_loss: 664634.6832 - val_mean_absolute_error: 664635.1689\n",
            "Epoch 235/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 757613.3017 - mean_absolute_error: 757613.7555 - val_loss: 263794.0773 - val_mean_absolute_error: 263794.5702\n",
            "Epoch 236/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 470470.5521 - mean_absolute_error: 470471.0566 - val_loss: 481068.2840 - val_mean_absolute_error: 481068.8131\n",
            "Epoch 237/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 413220.7657 - mean_absolute_error: 413221.2657 - val_loss: 223281.1460 - val_mean_absolute_error: 223281.6400\n",
            "Epoch 238/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 416075.9819 - mean_absolute_error: 416076.4743 - val_loss: 727519.3694 - val_mean_absolute_error: 727519.9319\n",
            "Epoch 239/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 512408.7177 - mean_absolute_error: 512409.2207 - val_loss: 440306.3503 - val_mean_absolute_error: 440306.8744\n",
            "Epoch 240/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 453199.6139 - mean_absolute_error: 453200.1145 - val_loss: 880139.3694 - val_mean_absolute_error: 880139.9177\n",
            "Epoch 241/250\n",
            "2648/2648 [==============================] - 2s 730us/step - loss: 559157.8017 - mean_absolute_error: 559158.2808 - val_loss: 362674.8431 - val_mean_absolute_error: 362675.3552\n",
            "Epoch 242/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 457773.0719 - mean_absolute_error: 457773.5946 - val_loss: 101874.7043 - val_mean_absolute_error: 101875.2043\n",
            "Epoch 243/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 665800.8339 - mean_absolute_error: 665801.3098 - val_loss: 415468.5028 - val_mean_absolute_error: 415469.0198\n",
            "Epoch 244/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 811150.5551 - mean_absolute_error: 811150.9887 - val_loss: 1194688.7983 - val_mean_absolute_error: 1194689.3948\n",
            "Epoch 245/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 1036656.0736 - mean_absolute_error: 1036656.5838 - val_loss: 1141576.3552 - val_mean_absolute_error: 1141576.9035\n",
            "Epoch 246/250\n",
            "2648/2648 [==============================] - 2s 725us/step - loss: 913634.7401 - mean_absolute_error: 913635.1616 - val_loss: 505515.2977 - val_mean_absolute_error: 505515.8097\n",
            "Epoch 247/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 843513.7268 - mean_absolute_error: 843514.2094 - val_loss: 760460.3026 - val_mean_absolute_error: 760460.8168\n",
            "Epoch 248/250\n",
            "2648/2648 [==============================] - 2s 726us/step - loss: 1249626.6586 - mean_absolute_error: 1249627.1360 - val_loss: 2852603.7896 - val_mean_absolute_error: 2852603.9035\n",
            "Epoch 249/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 1586391.0572 - mean_absolute_error: 1586391.6146 - val_loss: 2100507.4035 - val_mean_absolute_error: 2100507.9035\n",
            "Epoch 250/250\n",
            "2648/2648 [==============================] - 2s 757us/step - loss: 1840671.8097 - mean_absolute_error: 1840672.4305 - val_loss: 2724879.2327 - val_mean_absolute_error: 2724879.5396\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiBx0B-ctlSu",
        "colab_type": "code",
        "outputId": "42927f05-7f2e-45bf-eaee-16010ada0be6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "plt.semilogx(history.history['lr'], history.history['val_loss'])\n",
        "plt.legend(['Learning Rate', 'Validation Loss'])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe9dd93e278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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35LqiwtetIX0zMCOSfXpI6y99ei/pAymjgLvf4e717l4/ZcqU/blVEZGql6mS2WIG3AW8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\nrwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysjn6OdyzEzq",
        "colab_type": "code",
        "outputId": "126fa8cd-d958-4d9e-fde5-8abe809d411f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "plt.semilogx(history.history['lr'][:140], history.history['loss'][:140])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe9d0453400>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OfVSRzBo/egQNVcWs2XnM6ygigzZguZtZGHgEuA2YCXzQzGb2GfYK0OicuwJ4CvhGqoOK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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RWH9EZvszN-H",
        "colab_type": "code",
        "outputId": "6e7820cc-0788-43f7-d0c6-95403262a999",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# use 0.1 learning rate\n",
        "keras_model = Sequential([\n",
        "    Conv1D(filters=128, kernel_size=5, input_shape=(window_size, 1), strides=1, padding=\"causal\", activation=\"tanh\"),\n",
        "    Conv1D(filters=128, kernel_size=5, strides=1, padding=\"causal\", activation=\"tanh\"),\n",
        "    LSTM(128, return_sequences=True),\n",
        "    LSTM(128),\n",
        "    Dense(64, activation=\"tanh\"),\n",
        "    Dense(64, activation=\"tanh\"),\n",
        "    Dense(1)\n",
        "])\n",
        "\n",
        "optimizer = SGD(lr=0.1, momentum=0.9)\n",
        "keras_model.compile(loss=Huber(),\n",
        "                   optimizer=optimizer,\n",
        "                   metrics=[\"mae\"])\n",
        "history = keras_model.fit(X_train_reshape, Y_train_reshape, batch_size=256, epochs=250, validation_data=(X_test_reshape, Y_test_reshape))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 2648 samples, validate on 663 samples\n",
            "Epoch 1/250\n",
            "2648/2648 [==============================] - 4s 1ms/step - loss: 0.1141 - mean_absolute_error: 0.3795 - val_loss: 0.2911 - val_mean_absolute_error: 0.7132\n",
            "Epoch 2/250\n",
            "2648/2648 [==============================] - 2s 778us/step - loss: 0.0134 - mean_absolute_error: 0.1294 - val_loss: 0.3476 - val_mean_absolute_error: 0.7913\n",
            "Epoch 3/250\n",
            "2648/2648 [==============================] - 2s 796us/step - loss: 0.0073 - mean_absolute_error: 0.0800 - val_loss: 0.2771 - val_mean_absolute_error: 0.6907\n",
            "Epoch 4/250\n",
            "2648/2648 [==============================] - 2s 815us/step - loss: 0.0055 - mean_absolute_error: 0.0635 - val_loss: 0.2574 - val_mean_absolute_error: 0.6641\n",
            "Epoch 5/250\n",
            "2648/2648 [==============================] - 2s 754us/step - loss: 0.0048 - mean_absolute_error: 0.0553 - val_loss: 0.2241 - val_mean_absolute_error: 0.6158\n",
            "Epoch 6/250\n",
            "2648/2648 [==============================] - 2s 769us/step - loss: 0.0043 - mean_absolute_error: 0.0533 - val_loss: 0.1941 - val_mean_absolute_error: 0.5741\n",
            "Epoch 7/250\n",
            "2648/2648 [==============================] - 2s 757us/step - loss: 0.0036 - mean_absolute_error: 0.0464 - val_loss: 0.1485 - val_mean_absolute_error: 0.5014\n",
            "Epoch 8/250\n",
            "2648/2648 [==============================] - 2s 759us/step - loss: 0.0027 - mean_absolute_error: 0.0414 - val_loss: 0.0827 - val_mean_absolute_error: 0.3633\n",
            "Epoch 9/250\n",
            "2648/2648 [==============================] - 2s 760us/step - loss: 0.0017 - mean_absolute_error: 0.0324 - val_loss: 0.0365 - val_mean_absolute_error: 0.2259\n",
            "Epoch 10/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 7.3596e-04 - mean_absolute_error: 0.0217 - val_loss: 0.0107 - val_mean_absolute_error: 0.0966\n",
            "Epoch 11/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 2.3568e-04 - mean_absolute_error: 0.0127 - val_loss: 0.0129 - val_mean_absolute_error: 0.1223\n",
            "Epoch 12/250\n",
            "2648/2648 [==============================] - 2s 742us/step - loss: 1.5249e-04 - mean_absolute_error: 0.0098 - val_loss: 0.0176 - val_mean_absolute_error: 0.1489\n",
            "Epoch 13/250\n",
            "2648/2648 [==============================] - 2s 765us/step - loss: 1.5474e-04 - mean_absolute_error: 0.0086 - val_loss: 0.0151 - val_mean_absolute_error: 0.1349\n",
            "Epoch 14/250\n",
            "2648/2648 [==============================] - 2s 758us/step - loss: 1.3993e-04 - mean_absolute_error: 0.0094 - val_loss: 0.0123 - val_mean_absolute_error: 0.1170\n",
            "Epoch 15/250\n",
            "2648/2648 [==============================] - 2s 755us/step - loss: 1.3010e-04 - mean_absolute_error: 0.0084 - val_loss: 0.0117 - val_mean_absolute_error: 0.1138\n",
            "Epoch 16/250\n",
            "2648/2648 [==============================] - 2s 748us/step - loss: 1.3331e-04 - mean_absolute_error: 0.0086 - val_loss: 0.0117 - val_mean_absolute_error: 0.1141\n",
            "Epoch 17/250\n",
            "2648/2648 [==============================] - 2s 744us/step - loss: 1.2574e-04 - mean_absolute_error: 0.0074 - val_loss: 0.0116 - val_mean_absolute_error: 0.1129\n",
            "Epoch 18/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 1.3591e-04 - mean_absolute_error: 0.0091 - val_loss: 0.0120 - val_mean_absolute_error: 0.1161\n",
            "Epoch 19/250\n",
            "2648/2648 [==============================] - 2s 742us/step - loss: 1.4352e-04 - mean_absolute_error: 0.0099 - val_loss: 0.0114 - val_mean_absolute_error: 0.1124\n",
            "Epoch 20/250\n",
            "2648/2648 [==============================] - 2s 756us/step - loss: 1.2445e-04 - mean_absolute_error: 0.0074 - val_loss: 0.0115 - val_mean_absolute_error: 0.1122\n",
            "Epoch 21/250\n",
            "2648/2648 [==============================] - 2s 749us/step - loss: 1.2769e-04 - mean_absolute_error: 0.0081 - val_loss: 0.0117 - val_mean_absolute_error: 0.1151\n",
            "Epoch 22/250\n",
            "2648/2648 [==============================] - 2s 753us/step - loss: 1.3844e-04 - mean_absolute_error: 0.0095 - val_loss: 0.0112 - val_mean_absolute_error: 0.1106\n",
            "Epoch 23/250\n",
            "2648/2648 [==============================] - 2s 754us/step - loss: 1.5259e-04 - mean_absolute_error: 0.0107 - val_loss: 0.0116 - val_mean_absolute_error: 0.1150\n",
            "Epoch 24/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 1.3551e-04 - mean_absolute_error: 0.0094 - val_loss: 0.0111 - val_mean_absolute_error: 0.1106\n",
            "Epoch 25/250\n",
            "2648/2648 [==============================] - 2s 788us/step - loss: 1.2558e-04 - mean_absolute_error: 0.0079 - val_loss: 0.0109 - val_mean_absolute_error: 0.1093\n",
            "Epoch 26/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 1.2332e-04 - mean_absolute_error: 0.0075 - val_loss: 0.0106 - val_mean_absolute_error: 0.1059\n",
            "Epoch 27/250\n",
            "2648/2648 [==============================] - 2s 748us/step - loss: 1.4448e-04 - mean_absolute_error: 0.0104 - val_loss: 0.0108 - val_mean_absolute_error: 0.1091\n",
            "Epoch 28/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 1.2448e-04 - mean_absolute_error: 0.0079 - val_loss: 0.0107 - val_mean_absolute_error: 0.1077\n",
            "Epoch 29/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 1.3276e-04 - mean_absolute_error: 0.0088 - val_loss: 0.0112 - val_mean_absolute_error: 0.1123\n",
            "Epoch 30/250\n",
            "2648/2648 [==============================] - 2s 743us/step - loss: 1.3451e-04 - mean_absolute_error: 0.0097 - val_loss: 0.0110 - val_mean_absolute_error: 0.1112\n",
            "Epoch 31/250\n",
            "2648/2648 [==============================] - 2s 744us/step - loss: 1.6483e-04 - mean_absolute_error: 0.0122 - val_loss: 0.0109 - val_mean_absolute_error: 0.1098\n",
            "Epoch 32/250\n",
            "2648/2648 [==============================] - 2s 752us/step - loss: 1.2036e-04 - mean_absolute_error: 0.0074 - val_loss: 0.0105 - val_mean_absolute_error: 0.1061\n",
            "Epoch 33/250\n",
            "2648/2648 [==============================] - 2s 746us/step - loss: 1.4381e-04 - mean_absolute_error: 0.0101 - val_loss: 0.0106 - val_mean_absolute_error: 0.1082\n",
            "Epoch 34/250\n",
            "2648/2648 [==============================] - 2s 762us/step - loss: 1.2153e-04 - mean_absolute_error: 0.0078 - val_loss: 0.0105 - val_mean_absolute_error: 0.1065\n",
            "Epoch 35/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 1.2494e-04 - mean_absolute_error: 0.0078 - val_loss: 0.0108 - val_mean_absolute_error: 0.1104\n",
            "Epoch 36/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 1.4752e-04 - mean_absolute_error: 0.0106 - val_loss: 0.0101 - val_mean_absolute_error: 0.1028\n",
            "Epoch 37/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 1.4394e-04 - mean_absolute_error: 0.0106 - val_loss: 0.0103 - val_mean_absolute_error: 0.1065\n",
            "Epoch 38/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 1.2179e-04 - mean_absolute_error: 0.0078 - val_loss: 0.0102 - val_mean_absolute_error: 0.1051\n",
            "Epoch 39/250\n",
            "2648/2648 [==============================] - 2s 730us/step - loss: 1.1852e-04 - mean_absolute_error: 0.0071 - val_loss: 0.0102 - val_mean_absolute_error: 0.1047\n",
            "Epoch 40/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 1.1872e-04 - mean_absolute_error: 0.0075 - val_loss: 0.0101 - val_mean_absolute_error: 0.1047\n",
            "Epoch 41/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 1.2212e-04 - mean_absolute_error: 0.0081 - val_loss: 0.0101 - val_mean_absolute_error: 0.1048\n",
            "Epoch 42/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 1.2036e-04 - mean_absolute_error: 0.0077 - val_loss: 0.0102 - val_mean_absolute_error: 0.1064\n",
            "Epoch 43/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 1.6727e-04 - mean_absolute_error: 0.0125 - val_loss: 0.0098 - val_mean_absolute_error: 0.1009\n",
            "Epoch 44/250\n",
            "2648/2648 [==============================] - 2s 735us/step - loss: 1.7716e-04 - mean_absolute_error: 0.0135 - val_loss: 0.0100 - val_mean_absolute_error: 0.1044\n",
            "Epoch 45/250\n",
            "2648/2648 [==============================] - 2s 726us/step - loss: 1.4638e-04 - mean_absolute_error: 0.0110 - val_loss: 0.0096 - val_mean_absolute_error: 0.0992\n",
            "Epoch 46/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 1.2877e-04 - mean_absolute_error: 0.0087 - val_loss: 0.0102 - val_mean_absolute_error: 0.1061\n",
            "Epoch 47/250\n",
            "2648/2648 [==============================] - 2s 730us/step - loss: 1.3069e-04 - mean_absolute_error: 0.0095 - val_loss: 0.0099 - val_mean_absolute_error: 0.1030\n",
            "Epoch 48/250\n",
            "2648/2648 [==============================] - 2s 725us/step - loss: 1.1894e-04 - mean_absolute_error: 0.0076 - val_loss: 0.0100 - val_mean_absolute_error: 0.1042\n",
            "Epoch 49/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 1.1289e-04 - mean_absolute_error: 0.0066 - val_loss: 0.0096 - val_mean_absolute_error: 0.1000\n",
            "Epoch 50/250\n",
            "2648/2648 [==============================] - 2s 725us/step - loss: 1.5981e-04 - mean_absolute_error: 0.0120 - val_loss: 0.0097 - val_mean_absolute_error: 0.1030\n",
            "Epoch 51/250\n",
            "2648/2648 [==============================] - 2s 747us/step - loss: 1.6060e-04 - mean_absolute_error: 0.0121 - val_loss: 0.0092 - val_mean_absolute_error: 0.0969\n",
            "Epoch 52/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 1.1777e-04 - mean_absolute_error: 0.0075 - val_loss: 0.0095 - val_mean_absolute_error: 0.0997\n",
            "Epoch 53/250\n",
            "2648/2648 [==============================] - 2s 730us/step - loss: 1.2447e-04 - mean_absolute_error: 0.0083 - val_loss: 0.0098 - val_mean_absolute_error: 0.1030\n",
            "Epoch 54/250\n",
            "2648/2648 [==============================] - 2s 767us/step - loss: 1.2832e-04 - mean_absolute_error: 0.0090 - val_loss: 0.0096 - val_mean_absolute_error: 0.1009\n",
            "Epoch 55/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 1.1918e-04 - mean_absolute_error: 0.0081 - val_loss: 0.0096 - val_mean_absolute_error: 0.1008\n",
            "Epoch 56/250\n",
            "2648/2648 [==============================] - 2s 751us/step - loss: 1.4006e-04 - mean_absolute_error: 0.0104 - val_loss: 0.0096 - val_mean_absolute_error: 0.1013\n",
            "Epoch 57/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 1.2811e-04 - mean_absolute_error: 0.0088 - val_loss: 0.0097 - val_mean_absolute_error: 0.1017\n",
            "Epoch 58/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 1.2277e-04 - mean_absolute_error: 0.0081 - val_loss: 0.0096 - val_mean_absolute_error: 0.1012\n",
            "Epoch 59/250\n",
            "2648/2648 [==============================] - 2s 749us/step - loss: 1.1618e-04 - mean_absolute_error: 0.0077 - val_loss: 0.0094 - val_mean_absolute_error: 0.0999\n",
            "Epoch 60/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 1.1208e-04 - mean_absolute_error: 0.0068 - val_loss: 0.0094 - val_mean_absolute_error: 0.0993\n",
            "Epoch 61/250\n",
            "2648/2648 [==============================] - 2s 751us/step - loss: 1.1247e-04 - mean_absolute_error: 0.0069 - val_loss: 0.0095 - val_mean_absolute_error: 0.1012\n",
            "Epoch 62/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 1.1020e-04 - mean_absolute_error: 0.0066 - val_loss: 0.0093 - val_mean_absolute_error: 0.0987\n",
            "Epoch 63/250\n",
            "2648/2648 [==============================] - 2s 769us/step - loss: 1.1068e-04 - mean_absolute_error: 0.0068 - val_loss: 0.0091 - val_mean_absolute_error: 0.0964\n",
            "Epoch 64/250\n",
            "2648/2648 [==============================] - 2s 780us/step - loss: 1.1107e-04 - mean_absolute_error: 0.0067 - val_loss: 0.0094 - val_mean_absolute_error: 0.1007\n",
            "Epoch 65/250\n",
            "2648/2648 [==============================] - 2s 782us/step - loss: 1.1482e-04 - mean_absolute_error: 0.0074 - val_loss: 0.0092 - val_mean_absolute_error: 0.0980\n",
            "Epoch 66/250\n",
            "2648/2648 [==============================] - 2s 752us/step - loss: 1.1212e-04 - mean_absolute_error: 0.0072 - val_loss: 0.0094 - val_mean_absolute_error: 0.0999\n",
            "Epoch 67/250\n",
            "2648/2648 [==============================] - 2s 747us/step - loss: 1.1141e-04 - mean_absolute_error: 0.0069 - val_loss: 0.0091 - val_mean_absolute_error: 0.0969\n",
            "Epoch 68/250\n",
            "2648/2648 [==============================] - 2s 743us/step - loss: 1.3060e-04 - mean_absolute_error: 0.0097 - val_loss: 0.0092 - val_mean_absolute_error: 0.0982\n",
            "Epoch 69/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 1.3378e-04 - mean_absolute_error: 0.0100 - val_loss: 0.0087 - val_mean_absolute_error: 0.0930\n",
            "Epoch 70/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 1.5983e-04 - mean_absolute_error: 0.0127 - val_loss: 0.0089 - val_mean_absolute_error: 0.0957\n",
            "Epoch 71/250\n",
            "2648/2648 [==============================] - 2s 762us/step - loss: 1.3861e-04 - mean_absolute_error: 0.0109 - val_loss: 0.0086 - val_mean_absolute_error: 0.0911\n",
            "Epoch 72/250\n",
            "2648/2648 [==============================] - 2s 726us/step - loss: 1.2567e-04 - mean_absolute_error: 0.0090 - val_loss: 0.0089 - val_mean_absolute_error: 0.0954\n",
            "Epoch 73/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 1.1754e-04 - mean_absolute_error: 0.0082 - val_loss: 0.0088 - val_mean_absolute_error: 0.0941\n",
            "Epoch 74/250\n",
            "2648/2648 [==============================] - 2s 735us/step - loss: 1.4283e-04 - mean_absolute_error: 0.0112 - val_loss: 0.0092 - val_mean_absolute_error: 0.0988\n",
            "Epoch 75/250\n",
            "2648/2648 [==============================] - 2s 743us/step - loss: 1.3262e-04 - mean_absolute_error: 0.0099 - val_loss: 0.0089 - val_mean_absolute_error: 0.0951\n",
            "Epoch 76/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 1.0884e-04 - mean_absolute_error: 0.0067 - val_loss: 0.0090 - val_mean_absolute_error: 0.0970\n",
            "Epoch 77/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 1.1604e-04 - mean_absolute_error: 0.0077 - val_loss: 0.0088 - val_mean_absolute_error: 0.0937\n",
            "Epoch 78/250\n",
            "2648/2648 [==============================] - 2s 754us/step - loss: 1.2245e-04 - mean_absolute_error: 0.0086 - val_loss: 0.0089 - val_mean_absolute_error: 0.0959\n",
            "Epoch 79/250\n",
            "2648/2648 [==============================] - 2s 747us/step - loss: 1.1197e-04 - mean_absolute_error: 0.0073 - val_loss: 0.0088 - val_mean_absolute_error: 0.0951\n",
            "Epoch 80/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 1.1268e-04 - mean_absolute_error: 0.0075 - val_loss: 0.0087 - val_mean_absolute_error: 0.0937\n",
            "Epoch 81/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 1.1136e-04 - mean_absolute_error: 0.0073 - val_loss: 0.0089 - val_mean_absolute_error: 0.0968\n",
            "Epoch 82/250\n",
            "2648/2648 [==============================] - 2s 769us/step - loss: 1.2304e-04 - mean_absolute_error: 0.0091 - val_loss: 0.0087 - val_mean_absolute_error: 0.0940\n",
            "Epoch 83/250\n",
            "2648/2648 [==============================] - 2s 777us/step - loss: 1.2670e-04 - mean_absolute_error: 0.0093 - val_loss: 0.0087 - val_mean_absolute_error: 0.0934\n",
            "Epoch 84/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 1.0935e-04 - mean_absolute_error: 0.0068 - val_loss: 0.0087 - val_mean_absolute_error: 0.0941\n",
            "Epoch 85/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 1.0556e-04 - mean_absolute_error: 0.0061 - val_loss: 0.0088 - val_mean_absolute_error: 0.0948\n",
            "Epoch 86/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 1.2277e-04 - mean_absolute_error: 0.0091 - val_loss: 0.0090 - val_mean_absolute_error: 0.0979\n",
            "Epoch 87/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 1.3769e-04 - mean_absolute_error: 0.0104 - val_loss: 0.0086 - val_mean_absolute_error: 0.0924\n",
            "Epoch 88/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 1.1518e-04 - mean_absolute_error: 0.0077 - val_loss: 0.0087 - val_mean_absolute_error: 0.0941\n",
            "Epoch 89/250\n",
            "2648/2648 [==============================] - 2s 725us/step - loss: 1.0900e-04 - mean_absolute_error: 0.0071 - val_loss: 0.0085 - val_mean_absolute_error: 0.0914\n",
            "Epoch 90/250\n",
            "2648/2648 [==============================] - 2s 742us/step - loss: 1.2090e-04 - mean_absolute_error: 0.0091 - val_loss: 0.0086 - val_mean_absolute_error: 0.0932\n",
            "Epoch 91/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 1.1797e-04 - mean_absolute_error: 0.0084 - val_loss: 0.0084 - val_mean_absolute_error: 0.0909\n",
            "Epoch 92/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 1.1253e-04 - mean_absolute_error: 0.0074 - val_loss: 0.0087 - val_mean_absolute_error: 0.0950\n",
            "Epoch 93/250\n",
            "2648/2648 [==============================] - 2s 742us/step - loss: 1.0727e-04 - mean_absolute_error: 0.0065 - val_loss: 0.0086 - val_mean_absolute_error: 0.0933\n",
            "Epoch 94/250\n",
            "2648/2648 [==============================] - 2s 764us/step - loss: 1.1317e-04 - mean_absolute_error: 0.0077 - val_loss: 0.0087 - val_mean_absolute_error: 0.0946\n",
            "Epoch 95/250\n",
            "2648/2648 [==============================] - 2s 735us/step - loss: 1.0957e-04 - mean_absolute_error: 0.0071 - val_loss: 0.0084 - val_mean_absolute_error: 0.0914\n",
            "Epoch 96/250\n",
            "2648/2648 [==============================] - 2s 762us/step - loss: 1.1429e-04 - mean_absolute_error: 0.0082 - val_loss: 0.0085 - val_mean_absolute_error: 0.0922\n",
            "Epoch 97/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 1.1057e-04 - mean_absolute_error: 0.0075 - val_loss: 0.0083 - val_mean_absolute_error: 0.0898\n",
            "Epoch 98/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 1.1162e-04 - mean_absolute_error: 0.0075 - val_loss: 0.0083 - val_mean_absolute_error: 0.0901\n",
            "Epoch 99/250\n",
            "2648/2648 [==============================] - 2s 763us/step - loss: 1.0694e-04 - mean_absolute_error: 0.0070 - val_loss: 0.0085 - val_mean_absolute_error: 0.0924\n",
            "Epoch 100/250\n",
            "2648/2648 [==============================] - 2s 750us/step - loss: 1.1792e-04 - mean_absolute_error: 0.0086 - val_loss: 0.0083 - val_mean_absolute_error: 0.0894\n",
            "Epoch 101/250\n",
            "2648/2648 [==============================] - 2s 746us/step - loss: 1.2832e-04 - mean_absolute_error: 0.0096 - val_loss: 0.0084 - val_mean_absolute_error: 0.0922\n",
            "Epoch 102/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 1.0636e-04 - mean_absolute_error: 0.0069 - val_loss: 0.0084 - val_mean_absolute_error: 0.0914\n",
            "Epoch 103/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 1.0607e-04 - mean_absolute_error: 0.0068 - val_loss: 0.0085 - val_mean_absolute_error: 0.0935\n",
            "Epoch 104/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 1.2152e-04 - mean_absolute_error: 0.0092 - val_loss: 0.0083 - val_mean_absolute_error: 0.0900\n",
            "Epoch 105/250\n",
            "2648/2648 [==============================] - 2s 757us/step - loss: 1.1196e-04 - mean_absolute_error: 0.0080 - val_loss: 0.0083 - val_mean_absolute_error: 0.0910\n",
            "Epoch 106/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 1.1211e-04 - mean_absolute_error: 0.0079 - val_loss: 0.0083 - val_mean_absolute_error: 0.0904\n",
            "Epoch 107/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 1.1328e-04 - mean_absolute_error: 0.0081 - val_loss: 0.0084 - val_mean_absolute_error: 0.0920\n",
            "Epoch 108/250\n",
            "2648/2648 [==============================] - 2s 749us/step - loss: 1.1955e-04 - mean_absolute_error: 0.0087 - val_loss: 0.0081 - val_mean_absolute_error: 0.0877\n",
            "Epoch 109/250\n",
            "2648/2648 [==============================] - 2s 749us/step - loss: 1.4312e-04 - mean_absolute_error: 0.0115 - val_loss: 0.0084 - val_mean_absolute_error: 0.0923\n",
            "Epoch 110/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 1.2732e-04 - mean_absolute_error: 0.0095 - val_loss: 0.0082 - val_mean_absolute_error: 0.0889\n",
            "Epoch 111/250\n",
            "2648/2648 [==============================] - 2s 752us/step - loss: 1.1087e-04 - mean_absolute_error: 0.0076 - val_loss: 0.0083 - val_mean_absolute_error: 0.0903\n",
            "Epoch 112/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 1.0547e-04 - mean_absolute_error: 0.0069 - val_loss: 0.0082 - val_mean_absolute_error: 0.0899\n",
            "Epoch 113/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 1.1118e-04 - mean_absolute_error: 0.0078 - val_loss: 0.0083 - val_mean_absolute_error: 0.0910\n",
            "Epoch 114/250\n",
            "2648/2648 [==============================] - 2s 778us/step - loss: 1.2466e-04 - mean_absolute_error: 0.0097 - val_loss: 0.0082 - val_mean_absolute_error: 0.0903\n",
            "Epoch 115/250\n",
            "2648/2648 [==============================] - 2s 753us/step - loss: 1.1630e-04 - mean_absolute_error: 0.0083 - val_loss: 0.0081 - val_mean_absolute_error: 0.0880\n",
            "Epoch 116/250\n",
            "2648/2648 [==============================] - 2s 753us/step - loss: 1.0683e-04 - mean_absolute_error: 0.0069 - val_loss: 0.0082 - val_mean_absolute_error: 0.0906\n",
            "Epoch 117/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 1.1052e-04 - mean_absolute_error: 0.0079 - val_loss: 0.0081 - val_mean_absolute_error: 0.0889\n",
            "Epoch 118/250\n",
            "2648/2648 [==============================] - 2s 726us/step - loss: 1.0711e-04 - mean_absolute_error: 0.0071 - val_loss: 0.0084 - val_mean_absolute_error: 0.0927\n",
            "Epoch 119/250\n",
            "2648/2648 [==============================] - 2s 755us/step - loss: 1.0546e-04 - mean_absolute_error: 0.0068 - val_loss: 0.0080 - val_mean_absolute_error: 0.0878\n",
            "Epoch 120/250\n",
            "2648/2648 [==============================] - 2s 746us/step - loss: 1.0832e-04 - mean_absolute_error: 0.0077 - val_loss: 0.0082 - val_mean_absolute_error: 0.0907\n",
            "Epoch 121/250\n",
            "2648/2648 [==============================] - 2s 751us/step - loss: 1.1077e-04 - mean_absolute_error: 0.0079 - val_loss: 0.0081 - val_mean_absolute_error: 0.0886\n",
            "Epoch 122/250\n",
            "2648/2648 [==============================] - 2s 766us/step - loss: 1.0132e-04 - mean_absolute_error: 0.0059 - val_loss: 0.0081 - val_mean_absolute_error: 0.0892\n",
            "Epoch 123/250\n",
            "2648/2648 [==============================] - 2s 795us/step - loss: 1.0500e-04 - mean_absolute_error: 0.0069 - val_loss: 0.0081 - val_mean_absolute_error: 0.0897\n",
            "Epoch 124/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 1.0091e-04 - mean_absolute_error: 0.0063 - val_loss: 0.0083 - val_mean_absolute_error: 0.0917\n",
            "Epoch 125/250\n",
            "2648/2648 [==============================] - 2s 751us/step - loss: 1.1017e-04 - mean_absolute_error: 0.0080 - val_loss: 0.0081 - val_mean_absolute_error: 0.0896\n",
            "Epoch 126/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 1.2109e-04 - mean_absolute_error: 0.0095 - val_loss: 0.0081 - val_mean_absolute_error: 0.0886\n",
            "Epoch 127/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 1.1360e-04 - mean_absolute_error: 0.0085 - val_loss: 0.0082 - val_mean_absolute_error: 0.0903\n",
            "Epoch 128/250\n",
            "2648/2648 [==============================] - 2s 750us/step - loss: 1.2416e-04 - mean_absolute_error: 0.0097 - val_loss: 0.0079 - val_mean_absolute_error: 0.0868\n",
            "Epoch 129/250\n",
            "2648/2648 [==============================] - 2s 753us/step - loss: 1.1469e-04 - mean_absolute_error: 0.0084 - val_loss: 0.0080 - val_mean_absolute_error: 0.0881\n",
            "Epoch 130/250\n",
            "2648/2648 [==============================] - 2s 747us/step - loss: 1.0083e-04 - mean_absolute_error: 0.0062 - val_loss: 0.0080 - val_mean_absolute_error: 0.0880\n",
            "Epoch 131/250\n",
            "2648/2648 [==============================] - 2s 754us/step - loss: 1.0606e-04 - mean_absolute_error: 0.0074 - val_loss: 0.0079 - val_mean_absolute_error: 0.0871\n",
            "Epoch 132/250\n",
            "2648/2648 [==============================] - 2s 756us/step - loss: 1.0558e-04 - mean_absolute_error: 0.0070 - val_loss: 0.0081 - val_mean_absolute_error: 0.0894\n",
            "Epoch 133/250\n",
            "2648/2648 [==============================] - 2s 794us/step - loss: 1.0674e-04 - mean_absolute_error: 0.0070 - val_loss: 0.0079 - val_mean_absolute_error: 0.0870\n",
            "Epoch 134/250\n",
            "2648/2648 [==============================] - 2s 753us/step - loss: 1.0443e-04 - mean_absolute_error: 0.0072 - val_loss: 0.0080 - val_mean_absolute_error: 0.0882\n",
            "Epoch 135/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 1.0522e-04 - mean_absolute_error: 0.0074 - val_loss: 0.0079 - val_mean_absolute_error: 0.0862\n",
            "Epoch 136/250\n",
            "2648/2648 [==============================] - 2s 750us/step - loss: 1.0728e-04 - mean_absolute_error: 0.0077 - val_loss: 0.0079 - val_mean_absolute_error: 0.0876\n",
            "Epoch 137/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 9.8579e-05 - mean_absolute_error: 0.0058 - val_loss: 0.0079 - val_mean_absolute_error: 0.0862\n",
            "Epoch 138/250\n",
            "2648/2648 [==============================] - 2s 751us/step - loss: 9.9195e-05 - mean_absolute_error: 0.0058 - val_loss: 0.0079 - val_mean_absolute_error: 0.0865\n",
            "Epoch 139/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 1.0967e-04 - mean_absolute_error: 0.0077 - val_loss: 0.0078 - val_mean_absolute_error: 0.0846\n",
            "Epoch 140/250\n",
            "2648/2648 [==============================] - 2s 754us/step - loss: 1.1254e-04 - mean_absolute_error: 0.0083 - val_loss: 0.0079 - val_mean_absolute_error: 0.0879\n",
            "Epoch 141/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 1.0578e-04 - mean_absolute_error: 0.0071 - val_loss: 0.0079 - val_mean_absolute_error: 0.0873\n",
            "Epoch 142/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 1.0146e-04 - mean_absolute_error: 0.0064 - val_loss: 0.0079 - val_mean_absolute_error: 0.0869\n",
            "Epoch 143/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 1.0422e-04 - mean_absolute_error: 0.0074 - val_loss: 0.0078 - val_mean_absolute_error: 0.0865\n",
            "Epoch 144/250\n",
            "2648/2648 [==============================] - 2s 748us/step - loss: 9.7563e-05 - mean_absolute_error: 0.0059 - val_loss: 0.0078 - val_mean_absolute_error: 0.0858\n",
            "Epoch 145/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 1.0899e-04 - mean_absolute_error: 0.0082 - val_loss: 0.0078 - val_mean_absolute_error: 0.0862\n",
            "Epoch 146/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 1.2379e-04 - mean_absolute_error: 0.0096 - val_loss: 0.0078 - val_mean_absolute_error: 0.0865\n",
            "Epoch 147/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 9.9772e-05 - mean_absolute_error: 0.0065 - val_loss: 0.0078 - val_mean_absolute_error: 0.0860\n",
            "Epoch 148/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 9.7721e-05 - mean_absolute_error: 0.0058 - val_loss: 0.0077 - val_mean_absolute_error: 0.0849\n",
            "Epoch 149/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 1.0254e-04 - mean_absolute_error: 0.0065 - val_loss: 0.0077 - val_mean_absolute_error: 0.0847\n",
            "Epoch 150/250\n",
            "2648/2648 [==============================] - 2s 750us/step - loss: 1.0626e-04 - mean_absolute_error: 0.0076 - val_loss: 0.0078 - val_mean_absolute_error: 0.0861\n",
            "Epoch 151/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 1.0098e-04 - mean_absolute_error: 0.0066 - val_loss: 0.0077 - val_mean_absolute_error: 0.0854\n",
            "Epoch 152/250\n",
            "2648/2648 [==============================] - 2s 750us/step - loss: 1.0219e-04 - mean_absolute_error: 0.0066 - val_loss: 0.0078 - val_mean_absolute_error: 0.0864\n",
            "Epoch 153/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 1.0036e-04 - mean_absolute_error: 0.0066 - val_loss: 0.0078 - val_mean_absolute_error: 0.0859\n",
            "Epoch 154/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 9.8914e-05 - mean_absolute_error: 0.0062 - val_loss: 0.0077 - val_mean_absolute_error: 0.0845\n",
            "Epoch 155/250\n",
            "2648/2648 [==============================] - 2s 750us/step - loss: 1.0694e-04 - mean_absolute_error: 0.0080 - val_loss: 0.0077 - val_mean_absolute_error: 0.0846\n",
            "Epoch 156/250\n",
            "2648/2648 [==============================] - 2s 785us/step - loss: 1.0437e-04 - mean_absolute_error: 0.0076 - val_loss: 0.0077 - val_mean_absolute_error: 0.0848\n",
            "Epoch 157/250\n",
            "2648/2648 [==============================] - 2s 744us/step - loss: 9.8614e-05 - mean_absolute_error: 0.0065 - val_loss: 0.0077 - val_mean_absolute_error: 0.0849\n",
            "Epoch 158/250\n",
            "2648/2648 [==============================] - 2s 746us/step - loss: 1.0454e-04 - mean_absolute_error: 0.0076 - val_loss: 0.0078 - val_mean_absolute_error: 0.0863\n",
            "Epoch 159/250\n",
            "2648/2648 [==============================] - 2s 757us/step - loss: 1.2568e-04 - mean_absolute_error: 0.0100 - val_loss: 0.0076 - val_mean_absolute_error: 0.0825\n",
            "Epoch 160/250\n",
            "2648/2648 [==============================] - 2s 743us/step - loss: 1.1252e-04 - mean_absolute_error: 0.0085 - val_loss: 0.0077 - val_mean_absolute_error: 0.0849\n",
            "Epoch 161/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 1.0028e-04 - mean_absolute_error: 0.0064 - val_loss: 0.0076 - val_mean_absolute_error: 0.0841\n",
            "Epoch 162/250\n",
            "2648/2648 [==============================] - 2s 762us/step - loss: 9.9163e-05 - mean_absolute_error: 0.0065 - val_loss: 0.0076 - val_mean_absolute_error: 0.0847\n",
            "Epoch 163/250\n",
            "2648/2648 [==============================] - 2s 827us/step - loss: 9.7379e-05 - mean_absolute_error: 0.0059 - val_loss: 0.0076 - val_mean_absolute_error: 0.0835\n",
            "Epoch 164/250\n",
            "2648/2648 [==============================] - 2s 763us/step - loss: 1.0660e-04 - mean_absolute_error: 0.0072 - val_loss: 0.0077 - val_mean_absolute_error: 0.0856\n",
            "Epoch 165/250\n",
            "2648/2648 [==============================] - 2s 760us/step - loss: 1.0019e-04 - mean_absolute_error: 0.0066 - val_loss: 0.0076 - val_mean_absolute_error: 0.0841\n",
            "Epoch 166/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 1.0089e-04 - mean_absolute_error: 0.0070 - val_loss: 0.0076 - val_mean_absolute_error: 0.0845\n",
            "Epoch 167/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 1.0130e-04 - mean_absolute_error: 0.0069 - val_loss: 0.0075 - val_mean_absolute_error: 0.0822\n",
            "Epoch 168/250\n",
            "2648/2648 [==============================] - 2s 765us/step - loss: 1.0407e-04 - mean_absolute_error: 0.0077 - val_loss: 0.0076 - val_mean_absolute_error: 0.0835\n",
            "Epoch 169/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 1.0432e-04 - mean_absolute_error: 0.0077 - val_loss: 0.0075 - val_mean_absolute_error: 0.0822\n",
            "Epoch 170/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 1.3734e-04 - mean_absolute_error: 0.0111 - val_loss: 0.0075 - val_mean_absolute_error: 0.0827\n",
            "Epoch 171/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 1.1163e-04 - mean_absolute_error: 0.0083 - val_loss: 0.0076 - val_mean_absolute_error: 0.0835\n",
            "Epoch 172/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 1.0686e-04 - mean_absolute_error: 0.0081 - val_loss: 0.0075 - val_mean_absolute_error: 0.0823\n",
            "Epoch 173/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 1.2119e-04 - mean_absolute_error: 0.0099 - val_loss: 0.0077 - val_mean_absolute_error: 0.0861\n",
            "Epoch 174/250\n",
            "2648/2648 [==============================] - 2s 738us/step - loss: 1.0339e-04 - mean_absolute_error: 0.0074 - val_loss: 0.0075 - val_mean_absolute_error: 0.0835\n",
            "Epoch 175/250\n",
            "2648/2648 [==============================] - 2s 767us/step - loss: 9.5850e-05 - mean_absolute_error: 0.0059 - val_loss: 0.0075 - val_mean_absolute_error: 0.0833\n",
            "Epoch 176/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 9.5069e-05 - mean_absolute_error: 0.0057 - val_loss: 0.0075 - val_mean_absolute_error: 0.0836\n",
            "Epoch 177/250\n",
            "2648/2648 [==============================] - 2s 756us/step - loss: 9.7522e-05 - mean_absolute_error: 0.0063 - val_loss: 0.0077 - val_mean_absolute_error: 0.0860\n",
            "Epoch 178/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 1.3474e-04 - mean_absolute_error: 0.0110 - val_loss: 0.0074 - val_mean_absolute_error: 0.0814\n",
            "Epoch 179/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 1.3835e-04 - mean_absolute_error: 0.0112 - val_loss: 0.0076 - val_mean_absolute_error: 0.0850\n",
            "Epoch 180/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 1.0932e-04 - mean_absolute_error: 0.0088 - val_loss: 0.0074 - val_mean_absolute_error: 0.0808\n",
            "Epoch 181/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 1.3022e-04 - mean_absolute_error: 0.0106 - val_loss: 0.0075 - val_mean_absolute_error: 0.0832\n",
            "Epoch 182/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 1.0616e-04 - mean_absolute_error: 0.0082 - val_loss: 0.0074 - val_mean_absolute_error: 0.0817\n",
            "Epoch 183/250\n",
            "2648/2648 [==============================] - 2s 766us/step - loss: 1.0834e-04 - mean_absolute_error: 0.0082 - val_loss: 0.0075 - val_mean_absolute_error: 0.0834\n",
            "Epoch 184/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 1.0997e-04 - mean_absolute_error: 0.0084 - val_loss: 0.0075 - val_mean_absolute_error: 0.0829\n",
            "Epoch 185/250\n",
            "2648/2648 [==============================] - 2s 735us/step - loss: 1.1628e-04 - mean_absolute_error: 0.0094 - val_loss: 0.0074 - val_mean_absolute_error: 0.0824\n",
            "Epoch 186/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 1.1218e-04 - mean_absolute_error: 0.0088 - val_loss: 0.0076 - val_mean_absolute_error: 0.0845\n",
            "Epoch 187/250\n",
            "2648/2648 [==============================] - 2s 746us/step - loss: 1.1211e-04 - mean_absolute_error: 0.0089 - val_loss: 0.0074 - val_mean_absolute_error: 0.0824\n",
            "Epoch 188/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 1.1037e-04 - mean_absolute_error: 0.0084 - val_loss: 0.0076 - val_mean_absolute_error: 0.0859\n",
            "Epoch 189/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 1.3790e-04 - mean_absolute_error: 0.0115 - val_loss: 0.0074 - val_mean_absolute_error: 0.0817\n",
            "Epoch 190/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 9.9119e-05 - mean_absolute_error: 0.0066 - val_loss: 0.0075 - val_mean_absolute_error: 0.0840\n",
            "Epoch 191/250\n",
            "2648/2648 [==============================] - 2s 747us/step - loss: 1.0027e-04 - mean_absolute_error: 0.0069 - val_loss: 0.0075 - val_mean_absolute_error: 0.0848\n",
            "Epoch 192/250\n",
            "2648/2648 [==============================] - 2s 735us/step - loss: 9.9203e-05 - mean_absolute_error: 0.0072 - val_loss: 0.0075 - val_mean_absolute_error: 0.0834\n",
            "Epoch 193/250\n",
            "2648/2648 [==============================] - 2s 746us/step - loss: 1.0057e-04 - mean_absolute_error: 0.0070 - val_loss: 0.0075 - val_mean_absolute_error: 0.0834\n",
            "Epoch 194/250\n",
            "2648/2648 [==============================] - 2s 767us/step - loss: 9.7427e-05 - mean_absolute_error: 0.0066 - val_loss: 0.0074 - val_mean_absolute_error: 0.0827\n",
            "Epoch 195/250\n",
            "2648/2648 [==============================] - 2s 729us/step - loss: 9.9094e-05 - mean_absolute_error: 0.0070 - val_loss: 0.0076 - val_mean_absolute_error: 0.0861\n",
            "Epoch 196/250\n",
            "2648/2648 [==============================] - 2s 765us/step - loss: 1.1708e-04 - mean_absolute_error: 0.0097 - val_loss: 0.0074 - val_mean_absolute_error: 0.0830\n",
            "Epoch 197/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 9.6721e-05 - mean_absolute_error: 0.0063 - val_loss: 0.0076 - val_mean_absolute_error: 0.0855\n",
            "Epoch 198/250\n",
            "2648/2648 [==============================] - 2s 751us/step - loss: 9.9473e-05 - mean_absolute_error: 0.0072 - val_loss: 0.0074 - val_mean_absolute_error: 0.0827\n",
            "Epoch 199/250\n",
            "2648/2648 [==============================] - 2s 742us/step - loss: 1.0042e-04 - mean_absolute_error: 0.0073 - val_loss: 0.0074 - val_mean_absolute_error: 0.0829\n",
            "Epoch 200/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 1.0673e-04 - mean_absolute_error: 0.0083 - val_loss: 0.0074 - val_mean_absolute_error: 0.0817\n",
            "Epoch 201/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 1.0269e-04 - mean_absolute_error: 0.0077 - val_loss: 0.0073 - val_mean_absolute_error: 0.0808\n",
            "Epoch 202/250\n",
            "2648/2648 [==============================] - 2s 753us/step - loss: 9.8439e-05 - mean_absolute_error: 0.0068 - val_loss: 0.0074 - val_mean_absolute_error: 0.0821\n",
            "Epoch 203/250\n",
            "2648/2648 [==============================] - 2s 761us/step - loss: 1.2632e-04 - mean_absolute_error: 0.0103 - val_loss: 0.0073 - val_mean_absolute_error: 0.0804\n",
            "Epoch 204/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 1.2863e-04 - mean_absolute_error: 0.0106 - val_loss: 0.0074 - val_mean_absolute_error: 0.0839\n",
            "Epoch 205/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 1.1371e-04 - mean_absolute_error: 0.0090 - val_loss: 0.0073 - val_mean_absolute_error: 0.0802\n",
            "Epoch 206/250\n",
            "2648/2648 [==============================] - 2s 774us/step - loss: 1.0391e-04 - mean_absolute_error: 0.0079 - val_loss: 0.0074 - val_mean_absolute_error: 0.0822\n",
            "Epoch 207/250\n",
            "2648/2648 [==============================] - 2s 772us/step - loss: 1.0180e-04 - mean_absolute_error: 0.0074 - val_loss: 0.0073 - val_mean_absolute_error: 0.0804\n",
            "Epoch 208/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 1.0842e-04 - mean_absolute_error: 0.0085 - val_loss: 0.0074 - val_mean_absolute_error: 0.0838\n",
            "Epoch 209/250\n",
            "2648/2648 [==============================] - 2s 735us/step - loss: 1.2231e-04 - mean_absolute_error: 0.0100 - val_loss: 0.0072 - val_mean_absolute_error: 0.0805\n",
            "Epoch 210/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 1.2192e-04 - mean_absolute_error: 0.0098 - val_loss: 0.0073 - val_mean_absolute_error: 0.0820\n",
            "Epoch 211/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 1.2764e-04 - mean_absolute_error: 0.0106 - val_loss: 0.0073 - val_mean_absolute_error: 0.0801\n",
            "Epoch 212/250\n",
            "2648/2648 [==============================] - 2s 759us/step - loss: 1.0549e-04 - mean_absolute_error: 0.0080 - val_loss: 0.0073 - val_mean_absolute_error: 0.0809\n",
            "Epoch 213/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 9.5602e-05 - mean_absolute_error: 0.0064 - val_loss: 0.0073 - val_mean_absolute_error: 0.0825\n",
            "Epoch 214/250\n",
            "2648/2648 [==============================] - 2s 758us/step - loss: 1.0442e-04 - mean_absolute_error: 0.0082 - val_loss: 0.0072 - val_mean_absolute_error: 0.0807\n",
            "Epoch 215/250\n",
            "2648/2648 [==============================] - 2s 737us/step - loss: 1.3236e-04 - mean_absolute_error: 0.0108 - val_loss: 0.0073 - val_mean_absolute_error: 0.0813\n",
            "Epoch 216/250\n",
            "2648/2648 [==============================] - 2s 728us/step - loss: 1.1423e-04 - mean_absolute_error: 0.0093 - val_loss: 0.0073 - val_mean_absolute_error: 0.0803\n",
            "Epoch 217/250\n",
            "2648/2648 [==============================] - 2s 726us/step - loss: 9.1751e-05 - mean_absolute_error: 0.0055 - val_loss: 0.0072 - val_mean_absolute_error: 0.0801\n",
            "Epoch 218/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 9.4254e-05 - mean_absolute_error: 0.0061 - val_loss: 0.0072 - val_mean_absolute_error: 0.0805\n",
            "Epoch 219/250\n",
            "2648/2648 [==============================] - 2s 726us/step - loss: 9.1145e-05 - mean_absolute_error: 0.0055 - val_loss: 0.0072 - val_mean_absolute_error: 0.0806\n",
            "Epoch 220/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 9.7428e-05 - mean_absolute_error: 0.0069 - val_loss: 0.0073 - val_mean_absolute_error: 0.0814\n",
            "Epoch 221/250\n",
            "2648/2648 [==============================] - 2s 727us/step - loss: 1.0426e-04 - mean_absolute_error: 0.0079 - val_loss: 0.0072 - val_mean_absolute_error: 0.0800\n",
            "Epoch 222/250\n",
            "2648/2648 [==============================] - 2s 759us/step - loss: 1.0368e-04 - mean_absolute_error: 0.0079 - val_loss: 0.0073 - val_mean_absolute_error: 0.0813\n",
            "Epoch 223/250\n",
            "2648/2648 [==============================] - 2s 745us/step - loss: 9.8554e-05 - mean_absolute_error: 0.0071 - val_loss: 0.0072 - val_mean_absolute_error: 0.0798\n",
            "Epoch 224/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 1.1325e-04 - mean_absolute_error: 0.0092 - val_loss: 0.0072 - val_mean_absolute_error: 0.0779\n",
            "Epoch 225/250\n",
            "2648/2648 [==============================] - 2s 733us/step - loss: 9.5737e-05 - mean_absolute_error: 0.0068 - val_loss: 0.0072 - val_mean_absolute_error: 0.0798\n",
            "Epoch 226/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 9.7645e-05 - mean_absolute_error: 0.0071 - val_loss: 0.0073 - val_mean_absolute_error: 0.0811\n",
            "Epoch 227/250\n",
            "2648/2648 [==============================] - 2s 732us/step - loss: 9.3260e-05 - mean_absolute_error: 0.0062 - val_loss: 0.0073 - val_mean_absolute_error: 0.0817\n",
            "Epoch 228/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 9.1778e-05 - mean_absolute_error: 0.0059 - val_loss: 0.0072 - val_mean_absolute_error: 0.0810\n",
            "Epoch 229/250\n",
            "2648/2648 [==============================] - 2s 768us/step - loss: 9.3050e-05 - mean_absolute_error: 0.0060 - val_loss: 0.0072 - val_mean_absolute_error: 0.0796\n",
            "Epoch 230/250\n",
            "2648/2648 [==============================] - 2s 747us/step - loss: 1.1075e-04 - mean_absolute_error: 0.0087 - val_loss: 0.0072 - val_mean_absolute_error: 0.0806\n",
            "Epoch 231/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 9.8657e-05 - mean_absolute_error: 0.0072 - val_loss: 0.0071 - val_mean_absolute_error: 0.0782\n",
            "Epoch 232/250\n",
            "2648/2648 [==============================] - 2s 719us/step - loss: 9.6885e-05 - mean_absolute_error: 0.0071 - val_loss: 0.0072 - val_mean_absolute_error: 0.0806\n",
            "Epoch 233/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 9.7541e-05 - mean_absolute_error: 0.0074 - val_loss: 0.0073 - val_mean_absolute_error: 0.0830\n",
            "Epoch 234/250\n",
            "2648/2648 [==============================] - 2s 768us/step - loss: 1.1127e-04 - mean_absolute_error: 0.0090 - val_loss: 0.0071 - val_mean_absolute_error: 0.0781\n",
            "Epoch 235/250\n",
            "2648/2648 [==============================] - 2s 731us/step - loss: 1.3022e-04 - mean_absolute_error: 0.0109 - val_loss: 0.0072 - val_mean_absolute_error: 0.0798\n",
            "Epoch 236/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 1.2648e-04 - mean_absolute_error: 0.0109 - val_loss: 0.0073 - val_mean_absolute_error: 0.0809\n",
            "Epoch 237/250\n",
            "2648/2648 [==============================] - 2s 739us/step - loss: 1.1841e-04 - mean_absolute_error: 0.0101 - val_loss: 0.0071 - val_mean_absolute_error: 0.0788\n",
            "Epoch 238/250\n",
            "2648/2648 [==============================] - 2s 756us/step - loss: 1.1086e-04 - mean_absolute_error: 0.0088 - val_loss: 0.0071 - val_mean_absolute_error: 0.0797\n",
            "Epoch 239/250\n",
            "2648/2648 [==============================] - 2s 775us/step - loss: 9.8681e-05 - mean_absolute_error: 0.0073 - val_loss: 0.0072 - val_mean_absolute_error: 0.0806\n",
            "Epoch 240/250\n",
            "2648/2648 [==============================] - 2s 776us/step - loss: 9.6707e-05 - mean_absolute_error: 0.0072 - val_loss: 0.0071 - val_mean_absolute_error: 0.0783\n",
            "Epoch 241/250\n",
            "2648/2648 [==============================] - 2s 735us/step - loss: 1.0462e-04 - mean_absolute_error: 0.0083 - val_loss: 0.0071 - val_mean_absolute_error: 0.0797\n",
            "Epoch 242/250\n",
            "2648/2648 [==============================] - 2s 740us/step - loss: 9.4482e-05 - mean_absolute_error: 0.0065 - val_loss: 0.0072 - val_mean_absolute_error: 0.0808\n",
            "Epoch 243/250\n",
            "2648/2648 [==============================] - 2s 717us/step - loss: 9.1995e-05 - mean_absolute_error: 0.0061 - val_loss: 0.0072 - val_mean_absolute_error: 0.0803\n",
            "Epoch 244/250\n",
            "2648/2648 [==============================] - 2s 716us/step - loss: 9.2391e-05 - mean_absolute_error: 0.0063 - val_loss: 0.0072 - val_mean_absolute_error: 0.0810\n",
            "Epoch 245/250\n",
            "2648/2648 [==============================] - 2s 722us/step - loss: 1.0908e-04 - mean_absolute_error: 0.0088 - val_loss: 0.0071 - val_mean_absolute_error: 0.0787\n",
            "Epoch 246/250\n",
            "2648/2648 [==============================] - 2s 734us/step - loss: 1.0506e-04 - mean_absolute_error: 0.0088 - val_loss: 0.0071 - val_mean_absolute_error: 0.0798\n",
            "Epoch 247/250\n",
            "2648/2648 [==============================] - 2s 723us/step - loss: 1.0173e-04 - mean_absolute_error: 0.0081 - val_loss: 0.0072 - val_mean_absolute_error: 0.0805\n",
            "Epoch 248/250\n",
            "2648/2648 [==============================] - 2s 757us/step - loss: 9.4994e-05 - mean_absolute_error: 0.0068 - val_loss: 0.0071 - val_mean_absolute_error: 0.0782\n",
            "Epoch 249/250\n",
            "2648/2648 [==============================] - 2s 741us/step - loss: 9.5519e-05 - mean_absolute_error: 0.0068 - val_loss: 0.0071 - val_mean_absolute_error: 0.0795\n",
            "Epoch 250/250\n",
            "2648/2648 [==============================] - 2s 736us/step - loss: 8.8998e-05 - mean_absolute_error: 0.0054 - val_loss: 0.0071 - val_mean_absolute_error: 0.0803\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lC6__j-H0QJV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred_train_keras = scaler.inverse_transform(keras_model.predict(X_train_reshape, batch_size=256))\n",
        "y_pred_keras = scaler.inverse_transform(keras_model.predict(X_test_reshape, batch_size=256))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbebOEMs2qyB",
        "colab_type": "code",
        "outputId": "9f5da078-169c-4d57-e1ad-ee237055f147",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "# plot training\n",
        "df_keras = df.copy()\n",
        "df_keras.drop(['Open', 'High', 'Low', 'Close', 'Volume'], axis=1, inplace=True)\n",
        "df_keras = df_keras.iloc[window_size:ind_split] # past 32 days\n",
        "df_keras['Adj Close Train'] = y_pred_train_keras[:-window_size]\n",
        "df_keras.plot(label='BTC-USD', figsize=(16,8), title='Adjusted Closing Price', grid=True)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe990dd8f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z15YvxGSjdjJ0HzhVmNbkmGLT3LKuZXCVJEmSJJVtfmEDPaGOl+k/VbiYbAxR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\nhFnFAZ8HAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKq97m8B3LWr",
        "colab_type": "code",
        "outputId": "c2d493db-d3d7-4b1f-e9f4-e6fae08abac6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "# plot test\n",
        "df_keras = df.copy()\n",
        "df_keras.drop(['Open', 'High', 'Low', 'Close', 'Volume'], axis=1, inplace=True)\n",
        "df_keras = df_keras.iloc[ind_split+window_size:] # past 32 days\n",
        "df_keras['Adj Close Test'] = y_pred_keras\n",
        "df_keras.plot(label='BTC-USD', figsize=(16,8), title='Adjusted Closing Price', grid=True)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe990d0d208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IiIiISB/Il329+eabs5/vu+++vNft3r2b4cOHH9QeiUS45557uOeeew46tm7duuznW2+9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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vO7i1YWa3kBO",
        "colab_type": "code",
        "outputId": "f5d496a5-6c66-4394-951a-2f069e827524",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "df_train = df.copy()\n",
        "df_train.drop(['Open', 'High', 'Low', 'Close', 'Volume'], axis=1, inplace=True)\n",
        "df_train = df_train.iloc[window_size:ind_split] # past 32 days we don't know\n",
        "# add in all of our methods\n",
        "df_train['Adj Close Train Ridge'] = y_pred_train_ridge[:-window_size]\n",
        "df_train['Adj Close Train Gradient Boosting'] = y_pred_train_gb[:-window_size]\n",
        "df_train['Adj Close Train LSTM'] = y_pred_train_keras[:-window_size]\n",
        "# plot data\n",
        "df_train.plot(label='BTC-USD', figsize=(16,8), title='Adjusted Closing Price', grid=True)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe990b58128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3ZWPMLGAPULvd2OvAZUARUOUvi7X2qDHmF8C//OXuq92oSUREREREQtfYKOqdd95Z9/mJ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QSzBjCr+AuK3etZ7jepTNIoSsm1PqND1EWttil0RvHZmgyVybX9L+fgLvcx58cplM6axLSNs/kl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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_tXxBSJp4SlD",
        "colab_type": "code",
        "outputId": "c2011ede-13dd-4597-efe4-76eca55d24e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "# plot all testing data\n",
        "df_test = df.copy()\n",
        "df_test.drop(['Open', 'High', 'Low', 'Close', 'Volume'], axis=1, inplace=True)\n",
        "df_test = df_test.iloc[ind_split+window_size:]\n",
        "# add in all methods\n",
        "df_test['Adj Close Test Ridge'] = y_pred_ridge\n",
        "df_test['Adj Close Test Gradient Boosting'] = y_pred_gb\n",
        "df_test['Adj Close Test LSTM'] = y_pred_keras\n",
        "# plot\n",
        "df_test.plot(label='BTC-USD', figsize=(16,8), title='Adjusted Closing Price', grid=True)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe990a1e198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Arr32WgDuuOMOZs6cyVe+8hUWLlzIN7/5TYqKippsnz9/PmvWrKFHjx4cPXqU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c/4nauxzZlCu96KhQ5Re+RIrd2kR5LAKzmUut1UIutcSa/Hds7ZBN99l3Qs8zUKBAKBQCAQCIYW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\ngg==\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yFouVu5Y5PNR",
        "colab_type": "text"
      },
      "source": [
        "# Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nq_lbcYF5SNA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_days = 10 # predict next 10 days\n",
        "\n",
        "# get the last window_size (32) days\n",
        "data_seed = df['Adj Close'].values[-window_size:][None]\n",
        "# get the normalized data as well for the neural network\n",
        "data_seed_norm = scaler.transform(data_seed)\n",
        "input_values = {\"ridge\": data_seed, \"gb\": data_seed, \"keras\": data_seed_norm.copy()}\n",
        "values = {\"ridge\": [], \"gb\": [], \"keras\": []}\n",
        "for i in range(num_days):\n",
        "  values[\"ridge\"].append(ridge_model.predict(input_values[\"ridge\"])[0])\n",
        "  values[\"gb\"].append(optimized_gb_model.predict(input_values[\"gb\"])[0])\n",
        "  values[\"keras\"].append(keras_model.predict(input_values[\"keras\"][...,None])[0][0])\n",
        "  \n",
        "  # dump the oldest price and put the newest price at the end\n",
        "  for v in input_values:\n",
        "    val = input_values[v]\n",
        "    val = np.insert(val, -1, values[v][-1], axis=1)\n",
        "    val = np.delete(val, 0, axis=1)\n",
        "    input_values[v] = val.copy()\n",
        "    \n",
        "# convert all to NumPy arrays\n",
        "for v in input_values:\n",
        "  values[v] = np.array(values[v])\n",
        "  \n",
        "# unnormalize prices from NN approach\n",
        "values['keras'] = scaler.inverse_transform(values['keras'][None])[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXfTg4LO61d3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "807adb11-d4e0-49b1-c217-0eb244226663"
      },
      "source": [
        "# plot next 10 days\n",
        "from datetime import timedelta, datetime\n",
        "today = str(df.index[-1])\n",
        "last_date = datetime.strptime(today[:10], '%Y-%m-%d')\n",
        "df_forecast = pd.DataFrame()\n",
        "df_forecast[\"Ridge\"] = values[\"ridge\"]\n",
        "df_forecast[\"Gradient Boosting\"] = values[\"gb\"]\n",
        "df_forecast[\"Keras LSTM\"] = values[\"keras\"]\n",
        "df_forecast.index = pd.date_range(start = last_date, periods=num_days)\n",
        "df_forecast.plot(label='BTC-USD', figsize=(16,8), title='Forecasted Adjusted Closing Price', grid=True)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9909f5cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9pnljh+viqUW6ZFqxzs7v31mPjza26bXdNVqzo1qrd1Sr8mizJGlCYcxkVePylZ7SN9Xk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\nXD39qK3IOVfpPT+g4/8KlEj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z81igFVgMfA64MjNfAjwFvKc85D3AU2X7lWU/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\nVAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}