Time_Series_Forecasting_with_DNN.ipynb

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  "metadata": {
    "colab": {
      "name": "Time Series Forecasting with DNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOCHj4ZnBNMfsPeM8Gbx4TE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/adib0073/TimeSeries-Using-TensorFlow/blob/main/Time_Series_Forecasting_with_DNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aAaQyzrU-03x"
      },
      "source": [
        "## Time Series Forecasting with DNN using TensorFlow\r\n",
        "\r\n",
        "In this notebook we will use a custom Deep Neural Network which can be used for forecasting Univariate Time Series data using TensorFlow. As an example of the time series data, we will be using the [Sunspot Data from Kaggle](https://www.kaggle.com/robervalt/sunspots). The data can be easily downloaded from my GitHub project [TimeSeries-Using-TensorFlow](https://github.com/adib0073/TimeSeries-Using-TensorFlow/blob/main/Data/Sunspots.csv). Now, let's begin! "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6XH6Wyt91-2"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ufLfs2Z__pr",
        "outputId": "bd60241d-49b3-4151-de2c-2517626df59c"
      },
      "source": [
        "# Please validate the TF version and make sure it is above 2.0. Otherwise please download the latest TF version\r\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oeojQVv3Aqfp"
      },
      "source": [
        "### Import Modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zEtFIkeFAJ6m"
      },
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns\r\n",
        "plt.style.use('seaborn-darkgrid')\r\n",
        "tf.random.set_seed(51)\r\n",
        "np.random.seed(51)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZcTkxB4A8GH"
      },
      "source": [
        "### Downloading and loading the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mU4aMrlrA2FS",
        "outputId": "664d3c25-39ff-459b-a97e-6b388a2ec859"
      },
      "source": [
        "!wget --no-check-certificate https://raw.githubusercontent.com/adib0073/TimeSeries-Using-TensorFlow/main/Data/Sunspots.csv -O /tmp/sunspots.csv"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-12-23 05:21:20--  https://raw.githubusercontent.com/adib0073/TimeSeries-Using-TensorFlow/main/Data/Sunspots.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 70827 (69K) [text/plain]\n",
            "Saving to: ‘/tmp/sunspots.csv’\n",
            "\n",
            "/tmp/sunspots.csv   100%[===================>]  69.17K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2020-12-23 05:21:21 (6.21 MB/s) - ‘/tmp/sunspots.csv’ saved [70827/70827]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "qA6rr6dxBMzI",
        "outputId": "08737d76-cbb5-49e4-b785-d3d9bebda2a9"
      },
      "source": [
        "# Loading the data as a pandas dataframe\r\n",
        "df = pd.read_csv('/tmp/sunspots.csv', index_col=0)\r\n",
        "df.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Monthly Mean Total Sunspot Number</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1749-01-31</td>\n",
              "      <td>96.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1749-02-28</td>\n",
              "      <td>104.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1749-03-31</td>\n",
              "      <td>116.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1749-04-30</td>\n",
              "      <td>92.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1749-05-31</td>\n",
              "      <td>141.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date  Monthly Mean Total Sunspot Number\n",
              "0  1749-01-31                               96.7\n",
              "1  1749-02-28                              104.3\n",
              "2  1749-03-31                              116.7\n",
              "3  1749-04-30                               92.8\n",
              "4  1749-05-31                              141.7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "-oSBnGOvBexD",
        "outputId": "56bf4a25-1edf-4e6a-c1e8-8200d132323b"
      },
      "source": [
        "# Let's visualize the series\r\n",
        "df.plot()\r\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcDx0YfpD1ik"
      },
      "source": [
        "# We don't see any clear trend, but we can see some seasonal patterns in the time series data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46gsyvcVFNiU"
      },
      "source": [
        "### Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l26NrYiEFXeF"
      },
      "source": [
        "time_index = np.array(df['Date'])\r\n",
        "data = np.array(df['Monthly Mean Total Sunspot Number'])\r\n",
        "#time_index = time_index.reshape(-1,1)\r\n",
        "#data = data.reshape(-1,1)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3YufVYCPFrP2",
        "outputId": "9e9fc8e3-fe28-4c88-83cd-8a0c19943b60"
      },
      "source": [
        "print(time_index.shape)\r\n",
        "print(data.shape)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3235,)\n",
            "(3235,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMJKRg4VFzIM"
      },
      "source": [
        "# Certain Hyper-parameters to tune\r\n",
        "SPLIT_RATIO = 0.8\r\n",
        "WINDOW_SIZE = 60\r\n",
        "BATCH_SIZE = 32\r\n",
        "SHUFFLE_BUFFER = 1000"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Efzoq2CGUJ8",
        "outputId": "71ef816e-d606-4694-dacf-90865aac0008"
      },
      "source": [
        "# Dividing into train-test split\r\n",
        "split_index = int(SPLIT_RATIO * data.shape[0])\r\n",
        "print(split_index)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2588\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvzObJ_xGh-s"
      },
      "source": [
        "# Train-Test Split\r\n",
        "train_data = data[:split_index]\r\n",
        "train_time = time_index[:split_index]\r\n",
        "\r\n",
        "test_data = data[split_index:]\r\n",
        "test_time = time_index[split_index:]"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LK6vRlVGH6bc"
      },
      "source": [
        "def ts_data_generator(data, window_size, batch_size, shuffle_buffer):\r\n",
        "  '''\r\n",
        "  Utility function for time series data generation in batches\r\n",
        "  '''\r\n",
        "  ts_data = tf.data.Dataset.from_tensor_slices(data)\r\n",
        "  ts_data = ts_data.window(window_size + 1, shift=1, drop_remainder=True)\r\n",
        "  ts_data = ts_data.flat_map(lambda window: window.batch(window_size + 1))\r\n",
        "  ts_data = ts_data.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\r\n",
        "  ts_data = ts_data.batch(batch_size).prefetch(1)\r\n",
        "  return ts_data"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7fi9mRPMaPu"
      },
      "source": [
        "train_dataset = ts_data_generator(train_data, WINDOW_SIZE, BATCH_SIZE, SHUFFLE_BUFFER)\r\n",
        "test_dataset = ts_data_generator(test_data, WINDOW_SIZE, BATCH_SIZE, SHUFFLE_BUFFER)\r\n"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wMKz5uqjNQ2l",
        "outputId": "6670312a-a223-462c-de3a-3d0e8700617f"
      },
      "source": [
        "train_dataset"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PrefetchDataset shapes: ((None, None), (None,)), types: (tf.float64, tf.float64)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U-Iu1JPXJ-Kx"
      },
      "source": [
        "### TensorFlow DNN Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nx59dlURIs2T",
        "outputId": "52158527-a136-4344-b5c4-13ef4280d318"
      },
      "source": [
        "model = tf.keras.models.Sequential([\r\n",
        "    tf.keras.layers.Dense(20, input_shape=[WINDOW_SIZE], activation=\"relu\"), \r\n",
        "    tf.keras.layers.Dense(10, activation=\"relu\"),\r\n",
        "    tf.keras.layers.Dense(1)\r\n",
        "])\r\n",
        "\r\n",
        "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(lr=1e-7, momentum=0.9))\r\n",
        "model.fit(train_dataset, epochs=200,validation_data = test_dataset)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 2546.9874 - val_loss: 1215.2766\n",
            "Epoch 2/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 1355.8905 - val_loss: 1010.8966\n",
            "Epoch 3/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 1124.5238 - val_loss: 893.6034\n",
            "Epoch 4/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 1071.7694 - val_loss: 810.0725\n",
            "Epoch 5/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 932.4372 - val_loss: 767.9634\n",
            "Epoch 6/200\n",
            "79/79 [==============================] - 1s 6ms/step - loss: 876.1713 - val_loss: 740.3677\n",
            "Epoch 7/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 950.3283 - val_loss: 706.0848\n",
            "Epoch 8/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 849.1399 - val_loss: 675.4581\n",
            "Epoch 9/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 796.9535 - val_loss: 664.1207\n",
            "Epoch 10/200\n",
            "79/79 [==============================] - 1s 6ms/step - loss: 786.9360 - val_loss: 645.5507\n",
            "Epoch 11/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 804.0468 - val_loss: 661.7661\n",
            "Epoch 12/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 750.2587 - val_loss: 623.3406\n",
            "Epoch 13/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 728.5940 - val_loss: 625.2959\n",
            "Epoch 14/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 740.4011 - val_loss: 625.6844\n",
            "Epoch 15/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 702.1447 - val_loss: 604.7986\n",
            "Epoch 16/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 745.6790 - val_loss: 598.6310\n",
            "Epoch 17/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 724.8049 - val_loss: 594.4995\n",
            "Epoch 18/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 684.7678 - val_loss: 592.1094\n",
            "Epoch 19/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 698.0174 - val_loss: 583.4360\n",
            "Epoch 20/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 712.0149 - val_loss: 580.8572\n",
            "Epoch 21/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 721.4866 - val_loss: 588.6357\n",
            "Epoch 22/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 684.2198 - val_loss: 576.0804\n",
            "Epoch 23/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 694.6128 - val_loss: 568.3679\n",
            "Epoch 24/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 678.8668 - val_loss: 564.1708\n",
            "Epoch 25/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 692.2523 - val_loss: 561.6949\n",
            "Epoch 26/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 699.2770 - val_loss: 573.8082\n",
            "Epoch 27/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 644.6075 - val_loss: 555.6868\n",
            "Epoch 28/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 694.9334 - val_loss: 552.9712\n",
            "Epoch 29/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 645.4573 - val_loss: 561.7847\n",
            "Epoch 30/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 692.3103 - val_loss: 570.1151\n",
            "Epoch 31/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 688.4101 - val_loss: 553.4549\n",
            "Epoch 32/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 691.2685 - val_loss: 552.7026\n",
            "Epoch 33/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 669.0158 - val_loss: 553.3276\n",
            "Epoch 34/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 655.0273 - val_loss: 558.7534\n",
            "Epoch 35/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 613.0070 - val_loss: 546.2744\n",
            "Epoch 36/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 661.6266 - val_loss: 541.6135\n",
            "Epoch 37/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 678.6138 - val_loss: 544.5047\n",
            "Epoch 38/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 607.2249 - val_loss: 545.5169\n",
            "Epoch 39/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 625.7823 - val_loss: 533.4227\n",
            "Epoch 40/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 602.5804 - val_loss: 535.0149\n",
            "Epoch 41/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 676.0752 - val_loss: 545.4424\n",
            "Epoch 42/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 630.1611 - val_loss: 530.5930\n",
            "Epoch 43/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 660.4306 - val_loss: 551.7036\n",
            "Epoch 44/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 607.4098 - val_loss: 531.1237\n",
            "Epoch 45/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 628.8128 - val_loss: 529.3899\n",
            "Epoch 46/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 653.9381 - val_loss: 527.4434\n",
            "Epoch 47/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 624.6503 - val_loss: 527.6728\n",
            "Epoch 48/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 623.2351 - val_loss: 538.3395\n",
            "Epoch 49/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 635.0395 - val_loss: 524.3699\n",
            "Epoch 50/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 590.6425 - val_loss: 527.5285\n",
            "Epoch 51/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 659.2633 - val_loss: 550.2422\n",
            "Epoch 52/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 643.7707 - val_loss: 532.2659\n",
            "Epoch 53/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 625.7046 - val_loss: 533.6039\n",
            "Epoch 54/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 596.8678 - val_loss: 526.2125\n",
            "Epoch 55/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 614.5812 - val_loss: 530.9954\n",
            "Epoch 56/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 616.3928 - val_loss: 518.9602\n",
            "Epoch 57/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 599.0098 - val_loss: 521.4235\n",
            "Epoch 58/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 611.5574 - val_loss: 522.0380\n",
            "Epoch 59/200\n",
            "79/79 [==============================] - 1s 6ms/step - loss: 611.8717 - val_loss: 518.0168\n",
            "Epoch 60/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 626.9648 - val_loss: 516.7966\n",
            "Epoch 61/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 648.3503 - val_loss: 516.4186\n",
            "Epoch 62/200\n",
            "79/79 [==============================] - 1s 6ms/step - loss: 625.3061 - val_loss: 532.4888\n",
            "Epoch 63/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 618.8060 - val_loss: 541.7223\n",
            "Epoch 64/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 592.0740 - val_loss: 519.4359\n",
            "Epoch 65/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 622.3510 - val_loss: 523.2772\n",
            "Epoch 66/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 653.6183 - val_loss: 513.8393\n",
            "Epoch 67/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 607.4901 - val_loss: 536.6286\n",
            "Epoch 68/200\n",
            "79/79 [==============================] - 1s 10ms/step - loss: 613.6452 - val_loss: 514.2047\n",
            "Epoch 69/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 640.7402 - val_loss: 525.4171\n",
            "Epoch 70/200\n",
            "79/79 [==============================] - 1s 11ms/step - loss: 604.8513 - val_loss: 524.6630\n",
            "Epoch 71/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 625.3309 - val_loss: 528.0048\n",
            "Epoch 72/200\n",
            "79/79 [==============================] - 1s 11ms/step - loss: 624.2467 - val_loss: 515.0325\n",
            "Epoch 73/200\n",
            "79/79 [==============================] - 1s 11ms/step - loss: 611.7377 - val_loss: 515.5065\n",
            "Epoch 74/200\n",
            "79/79 [==============================] - 1s 11ms/step - loss: 581.3370 - val_loss: 513.1246\n",
            "Epoch 75/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 582.1123 - val_loss: 519.0317\n",
            "Epoch 76/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 626.8258 - val_loss: 513.6022\n",
            "Epoch 77/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 610.8191 - val_loss: 518.2590\n",
            "Epoch 78/200\n",
            "79/79 [==============================] - 1s 10ms/step - loss: 610.6505 - val_loss: 524.1370\n",
            "Epoch 79/200\n",
            "79/79 [==============================] - 2s 17ms/step - loss: 618.4342 - val_loss: 518.4077\n",
            "Epoch 80/200\n",
            "79/79 [==============================] - 2s 16ms/step - loss: 603.8361 - val_loss: 515.8859\n",
            "Epoch 81/200\n",
            "79/79 [==============================] - 2s 18ms/step - loss: 572.4450 - val_loss: 522.0485\n",
            "Epoch 82/200\n",
            "79/79 [==============================] - 2s 17ms/step - loss: 590.5465 - val_loss: 515.5558\n",
            "Epoch 83/200\n",
            "79/79 [==============================] - 1s 13ms/step - loss: 612.0675 - val_loss: 513.4659\n",
            "Epoch 84/200\n",
            "79/79 [==============================] - 1s 15ms/step - loss: 624.5083 - val_loss: 535.3964\n",
            "Epoch 85/200\n",
            "79/79 [==============================] - 1s 12ms/step - loss: 582.4638 - val_loss: 519.3719\n",
            "Epoch 86/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 599.8762 - val_loss: 554.8516\n",
            "Epoch 87/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 582.0071 - val_loss: 516.1750\n",
            "Epoch 88/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 582.6027 - val_loss: 513.7328\n",
            "Epoch 89/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 586.3740 - val_loss: 543.9814\n",
            "Epoch 90/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 539.1055 - val_loss: 536.3013\n",
            "Epoch 91/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 616.5437 - val_loss: 516.6400\n",
            "Epoch 92/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 621.6134 - val_loss: 514.3275\n",
            "Epoch 93/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 567.6152 - val_loss: 532.0311\n",
            "Epoch 94/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 611.7424 - val_loss: 512.8068\n",
            "Epoch 95/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 575.7826 - val_loss: 512.0933\n",
            "Epoch 96/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 563.4585 - val_loss: 513.1031\n",
            "Epoch 97/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 568.1158 - val_loss: 512.6827\n",
            "Epoch 98/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 579.7429 - val_loss: 513.0075\n",
            "Epoch 99/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 605.6214 - val_loss: 532.5446\n",
            "Epoch 100/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 593.8855 - val_loss: 519.8869\n",
            "Epoch 101/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 575.8266 - val_loss: 517.1534\n",
            "Epoch 102/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 604.0991 - val_loss: 511.2162\n",
            "Epoch 103/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 641.9805 - val_loss: 510.2909\n",
            "Epoch 104/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 585.1678 - val_loss: 512.8585\n",
            "Epoch 105/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 591.8066 - val_loss: 536.4153\n",
            "Epoch 106/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 573.7270 - val_loss: 511.0070\n",
            "Epoch 107/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 609.9943 - val_loss: 512.0884\n",
            "Epoch 108/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 594.9874 - val_loss: 513.3677\n",
            "Epoch 109/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 554.2273 - val_loss: 520.4348\n",
            "Epoch 110/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 587.9504 - val_loss: 511.8255\n",
            "Epoch 111/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 564.2744 - val_loss: 516.8434\n",
            "Epoch 112/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 568.4417 - val_loss: 516.6830\n",
            "Epoch 113/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 548.6829 - val_loss: 522.4886\n",
            "Epoch 114/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 559.6687 - val_loss: 531.8823\n",
            "Epoch 115/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 563.3755 - val_loss: 516.8097\n",
            "Epoch 116/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 558.6696 - val_loss: 514.6533\n",
            "Epoch 117/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 597.5707 - val_loss: 512.9219\n",
            "Epoch 118/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 585.7630 - val_loss: 513.6171\n",
            "Epoch 119/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 577.2594 - val_loss: 511.6598\n",
            "Epoch 120/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 562.1539 - val_loss: 530.3802\n",
            "Epoch 121/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 555.9707 - val_loss: 511.4373\n",
            "Epoch 122/200\n",
            "79/79 [==============================] - 1s 10ms/step - loss: 614.8986 - val_loss: 520.1447\n",
            "Epoch 123/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 568.8638 - val_loss: 518.3849\n",
            "Epoch 124/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 551.1043 - val_loss: 514.5498\n",
            "Epoch 125/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 561.2958 - val_loss: 512.5775\n",
            "Epoch 126/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 553.0963 - val_loss: 518.0500\n",
            "Epoch 127/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 578.8930 - val_loss: 512.1352\n",
            "Epoch 128/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 593.3261 - val_loss: 528.3103\n",
            "Epoch 129/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 584.4374 - val_loss: 514.8653\n",
            "Epoch 130/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 570.8473 - val_loss: 512.7128\n",
            "Epoch 131/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 591.3961 - val_loss: 518.8230\n",
            "Epoch 132/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 562.9865 - val_loss: 510.7832\n",
            "Epoch 133/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 582.9577 - val_loss: 537.0903\n",
            "Epoch 134/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 553.1890 - val_loss: 536.2081\n",
            "Epoch 135/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 571.6480 - val_loss: 527.6889\n",
            "Epoch 136/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 567.9182 - val_loss: 509.8418\n",
            "Epoch 137/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 560.5491 - val_loss: 509.1056\n",
            "Epoch 138/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 560.6217 - val_loss: 514.7151\n",
            "Epoch 139/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 567.1347 - val_loss: 512.5084\n",
            "Epoch 140/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 564.5821 - val_loss: 511.3087\n",
            "Epoch 141/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 550.6407 - val_loss: 535.8087\n",
            "Epoch 142/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 619.1692 - val_loss: 518.1783\n",
            "Epoch 143/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 568.1100 - val_loss: 513.1294\n",
            "Epoch 144/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 593.6150 - val_loss: 509.9595\n",
            "Epoch 145/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 570.0384 - val_loss: 523.3079\n",
            "Epoch 146/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 590.3354 - val_loss: 509.2140\n",
            "Epoch 147/200\n",
            "79/79 [==============================] - 1s 6ms/step - loss: 594.3069 - val_loss: 515.7897\n",
            "Epoch 148/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 554.0997 - val_loss: 514.2733\n",
            "Epoch 149/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 543.4985 - val_loss: 516.4089\n",
            "Epoch 150/200\n",
            "79/79 [==============================] - 1s 6ms/step - loss: 519.7058 - val_loss: 522.8925\n",
            "Epoch 151/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 547.0541 - val_loss: 540.5236\n",
            "Epoch 152/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 563.5523 - val_loss: 516.0596\n",
            "Epoch 153/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 561.7312 - val_loss: 520.6255\n",
            "Epoch 154/200\n",
            "79/79 [==============================] - 1s 6ms/step - loss: 579.7725 - val_loss: 509.1056\n",
            "Epoch 155/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 559.5165 - val_loss: 510.5073\n",
            "Epoch 156/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 549.2403 - val_loss: 515.6622\n",
            "Epoch 157/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 572.4585 - val_loss: 511.8316\n",
            "Epoch 158/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 542.6872 - val_loss: 511.1702\n",
            "Epoch 159/200\n",
            "79/79 [==============================] - 1s 11ms/step - loss: 548.2897 - val_loss: 520.7610\n",
            "Epoch 160/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 577.4156 - val_loss: 514.6565\n",
            "Epoch 161/200\n",
            "79/79 [==============================] - 1s 11ms/step - loss: 564.0941 - val_loss: 515.5186\n",
            "Epoch 162/200\n",
            "79/79 [==============================] - 1s 11ms/step - loss: 546.7212 - val_loss: 530.7937\n",
            "Epoch 163/200\n",
            "79/79 [==============================] - 1s 11ms/step - loss: 562.1767 - val_loss: 516.1628\n",
            "Epoch 164/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 540.7807 - val_loss: 513.2947\n",
            "Epoch 165/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 564.1052 - val_loss: 512.4519\n",
            "Epoch 166/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 533.9276 - val_loss: 511.6421\n",
            "Epoch 167/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 555.6395 - val_loss: 558.4973\n",
            "Epoch 168/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 595.5804 - val_loss: 509.8106\n",
            "Epoch 169/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 574.6756 - val_loss: 528.5756\n",
            "Epoch 170/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 586.8977 - val_loss: 511.9303\n",
            "Epoch 171/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 574.9761 - val_loss: 511.5114\n",
            "Epoch 172/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 573.0180 - val_loss: 521.5530\n",
            "Epoch 173/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 568.1816 - val_loss: 521.3835\n",
            "Epoch 174/200\n",
            "79/79 [==============================] - 1s 6ms/step - loss: 534.9744 - val_loss: 526.9485\n",
            "Epoch 175/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 550.0962 - val_loss: 518.7127\n",
            "Epoch 176/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 506.8761 - val_loss: 518.1097\n",
            "Epoch 177/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 570.2542 - val_loss: 521.7633\n",
            "Epoch 178/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 588.8215 - val_loss: 511.8166\n",
            "Epoch 179/200\n",
            "79/79 [==============================] - 1s 6ms/step - loss: 590.7961 - val_loss: 550.7400\n",
            "Epoch 180/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 532.1070 - val_loss: 516.6417\n",
            "Epoch 181/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 550.2999 - val_loss: 516.9287\n",
            "Epoch 182/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 539.0050 - val_loss: 527.0582\n",
            "Epoch 183/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 564.6014 - val_loss: 514.6245\n",
            "Epoch 184/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 603.3114 - val_loss: 514.1875\n",
            "Epoch 185/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 541.7545 - val_loss: 518.2525\n",
            "Epoch 186/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 566.9104 - val_loss: 514.6080\n",
            "Epoch 187/200\n",
            "79/79 [==============================] - 1s 9ms/step - loss: 543.3987 - val_loss: 532.1966\n",
            "Epoch 188/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 534.8211 - val_loss: 516.6996\n",
            "Epoch 189/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 554.9591 - val_loss: 515.1008\n",
            "Epoch 190/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 561.7818 - val_loss: 514.5386\n",
            "Epoch 191/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 537.8290 - val_loss: 512.7344\n",
            "Epoch 192/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 586.3818 - val_loss: 525.8991\n",
            "Epoch 193/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 543.3024 - val_loss: 528.3223\n",
            "Epoch 194/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 578.2735 - val_loss: 542.6617\n",
            "Epoch 195/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 605.5571 - val_loss: 523.3635\n",
            "Epoch 196/200\n",
            "79/79 [==============================] - 1s 6ms/step - loss: 561.3013 - val_loss: 518.7684\n",
            "Epoch 197/200\n",
            "79/79 [==============================] - 1s 6ms/step - loss: 586.3300 - val_loss: 525.8988\n",
            "Epoch 198/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 587.9532 - val_loss: 534.7031\n",
            "Epoch 199/200\n",
            "79/79 [==============================] - 1s 7ms/step - loss: 524.0233 - val_loss: 521.0173\n",
            "Epoch 200/200\n",
            "79/79 [==============================] - 1s 8ms/step - loss: 567.2514 - val_loss: 520.7255\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff9fb1e1438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bk8k0SgrQDPb"
      },
      "source": [
        "### Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VJ-JrJezRbrz",
        "outputId": "2a870e9c-0092-4a72-d78f-ad99065dc34f"
      },
      "source": [
        "time_int = np.array(list(range(len(data))))\r\n",
        "time_int"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   0,    1,    2, ..., 3232, 3233, 3234])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TtLq5_jZKnsV"
      },
      "source": [
        "forecast=[]\r\n",
        "for time in range(len(data) - WINDOW_SIZE):\r\n",
        "  forecast.append(model.predict(data[time:time + WINDOW_SIZE][np.newaxis]))\r\n",
        "\r\n",
        "forecast = forecast[split_index-WINDOW_SIZE:]\r\n",
        "results = np.array(forecast)[:, 0, 0]"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WOIs3aOZPpv",
        "outputId": "3357db47-abd7-48c6-8260-084465863d8b"
      },
      "source": [
        "# Overall Error\r\n",
        "error = tf.keras.metrics.mean_absolute_error(test_data, results).numpy()\r\n",
        "print(error)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16.528038\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "uOcWBLv8QgR5",
        "outputId": "dc37e6a0-4a39-4f35-8140-363dce85f7e5"
      },
      "source": [
        "plt.figure(figsize=(15, 6))\r\n",
        "\r\n",
        "plt.plot(list(range(split_index,len(data))), test_data, label = 'Test Data')\r\n",
        "plt.plot(list(range(split_index,len(data))), results, label = 'Predictions')\r\n",
        "#plt.fill_between(range(split_index,len(data)), results - error, results + error, alpha = 0.5, color = 'red')\r\n",
        "plt.legend()\r\n",
        "plt.show()\r\n",
        "\r\n",
        "plt.figure(figsize=(15, 6))\r\n",
        "# Plotting with Confidence Intervals\r\n",
        "#plt.plot(list(range(split_index,len(data))), test_data, label = 'Test Data')\r\n",
        "plt.plot(list(range(split_index,len(data))), results, label = 'Predictions', color = 'k', linestyle = '--')\r\n",
        "plt.fill_between(range(split_index,len(data)), results - error, results + error, alpha = 0.5, color = 'red')\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PS3iGjSSRsKT"
      },
      "source": [
        "# Since forecast can never be accurate, it is always advisable to keep forecasts within confidence intervals or error bands"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N1lhpYH4eooz"
      },
      "source": [
        "### Combination Model\r\n",
        "\r\n",
        "In order to add complexity, we will use 1D CNN and LSTM for feature extraction part and finally DNN for the prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3eJvZ8NUQK3"
      },
      "source": [
        "# Expanding data into tensors\r\n",
        "tensor_train_data = tf.expand_dims(train_data, axis=-1)\r\n",
        "tensor_test_data = tf.expand_dims(test_data, axis=-1)\r\n",
        "\r\n",
        "tensor_train_dataset = ts_data_generator(tensor_train_data, WINDOW_SIZE, BATCH_SIZE, SHUFFLE_BUFFER)\r\n",
        "tensor_test_dataset = ts_data_generator(tensor_test_data, WINDOW_SIZE, BATCH_SIZE, SHUFFLE_BUFFER)"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVLax1aq2dHG"
      },
      "source": [
        "# Combination model of 1D CNN and LSTM\r\n",
        "model = tf.keras.models.Sequential([\r\n",
        "  tf.keras.layers.Conv1D(filters=32, kernel_size=5,\r\n",
        "                      strides=1, padding=\"causal\",\r\n",
        "                      activation=\"relu\",\r\n",
        "                      input_shape=[None, 1]),\r\n",
        "  tf.keras.layers.LSTM(64, return_sequences=True),\r\n",
        "  tf.keras.layers.LSTM(64, return_sequences=True),\r\n",
        "  tf.keras.layers.Dense(30, activation=\"relu\"),\r\n",
        "  tf.keras.layers.Dense(10, activation=\"relu\"),\r\n",
        "  tf.keras.layers.Dense(1)\r\n",
        "])\r\n"
      ],
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6NZlmoO3jP-",
        "outputId": "7b9dc557-d04a-4728-9b2a-0813d16c0ff6"
      },
      "source": [
        "# Using callbacks to optimize the learning rates\r\n",
        "lr_schedule = tf.keras.callbacks.LearningRateScheduler(\r\n",
        "    lambda epoch: 1e-8 * 10**(epoch / 20))\r\n",
        "optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)\r\n",
        "model.compile(loss=tf.keras.losses.Huber(),\r\n",
        "              optimizer=optimizer,\r\n",
        "              metrics=[\"mae\"])\r\n",
        "history = model.fit(tensor_train_dataset, epochs=100, callbacks=[lr_schedule])"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "79/79 [==============================] - 10s 70ms/step - loss: 54.6047 - mae: 55.1022\n",
            "Epoch 2/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 54.6271 - mae: 55.1258\n",
            "Epoch 3/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 53.5968 - mae: 54.0950\n",
            "Epoch 4/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 54.6908 - mae: 55.1894\n",
            "Epoch 5/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 54.4149 - mae: 54.9132\n",
            "Epoch 6/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 55.4203 - mae: 55.9186\n",
            "Epoch 7/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 54.9853 - mae: 55.4839\n",
            "Epoch 8/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 54.9877 - mae: 55.4863\n",
            "Epoch 9/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 55.1938 - mae: 55.6917\n",
            "Epoch 10/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 55.3503 - mae: 55.8484\n",
            "Epoch 11/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 54.2151 - mae: 54.7133\n",
            "Epoch 12/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 54.9600 - mae: 55.4582\n",
            "Epoch 13/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 55.4235 - mae: 55.9220\n",
            "Epoch 14/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 54.8399 - mae: 55.3385\n",
            "Epoch 15/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 54.6075 - mae: 55.1057\n",
            "Epoch 16/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 56.6245 - mae: 57.1229\n",
            "Epoch 17/100\n",
            "79/79 [==============================] - 6s 69ms/step - loss: 53.7742 - mae: 54.2721\n",
            "Epoch 18/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 54.9669 - mae: 55.4654\n",
            "Epoch 19/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 55.1692 - mae: 55.6673\n",
            "Epoch 20/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 53.5119 - mae: 54.0100\n",
            "Epoch 21/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 54.6728 - mae: 55.1713\n",
            "Epoch 22/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 53.4781 - mae: 53.9766\n",
            "Epoch 23/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 54.2077 - mae: 54.7061\n",
            "Epoch 24/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 54.0786 - mae: 54.5771\n",
            "Epoch 25/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 54.0072 - mae: 54.5047\n",
            "Epoch 26/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 55.1690 - mae: 55.6673\n",
            "Epoch 27/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 55.1955 - mae: 55.6942\n",
            "Epoch 28/100\n",
            "79/79 [==============================] - 6s 75ms/step - loss: 55.6562 - mae: 56.1542\n",
            "Epoch 29/100\n",
            "79/79 [==============================] - 6s 74ms/step - loss: 54.7752 - mae: 55.2737\n",
            "Epoch 30/100\n",
            "79/79 [==============================] - 6s 73ms/step - loss: 54.7946 - mae: 55.2924\n",
            "Epoch 31/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 54.3074 - mae: 54.8060\n",
            "Epoch 32/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 55.1277 - mae: 55.6257\n",
            "Epoch 33/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 54.7629 - mae: 55.2607\n",
            "Epoch 34/100\n",
            "79/79 [==============================] - 6s 74ms/step - loss: 54.1586 - mae: 54.6567\n",
            "Epoch 35/100\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 55.0523 - mae: 55.5505\n",
            "Epoch 36/100\n",
            "79/79 [==============================] - 7s 82ms/step - loss: 54.7367 - mae: 55.2350\n",
            "Epoch 37/100\n",
            "79/79 [==============================] - 6s 73ms/step - loss: 53.6479 - mae: 54.1460\n",
            "Epoch 38/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 53.7466 - mae: 54.2452\n",
            "Epoch 39/100\n",
            "79/79 [==============================] - 6s 73ms/step - loss: 54.9770 - mae: 55.4755\n",
            "Epoch 40/100\n",
            "79/79 [==============================] - 6s 74ms/step - loss: 53.8638 - mae: 54.3622\n",
            "Epoch 41/100\n",
            "79/79 [==============================] - 6s 76ms/step - loss: 54.4189 - mae: 54.9176\n",
            "Epoch 42/100\n",
            "79/79 [==============================] - 6s 75ms/step - loss: 54.6725 - mae: 55.1711\n",
            "Epoch 43/100\n",
            "79/79 [==============================] - 6s 73ms/step - loss: 54.9109 - mae: 55.4097\n",
            "Epoch 44/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 55.4043 - mae: 55.9028\n",
            "Epoch 45/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 54.4856 - mae: 54.9834\n",
            "Epoch 46/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 54.9459 - mae: 55.4443\n",
            "Epoch 47/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 54.1763 - mae: 54.6749\n",
            "Epoch 48/100\n",
            "79/79 [==============================] - 6s 73ms/step - loss: 54.3852 - mae: 54.8840\n",
            "Epoch 49/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 54.5114 - mae: 55.0094\n",
            "Epoch 50/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 55.0513 - mae: 55.5497\n",
            "Epoch 51/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 55.0577 - mae: 55.5564\n",
            "Epoch 52/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 54.4389 - mae: 54.9375\n",
            "Epoch 53/100\n",
            "79/79 [==============================] - 6s 73ms/step - loss: 54.8659 - mae: 55.3647\n",
            "Epoch 54/100\n",
            "79/79 [==============================] - 6s 73ms/step - loss: 55.4545 - mae: 55.9532\n",
            "Epoch 55/100\n",
            "79/79 [==============================] - 6s 75ms/step - loss: 54.3204 - mae: 54.8192\n",
            "Epoch 56/100\n",
            "79/79 [==============================] - 6s 75ms/step - loss: 54.7933 - mae: 55.2923\n",
            "Epoch 57/100\n",
            "79/79 [==============================] - 6s 75ms/step - loss: 54.3467 - mae: 54.8456\n",
            "Epoch 58/100\n",
            "79/79 [==============================] - 6s 75ms/step - loss: 54.4178 - mae: 54.9170\n",
            "Epoch 59/100\n",
            "79/79 [==============================] - 6s 75ms/step - loss: 54.8634 - mae: 55.3627\n",
            "Epoch 60/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 54.1401 - mae: 54.6391\n",
            "Epoch 61/100\n",
            "79/79 [==============================] - 6s 73ms/step - loss: 53.5567 - mae: 54.0547\n",
            "Epoch 62/100\n",
            "79/79 [==============================] - 6s 73ms/step - loss: 53.4024 - mae: 53.9007\n",
            "Epoch 63/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 54.2818 - mae: 54.7788\n",
            "Epoch 64/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 53.3576 - mae: 53.8548\n",
            "Epoch 65/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 53.3594 - mae: 53.8572\n",
            "Epoch 66/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 54.0781 - mae: 54.5759\n",
            "Epoch 67/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 54.6316 - mae: 55.1300\n",
            "Epoch 68/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 53.5497 - mae: 54.0468\n",
            "Epoch 69/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 53.1177 - mae: 53.6163\n",
            "Epoch 70/100\n",
            "79/79 [==============================] - 6s 73ms/step - loss: 53.0821 - mae: 53.5814\n",
            "Epoch 71/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 52.9762 - mae: 53.4749\n",
            "Epoch 72/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 54.0217 - mae: 54.5209\n",
            "Epoch 73/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 53.3375 - mae: 53.8359\n",
            "Epoch 74/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 51.7151 - mae: 52.2124\n",
            "Epoch 75/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 53.2114 - mae: 53.7094\n",
            "Epoch 76/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 53.2214 - mae: 53.7187\n",
            "Epoch 77/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 52.7588 - mae: 53.2556\n",
            "Epoch 78/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 52.5605 - mae: 53.0584\n",
            "Epoch 79/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 52.5933 - mae: 53.0903\n",
            "Epoch 80/100\n",
            "79/79 [==============================] - 6s 76ms/step - loss: 52.8534 - mae: 53.3504\n",
            "Epoch 81/100\n",
            "79/79 [==============================] - 6s 76ms/step - loss: 51.9462 - mae: 52.4442\n",
            "Epoch 82/100\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 53.6072 - mae: 54.1048\n",
            "Epoch 83/100\n",
            "79/79 [==============================] - 7s 80ms/step - loss: 52.7277 - mae: 53.2237\n",
            "Epoch 84/100\n",
            "79/79 [==============================] - 6s 74ms/step - loss: 51.7996 - mae: 52.2972\n",
            "Epoch 85/100\n",
            "79/79 [==============================] - 6s 73ms/step - loss: 51.2683 - mae: 51.7654\n",
            "Epoch 86/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 51.4196 - mae: 51.9191\n",
            "Epoch 87/100\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 51.2182 - mae: 51.7176\n",
            "Epoch 88/100\n",
            "79/79 [==============================] - 6s 74ms/step - loss: 52.3982 - mae: 52.8971\n",
            "Epoch 89/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 51.1973 - mae: 51.6955\n",
            "Epoch 90/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 52.4622 - mae: 52.9598\n",
            "Epoch 91/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 51.6513 - mae: 52.1498\n",
            "Epoch 92/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 51.2368 - mae: 51.7350\n",
            "Epoch 93/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 51.5100 - mae: 52.0085\n",
            "Epoch 94/100\n",
            "79/79 [==============================] - 6s 72ms/step - loss: 50.7105 - mae: 51.2087\n",
            "Epoch 95/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 51.0044 - mae: 51.5028\n",
            "Epoch 96/100\n",
            "79/79 [==============================] - 6s 73ms/step - loss: 48.5697 - mae: 49.0677\n",
            "Epoch 97/100\n",
            "79/79 [==============================] - 6s 75ms/step - loss: 48.8760 - mae: 49.3737\n",
            "Epoch 98/100\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 48.7669 - mae: 49.2640\n",
            "Epoch 99/100\n",
            "79/79 [==============================] - 6s 74ms/step - loss: 48.3085 - mae: 48.8065\n",
            "Epoch 100/100\n",
            "79/79 [==============================] - 6s 74ms/step - loss: 46.2973 - mae: 46.7948\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "WPd2hKGp66NO",
        "outputId": "94c4601b-f362-4751-92f5-3ab7719ab1cc"
      },
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff9f85bb470>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uuum4P6ShoW1QRX4cvz9NgQCjCweLPkYHfYyO4dDHS84co7qGNm14N6Bbf/OqvnPRCXI6rB3bOhz6GAsG20e/P+2o7/X7G5Gfn6958+bJMAwVFxfL4/HopZde0pIlS3TppZeqtrZWV1555YCLA4DhxOEwdPUXpmhyaZY2bq/Tb/++VZFjH2BEAup3z3fNmjUKBAK66qqrFAgEFAwGtWnTpr5zvXPnztWjjz465IUCQLxwuxyquGiq7vrDRr30VrV8XrcunTNOhmHYXRpiRL97vnPnztVrr72mBQsW6Nvf/raWLl3KICsA6Eeyx6XvXTJNhTlePbOuSk9X7ra7JMSQfvd8fT6fHnjggaO+399gKwBIVGlej3542Qzd/ugGPfnCDvlS3Dpz+gi7y0IMYIYrABhC2enJ+uFlM+RLcet3/7NVG96ttbskxADCFwCGWGFOqr5/6XR53E49uGaLNm3nXsCJjvAFAAuMLkzX4i913wv4V3/erDd3EMCJjPAFAItMKsnS4kumy2EYuu8pAjiREb4AYKGykiwtvnjaIQFcb3dJsAHhCwAWKyvN1nUXT5NhGLrvqTcJ4ARE+AKADSaXZh+yB/wmh6ATDOELADY5PIA3ayOjoBMG4QsANiorze4bhPWrpzZr43sEcCIgfAHAZmUlWfreJdPldHZfhrTunRq7S8IQI3wBIAZMKsnS9y+ZLrfLoQf/e4teeGOv3SVhCBG+ABAjJhZn6YYFn5LP69bDz7yrv73ygUxuRzgsEb4AEENKCtJ045UnKSc9SX96caeeeH479wMehghfAIgxBdle3XjlSX23I3xg9VvqDIbtLgtRRPgCQAzKTk/WjVeepAlFmVr/bkB3/v51NTR32l0WooTwBYAY5Utxa8nlM3T61EJ9UN2sW373mnZVN9tdFqKA8AWAGOZyOvS1eZN0yZyxamrp0h2PbtCrb1fbXRYGifAFgBhnGIbKZ5Wo4ktT5XAY+vWat/X7/92mUDhid2kYIMIXAOLEieP9+slXTtaI3FQ9t2GP7nyM88DxivAFgDhSmJOqHy86SbMm52vH3oNa9tA6vfU+d0WKN4QvAMSZZI9LV39hshacM16tHSH9YtUmPfa/29TF5Uhxg/AFgDhkGIbOOblIP150sgpzvPrHhj265XfrtbuG0dDxgPAFgDhWUpCmm756iuZ+aqT21rXq1ofX64l/MBgr1hG+ABDnktxOXXnuRH3vkmlKTXbrkaff0U9/u1479x20uzQcBeELAMPEtLG5uvWbs3TurBLtCbTotkfW6w//eE8dXSG7S8MRCF8AGEZSk9367qUzdP2XT1ReZor+d32Vlv7Xa9qxt8nu0nAIwhcAhqFJJVla9vWZ+tysYgUa23XHo69r9dqdCkc4FxwLCF8AGKY8bqcunTNO//HlE5WZ5tGalz7QHY++rpoDbXaXlvAIXwAY5iaVZOmnX5+pUyfna+e+g7rpv9bp6cpd7AXbiPAFgATgTXbr6vlTdO0FU5TsceqPz+/QrQ9v4Lpgm7j6W6GyslKLFy/W+PHjJUkTJkzQ+eefr+XLl8vlcsnj8eiuu+5Sdnb2kBcLABicmWX5mlyarT/84z29sqVaP/3tep1z8iiVzypWhi/J7vISRr/hK0kzZ87UihUr+l5fd911Wr58uYqKinTffffpiSee0LXXXjtkRQIAoseX4tY3vzBZp07J18P/866efa1Kz7+xV2dMK9TnZhUrNyPF7hKHveMK3yP1BrFpmqqpqdFJJ50U1aIAAENv6pgc3X71LP17c7WefnWX/vn6Xr24cZ9OnZKvz59WqoJsr90lDluGaZrmsVaorKzUsmXLVFxcrKamJlVUVGj27Nn617/+pdtuu01jxozRr371KzkcRz99HAqF5XI5o148ACA6QuGI/vXGHv3xufe0p7ZFhiGdPn2kLvnMeI0ekWF3ecNOv+FbU1OjDRs2qLy8XFVVVVq0aJGeffZZeTwemaapu+++W2lpacc87BwIRP+Evt+fNiTbTTT0MTroY3TQx+gYTB8jpqnX3w3or698oN01LZKkaWNzdPrUQk0flyu3K3HG6Q7299HvTzvqe/0eds7Pz9e8efMkScXFxcrNzdVjjz2mr371qzIMQ+edd57uvffeARcHAIgdDsPQyZPydNJEvzbvPKC/vvKB3txRrzd31Cs12aWZZfn69AkFGjMiXYZh2F1u3Oo3fNesWaNAIKCrrrpKgUBA9fX1WrVqlWbNmqWysjJt2rRJo0ePtqJWAIBFDMPQtLE5mjY2R3sCLXp5c7Ve2VKt59/Yq+ff2Kv8bK8+fUKBPj2lQDkZyXaXG3f6Pezc0tKiJUuW6ODBgwoGg6qoqFB2drZuu+02OZ1OJScna/ny5crJyTnqNjjsHLvoY3TQx+igj9ExVH0MRyLa8n6DXn5rv954r07BUPckHWUlWfrCp0s1qSQr6p9pp6E87Nxv+EYD4Ru76GN00MfooI/RYUUf2zpCWv9urV5+q1rbqholSSeMydaXzhyrkoKjh048sfWcLwAAR/Imu3Tm9BE6c/oI7dx3UH96cYfe2nlAb+08oFmT83XhGaOVn8WlSkdD+AIABmXMiHQtuXyG3v6gQU++sEOVb9do/dZanTl9hL4wu1SZzJz1EYQvAGDQDMPQlNHZKivN0vqttfrzv3bq+Tf26qW39uuzJxfp86eVKNlD5PSiEwCAqHEYhmaW5etTE/x6afN+/fe/39ffXtml17bW6pr5UzS6MN3uEmNC4lwtDQCwjMvp0FkzRupn15ym8lnFCjS06/ZHNuhvr3ygSGTIx/nGPMIXADBkPG6nLpkzTj+8fIbSvG796cWduvvxN9TQ3Gl3abYifAEAQ25yabZ+etUsnTg+V1t3N+r2Rzao5kCb3WXZhvAFAFjCl+JWxUVT9cUzRqv+YIduf3SDdlUn5nXdhC8AwDKGYegLs0dr4XkT1dIW1J2Pva6tuxrsLstyhC8AwHJzThypay6YomAool88sUlv7qi3uyRLEb4AAFvMLMvX9y6ZLochPbhmi2ob2+0uyTKELwDANlNGZ2vheRPV3hnS/avf6rtZw3BH+AIAbDV7aqFOn1qoXdXNeuL57XaXYwnCFwBguyvOnaCRual6bsMerd9aa3c5Q47wBQDYLsnt1LUXniCP26GHnn5HtQ3D+xpgwhcAEBNG5qZq4bkT1d4Z1kN/3yoLbjdvG8IXABAzZk8t1LSxOXq3qlHvDOPrfwlfAEBM+eIZYyRJq9e+P2z3fglfAEBMKSlI04njc7V9b5O2vH/A7nKGBOELAIg5F5w+WpL052G690v4AgBiTnF+mk6e6Nf7+w9q0zCcepLwBQDEpPmnj5YhafXancNu75fwBQDEpFF+n04py9Pumha98V6d3eVEFeELAIhZF5w+WoYh/f3VXXaXElWELwAgZhXmpGr8yAy9v/+g2jpCdpcTNYQvACCmjS/KlGlKO/Y12V1K1BC+AICYNn5UpiRpW1WjzZVED+ELAIhp40ZmyJD0HuELAIA1vMkuFeX5tHN/s4KhiN3lRAXhCwCIeeOLMhUKR/T+/oN2lxIVrv5WqKys1OLFizV+/HhJ0oQJE/SNb3xDN954o0KhkFwul+666y75/f4hLxYAkJgmFGXquQ179N6eRk0oyrS7nEHrN3wlaebMmVqxYkXf6xtuuEGXXnqp5s2bp9///vd66KGHdP311w9ZkQCAxDZhVIYkaVtVkz5/ms3FRMFxhe+Rbr75ZiUlJUmSsrKytGXLlqgWBQDAoTJ8ScrLStH2vY2KREw5HIbdJQ3KcYXv9u3bde2116qpqUkVFRWaPXu2JCkcDuuxxx7Td77znWN+f1aWVy6Xc/DVHsHvT4v6NhMRfYwO+hgd9DE6hmMfp43z6x+v7VZryNSYkemWfOZQ9bHf8C0tLVVFRYXKy8tVVVWlRYsW6dlnn5XT6dT111+vU089VaedduxjAA0NbVEruJffn6ZAoDnq20009DE66GN00MfoGK59LPJ7JUnrNu9TmmfoxwsPto/HCu5+q8/Pz9e8efNkGIaKi4uVm5urmpoa3XjjjSopKVFFRcWACwMA4Hj1DrQaDpNt9Bu+a9as0cqVKyVJgUBA9fX1Wr9+vdxut6677rohLxAAAEnKy0xRRqpH2/Y0xv0tBvs97Dx37lwtWbJEzz33nILBoJYuXar7779fnZ2dWrhwoSRp7NixWrp06VDXCgBIYIZhaHxRptZvrVWgsV15WV67SxqwfsPX5/PpgQceOGzZWWedNWQFAQBwNONHZWj91lptq2qK6/BlhisAQNyY0HuThT3xfd6X8AUAxI2iPJ9Skpxxf5MFwhcAEDccDkMl+WmqaWhXZ1fY7nIGjPAFAMSVwtxUSVL1gejPIWEVwhcAEFdG5HSH7/76VpsrGTjCFwAQVwpyukc576tnzxcAAEuw5wsAgMUyfR4le5zaz54vAADWMAxDhTmpqjnQpnAkYnc5A0L4AgDizogcr8IRU4HGDrtLGRDCFwAQd3oHXe2vi8/zvoQvACDu9A662heng64IXwBA3OmdaCNeB10RvgCAuOPPTJbLacTt5UaELwAg7jgdDuVnebW/vk2madpdzidG+AIA4lJhjlcdXWE1tnTZXconRvgCAOJSQRwPuiJ8AQBxaUQcX25E+AIA4lJhTvyOeCZ8AQBxqSDHK0PxeYMFwhcAEJeS3E7lZCSz5wsAgJUKc1LV1Nqlto6g3aV8IoQvACBuFfYMutoXZ3u/hC8AIG4VxumIZ8IXABC3+kY8H2DPFwAAS4zovcECe74AAFjDl+JWmtcddyOeCV8AQFwrzPYq0NSuYChidynHjfAFAMS1rPRkmabU3BY/N1ggfAEAcS3N65YkNbfFz7W+/YZvZWWlTj31VC1cuFALFy7ULbfcIkl6+OGHNWXKFLW2xtdJbgDA8JLm9UiKrz1f1/GsNHPmTK1YsaLv9erVq1VfX6+8vLwhKwwAgOOR3rPne3C4he+RzjnnHPl8Pv3lL3+Jdj0AAHwi6T17vgdb4+ew83GF7/bt23XttdeqqalJFRUVmj179if6kKwsr1wu54AKPBa/Py3q20xE9DE66GN00MfoSKQ+FveEbljR/7mHqo/9hm9paakqKipUXl6uqqoqLVq0SM8++6w8Hs9xf0hDQ/Svv/L70xQINEd9u4mGPkYHfYwO+hgdidbHUFd3+NbUtUb15x5sH48V3P0OuMrPz9e8efNkGIaKi4uVm5urmpqaARcDAEA09R12jqNzvv2G75o1a7Ry5UpJUiAQUH19vfLz84e8MAAAjkeyxymX0zG8RjvPnTtXS5Ys0XPPPadgMKilS5dq5cqVevnllxUIBPTNb35TM2bM0PXXX29FvQAAHMYwDKWnuofXgCufz6cHHnjgsGVnnXWWvvWtbw1ZUQAAfBJpKR7tPxA/804wwxUAIO6lpbrVFYyosytsdynHhfAFAMS9eBt0RfgCAOIe4QsAgMXi7eYKhC8AIO713VyhlT1fAAAskZ4aXzdXIHwBAHHvw9sKctgZAABLpMfZPX0JXwBA3Evru6cve74AAFjC43YqyeNkwBUAAFZK97oZcAUAgJXSvR41twVlmqbdpfSL8AUADAtpXo/CEVPtnSG7S+kX4QsAGBaONujqnV0Nuv3RDWrtiJ3BWIQvAGBYSE/tmd/5iEFXL2/er+17mrRz30E7yvpYhC8AYFg42kQbewLd9/k9MpTtRPgCAIaFD2+u8GHIhiMR7a1r7VnOYWcAAKLq424rWHOgXaFwRJLU3M6eLwAAUdW359v64R7unkBL3/NDl9uN8AUADAu9A64O3cM9LHxjaAIOwhcAMCz4UnouNTpkYNWe2u7zvYak5vbY2fN12V0AAADR4HI6lJrsOmxgVVVtizJSPTIMRjsDADAk0ryevgFXbR0h1R/s0Ch/qtK8npja8yV8AQDDRrrXrZb2oCIRU3vrus/3jsrzKc3rVmdXWF3BsM0VdiN8AQDDRprXI9OUWjqC2lPbE75+X99lSLFyrS/hCwAYNtJ6Rzy3dqmqZ2arojyffL2XIcXItb6ELwBg2Eg/5OYKewItchiGCnNSjzr1pF0IXwDAsPFhyHZpT7krjbMAAArpSURBVG2LCnK8crscfaEcK9f6Er4AgGGjd5ar9/cfVEdXWKP8qT3Le+94xJ4vAABR1Tuw6p0PGiR1n++VDpl6MkbO+fY7yUZlZaUWL16s8ePHS5ImTJigb3zjG7r++usVDofl9/t11113yePxDHmxAAAcS++Aq909I51H+rvDN9ZGOx/XDFczZ87UihUr+l7feOONWrBggcrLy/WLX/xCTz75pBYsWDBkRQIAcDx6z+32KvIfsecbI7NcDeiwc2VlpT7zmc9IkubMmaNXXnklqkUBADAQqSluGUb385Qkl7LTk/qeOx1GzMxydVx7vtu3b9e1116rpqYmVVRUqL29ve8wc05OjgKBwDG/PyvLK5fLOfhqj+D3p0V9m4mIPkYHfYwO+hgdidzHjNQkNbZ0aszIDOXlpX+43Jekts7QJ+rNUPWx3/AtLS1VRUWFysvLVVVVpUWLFikc/nB6LtM0+/2Qhoa2wVX5Mfz+NAUCzVHfbqKhj9FBH6ODPkZHovcxNcWlxpZO5WUmH9aH1GSXahvbj7s3g+3jsYK738PO+fn5mjdvngzDUHFxsXJzc9XU1KSOjg5JUk1NjfLy8gZcHAAA0dQ7uKr3fG+v3vmdgyH753fuN3zXrFmjlStXSpICgYDq6+t10UUX6ZlnnpEkPfvsszrjjDOGtkoAAI5T7+CqUXmHh28sjXju97Dz3LlztWTJEj333HMKBoNaunSpysrKdMMNN2jVqlUaMWKELrzwQitqBQCgX6dPK5Tb6VBpweGHfX19U092KTs92Y7S+vQbvj6fTw888MBHlj/00ENDUhAAAINxwugcnTA65yPLY2l+Z2a4AgAkhFia35nwBQAkBPZ8AQCwWNoh53ztRvgCABJCLI12JnwBAAmhd8+3hfAFAMAavfM7c9gZAACLGIahNK+b0c4AAFgpzevhnC8AAFZK87rVEQPzOxO+AICEESsjnglfAEDC8PXNckX4AgBgid5Zruwe8Uz4AgASRqzM70z4AgASRqzM70z4AgASRhrnfAEAsFY653wBALBWrMzvTPgCABJG7/zODLgCAMAivfM7c9gZAAALxcL8zoQvACChxML8zoQvACChxML8zoQvACChxML8zoQvACChfDjLlX2DrghfAEBCyfR1h++B5k7baiB8AQAJpSDbK0mqPtBmWw2ELwAgoeT3hm894QsAgCXSUtxKTXappoHwBQDAEoZhKD/bq9qGdoUjEVtqIHwBAAmnINurcMRUXVOHLZ9/XOHb0dGhc845R0899ZR27NihK664QldeeaV+/OMfKxQKDXWNAABEVYHN532PK3zvv/9+ZWRkSJLuvvtuXX311Xr00UdVWFiop59+ekgLBAAg2uwe8dxv+O7YsUPbt2/X2WefLUnatWuXpk2bJkk644wz9NJLLw1pgQAARFtv+NbYFL6u/la488479ZOf/ESrV6+WJE2YMEEvvviiLrzwQq1du1Z1dXX9fkhWllcul3Pw1R7B70+L+jYTEX2MDvoYHfQxOujjsaVnemUYUn1z1zF7NVR9PGb4rl69WjNmzFBRUVHfshtuuEFLly7VU089pZkzZ8o0zX4/pGEIhnP7/WkKBJqjvt1EQx+jgz5GB32MDvp4fLLTklVVc/CovRpsH48V3McM3xdeeEFVVVV64YUXVF1dLY/Ho4KCAj344IOSpLVr16q2tnbAhQEAYJeC7BRt+aBB7Z0hpST1eyA4qo75affcc0/f83vvvVcjR47U+vXr1dXVpbPPPltPPfWULrjggiEvEgCAaCvITtWWDxpU29CukgJrD9N/4ut8zz//fN1333360pe+pLy8vL6BWAAAxJOCnO5BV/sPtFr+2ce9n/3d73637/mTTz45JMUAAGCV/OwUSfZc68sMVwCAhNR3uVFDu+WfTfgCABJSdnqy3C4He74AAFjFYRjKz0pRdUPbcV02G9XPtvTTAACIIfnZXnV2hdXY0mXp5xK+AICEZdccz4QvACBh2TXHM+ELAEhY7PkCAGCxfMIXAABr+VLc8qW4CV8AAKxUkONVXWOHQuGIZZ9J+AIAElpBtlcR01Sg0bqZrghfAEBC6xt0ZeFMV4QvACCh9d5OsDMYtuwzrb17MAAAMWZySZaWfu0UjcrzWfaZhC8AIKEZhqHi/DRLP5PDzgAAWIzwBQDAYoQvAAAWI3wBALAY4QsAgMUIXwAALEb4AgBgMcIXAACLEb4AAFiM8AUAwGKELwAAFjNM0zTtLgIAgETCni8AABYjfAEAsBjhCwCAxQhfAAAsRvgCAGAxwhcAAIsRvgAAWIzwBQDAYi67C4imF198UWvXrpVpmrriiis0ZswYu0uKS3/961+1ZcsWHThwQGPGjNE111xjd0lxKRAI6MEHH1QoFNLll1+uSZMm2V1SXLr33ntVXV2t9PR0zZ8/X2VlZXaXFLcCgYC++MUv6oUXXpDLNaz++bfMhg0b9PjjjysYDOqqq67S1KlTB7SdmNzz3bZtm8455xw9+uijfctuv/12XXbZZbr88sv15ptvfuz3rV27Vtdcc43mz5+vN954w6pyY9ZA+3j++efrhhtukN/v1xVXXGFVuTFroH188sknNXLkSCUnJ8vv91tVbswaaB8lKTk5WcFgUHl5eVaUGtMG08eHHnpIp5xyihVlxryB9tHn8+nWW2/V17/+da1bt27Anx9z//Vpa2vTLbfcotNOO61v2bp167Rr1y6tWrVKO3bs0H/+539q1apV+u1vf6vXX39dkjRu3Didd955uvnmmxWJRHTzzTfb9SPEhMH08brrrtP777+vnJwc+Xw+u36EmDCYPtbV1emaa65RV1eXHn74YX3/+9+368ew3WD6eNlllykzM1OBQEC/+93v9IMf/MCuH8N2g+ljSUmJzj33XD3++ON2lR8zBvvv44svvqiVK1fq1ltvHXgRZowJBoNme3u7uWLFCvORRx4xTdM077nnHvOJJ57oW+e8884zm5ubP/K91113nRkMBs2amhrzl7/8pWU1x6LB9NE0TXPFihXm/v37Lak1lg2mj/fcc49ZVVVlVldXm7fddptlNceiwfTx5ZdfNkOhkNnU1GTecccdltUciwbTx2XLlpkrVqww58+fb65evdqymmPRYPq4ceNGMxKJmPX19eayZcsGXEPM7fm6XK6PnIuoq6vTlClT+l5nZ2crEAh8ZK9szpw5uuuuuxQOh1VeXm5JvbFqMH2UpKqqKhUUFAx5nbFuMH28+OKLtWLFCoXD4YQ/bz6YPnZ0dOhHP/qRXC6Xrr76akvqjVWD6eNNN90kSdq7d68+//nPD32xMWwwfWxqatJNN92ktrY2zZ8/f+A1DPg7bWQe5UZMF154oS688EKLq4lfR+ujJC1fvtzCSuLb0fo4cuRI3XnnnRZXE7+O1sc5c+Zozpw5FlcTv471dy1JP/vZzyyqJL4drY9nnnmmzjzzzEFvPyYHXB0pLy9PdXV1fa9ra2sZwDIA9DE66GN00MfooI/RYXUf4yJ8Z8+erWeeeUaStGXLFuXl5SX8QKCBoI/RQR+jgz5GB32MDqv7GHOHnd966y3deeed2rt3r1wul5555hnde++9mjJlii6//HIZhpHwI5mPB32MDvoYHfQxOuhjdMRCHw2zvxMEAAAgquLisDMAAMMJ4QsAgMUIXwAALEb4AgBgMcIXAACLEb4AAFiM8AUAwGKELwAAFiN8AQCw2P8HLdpTxBhpgPoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-jA9o8TGA3J"
      },
      "source": [
        "# From the curve we will select a learning rate of 10^-4"
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZRoUn6o4Gt1F",
        "outputId": "81dccedd-c133-469a-a87e-eaa8076f1040"
      },
      "source": [
        "optimizer = tf.keras.optimizers.SGD(lr=1e-4, momentum=0.9)\r\n",
        "model.compile(loss=tf.keras.losses.Huber(),\r\n",
        "              optimizer=optimizer,\r\n",
        "              metrics=[\"mae\"])\r\n",
        "history = model.fit(tensor_train_dataset, epochs=200, validation_data=tensor_test_dataset)"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "79/79 [==============================] - 10s 87ms/step - loss: 46.2903 - mae: 46.7881 - val_loss: 52.2250 - val_mae: 52.7225\n",
            "Epoch 2/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 46.0783 - mae: 46.5761 - val_loss: 51.7771 - val_mae: 52.2748\n",
            "Epoch 3/200\n",
            "79/79 [==============================] - 6s 76ms/step - loss: 47.4932 - mae: 47.9907 - val_loss: 51.6723 - val_mae: 52.1702\n",
            "Epoch 4/200\n",
            "79/79 [==============================] - 6s 75ms/step - loss: 47.2067 - mae: 47.7039 - val_loss: 50.8445 - val_mae: 51.3427\n",
            "Epoch 5/200\n",
            "79/79 [==============================] - 6s 76ms/step - loss: 46.5630 - mae: 47.0601 - val_loss: 50.3701 - val_mae: 50.8677\n",
            "Epoch 6/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 45.7445 - mae: 46.2420 - val_loss: 50.4906 - val_mae: 50.9882\n",
            "Epoch 7/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 45.4369 - mae: 45.9345 - val_loss: 51.4204 - val_mae: 51.9182\n",
            "Epoch 8/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 45.5472 - mae: 46.0447 - val_loss: 50.9402 - val_mae: 51.4382\n",
            "Epoch 9/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 46.6955 - mae: 47.1927 - val_loss: 49.9487 - val_mae: 50.4463\n",
            "Epoch 10/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 45.2434 - mae: 45.7404 - val_loss: 50.3468 - val_mae: 50.8448\n",
            "Epoch 11/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.9889 - mae: 45.4861 - val_loss: 49.7713 - val_mae: 50.2682\n",
            "Epoch 12/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 44.7472 - mae: 45.2444 - val_loss: 49.2275 - val_mae: 49.7252\n",
            "Epoch 13/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 45.5317 - mae: 46.0292 - val_loss: 48.5717 - val_mae: 49.0698\n",
            "Epoch 14/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 46.2300 - mae: 46.7278 - val_loss: 48.7183 - val_mae: 49.2161\n",
            "Epoch 15/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.7862 - mae: 45.2838 - val_loss: 49.3205 - val_mae: 49.8183\n",
            "Epoch 16/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 45.9798 - mae: 46.4773 - val_loss: 48.9421 - val_mae: 49.4388\n",
            "Epoch 17/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 46.5674 - mae: 47.0649 - val_loss: 48.9246 - val_mae: 49.4216\n",
            "Epoch 18/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 46.0290 - mae: 46.5269 - val_loss: 49.4159 - val_mae: 49.9124\n",
            "Epoch 19/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 45.9986 - mae: 46.4962 - val_loss: 48.9170 - val_mae: 49.4134\n",
            "Epoch 20/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 46.6938 - mae: 47.1913 - val_loss: 49.1393 - val_mae: 49.6361\n",
            "Epoch 21/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.8609 - mae: 45.3583 - val_loss: 49.1479 - val_mae: 49.6450\n",
            "Epoch 22/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.6402 - mae: 45.1373 - val_loss: 49.1631 - val_mae: 49.6608\n",
            "Epoch 23/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 44.2980 - mae: 44.7949 - val_loss: 49.7277 - val_mae: 50.2256\n",
            "Epoch 24/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 44.6503 - mae: 45.1477 - val_loss: 48.8751 - val_mae: 49.3724\n",
            "Epoch 25/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 45.2340 - mae: 45.7315 - val_loss: 49.0075 - val_mae: 49.5048\n",
            "Epoch 26/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 44.1672 - mae: 44.6644 - val_loss: 49.1301 - val_mae: 49.6276\n",
            "Epoch 27/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 45.1903 - mae: 45.6878 - val_loss: 50.4846 - val_mae: 50.9822\n",
            "Epoch 28/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 45.2566 - mae: 45.7539 - val_loss: 48.7881 - val_mae: 49.2850\n",
            "Epoch 29/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 45.7716 - mae: 46.2690 - val_loss: 50.1638 - val_mae: 50.6617\n",
            "Epoch 30/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 45.6270 - mae: 46.1246 - val_loss: 50.1398 - val_mae: 50.6375\n",
            "Epoch 31/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 46.7130 - mae: 47.2106 - val_loss: 50.7385 - val_mae: 51.2359\n",
            "Epoch 32/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 46.5726 - mae: 47.0702 - val_loss: 50.0689 - val_mae: 50.5664\n",
            "Epoch 33/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 45.1514 - mae: 45.6486 - val_loss: 50.2788 - val_mae: 50.7767\n",
            "Epoch 34/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 46.1511 - mae: 46.6485 - val_loss: 49.9045 - val_mae: 50.4017\n",
            "Epoch 35/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 45.4677 - mae: 45.9647 - val_loss: 49.6758 - val_mae: 50.1731\n",
            "Epoch 36/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 46.7984 - mae: 47.2957 - val_loss: 49.9493 - val_mae: 50.4464\n",
            "Epoch 37/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 46.1133 - mae: 46.6100 - val_loss: 49.3439 - val_mae: 49.8411\n",
            "Epoch 38/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 45.4865 - mae: 45.9839 - val_loss: 49.4061 - val_mae: 49.9031\n",
            "Epoch 39/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 45.1001 - mae: 45.5970 - val_loss: 49.2916 - val_mae: 49.7889\n",
            "Epoch 40/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 47.3935 - mae: 47.8907 - val_loss: 49.5427 - val_mae: 50.0395\n",
            "Epoch 41/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 45.1860 - mae: 45.6829 - val_loss: 49.0829 - val_mae: 49.5802\n",
            "Epoch 42/200\n",
            "79/79 [==============================] - 6s 81ms/step - loss: 45.9218 - mae: 46.4190 - val_loss: 49.3771 - val_mae: 49.8740\n",
            "Epoch 43/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 45.3615 - mae: 45.8588 - val_loss: 49.2831 - val_mae: 49.7801\n",
            "Epoch 44/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 44.9418 - mae: 45.4387 - val_loss: 49.2647 - val_mae: 49.7623\n",
            "Epoch 45/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 44.3990 - mae: 44.8959 - val_loss: 48.5903 - val_mae: 49.0877\n",
            "Epoch 46/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 45.4757 - mae: 45.9729 - val_loss: 48.2545 - val_mae: 48.7520\n",
            "Epoch 47/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.9795 - mae: 45.4766 - val_loss: 48.5071 - val_mae: 49.0043\n",
            "Epoch 48/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.5664 - mae: 45.0635 - val_loss: 48.2757 - val_mae: 48.7736\n",
            "Epoch 49/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 45.0117 - mae: 45.5091 - val_loss: 47.8712 - val_mae: 48.3689\n",
            "Epoch 50/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 46.3983 - mae: 46.8958 - val_loss: 47.8752 - val_mae: 48.3731\n",
            "Epoch 51/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 46.0212 - mae: 46.5183 - val_loss: 48.7411 - val_mae: 49.2376\n",
            "Epoch 52/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.5518 - mae: 45.0487 - val_loss: 48.7085 - val_mae: 49.2061\n",
            "Epoch 53/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 44.3853 - mae: 44.8822 - val_loss: 48.6046 - val_mae: 49.1021\n",
            "Epoch 54/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 44.9622 - mae: 45.4593 - val_loss: 47.4621 - val_mae: 47.9592\n",
            "Epoch 55/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 45.3383 - mae: 45.8355 - val_loss: 48.5851 - val_mae: 49.0816\n",
            "Epoch 56/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 45.5060 - mae: 46.0029 - val_loss: 48.0551 - val_mae: 48.5523\n",
            "Epoch 57/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 45.3365 - mae: 45.8339 - val_loss: 48.7871 - val_mae: 49.2839\n",
            "Epoch 58/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.4079 - mae: 44.9049 - val_loss: 48.0315 - val_mae: 48.5279\n",
            "Epoch 59/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.1994 - mae: 44.6965 - val_loss: 48.0114 - val_mae: 48.5085\n",
            "Epoch 60/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 45.2839 - mae: 45.7808 - val_loss: 48.5256 - val_mae: 49.0229\n",
            "Epoch 61/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 44.6324 - mae: 45.1292 - val_loss: 48.5377 - val_mae: 49.0347\n",
            "Epoch 62/200\n",
            "79/79 [==============================] - 7s 84ms/step - loss: 45.9825 - mae: 46.4795 - val_loss: 48.4698 - val_mae: 48.9665\n",
            "Epoch 63/200\n",
            "79/79 [==============================] - 7s 84ms/step - loss: 44.3998 - mae: 44.8973 - val_loss: 48.6926 - val_mae: 49.1898\n",
            "Epoch 64/200\n",
            "79/79 [==============================] - 7s 83ms/step - loss: 45.1866 - mae: 45.6839 - val_loss: 49.1901 - val_mae: 49.6880\n",
            "Epoch 65/200\n",
            "79/79 [==============================] - 7s 83ms/step - loss: 44.1435 - mae: 44.6409 - val_loss: 49.3967 - val_mae: 49.8935\n",
            "Epoch 66/200\n",
            "79/79 [==============================] - 7s 83ms/step - loss: 45.1617 - mae: 45.6588 - val_loss: 48.2264 - val_mae: 48.7244\n",
            "Epoch 67/200\n",
            "79/79 [==============================] - 7s 83ms/step - loss: 44.1801 - mae: 44.6773 - val_loss: 48.2078 - val_mae: 48.7053\n",
            "Epoch 68/200\n",
            "79/79 [==============================] - 7s 82ms/step - loss: 44.9254 - mae: 45.4228 - val_loss: 48.1603 - val_mae: 48.6573\n",
            "Epoch 69/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 45.8857 - mae: 46.3832 - val_loss: 47.3041 - val_mae: 47.8020\n",
            "Epoch 70/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 46.3615 - mae: 46.8587 - val_loss: 48.0310 - val_mae: 48.5281\n",
            "Epoch 71/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 46.1077 - mae: 46.6050 - val_loss: 48.3260 - val_mae: 48.8231\n",
            "Epoch 72/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 45.6781 - mae: 46.1754 - val_loss: 47.9426 - val_mae: 48.4402\n",
            "Epoch 73/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 44.5874 - mae: 45.0846 - val_loss: 48.1270 - val_mae: 48.6247\n",
            "Epoch 74/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 45.7358 - mae: 46.2330 - val_loss: 47.8689 - val_mae: 48.3658\n",
            "Epoch 75/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.8518 - mae: 45.3492 - val_loss: 47.9604 - val_mae: 48.4579\n",
            "Epoch 76/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.5440 - mae: 45.0412 - val_loss: 47.8167 - val_mae: 48.3139\n",
            "Epoch 77/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 45.2158 - mae: 45.7129 - val_loss: 49.2568 - val_mae: 49.7543\n",
            "Epoch 78/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 44.0505 - mae: 44.5478 - val_loss: 48.6499 - val_mae: 49.1471\n",
            "Epoch 79/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 43.5442 - mae: 44.0413 - val_loss: 48.0488 - val_mae: 48.5455\n",
            "Epoch 80/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 45.1893 - mae: 45.6865 - val_loss: 48.2369 - val_mae: 48.7339\n",
            "Epoch 81/200\n",
            "79/79 [==============================] - 7s 86ms/step - loss: 44.8539 - mae: 45.3510 - val_loss: 49.1604 - val_mae: 49.6579\n",
            "Epoch 82/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.8099 - mae: 45.3069 - val_loss: 48.5234 - val_mae: 49.0207\n",
            "Epoch 83/200\n",
            "79/79 [==============================] - 6s 81ms/step - loss: 43.6806 - mae: 44.1777 - val_loss: 48.4338 - val_mae: 48.9310\n",
            "Epoch 84/200\n",
            "79/79 [==============================] - 7s 83ms/step - loss: 45.1110 - mae: 45.6082 - val_loss: 48.1410 - val_mae: 48.6385\n",
            "Epoch 85/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 44.5326 - mae: 45.0297 - val_loss: 48.1111 - val_mae: 48.6082\n",
            "Epoch 86/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 45.5239 - mae: 46.0211 - val_loss: 48.1983 - val_mae: 48.6957\n",
            "Epoch 87/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 44.4229 - mae: 44.9199 - val_loss: 48.5677 - val_mae: 49.0648\n",
            "Epoch 88/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 44.1974 - mae: 44.6944 - val_loss: 48.2625 - val_mae: 48.7592\n",
            "Epoch 89/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.8809 - mae: 45.3777 - val_loss: 48.3211 - val_mae: 48.8181\n",
            "Epoch 90/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.3356 - mae: 44.8325 - val_loss: 48.1471 - val_mae: 48.6445\n",
            "Epoch 91/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.4270 - mae: 44.9238 - val_loss: 47.8610 - val_mae: 48.3585\n",
            "Epoch 92/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.2429 - mae: 44.7396 - val_loss: 47.8456 - val_mae: 48.3431\n",
            "Epoch 93/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.8814 - mae: 45.3781 - val_loss: 47.9805 - val_mae: 48.4779\n",
            "Epoch 94/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 44.3816 - mae: 44.8783 - val_loss: 47.8870 - val_mae: 48.3845\n",
            "Epoch 95/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 43.8174 - mae: 44.3146 - val_loss: 48.2340 - val_mae: 48.7309\n",
            "Epoch 96/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.9299 - mae: 45.4264 - val_loss: 48.4339 - val_mae: 48.9309\n",
            "Epoch 97/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.0897 - mae: 44.5865 - val_loss: 47.9137 - val_mae: 48.4115\n",
            "Epoch 98/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.2471 - mae: 44.7440 - val_loss: 47.7348 - val_mae: 48.2321\n",
            "Epoch 99/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.7066 - mae: 45.2033 - val_loss: 48.6313 - val_mae: 49.1284\n",
            "Epoch 100/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.5780 - mae: 45.0750 - val_loss: 48.2279 - val_mae: 48.7253\n",
            "Epoch 101/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 45.3687 - mae: 45.8657 - val_loss: 48.0511 - val_mae: 48.5483\n",
            "Epoch 102/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 44.6324 - mae: 45.1290 - val_loss: 47.8740 - val_mae: 48.3712\n",
            "Epoch 103/200\n",
            "79/79 [==============================] - 6s 81ms/step - loss: 43.7903 - mae: 44.2872 - val_loss: 48.0394 - val_mae: 48.5368\n",
            "Epoch 104/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.1228 - mae: 44.6195 - val_loss: 48.1172 - val_mae: 48.6138\n",
            "Epoch 105/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.0670 - mae: 44.5639 - val_loss: 47.6995 - val_mae: 48.1967\n",
            "Epoch 106/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.7341 - mae: 45.2308 - val_loss: 48.1029 - val_mae: 48.6006\n",
            "Epoch 107/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 43.4990 - mae: 43.9957 - val_loss: 47.4425 - val_mae: 47.9399\n",
            "Epoch 108/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.7358 - mae: 45.2328 - val_loss: 47.6143 - val_mae: 48.1117\n",
            "Epoch 109/200\n",
            "79/79 [==============================] - 7s 82ms/step - loss: 45.0414 - mae: 45.5382 - val_loss: 47.4842 - val_mae: 47.9814\n",
            "Epoch 110/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 44.7465 - mae: 45.2431 - val_loss: 47.9904 - val_mae: 48.4873\n",
            "Epoch 111/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 44.4153 - mae: 44.9120 - val_loss: 48.2407 - val_mae: 48.7382\n",
            "Epoch 112/200\n",
            "79/79 [==============================] - 7s 82ms/step - loss: 43.6280 - mae: 44.1251 - val_loss: 47.8889 - val_mae: 48.3864\n",
            "Epoch 113/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 45.2156 - mae: 45.7125 - val_loss: 47.7241 - val_mae: 48.2210\n",
            "Epoch 114/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.7989 - mae: 45.2957 - val_loss: 47.6144 - val_mae: 48.1115\n",
            "Epoch 115/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 43.7645 - mae: 44.2616 - val_loss: 47.5314 - val_mae: 48.0287\n",
            "Epoch 116/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 43.8030 - mae: 44.2998 - val_loss: 47.5808 - val_mae: 48.0781\n",
            "Epoch 117/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.5486 - mae: 45.0456 - val_loss: 47.9994 - val_mae: 48.4971\n",
            "Epoch 118/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 43.5114 - mae: 44.0086 - val_loss: 47.3453 - val_mae: 47.8427\n",
            "Epoch 119/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 43.6924 - mae: 44.1896 - val_loss: 47.6704 - val_mae: 48.1678\n",
            "Epoch 120/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.5613 - mae: 45.0582 - val_loss: 47.3801 - val_mae: 47.8775\n",
            "Epoch 121/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.1087 - mae: 44.6052 - val_loss: 47.4893 - val_mae: 47.9866\n",
            "Epoch 122/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 45.5663 - mae: 46.0630 - val_loss: 47.3466 - val_mae: 47.8435\n",
            "Epoch 123/200\n",
            "79/79 [==============================] - 7s 82ms/step - loss: 44.7035 - mae: 45.2004 - val_loss: 49.4711 - val_mae: 49.9686\n",
            "Epoch 124/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.0263 - mae: 44.5230 - val_loss: 48.2406 - val_mae: 48.7380\n",
            "Epoch 125/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 44.9231 - mae: 45.4203 - val_loss: 48.3935 - val_mae: 48.8914\n",
            "Epoch 126/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 43.5742 - mae: 44.0711 - val_loss: 48.1416 - val_mae: 48.6395\n",
            "Epoch 127/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.0426 - mae: 44.5394 - val_loss: 48.7384 - val_mae: 49.2353\n",
            "Epoch 128/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 45.4669 - mae: 45.9640 - val_loss: 48.2517 - val_mae: 48.7486\n",
            "Epoch 129/200\n",
            "79/79 [==============================] - 7s 86ms/step - loss: 44.3314 - mae: 44.8286 - val_loss: 48.1207 - val_mae: 48.6179\n",
            "Epoch 130/200\n",
            "79/79 [==============================] - 7s 85ms/step - loss: 44.1280 - mae: 44.6246 - val_loss: 47.5071 - val_mae: 48.0042\n",
            "Epoch 131/200\n",
            "79/79 [==============================] - 8s 102ms/step - loss: 43.7540 - mae: 44.2508 - val_loss: 47.4689 - val_mae: 47.9663\n",
            "Epoch 132/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.0280 - mae: 44.5247 - val_loss: 47.6055 - val_mae: 48.1027\n",
            "Epoch 133/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 44.3814 - mae: 44.8786 - val_loss: 47.6927 - val_mae: 48.1895\n",
            "Epoch 134/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 44.5437 - mae: 45.0408 - val_loss: 47.2459 - val_mae: 47.7431\n",
            "Epoch 135/200\n",
            "79/79 [==============================] - 7s 84ms/step - loss: 45.1643 - mae: 45.6613 - val_loss: 47.1551 - val_mae: 47.6521\n",
            "Epoch 136/200\n",
            "79/79 [==============================] - 7s 83ms/step - loss: 44.0028 - mae: 44.5001 - val_loss: 47.2828 - val_mae: 47.7801\n",
            "Epoch 137/200\n",
            "79/79 [==============================] - 7s 84ms/step - loss: 44.2431 - mae: 44.7402 - val_loss: 47.5763 - val_mae: 48.0732\n",
            "Epoch 138/200\n",
            "79/79 [==============================] - 7s 82ms/step - loss: 43.3712 - mae: 43.8681 - val_loss: 47.6192 - val_mae: 48.1160\n",
            "Epoch 139/200\n",
            "79/79 [==============================] - 7s 87ms/step - loss: 45.2031 - mae: 45.7003 - val_loss: 47.5447 - val_mae: 48.0420\n",
            "Epoch 140/200\n",
            "79/79 [==============================] - 6s 81ms/step - loss: 42.5582 - mae: 43.0550 - val_loss: 47.8458 - val_mae: 48.3432\n",
            "Epoch 141/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 44.0691 - mae: 44.5661 - val_loss: 47.6715 - val_mae: 48.1687\n",
            "Epoch 142/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.8299 - mae: 45.3267 - val_loss: 47.9703 - val_mae: 48.4678\n",
            "Epoch 143/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.4156 - mae: 44.9125 - val_loss: 47.5822 - val_mae: 48.0795\n",
            "Epoch 144/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 43.4849 - mae: 43.9821 - val_loss: 47.3673 - val_mae: 47.8647\n",
            "Epoch 145/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.4676 - mae: 44.9646 - val_loss: 47.5308 - val_mae: 48.0281\n",
            "Epoch 146/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.2562 - mae: 44.7531 - val_loss: 47.7249 - val_mae: 48.2223\n",
            "Epoch 147/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.3342 - mae: 44.8310 - val_loss: 47.9944 - val_mae: 48.4917\n",
            "Epoch 148/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 43.7979 - mae: 44.2947 - val_loss: 46.9921 - val_mae: 47.4892\n",
            "Epoch 149/200\n",
            "79/79 [==============================] - 6s 81ms/step - loss: 44.5987 - mae: 45.0959 - val_loss: 47.2026 - val_mae: 47.7003\n",
            "Epoch 150/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.4821 - mae: 44.9790 - val_loss: 47.2700 - val_mae: 47.7674\n",
            "Epoch 151/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.4371 - mae: 44.9344 - val_loss: 47.4538 - val_mae: 47.9510\n",
            "Epoch 152/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 43.9942 - mae: 44.4911 - val_loss: 47.4636 - val_mae: 47.9606\n",
            "Epoch 153/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 43.5543 - mae: 44.0516 - val_loss: 47.2663 - val_mae: 47.7634\n",
            "Epoch 154/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.0920 - mae: 44.5892 - val_loss: 47.1619 - val_mae: 47.6589\n",
            "Epoch 155/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 43.7910 - mae: 44.2877 - val_loss: 49.6307 - val_mae: 50.1285\n",
            "Epoch 156/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 45.1689 - mae: 45.6660 - val_loss: 48.7544 - val_mae: 49.2521\n",
            "Epoch 157/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.4528 - mae: 44.9499 - val_loss: 47.0349 - val_mae: 47.5319\n",
            "Epoch 158/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.7360 - mae: 45.2331 - val_loss: 47.5795 - val_mae: 48.0764\n",
            "Epoch 159/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.4561 - mae: 44.9531 - val_loss: 47.6878 - val_mae: 48.1852\n",
            "Epoch 160/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 43.6463 - mae: 44.1432 - val_loss: 47.5557 - val_mae: 48.0528\n",
            "Epoch 161/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 43.8496 - mae: 44.3465 - val_loss: 47.1526 - val_mae: 47.6495\n",
            "Epoch 162/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 43.9763 - mae: 44.4736 - val_loss: 46.8375 - val_mae: 47.3350\n",
            "Epoch 163/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.9421 - mae: 45.4392 - val_loss: 46.8267 - val_mae: 47.3237\n",
            "Epoch 164/200\n",
            "79/79 [==============================] - 6s 77ms/step - loss: 43.5657 - mae: 44.0627 - val_loss: 46.8124 - val_mae: 47.3098\n",
            "Epoch 165/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 43.9290 - mae: 44.4259 - val_loss: 46.6830 - val_mae: 47.1803\n",
            "Epoch 166/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.8402 - mae: 45.3374 - val_loss: 46.6920 - val_mae: 47.1891\n",
            "Epoch 167/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 44.1452 - mae: 44.6418 - val_loss: 46.4321 - val_mae: 46.9287\n",
            "Epoch 168/200\n",
            "79/79 [==============================] - 6s 81ms/step - loss: 43.1822 - mae: 43.6791 - val_loss: 47.2872 - val_mae: 47.7845\n",
            "Epoch 169/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 44.3040 - mae: 44.8005 - val_loss: 47.1503 - val_mae: 47.6477\n",
            "Epoch 170/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 43.8972 - mae: 44.3938 - val_loss: 47.4419 - val_mae: 47.9395\n",
            "Epoch 171/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 42.8585 - mae: 43.3556 - val_loss: 48.5015 - val_mae: 48.9990\n",
            "Epoch 172/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 42.8372 - mae: 43.3340 - val_loss: 47.4643 - val_mae: 47.9613\n",
            "Epoch 173/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 43.9727 - mae: 44.4700 - val_loss: 47.1811 - val_mae: 47.6781\n",
            "Epoch 174/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 43.5819 - mae: 44.0786 - val_loss: 47.5176 - val_mae: 48.0150\n",
            "Epoch 175/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 43.7973 - mae: 44.2942 - val_loss: 47.5146 - val_mae: 48.0119\n",
            "Epoch 176/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 44.3592 - mae: 44.8558 - val_loss: 47.5904 - val_mae: 48.0874\n",
            "Epoch 177/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 43.3949 - mae: 43.8917 - val_loss: 47.4619 - val_mae: 47.9585\n",
            "Epoch 178/200\n",
            "79/79 [==============================] - 7s 86ms/step - loss: 43.7555 - mae: 44.2526 - val_loss: 47.2955 - val_mae: 47.7926\n",
            "Epoch 179/200\n",
            "79/79 [==============================] - 9s 109ms/step - loss: 44.1416 - mae: 44.6387 - val_loss: 48.1977 - val_mae: 48.6950\n",
            "Epoch 180/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 43.3781 - mae: 43.8749 - val_loss: 47.5326 - val_mae: 48.0295\n",
            "Epoch 181/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.1048 - mae: 44.6016 - val_loss: 47.2802 - val_mae: 47.7774\n",
            "Epoch 182/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 42.7915 - mae: 43.2888 - val_loss: 47.2647 - val_mae: 47.7620\n",
            "Epoch 183/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 43.4228 - mae: 43.9201 - val_loss: 46.6779 - val_mae: 47.1749\n",
            "Epoch 184/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 43.9520 - mae: 44.4491 - val_loss: 47.2307 - val_mae: 47.7277\n",
            "Epoch 185/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 42.6858 - mae: 43.1832 - val_loss: 47.1329 - val_mae: 47.6298\n",
            "Epoch 186/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 43.4366 - mae: 43.9337 - val_loss: 47.7497 - val_mae: 48.2469\n",
            "Epoch 187/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 43.7655 - mae: 44.2627 - val_loss: 47.2398 - val_mae: 47.7367\n",
            "Epoch 188/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 43.7693 - mae: 44.2659 - val_loss: 47.8496 - val_mae: 48.3469\n",
            "Epoch 189/200\n",
            "79/79 [==============================] - 7s 82ms/step - loss: 44.3616 - mae: 44.8582 - val_loss: 47.4352 - val_mae: 47.9325\n",
            "Epoch 190/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 43.5420 - mae: 44.0390 - val_loss: 47.8201 - val_mae: 48.3174\n",
            "Epoch 191/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 42.9081 - mae: 43.4053 - val_loss: 47.3580 - val_mae: 47.8551\n",
            "Epoch 192/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 43.5088 - mae: 44.0058 - val_loss: 48.0835 - val_mae: 48.5809\n",
            "Epoch 193/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 42.8794 - mae: 43.3766 - val_loss: 47.2421 - val_mae: 47.7395\n",
            "Epoch 194/200\n",
            "79/79 [==============================] - 6s 80ms/step - loss: 42.6665 - mae: 43.1636 - val_loss: 47.9845 - val_mae: 48.4820\n",
            "Epoch 195/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 44.1659 - mae: 44.6630 - val_loss: 47.3131 - val_mae: 47.8105\n",
            "Epoch 196/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 42.9502 - mae: 43.4469 - val_loss: 46.7281 - val_mae: 47.2253\n",
            "Epoch 197/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 43.6743 - mae: 44.1716 - val_loss: 47.0857 - val_mae: 47.5829\n",
            "Epoch 198/200\n",
            "79/79 [==============================] - 7s 81ms/step - loss: 44.1287 - mae: 44.6257 - val_loss: 47.0026 - val_mae: 47.4995\n",
            "Epoch 199/200\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 43.3745 - mae: 43.8714 - val_loss: 47.9267 - val_mae: 48.4244\n",
            "Epoch 200/200\n",
            "79/79 [==============================] - 6s 78ms/step - loss: 44.5657 - mae: 45.0630 - val_loss: 47.1561 - val_mae: 47.6531\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "3eHRH2N1HhTw",
        "outputId": "7fd5ec17-3b7b-4d99-f642-debfee4d0cb4"
      },
      "source": [
        "plt.plot(list(range(200)), history.history[\"loss\"], label = \"training_loss\")\r\n",
        "plt.plot(list(range(200)), history.history[\"val_loss\"], label = \"testing_loss\")\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qEV+ABvs8/LEREukkMGr5qjFfu2b5CvEVCAQCwexRVeJbb5uHjIwvY/3mup1Vy1c/43eu8OM9/81v2x+f7WUIBAKBoARUl/jaPQAMRJQuVhE14WqOW76JdJI3vXt5y/v2bC9FIBAIBCWgusTXNg8YbSE5avmqpUZqzHduWb4jsRHlazw4yysRCAQCQSmoLvG1Z8Q3kut2zli+5rlp+frjivhGU1HiqcQsr0YgEAgE06WqxLchI74DquWbUCzcUbfz3LR8hzOWL5BVSiUQCASCuUlViW+NxY3JYGJQjfkmc5tsKF/Dc83y1YnviBBfgUAgmPNUlfgaJAPzbHVZMV+DZMBqtAI6yzcxl8VXxH0FAoFgrlNV4gtK0lUoESaajBJORLCbbEiSBGR3uJpLDAvLVyAQCKqK6hNfNekq6iOcjGiCC2A2mDFKxooU35e6X6Mv1J/3OTXhCkTMVyAQCKqBqhPfhky50au9rzMSD9DsaNKekyQJu8lWcdnOQ+FhfrH/1zxx/Lm8z/uzEq6E21kgEAjmOlUnvqrl+2znCwC8e8mWrOcrUXyD8RAA0VQs7/P+mJ9aizKUWbidBQKBYO5TdeKrWr5pOc3qeatYUdeW9bzdZK+49pKxVByARJ4a3mgySjQVo9U1H4NkEAlXAoFAUAVUnfiqli/AFW3vHvO8w2QnkU6QzAxfqASiScXiTaTHiq8/Y+l6rLW4zE5h+QoEAkEVMOE837mGw2SnrWYxjY4G2mqXjHle32jj+RMv0hf28onTPlbuZWYRy4hvvu5Vary31lpDjcWNNzJQ1rUJBAKBoPRUnfhKksQXzroOWZbzPq8frvBKzy58sWG2pRJYjOZyLjOLcS1fnfi6LS5OBLuJpeJYjZayrlEgEAgEpaPq3M4qam1vLnazYvkOx0bwxYYz3/vLtq58RJNKzDeeR3zVtdVZa6nJJF0F4gFe6dnJjp5d5VukQCAQCEpG1Vm+E2E3Kpbv8ZFO7TF/zE+To2G2ljRq+eZzO2dqfGstNZr4DkSG+OWBhzEbzGxuObPgRkMgEAgElUnVWr6FUC3fYzrx9c265ZuJ+RbhdgbY2bebZDpJJBkRCVgCgUAwBznpxFfteHVspEN7TN/EYjaITRDzlZBwW1ya5bur/03t+Z5QX3kWKRAIBIKScfK5nU1qzHfU2p39mK8ivsl0krScRkLi5/t/TSKVoC/spcbixiAZNMs3nqkLBugN9XPqvJWzsm6BQCAQTI2TUHxHez1LSMjIWYMLZoNYclRMk+kkKTnFKz07tceWuBcBaJYvQIujid5wPz1hYfkKBALBXOOkdTsDzHc2Y5SMFWP5glLrq3a8Wla7hC0Lz2dr26VAtvhesugCJCR6hdtZIBAI5hwTWr47duzghhtuYOVKxbW5atUqPvnJT3LTTTeRSqVobGzkO9/5DhbL3Kg7Vd3OAC3OJqKpWAWI72i7y0Q6ocV+WxzNfHjVB7TnHGY7BsmALMusbzyNJzu201tgEpJAIBAIKpei3M6bN2/m+9//vvbzl770JT760Y+ydetWvvvd7/Kb3/yGj370ozO2yFKiF99mRyPDMT/HRjpJy2kM0uw4AmK6GG48ndBiurmNNAySgVV1y7GbbLgsTuY7m9gz8A6BeFCLBwsEAoGg8pmS2uzYsYNLL1VcoRdffDEvv/xySRc1k1iNViSUutgmRyO11lrScnpWR/VFE6Nu54TO7Zyvi9X1Gz7FJ9dtAxTLGBDWr0AgEMwxirJ829vbueaaa/D7/Vx33XVEIhHNzVxfX4/X6x339z0eByaTcfqrzaGx0T3xi/LgtDgIxkOsXrCUgaSXN/oBe4LG+qkdb7roRwk6a8xICWXog6fGPe41rgou4akOCBr8U34vZoJKWst0EddSmYhrqUzEtRTPhOK7dOlSrrvuOrZu3UpnZydXX301qVRKe75QD2U9Pl94eqvMQ2OjG693ag0mbAYrQUKYYw4s6UzTjb4eatP1pVxi0egTrvoH/YQz84aTUca9Rme6BoBDfR1sqFVet9u7l4HIIJctvmgGV1yY6XwulYa4lspEXEtlIq4l/3EKMaHbubm5mcsvvxxJkli8eDENDQ34/X6iUSVJqK+vj6ampmkvspwsr2tjVd1ybCYbddZagFktN9KXGiXSCWIZS9gywfCEZofyvusznn/b/ji/bX8874QkgUAgEFQGE4rvY489xr333guA1+tlcHCQD33oQzzxxBMAPPnkk1xwwQUzu8oSc/Wa/8UNZ34GQCe+s5PxLMtydqlRVsLV+JOWbCYr82weuoI9yLJMIB5kIDIIINpOCgQCQQUzodv5kksu4Qtf+ALPPPMMiUSC2267jdWrV3PzzTfzwAMP0Nrayl//9V+XY60zwmyLr9rVSiU74co64e8vq13Czr7d9IT68GaEF5S2lA32eaVfsEAgEAimzYTi63K5uOeee8Y8ft99983IgspNrVWJm86W2zmWjmf9HE+Piu9EbmeAUz0r2dm3m/2+Q4zERq1ddRqSQCAQCCqPk669ZC5mgwmX2Yl/lixfNd5rNVqIpeIkxqnzzYfa1/nA0KGsrOnZHhYhEAgEgsKc9OILivXrjQwiy3LZZ+PGM5avy+wklopn2ksqIlqM+HpsdTQ7Gjk4fARZljFIBtJyWoivQCAQVDAnXW/nfHistcRTcSK6No+l5vhIJ8l0cszjqtC6zEqHKiXbWclULsbtDIr1G89Yzad4VgCzm70tEAgEgvER4gvU2eqAmUu6Ouo/zv/beTcvdb865jnVxeyyOAEl4Uq1hotJuAI4xTM6UnBD4zpAxHwFAoGgkhHiC3isivj6YsMzcvyeTPvHgcjQmOfU5CqXOSO+6QSxZHF1viqrPMu0vtQrPctwmh3C7SwQCAQVjBBfFLczgC86M+I7nBH1UGJspy9VaFXLN56xfA2SAZNUXEtOu8nO2vpTaHY00WhvoNZSI8RXIJjjJFKJojoICuYmIuEK8Ngy4jtDbmc1/hpK5hHfdB7LNxXHarRMKvnrU6ddTRolYazWWkN3qFc7jkAgmFskUgm+8tI/cV7rZj6wfOtsL0cwAwjLF6hT3c4zZvkqop7X8tXczkrClVrnazFMTjSNBiNmg7KXUmuXhfUrEMxNQskwwUSIzkDXbC9FMEMI8WXU7TxTCVfqccN5xFdNuHLrE65ScaymqVusdZbC4ptKpwgnIlM+tkAgmHmSaWV4jfhfrV6E+AJmoxmX2TljCVfD0Yzlm8/tnBFfh8mBhJSxfGNYJ2n56tEs3zwZz3849jRffulbszq/WCAQjE8qU5YYznPPEFQHQnwzeGx1+KL+kic4xFMJTXRDifCY4+sbapgNJhKZRhuWIsuM8lEzjtv5Te9e4qk4XcGeKR9fIBDMLElZWL7VjhDfDHXWWhLpRF7rdDroXdlpOa2JrYp+iILZaCacjCAjTytRqraA2zkYD9GTGT84mKfsSSCoBHzRYe556z76wt7ZXsqskdQs30jW4BVB9SDEN4NW6xstbdw3N46cm3SlH6JgMVgIJELaz1OlroDbud1/VPt+ICrEV1CZHBo+wp6Bd9g3eGC2lzJrpDKWr4xMdAY77wlmDyG+GdRyo+FJxH0P+Y5w+45/GTdLWhVfi0GZzZtrWeuHKJiNpkkNVShEjcUNKJbvgaF2nu34M7Is0z58RHvNgG784FwlmozRHeyd7WUISoyabJTrJTqZ0LeiDQnXc1UixDfDVCzfPQP76A31cUgnarmoyVatrvnA2BiOeoOxGM2YMwIN0xNfo8GI2+yiI3CCH7z5Ex5q/z27vXtpHz6KSTJiMpi0bls9oT4ePvT7Obm7fujQY/zza98TyWNVRjKt9DZXvUInI+oGBETSVbUixDdDndrlahKW72DUp3yN+Aq+ZjiuiO8CVwsAoYxbWSWeaYRhkAyadQzF93UuRK21hlgqjslgwiAZeKT9cU4EullSs5h62zwt5vtsxws80/lnHjn8x2mdr9yk0ine9L5NSk4xEg9M/AuCOYNq9UWTJ6/lq7qdIX9/AMHcR4hvBo+tsOXbPnyUn73z4JipREOZuOlQdBzxzbF8c11IsVQcm0kRWrPO2rUYzUyHVlcLFoOZa0//G86bv4mB6BAyMis9y2iwzyOUDBNORDge6ATgha6XOeQrbMFXGkdHOjQX/sl8k65GEpn/s5PZ7ZzQ3Wvy9QcQzH2E+Gaos9YgIeWN+b7U/Sqv9OzkqL8j63HV8h1PfH0xPyaDiWZ7I5A/4UptqGExjHb7nE7CFcD/OfV/cvv5t7LSs5ytbZdpLu0VdW002OcBisu5J9RHnbUWCYlf7P81h3xHSOlcXpXK3oF3tO+jqbnnMhcURrN8T2Lx1f8PhpIi5luNCPHNYDKYcFtceZOn1JKd3nCf9lg0GdWEdFzLN+anzlKD0+wAxsZvFMvXBpAT852e29lkMGn9ouustXxg+VaW1S5heW0bDTZFfHd795CW02xoXMcliy/AGxnkrjfu4csvfov+8MC0zj/TvD24X/t+pixfWZb549FnRE10mVFrXGMnsUcjqXM7i1rf6kSIrw6PtY7hmH9MXd1wpmSnNzMaEGBIJ9JDseG8tXipdIpAPEidrVYT37GWb0yzfM3G0iRc5ePiRe/iHzb+HRajmQZ7PQCv978FwJKaRXxw+RVcu/5vWVt/KoFEkGMjHeMdblYZjAzRHerVxijOlOXbHejj90ef4OmO52fk+IL8JLSEq5NYfPVuZ5FwVZUI8dVRZ60hKafGCKQ/Uy6kF99BXZ1sMp3Mm3Hrj48gI1NnrcWRR3yT6SRpOa3FfC0lynaeCFV81TKoJTULkSSJtfWncn7r2QAVncS0N2P1nuJZAcychRRJKKIuBlSUFzXTV7idFUTCVXUixFeH2pZRL6SxVJxIpgynN6wXX8XVrGZJ57qew4kwb3n3AYpFbTNaMUiGrF2s1t1KS7gaFd/pxnzHoz4T8wVlFnCjvUH7ucaiTFeq5PKdg77DAJzZtB6YuZu0+vmMJ75H/MfoP4k7Mc0EquV7MifSJWVh+VY7Yp6vDlV4RuIBWlFKg/Q33uGYn0gyit1k08R2Zd0yXut7g8GoD5fZxf0HHqIv1IdfZzm2OJuQJAmnyZG1i1Xdalq2c5ksX6vRgtviIhAPssS9MGtusDvToKOSxXcwOoTZYKbV1QzM3E1atajzDagApV3o3bt/wtKaxdyw4dMzsoaTEdXlGhd1voBoslGtCPHVoQqP3uWaa/X0hvppq13MUCRbfIeiPrqCPRz0teOx1nGqZyXLapewyrOcFXXLAHCYHVl1vicC3ZnzKolRpazznYgGW70ivjWLsh7Xb0AqlaGoj3k2Dzajkqg2U5ZvNKnc/CPJqJKVnrMhiiajxFNxhkSf7JIi3M6jU41AlBpVK0WJbzQa5corr+Taa69l/fr1fO1rX0OSJJYuXcptt92GyVQdGl6TT3wzVs8C13y6gj30hhXxHYz6MBtMLK1dDChu6OMjnRglI1895wt5LVen2YE3MqBNNvrjsWcAuHTZ+ZDIcTtPY6RgMTTY53F05PgY8bUYLdiM1ooV32gyRigRZrF7oeYxmKnuXPpYsj82QpOjIev5cKYExB8PIMtylgdBMHXUDlcpOUUincRsqI77y2TQZzuXetiLoDIoKub7ox/9iNpaJbZ555138ulPf5qf//znzJ8/nz/+cW51RhoPTXxjYy1fNbmnNzMVSLW+5tk8AHSMnKAz0MXy2qUFXcZOs520nCaaivL24H46AifY0LiOxXULgBzL1zSz4rupZQOnelayyrN8zHOqS7oSUd39iuWriO9MZcXq2xvmi/uqJSCJdELUGpcQvcv1ZC03Ul3vRslIOBEp+ahTwewzofgePnyY9vZ2tmzZAsDx48c5/fTTAbjgggt48cUXZ3SB5SSv5TtGfPuJJmMEEyHm2TzYTTbsJjsdgRMArK5fVfD4DtNoxvMfjj4NwNa2y7Tn9THfmbZ819afyvUbPoU9U2Osx21xE0yEKnKUmSq+9TYPFqMFCWkG3c46yzdP3Desa36g37AJpoe+u9PJ6npW20vWWNyk5NRJ3ee6WplQfO+44w5uueUW7edVq1bx/PNK3eMLL2uJDW4AACAASURBVLzAwEBlN2OYDPninXq3s8vspDfcn2V9gSIEKqvnFRZftdZ3Z9+bHA90ckbjOhZk2k5Cbrbz9NpLTocai4u0nK7IEgf9e2+QDFiNlhlMuJrA8tWJr79C3fRzEX2N68la66u+B6pBEBFdrqqOcYMpjzzyCGeccQaLFo3GBW+++WZuu+02Hn74YTZv3lyUO8TjcWAyGae/2hwaG90lP6bTbCecCmnHDqdDSEgsW9DKokOt7Pe2EzEpN9rF9S00NrppqW3kRLCbWqubM9pWac0fcmnyeqATnu7cDsBVG95Ho8etXUtDXCl1shotNDfVlvzaiqWpZh54weRM01g3+fd4Jj4XlUi3krC2fP4CGhvc2C02EiRm5JyxrlHxjRujY85hHBn1DMjWmVlDKan09WkYRt9Xu9tIY8PYdc+ZaymCfNdiOqLcQxrcdRwPdGJxSdq9opKp9s+llIwrvtu3b6ezs5Pt27fT29uLxWKhpaWFf//3fwcUy7e/v3+8QwDg85XegmpsdOP1lt7acJnd+CIj2rEHgj5cFie+wTANlgZkDnH3Kz8FwJZy4PUGcEvKh7SqbgWDA6FCh4a4sgGJJKKc6lmJK1mH1xvQriUSVHa7ZoN5Rq6tWMxpJZZ6vK8Pe6JmUr87U5+LyokhJeZuiFrxegNYJCuhWGhGzqmPN/YOD4w5R59vtLa7a6Afr6Nyrd+Z/lxKSSShe98HfMyTs9c9l65lIgpdSyii5BDYsAPQ5fXiTM7ehrwYTobPZSrHKcS44nvXXXdp3999990sWLCAnTt3Eo/H2bJlCw8//DAf+MAHpr3ASqLG4qIv3E8qncIgGRiOj9CcaUJxyaILiCQjvDN4kJScYlHNQgCancrQhNPqTx332GrMF+DdS7aMeV6N+c50mdFE5Cu5qhQGoz6MklFzx9mMVm26VKmZ0O2cEG7nmUCfcHWyxnxH3c7K5lfU+lYfk87hv/LKK7npppu4++67Oeuss7RErGpBvakHEkGsRivxVJzaTOerJkcDf7P2o6TlNLFUXEtWOmf+JjzWOtZOIL5qzHeRe4GWwKXHoonvzCZbTUQld7kaivrw2Oo0177NZCWRTpJKpzAaShvaUG/8Rsk4ccxXJFyVjOyY78mZaKRuQNT7kaj1rT6KFt/rr79e+/43v/nNjCymEqixjpYbWYzKzVcVXxWDZMjKEjYbTJzWsHrCY7fVLmF942lctvjCvDWhasLVTLaWLIZKtXzjqQSBeJBWT4v2mFpuFE3FcBochX51SqiWb4N9Hr6Yf0wtr158AxX2Xs1l9NnOJ2upkZbtnLkfiVrf6uPkq16fAH25keoGrrVMLu5ZCKvRwqfXXV3w+cqxfCuzxaQvJ8scRvtiR5MxzbNQKmLJGBIS9fZ59IW9RFOxrE1XJOMKtBgtBVtQCiZPUpQajcl2FmMFqw8xWCEHvfiqN9Rcy3emUC3e2Y75lqvF5I/e/E9+uf+hol8/qKvxVRltMVn6JhexZByL0UydRUl0yXU9h5NhLAYzHmtdxXkJ5iqyLJNMJ7WwwslbapTCKBkLjiIVzH2E+OaQJb6x8oqvy+zkPUsu5qKF55XlfIWwGC1YjZZJW76v97/FUV9nUa9Ny2neHjzAO0MHiz5+bn01jA6lmImbdDQVw2K0aJ//GPFNRHCYHdRa3IQS4SyLTTA10nIaGRlnJjmx0Od60He4qq3BlJzEaDBq74OYbFR9CLdzDvp4p4QS3yuX+EqSxAeWby3LuSbCbXFPyprrDfVz796fw17Y2LSeD628Uhu3mI9wIoKMTCAxTmlWDoP5xDfjJYjMQGwwloxjMejEN55r+UaotdaM5gnEA1lrE0weNd7rtDgJJIJ5G6h0jfTyvTf+ncsWX8QHV1xR7iWWhWQ6hVkyaWGVkzXxrJoRlm8OquXrj42wb+gAJslIY2b4/MlEzSRbTI5khMlqtLCr/01+sudn4/5uMKFY1fFUnHgqUdQ58lm+1hm0fNVJRnV5LN+0nCaSjOIw2fO2JRVMDdV74DIXtnx7g8r85N7QxD0G5irJjOVrkoxISCf1eMVqRYhvDm6LEwmJfUMH6Q8PcGbzeuwm+2wvq+xMtsWkWof4v9e9nzMa13F0pINXe18v+Hq9SztUhPUryzJH/Mexm+x4bKMWtZbtPFOWr9GiJdzpxTeajCEj4zDbdW5pIb7TRR0i7zIrYzbzfa6+yDCgzHWuVtSYryRJmA0mEuniNqiCuYMQ3xwMkgGXxantNC9cMLvx19nCPcmMZ7UO0W118T9WXonZYOaRw38gUmDcn97dHEhMfA5vZIChqI9TPSuy2nfaTDOTcJVKp0imk9kxX53bWS0zcpgcOst39jOeg4kQP9j9Ezozs6LnGqrlazPZMEiGvJbvkCa+vqqd9pPSjVI0G83ERT5B1SHENw/qzXSxewFLc+bdniy4J5nxrNYhuixO5tk8vHfJxQTiQZ7peF57zWDEp4l0UCfqwfjElu++TGJW7uAKbaxgiS3feFrZfFmNZtwWFxJSluWrJsBkuZ0rwPJt9x3hnaGDvDaO16GSUWO+JoMJq9GaN9Y5FPEDSsiiWrOAk/Jo0xizwUyiyNCMYO4gxDcP6s30wgXnnbQD0usyrlZfdLio16uZpy6LEqu7YOG5AHQFewHFkrxj5/e4P1NapLd8g0W4nfdnxPfUXPHNxHwjJbZ81Zu+xWDBIBmosbizxTdzvXa927mIjcru/j08eey5kq5Vj2qR94W9M3aOmUS1fM0GEzajNW+dry8jvlC9rudkOokpY/laDGbhdq5CRLZzHjY2rUdCYmPzGbO9lFljvkvpItUV6inq9aoF4rI6IQp2tf4243aOpWKEEmHteHprN1d8h6I+3vLuozPQhcvi5Mq293DQd5hmRyP19uxs4hmzfFOq5avUXtda3fSE+rQuV6NuZ/uk3M6PH32K7lAv57Zu0rwLpUT9HPrnqPhqlm8m0zeQx5ugxnxBcT0vqULvVCqdwiRlLF+juajQjGBuIcQ3D+e2buLc1k2zvYxZpdWZEd+M5RpKhHn08B/4q6WX5i2n0budY1EZo8GIxWDWYrFqKZAvOowsy1k3k1y38717f8GxkQ7t56P+DmKp+BirF0YbkpS6E1Is4+azaOJbQ0egi0hSqe1Vu1s5THYcJjtmg5nOQLeWIZ2PVDqlWaQdgS7W1p9S0jXDqOU7EB3Ksp7mCrmWrzc1dl74kF58I9Vn+cqynOV2tsyS2zmSjPJG/1ucM/+sgmNSBVNHvKOCvNhMVhrs9XQFu5FlmVd7X+fF7ld5/sRLeV+vxnKdZrvuGDZNFFURTqSThBLh7Jhvzq6+L9xPg20eN5/1ORa45nPYfxSANXnEV0u4miHLVxPfjBt+OON61ixfsx1Jkrhw4bkMx/w80v6Hgsf0Rga1nr2dgRMlXa+K+jmk5TQDc1CYkrqYr81oJSWnspqXJNNJRmJBrfmEWn5WTah/IyYpk3BlMJOUU0WX/ZWKl7tf5Rf7f8P+oUNlPe/JghBfQUEWuuYTSoTxx0doHz4CwIEC/4ihRBi7yZY1WchmsmqiqBfHoZiPQCKk7aaDuqSZSDJCJBml2dnE4pqF/N36T1Bv82A1WlhRt2zMeVUrs9R1vnFdzBcYk/Gsz3YGeF/be2lxNvPnrpcK3qx6Qn3a9x0j0xNfWZY1N7iekG7Yw1x0PWcnXCnvvd6roSYAttUuBkYbr1QT6kQjk2HU7QwUXQ9fKtS8jErr8V4tCPEVFGSBaz4AJwLdtA8r1ueJYE/e7ORwMpI1rxiUeKwa89WXAvmifoLxIA22eUhIWVbwUCbBS3Vt11pruHnTDdyy6UYtuUqPQTJgNVq085QKfbazug4YrfVVLUxHxtI3G83839X/C4Nk4OH23+c9Zq9efANd01rfjt5dfHPHv4xpzxnRtVyci0lXerez1kBFt3FT3/9mZxMOk70q3c6q5Ws0jFq+QNmTrtT/qVInMwoUhPgKCrLA1QrArv43CWYsVRmZg8OHx7w2lAhnuZxBGXoQTydIpVNZ4jgYHSKUCOO2uHGaHVkJV2p29TxrnfaY0+ygydFQcJ2FsmKnQ6yA29mf63bWNWBZXLOQ5bVL6Q725t0MqJZvq7MFX2x4WhbFzr7dAHQFsxPi9KPn5qLlm+t2BugO9fLtV+9i/9Ahze1fZ62l3uapylpf7T2QRkuNoPziq9bol3pjK1AQ4isoiGr5vt73JgDntGwEGONWjacSJNIJnJmuRCpqPDaWime5nU8EupGRcVucuMzOLEt6tIVkHcViNZVefMfEfDN9qjW3c2Ks+AIsqVmEjJzXsu0J9WE1WljfeBoAHVOM+0aSUQ76lA3QUE4pWDgRwWVWurTNZctXrfMFeOLYc5wIdvNyz2ujw04sNdTb55FIJ4oqVZtLjLqdM6VGRuVruZOuVPEt1ChHMD2E+AoKUm/zYDPaSGbcYBcvugC7yTYm7qtvOKFnNBM5muW66swIk8viwmVxEk5GSGVuOKqYeCYxoMBmtBWVcBWIB3nw4KNFNWbILTXK7e8cTkYwG0xaPE5FLXs5PpI93SmVTtEf9tLiaGZJzUIAOka66A728qb37QnXo+edoYOaa9IXy455hpNhaixubQbxXCMhjxXfoyPHAWgfPspwTKnxrbXWaKGJaqv1TWXeA2OO5VvuLldCfGcWIb6CgkiSxIJMva/b7GK+s5lVdcsZiA5lZdKqVmDuMHu7btC9XhxV96vb7MRldiEja27cqVi+NqOVRMa9PR4vd7/G8yde5LXeNyY8Zm6pkdPswCAZssQ3d7MBsMSdX3wHIoMk5RTznc0sci8A4LW+N/jOzrv58Z7/GuM+Ho+3vPu07/WWrzbswWyn2dFIMBHSYtNzhaxSI12MX0JiOObniP8YoLqd5wHVV26Ua/nOWsxXKxOs3tGNs4kQX8G4qK7nFXVtSJLEKfNWAnDQ1669JqQlH+UkXOn6LusTrmSUGJ3LrFi+MJpR6YsNY5AMkxrjOOreHt/6PR5QBLE3PPE0HDXhSs12NkgGai01Wswxkohgz7leUDYNLrOTYzniq244WpxN1FlrqbG46Qv3E8/cUNUY7kSk0ineHnyHOmstzY6mrA5k6gbGaXLQ7GgE5l7SVVLfZMM4Kr7qjOvDGfGttbi1hivVlvGc1Kz/TJ1vxrtSbrdzVFi+M4oQX8G4LHYrLtJVnuUAWq9rvaWmup2dptyEq7GWb22mGxQoE6TcmTixGrcbig7jsdZOqqhfvUlPlMCkCqI+67gQuQlXoLg6R+IB0nK6oOUrSRJLaxbhiw1n9cXuyYy/m+9sBpT302Iw85l1/xeb0crOvt1FJQ4d9h8jnIxwesMa6m0ewsmIdpPUt7xUE9RmQnyfOr69YEb3dMlKuMpYvqd4VnDu/NGmNy6LE7PRrFm+k/EazAVUy3es27m8YwVFwtXMIsRXMC6bW87k42uu4vzWswFoymNRFbJ81VKRaCqmJUTNz3TOAsXyderEN5VO4Y+N4JmEyxkUEQf45qvf5d/evBdveHDMa/yxgBYvLEaQRmO+ozHdWmsNKTnFQd9hZGTqC8Sl88V9e0JKpzBVfD+2+iPcfv6tnN64lvWNpzEU9WmxzfE4lEm0WlN/ivY++TLXNboJctDiaM6sobTNPFLpFH869gzPdrwwIwPe9XW+bTWLWehq5fK2d9PqatFGe3rsSvJbi7OJZkcjb/TvKboH+VwgpXsPYLTON1HGmK8sy9r/7HQs3/bho1WXEFcq5rT4HukeocsrCsBnEqPByKaWDVrzDLvJRo3FnVXGEtK6W+XEfHX9ndXd83xXs/a82+IatXzjIXwxPzJy3vaV4/HeJZdwRdu7Weiaz77BA9yx83vsGdiX9ZqOwKgQjsQDmpVYiNxsZxgtN9p+4i8AnN64Nu/vjoqvInzDMT/vDB3EbrJpgmk2mLSZtZuaNwAUFYvuyoj4IvcCLS6uxn21DGyznWW1S3CaHbzhfauknZG6gj1EU8os42I8CJNFH/P12Or40uYbWVHXhkEysLx2KQDz7Mp1GyQDly3eQkpO8WznCyVfy2yhJjiadO0lobxu51gqrv3dTFV8fdFh7nr9Hv5w9KlSLq1qmLPie6I/wLd+tpOv3vsqX/6PV3j7WHUlXVQyzY5GhqLD2s0gt9uTik1LuIoSTcYwSkaa7KP1ui6LE1dmuEAwEcSnJltZJ2f5uixOLm97NzdvuoGPrf4IyXSSf3/rv7IsXNUKbclYnhPFfUebbGS7nQH2DuzHbDCztv7UvL+rJl0dGj5MMp3kZ/seJJyM8P5lW/O601d5luM2u3i9/60Jk8a6gz04TQ5qLTV4rKr4Ku+b1vjD5MBoMLKhcR2BeFArSyoFhzKdzpS19JbsuCqJHKtPz0qP0uFMtXwBNrVsoM5ay4vdO6pmvGAy1/LV3M7lE199jsZUm2wMZzbTc7HNaTmYs+L71I4OZBmWt9bQNxTh/qcOTvxLgpLQ5GhARsYbUdy7hSxfW8byjaRiRFNRbCZrlkvZaXJo1l8wERrT3WoqnDv/LC5f+m5kZPpCowKrWqFnN58JQG+on85AF//w/NfYO/DOmONo2c6GseIrI7Om/pSCAxRcFieLXK20Dx/l1he/yX7fIdbWn8oFC87J+3qjwcj6xrUEEyFOBLsLXlssFWcgMkSrqwVJkjTLV3W56vtNA5yVmcq1K08yVyKV4MGDj7Jv8ID22Eg8MGHJVpb4hkovvrnCo2fNvFMwSAaW1i3UHjMbTFy86F3EUnFe6Hql5OuZDVK5MV9j+bOd9XHeeCo+4aYwH+rfY6DImeAnG0WLbzQa5bLLLuPhhx/mtdde46qrrmLbtm185jOfwe/3T3yAEpJMpXl2ZydOm4mbPnomZ6xsoGcwTPeAiC2Ug9y4b7iQ+OraA0aTMWxGmyasTrNinanZzsF4SFfjOznLNxd1VJ+6KZBlmeOBTupt81he1wZAb7iPl7pfJZqK8nr/W2OOEU/FMRlMWb2q1RnHABsa1427hmvW/w0XLTyPWCqO2+zi/5z64XFnQ7fVLgHgqG6aUy69oT5kZFoz5V9qLbT6voUSo9nOAMvr2qi11PCGdy/JVHa88NeHHuX5Ey9q7tpkOsm3dnyX+/f/puD503Kaw8NHtfd3Jixfvds5l1ZXC98878v81YotWY+f03IWAMeKiJnPBXLdzuZZcDvnupqn0sRG/f8bKUNv6O5gL38+8fKMn6eUFC2+P/rRj6itVdw93/72t/nWt77Fz372MzZs2MADDzwwYwvMx5vtgwwHY5y7tgWzycDGUxQx2HVg4hISwfTJLWMJ5Wm1CLps50ypkc1kxZPpFOUyKzdwZ5blq9b4Tt3yhdHEL7XV4mDURygRZknNQlqcTYBS+vOGdw+A1rdaTzwVx2rKtmxVy9ckGTmtYfW4a6iz1vKRVX/Nt877Ml8++/PUWt3jvn5pjTIoILc+WI863lEd9+ix1iIhaY02tGYnGcvXIBnY2LyeSDLC7t7RGPiL3Tt4sftVAPrDysi+gcgQwUQoa/hDLj2hPsLJCGvmnYLHWld2yxeUucoGQ/Zty2l2YDaY8OeZ/TsX0ZdbAVgy70U5Ld9c8Z1Kra9q+aoVAjPJE8ef5YGDv51TNd9Fie/hw4dpb29ny5YtAHg8HoaHld223+/H45nezXKyvPCW4pq7YL3Se3j98nqMBoldB+dWTeNcRRXffp3lazNas6xEGK2/jWRivjajDbvJzgLXfJZlLD1lbquNE8FurWf0dC1f1QJXE5BUQVtSswin2YHb7GL/0CGtNGkwOqRlQqvkm8vrsdVhMZg5rWEN9sy1TYTL4tQsxfFocjRgN9my5hjn0h1SSmpaM7XXRoORWmvNqNs5MTb2rrqeX+rYCSgu6gcPPqrVAg9FfSTSSW0jNTKOi/CQT3E5r6hbxgJXCyPxQN4hG9NB3+GqWCRJosZSozVAmetobmetzlf5OyznVCNVfFXX91SSrtQhH2pp3kwyktl4BRJzJwG3KPG94447uOWWW7Sfb731Vv7u7/6O9773vezatYsPfvCDM7bAXKLxJHuODLJiUR2LmpSbmsNmZs3SeXT0BekfFt1YZpp62zwMkkET31AiPKbMCEbrb/2xADIydpMVSZK4dfPf87HVH9Zed2bT6YQSYQYig7gtroKx1GJx5li+A5nYdItDsXqbnY3aTvxUj9I05HCO9ZvP8rWbbNyy+UY+tvp/Tmt9+TBIBpa4F9EfHsjqSpWW09omoVuzfEczxj3WOnwxf9YNzqEbcLHYvZAGez2vdb9FPBXntd43SKaTvG/5e1leuzSTEDOofZZqyVc+1LGSK+uWaSVjxVi/B32Hue/t+4sqlVHd4+pQgWKptdYQSATLPvO2EKFEmEcP/3FKNbKjbufZ63Clrrsu46maivjqh3yMzLBXQi1nUl3d8VRiXC9OJTDh9vKRRx7hjDPOYNGiRdpjt99+Oz/4wQ/YuHEjd9xxB/fffz9XX311wWN4PA5Mpsn9MxUilZb5yKWrOGtNM42No668izYuYs+RQQ52jbB2ZVNJzlVO9NcyF2hxNdIfHaChwUUkFWG+q0m7BvWrLLuUkYFJ5R+v1unKe503Nv4NV4c/yM7uN2nRHWeqmF3KDThpiNPY6CZ1QrlpLW5upnGem6X1C2kfPorb4uSqDe/j689+l65YF5fVn8f2oy+xsfV04nICm9E6Zi2NzNzntGb+cvb7DuGTBlnSqAjsQ2//gV+//Ti3XngdveE+mpz1LJrfqP3O/NoGjo4cx+ySSUgxJCQWtzRmuWYvbNvEw/v+xPH4UXZ638BsMPHeNe/iqcMv8FLPa8TMIUbSo3WylhqYZx97nSfC3dRa3axevIRBuZ+nOrYTkIYn/Lx+0b6LnX27+eC697Cyvm3c10pGGYNkoKV5fO9H7jmb3PM44j+G1Q11edZeKl7v3suzR1/kc+f8rdZ5Kh8v7PsLTx5/jlUtS7hw/tnjHjP3WmxDyr2yvk75f0nZFeEzWMp3nzBmSuWb3fUMRoewOKWizq1/TfrI6GbL6EjN6NpDSUV8jTaZxkY3D+/7Iw/s/R3f23obLe6p6cFMv9cTiu/27dvp7Oxk+/bt9Pb2YrFYGBkZYeNGZcLNeeedx+9+97txj+HzlbYE4L1nLaSx0Y3XO7qbWjFfsYJf3dvDu9Y2F/rViiT3WuYC9dZ6ugN9tHd1EU3GsEhWvN7AmGuxmawMhpWYpJQ0jnOdRjbUKpnI030vkmlFfH3BEbzeAP1+JQ6UCEp4UwHqDEqYZF39WurSDZgNJvb2HuTnOx/lD0efYmPT28STiuVbzs+lyaRYk2927meBaRHRZIzf7X+atJzm+y/fx0g8wDr3mqw1OSTl7/5Q9wn84SA2k43BwWxX8GrXGuBP3L/7UfojA5zZdDphfwqnrMSwD/V0cHxoNMv6aE8PKXf2ZjmVTjEY9rG0ZhEDA0FcacUiOtB7jI11479HJ3yKu/x4Xx916cKjIQEi8Rgmaby/k/z/LzYUa/9wTzeL3TNXxPHYvqfZN3iAXUfeYUVd4Y3Enm6l+qJ3aAivc3LX4h9R7pfBQAKvN0AgpiQ7BULhsv09DviVzZjLqAhQ36APr2X8c+dey1BwNAzQ0d9Ps2HBDKxU8Q6NxBTvUM/QIF5ngCPeE8iyzP6u4xjrx3ai0//uQd9hVnmWZ5UBluqePJ6AT/hXetddd/HQQw/x4IMP8uEPf5hrr72W5uZm2tuV3r579uxhyZIl017kdKlxWLCajYyEy9uC7WRFjfse9SsxynxuZyBrKpKtyDjpdFEm4lg0963qklI7Ya1vXMspnhVcsvgCTAYTS2sW0x3s5U/HngHg9f63kJG1Dl3lQm3OobbBfKVnJ+FkhHqbR4vFLtB1CIPR+PhgRJmRnNviE5Qs4UW1rfRHlOSqszOjIdWaa29kIKsmOp+L0B8fIS2ntWS4ZmcTBsmgde4qhCzLWlLXePFklUQ6qblZJ4PaAGUm3ZuyLNORKVnTl7HlkpbTHPUrmddTSVTS4t5STpONMna4Ut3Mas39VNzO+vBJMZ/9VAknIlq/eNXtrOYiTPT3sHfgHe7e/R/a2NRyMqUt4je+8Q2+8pWvsG3bNvbt28e2bdtKva4p4bKbCEXKO3brZEUV3yePPweM7eusohcwte63HDhMDoKZf8RAPIjFaNESV+bZPHxuw6e1Vo/L69qQkUnLaTY2rdf+kXNjvjNNjcXNPJuHYyMdpNJK1yazwcTfn/lZLV6tlhmpqA099g0dUPpNm/N/DucvVspx3BYXq+etAqDRXo+ExLGRzqwWgPlKQ3JrsM0GE032BrqCPVp2bj4CiaBWplLMDTiZTmolNpOhNmfk40wwFB3W3qfxmrT0hb1a/H0qopWqgKlG6rrVzd2UxFe38Zio7/p00CdZqRn/wcxjE51X/bsemIXhHMWnFALXX3+99v2vfvWrki9mujhtZpFwVSZUK00dCK8OYMjFrptMYyujJek0O/BmLL1gIoTbXDjjePW8Vfzp2DOc37qZ/7Hy/ewbOkgkGdFKpcpJW81idvW/yU0vfINoKsq7FpyDx1bHJ077GH/uepnTGtZkvX5pzSLm2Tzs9u4lkU6M6TKmcsGSzfx235+4cMG5What2WjGY6vTBhM0O5roC/fnbYqQb9Tj6vpVPNf5F94e3M/6xtPynle1eqFY8U1hmorlq4pvvLTie2ConUPDR7i87TLtbx3GF1917CFMTbRypxqZDCYkpLJmO6sbJrWWPJKaQqlRIoxRMpKSU9O2fF/oeoXByBB/veLyMc8FdQIb0jbcGct3gvOqYh2ahf7TkxLfSsdpNxPtD5JMpTEZ52zzrjnBAtd8bjvnZlJySinfKVBOo3c1l8vtDIr4ngjGSaaTBOJBFrpbC752RV0bt2y6gQWu+RgkBkvZZwAAIABJREFUA+fN38QznX8uu+ULcHnbZRgkQ6aTlMyliy4EFIv3f58ytqpAkiQ2Nq3nqY7tAAUt30ZnPXe86+tjSnia7A2asK6sa6Mv3J/3hpWvBvuclrN4rvMv7OjZVZT4BopwCSfSCa32ezLUZKZlldryffzoUxz2H+XUeSuzarD7QoXLGo/4R5t9TMXtnDvVSJIkTAYTiTJONdIs30y281SytsPJCI2OBnpDfdMW3+2df6E33M/WtsuwGi3s6NlF+/ARPnrq/ySgE85QIowsy5rlO7H4Kp/PbAx/qC7xtSmXE4omqXWW/8Z5stHoqJ/wNfqZrPYyWpJqDHowMkRKTo1r+QLagHuAixaez+v9b02YmTsTtDib+fjaq5BlGRm5qNGKG5v14pvf8oXRNoV6mhwN7PcdApQSor907yhafBe6W1ngms+ewXcIxIN5N2D6ARzFW76TdzvXaZZv6WKLaTlNZ7ALgNf63sCb2UgsdLXSFewhnkrkzXg+6j+O1WghlopP0e08ttbZYjQTL2PMN5qMYDGYtY1QeJLXEU8lSKST1FlqGI4OT1t8VY+GNzzAQncr20/8hY5AF5e3vTvLtRxKhIml4lp8fCK3s1obX+p69WKoKvPQZVf+EUKR8g6dFhRG72ouq+WbiUH3Zm7+xTS6UKm3e/jm+beype3cGVlbMUiSVPRM44WuVi15Kt+M4fFodIxmHy+rW4qElPeGVajv9jktG0nLaXbm6R8NaK5/s8FcpPgmppRwZTfZMRlMJbV8+8JebbrVG31v0RE4QZOjgaW1i5GRszYWKsF4iL6wl2W1S7EYzFOzfHPaS4Ly/pW7vaTdZNOayUzW8tV3W3NbXJMW32MjHVrSWly3iemPDJCW09r/9UBkaIzbOTt/oXIt36oSX2dGfINCfCsGu1Hvdi5nzFfZsas3SNcUXJlzBUmS2Ni8Hpi8+KqibTVa8FjrcJodBS1fp9kxpgHKWS0bMEgGdvTsRJblMb/XHx7AarQw39nESDyQ9zUqsiyTlKdm+UqSRK2lpqRZtWpms9PsIJQME0lGWexeqCW/5Yv7qjOZl9UuwW6yTdpihPwtNi0Gc9kTrmwmG2aDCaNknLQFPzre0kGNxU0wHppUA5T73v4l9+79BUBW29D+sBd/bETbFA1EhzS3s1EyZsR3VIyLtXxnI+ZbXeJry1i+USG+lYJttrKdzarlq9wgJ2P5zkUuXHgem5o3sKFp/IEPuahDMpodjZk2je4xAibLMkPR4bw9t2ssbtY1rKEz2M3bg/uznkvLabyRAZrsDdRYakikk1mj6nLRLD5patGwWqu7YB/hV3p2sn/o0KSOdzyTYHX50ndrjy2pWaSJb75yo3eGlPreFXVt2E32KcVKc6cagRIyyBXfo/4OLVmu1ERTMWwmG5IkYTfZJi++un7vbosbGblo6zItpxmK+vDFlLGl+iS6/vAAvbr3fTAyqFm+jY4GoqlolliHkuFxs/FVz0RAiO/0cNozMV9RblQxWGcr2zmT9asmxlSz5QuKCH587VU02CeOw+tpsM9jTf0pbM7U/tZY3ESS0SwXZzARIpFOFBx4cWXbe5CQ+G3741mtKYdjfhLpJE2ORi0haiQW4Ij/GHe/8R9j4mzJjLiYjVMUX0tNph1n9nHf8r7Nz955kF8ffHRSx+sYOaEk4LVu1t7Xxe7R4Rx9OW5nWZZ50/s2DpOd5bWK+IaTkXGt/XwU43YejAxx1xv3cPfu/yi5OzqRTpJMJzWvld1kIzpJ97l+0pn62RdbbhRKhLUNlC82jF/Xd70/7M3yOAxEfAQSQSQkzYujerskpAnPq24S4ql4WbPJocrE12UTbudKIyvbuYwJV2p/576TxPKdKgbJwN+t/wQXL3oXAG5VJHU3rHxlRnpaXS2c17qZ3nA/L/W8qj2uZjo3OhqosarHDfBy9072+w6xdzB7jrKa5TtVy7cmk3Q1orOURuIBfpEZkzgQHZrQ9ZlKp4gmY6TSKU4Eu2h1tmAxmrmy7T2sa1jDkppF1FlrsRgtY9zOHYETDMf8rGtYg9FgxG6ykZbTxCfpLs6dagRKXXVSTmnrf/TwH7VM/ld6d03q+BOhWutqvHe6lq9+41UM+rj9UHQ46+f+yEDW+z4YHSQQD2Viy8oGW32+3j4PmEB8dY1Ayu16rirxVWO+wu1cOagZzhKS1uSiHKhZv+pNQJ0bLBifGquySRmJj/Bvu+/lv/c9wGARox6vXPYerEYLvz/ypHazU8VXcTuPiu+JTAZx7vhE1a06mYlGetR5y/qb9f37f0MwEcJuspFMJ8dNyDriP84/7riTr7/8z+wZ2EcindTq1ze1bOCa0z+O2WBCkiRaHI30h71ZYr7buxeA0xvXAqPiNdmkq9ypRjCaqZ5IJzniP8au/jdpdbZgkow80/F8SQdKqOtV128z2YmnEwUHbkC2iOl/VsRX/ZsqUnzjevH1aRnsLrOTUCLMkeFjSCgxfmUUZhC32aXleajeLrUbXKHzJtPJrI1RuZOuqkt8daVGgspAtXytRmvR2bulwJlTcjNRqZFAQRXJvYP72Td0gB29u3gzIyrjiW+Nxc0Vbe8hmAjx3/seIJVOaSMi9W5nX8xPd2bazLEc8VUtPvMUxVe1rtWb91H/cfYMvMPKumVcsEDJXPdmJlzl8kLXK3x31w8ZzMw1vu/t+wFYUpO/eUyzo4lEOslgZLQz0lvetzEbzKzJdBCzZ/IOJms1JuUUBsmQ9f+itpiMp+L8tv1xAP73KR/i7Pkb8UYGNeEvBep6bTrLFyBSIF7/au/r3PTCN3hs/1PaY6MTthxZXo9i0Mdsh6I+bcO0sm4ZoEzSqrd5mO9sZiQeIJQI47a4tGRD1e2sdoPL17FNv0YVIb7TQJQaVR5qzLec8V4YK74u4XYuClUkXzjxsvaYWkZUyO2scvGid7Gm/hT2DR3gm6/+C2/0v0WTo4EFrvnacQ/5jmgi2xXsyYpXJnPaKk6W3BaTz3S+AMDWpZfRmIkHDhQQ3z8dewar0coNGz7DxYvepcVdFxcQX/XxQ5kxi32hfnrD/ayZt0rz8Kgx00mLbzo5ZqSiWn4VTkY44j/O8tqlLK9byqWLL0JC4rnOv0zqHOMRTSrdrTTxVa8jMfY6jo908ov9v0FG5pF3ntB+NzvhanKW70g8v9t5RUZ8AVqcTZpbGRSrWLV81VGG6tjLfB3bYDTTWd3khMpc61tV4usQMd+Kw6a5rsqX6QzZJTc2o23K1tTJhiqSoWQYp9nBItdoZ7DxLF9QbmJXr/5f1Frc9IcHONWzki9svA6L0awd96BPGchiNVpIySlO6LJ1p+t2rtW5nQciQ+zu38NCVyurPMtpzNyo81m+/liA4ZifFXVtrPQs44PLr2Bdw2o81jpacwZZqKyZdwoA+zIZ3m949wCjLmcY/RuctNtZTmHMeQ/UZh5qow81AazZ0chCdyudgRMlcz2rFq59jOWrXMdwzM8P3/xPfrD7J/zozftIpVOsqT+FYDzEi907gNE2j06zQ/tcfLrEqfHIjvkqbmeX2Umra3RaXbOjiQad+LotrqwNt8lg0poAFRJ9dYOg/l2XO+O5qu5IZpMBq9k44zHfI90j/O7Fo3z6/WuxW6vqLSw59ozFW87uVjA62SiWimuJGIKJUUUS4Mym9azyLOfevT/HYrRoGeTj4ba4uH7DpzniP8Y5LWdpcUvV+lFjbGc1b+DF7h0cH+nEaDDwSPsfOGe+MvxhquLrsdVhNph4qec1jo50ICNz6eILkSRJayaSz/LtCCju76WZfuVGg5HPrPs4aTmdFXfV0+xopN7mYb/vEKl0ih29uzAbTKzXia/drFqMkxPf8SxfdfPg0Xkhmh2NdAa68EWHs6zBqaJa6qrFa8tptLG7f69WVmaQDHxoxRWcPf8svvbyt3mm43kuXHCuJmx2kx2r0YLL7OSo/ziyLCNJ0rjnV2O8ZoOZoegwoUSYertHK4sDxfLVb+gVy9eR9fNEWdZqXLrJ3sBAZLDsCVdVpxzOMkw22nWwnzcPD3LoxDCnLx9/PunJzqjbubyWLyiTjWKpOC4R7y0avfhuat5AW+1ilrgX4TQ7Jrxpqsx3NmsTo1RsJqu2GQI4v3UzL3bv4OjIcXb07qQj0IUvpnTRmqr42k02PrXu//LL/Q/R9f/Ze8/AuOo7bfs605tm1EZdsqqrbMvGNsamGAMhIQQIwWRJIGXDQtYbkpDsbkg25GGz7y4hebLLQt4lybZkQ8IGAnEqhGowxR03uanYVu9lpJnR1PN8OHOONNKoV8v/64vl0ZRzZiTd59fuX38zLpOT9RlrtPMy6gwJxVetPRfExBcU0w69NLrZhyRJrExbzp7G93i9fg9tvg42ZFZgHZJx0dK1CWqlwUiQ56t+R/P7LbT2d/Cxso+wKUvZZ61YbMa/B6r4qvVM1XMZBme1W33tMyK+w7udbbF/VcOQxn5l9/PfbniATFuGVlL6QOnV/Pb0K+xvOYwv5Ecv6THrTUiSRGlyMUfaj9M50DXuOFxvwINe0pPjyKLO04CMjMvkxGVyYtIZCUZDZNkz4rrBh0e+SUa71ucxXuTrtqVD1xltC9pcsajSzqCMG/XPcuQbDCrpnV6v2B08Hg6jnQ2ZFWzIrJiH11Z+GcWY0cSxGa2YdEbSLKkUu5agk3R89bKd7Fz759N+bnWMKd2aRn5SLlaDhcNtx6jrU7qf1e7o6ZQIVqUt45uXf5WPln6Yz676hCZiOklHmjWNdn/niLlbtet6tOaqsV4L4PfnXgaURRND0RquEtRKD7Ue5e2mfVzobcQb8rGncbDGrqSdh0W+elV8lfcoeYj4Zsbq2cPnjqeKGgGqF8yqWDb3K41yjf0tGCQ9eY6cuF6Om5ZuR0Li3eYD+MI+bAardsFWmqz4pFf1nBv39XsDHpymJNIsKdp6T5fZGZfBGJ52dgwTX4fJgV6njzm2jd1wlRF7zv5ZXHuYiEUnvnarkUAwQjgysv7hHQjNSD04EFKaMfp8orY8HpIk8dlVn9BSinOJTRNfkXaeKDpJx1+u/Sz3r/m09odTr9NPOOodCzWqznfkoJN0LEnKJypHMegMXJO3VbvfVCNfFYvBzPUF11CWUhx3u9uaij88oDXkgGKMUedpIN2SOmkjlqUppRgkPeFomGSzi2WppXHfV6Pg4V21AIfbjwHwzx98mNLkIs711mldvonSzmq3syqwQ9POGXYl8k3kNT0V6vuUyFZtWCp0FQBw3nOBqBylydtClj1zxAVCqjWZFalLOe+po9PfHbdhS22Wqo41qI2GLMt4gn24zM64c3TFfnZuKryejxTfiN1ow2a0ae9xktEe1+ehfpZJpqRRG67UcoA7dnEhup2nyVjjRv/6q2N8/38TG8BPBlV8PSLyXdCo4ivSzpNjaUopuY7sGX9eVXzV9Y5qjXV7/lXcXHSDJjBTNdkYDzWCG5p67vB34Q37tP3Uk8GsN2misilr/YhRutFGdLwhH6e7qshPyiUrKYM16SuRkTnRcRIYO+2spuZTzEPE16qKbwfTRZZlLvTVk2JOxhUbEVKj0POeetp8HYSioVF/Pi6Ppc4jciRut3SuIwurwUL1OJGvN+QjIkdwmZLiGvzUTvaKjNV8sPA67XY1+k0yOTDoDJqRj5rtcpqS8IX92pajoWgeADHhFuI7TcZartDU7qWhvZ/oJO3ehqOJr0+I70LGLtLOCwpNfGMd1Nvyr+SjpR/mQ4XXYTPa2JStWFwmWn04E2ji6xsU3wt9asp58uILsDX3ctIsqVyZc/mI72niO6zh6lh7JVE5ynq3Uo9WO6SPdVQCEJHDCdLOg2Js1pu05wYl0k82u2Yk7dwd6KEv2D/i/Sh0FtAf8nKsXTnG0cR3jbtc83AfGvnqJB0lrkI6/J30jNH1rM5ou8xO0hKI73DyHbmY9CZcsTS8XbvgViJf1eAjUUpZW/5gsOEw2oX4TpfRZn3DkSi+QJhIVJ526jkQFJHvxYDanbvYfZ0vFrbmbOKavC0sSy0DlIui6wuu0eZibyq8gW15W1mdtmJWXl9NL7b7u7TbBuu9UxPf9Rlr+PaWhxI2OmmjRsMiXzXlvC7WDJZuTSPHnsXp7moGwgEl8pUSR76gRL3DywAZNjfdgR6toW2yqO5VavNZ4QjxVf6vjhKNJr4mvZH1seUetmHd8YOp59GjX3XMyGlyJox8h3N72c18Y+ODg81hqvia1LTz6E1Xg0YgVhwmxT1rsj7c02HRie9om42GpqF7+gLTeg2Rdr44KHIVYNQZRjVKEMwteUk53Ln0tlEbqlzmJHYsvXXWrEATpZ3P9V5AQiI/KXfGX8+oM6KTdHENV0NTzuocKijRbzgaprLzFDLyiLWKQ61ZhzZbqWTapp563t3wDl97+9u0+TpGvRgpdC0BFH9sGF18ATZnbwQgxRJ/nKr4vnT+NV6+8EbCCFgdM3KZk+JMXdRZ4eFYDZa493HwgtsR97hEtqLqzmGrwYLdaCcqR6e0f3mqLELxTbzZqG9Iini6XcqDaWfRcLWQWZ2+kse3/ZP2h0lwaZNmSUFCos2vCNQFTz3nPHUsTSkZsad4JpAkCZvBqv1Bj0Qj/OTkM0Tl6Ijuf3U+WHWqGpF2Hhr5JnAaGxTfkWsOx6PO04A/7OeN+j1c8NQjIVEw7GIk35GjjV65TEljlnJKkgt5cP1fckPBtrjbC5JyKXEV0ext5Tc1L/LM6Re07/lCPgbCAU0kXWYnVoNVS2EPHYEbC/uwJks1Yu5JJL4hP1aDBZ2kIymWHZvL1POim/N1jFLz7R8ilDMV+fb5gkSjMjrd9DtBBQLB7GLQGShIyqO29zynu6p4q+FdAG5csn3WXlPdCBSVozx9+jlOdp5hZeoyrs27Mu5+BUl5lKct50TMvMI4LO1sGpItSEkQ+Q6d9Z0sqhvVe80HkYDMYQYWoNThcx3Z1PU1kDOBZjx1tGgoep2er1z2l/QG+viHff9Xi6IBHjv4JCadkaJYhO2MRaxLnHl4gn2jmp0MJz8plxOdpzQ7UTVLMHRZg4ov7NdKA3ZNfH1kTOiVps+iE9/RNhsNFeOe6Ua+sZqvLEP/QAinbe629QgEgqnzZ8s+yvcO/YCfnHyGvmA/Rc4ClqaUzNrrWQ0WegMejnWcZH/LYZY487l39T0JxeRjZR/hVFfVmHO+MHbkOx3xVe09C5MS17+LXAXU9TVoDXNTxWVOIsXs0jq3B8IBrRSgjkslxyLWv1j9KeRJ2GZeV3A11+Rt0dL0yVrkOzLF7Qv7tRlptdQxl7O+izftPGzUqG+o+PZPN/Id/GGYj7pvJBpl155aXjlYP/6dBQKBRoEzjxuXXKtZDt5YuH1GZphHwxpbx3ekTfF+/vjS20ZNcWfY3GzPvwoA/TgNV8NJtSRj0BmmNOvrC/uw6C1aine05rNVacsBZuRixWV24g8PEIgE44RR3eikpo+tBovWRDURdJIurj6udkEPr/lGohGCkSDWYd3Rc+lytWgj3+Fp57iab//UBTMSjcYZeHi8QZjDkqJvIMwPf3uCE7VdmIw6tq/PRa9bdNdQAsGs8cHC6zjdVY1Okiifpc5qFdUE4mhHJUkmx7iNXR8s3E6Hv5PLMtfE3T5UfJMtI9POOklHps1Ni7eNqByd1PpOb8iHy+xknbucl+t2s3SYOYnKqrTlfOfKb83I6N7gBqpeTXwLknKp62vEaUqasfWj6ljW8Mh36NYl5bXzcBjtpFvHXh4ykyw+8bUkHjWKq/lOI/INBONTIHM96/vE88c4W9+DyaAjGIrS0OZlSdbEmhEEAoFS+/3qZTtnNeJVUUdggpEg691rxhUVi8HCvavvGXG7aWjaOUHNF5T56cb+Ztp87WQN89YeDVmW8YX9ZNjS+XDxB7gyd3PCtLbKTM3Mq7XYnoBH23Z0Ve4VtPs7teanmcJldmmRbzASoqqnRhtjUn2rcxxZfOfKb83Jz4TKhMV3YGCAm2++mZ07d7J79266u5Ul0j09PVRUVPAP//APs3aQk8Fo0OGwGunsjZ+tUyNhg16asPhGZZlIRMZoGPyFUZutDHod4UgUj3fuOp57vUHO1vdQmudia3kWP33pDDVNvUJ8BYJJMld/ZIeaYaxKXz7l51EjX6vBMuqSkvykXPa1HKK+r2nC4jsQUZrB7EYbOkk3pvDOJENrsT0Dg65dW3I2zfxrmZy0eFsJRkK827Sf56p+w9IUxQrUOsQIZC6FFyZR833qqadwuZSrlSeeeIKf/exn/OxnP6O8vJwdO3bM2gFOhTy3nfYev9YYBYNp5+w0O739wQm5XP3h3fN85Qdv4xtSPw7GxDfdZYl73rmgukH5IV1dnEZZnvJLUtM4sR2ZAoFg7lHTmjpJx4qYuchUUK03E9V7VdSUdn1sUcVE8A5xeZpLhs7fqpFvonT6TDAYZfdq7426V3quz3soExLfmpoaqqur2bZtW9zttbW19PX1sWbNmsQPnCdy0x3IQFPn4MxWnz+E2ajHnWydsMvV+ZY+vANhOj2DUbQa+WakKL9Uc7nZqKpB+SEty3WRlWbDZjZQ0ziyhV4gECwM1Ci1xFUYt25wshh0BpYk5bN8DAHPi40ATU58lb+R9kk0Nc0EyUMaoQb9qmdLfAfry03eFnSSTltcYZvGZzJdJpR2fuyxx3j44YfZtWtX3O3/8z//w9133z3u41NSbBgME5vTmgxud+J067LiNF473IBnIKzdxxeI4Eoyk+12wNl2dEbDqI9XGYh1NRstRu2+bX2K2OZnOTlW08lAKDru80znXIZyobUfvU5i45ocLCYDy4tSOXy6DaPFRHLS3C6rH4uZeD8WCuJcFiYXy7nk+d1QBZuXrBv1mCd6Lt+76Rvj3COJ7KQMGr1NpKc7JpRGbYolBzOSU+bs7xiAMTau5MdHf7gfq9FCfvbsdK7m9mTABYiYgrT4Wsl35XBDyZX85+FfsjKvaNqfy1QZV3x37dpFRUUF+fnx7efBYJBDhw7xyCOPjPsi3d0z377tdifR3p54VVRybNzoVG0na4sUz1VPf4CcdDsmvfIDea6+G4dx7MC/OxbxNrV4yHIq4tYWe02TXsKg19HZ4xv1OGbiXFQCoQjVDT0UZDro6/XTBxSk2zkM7D/eyLqyheHiNJFzuVgQ57IwuZjOpchcwj0r7uSylIqExzzT55Jjzaa5r43T9XVx+25Ho6lDma+VA/o5+TumEpWVVHyrp5MOXxcus2vWPlN9SPnb/X7DKYKREBlmN+tc6/nnq8sxYZrVz2UsAR9XfHfv3k19fT27d++mpaUFk8lEVlYWsiwvuHSzSk660i3X2K7M8gWCEYLhKA6bkWSHMgM2kaYrNTU91LBDTTtbjHpcduOczfmeb/YQicparRegJFdJ09Q0ehaM+AoEgkEMOsOc7rLOT8rlUNtR6vsaJyS+qsGG3Ti36VedpMNpSqLd34Ev7J/yYouJoKadT3WeBSAntqfYNAuWopNhXPF9/PHHta+ffPJJcnNz2bJlCz/84Q9Zvnzq3Xuzic1iIM1ppqFDqWf0+RWBTLIaSXYoV0E948z6RqOyNq40tOFqINbEZTbpSbKZaOzwIsvyrHfKqfXe0tzBukhxjhMJOFvfMyfHIBAIFjZDm67WxbYLjYVPE9+53/yVbHZx3lMHzF69V30dgM6YnWW2Y2Kd4LPNlCeZ29vbSUtLG/+O80Su20Fvf5B+f0iLYJNspiHiO3bk6x0IIWtfj+x2Nhl1OO0mQuGoJsizSXWsq7ksb/CH1Go2UJzrpLqxl3959ijd0/SsFggEFzd5SUottb5/Yk1X3thmH9scR74wGJEqX8+e+CaZHHHz1WrkO99MymTjgQce0L5++OGHZ/xgZpJct51jNZ00tvcTCiuNUw7rYNp5PJerod3Qvri0s/JcFqNB83T2eINYzbPnVxKORKlq6MWdbMHliG+s+quPrua//niKE7VdPPLf+/nC7avjUtMCgeDSwWG0k2pJ4WTnGR557zGWOPOVpQ3pKxJu99LSzvMwcjN0R+9szherKe6eQC8mvWnOZpnHY9H6EualK04sDe1e+mLuVg6bEYfViF43vtFGvPgORr5qzdcci3xh9l2uztb34A+EKS8emWlIdph5cMda7rquDK8/zPeeeZ/3Kltm9XguVsKRKO9VtsTZgwoEi42Pld7M8pQy+kM+DrYe4YXq3/Od/Y8nXi6gpZ3nXnyTTYPR7mxGvjAo9Nn2zBmzrpwui85eUiXXPdh0lZWmfJ1kNSFJEskO0/jiO8SOcmjaWTXuMJn0pMTGezp7ByibxX3t759V9o+uX5q4qUqSJG7YmE+O285Tvz7Bf/7+FIVZSWSnzX0dZyHz7okWfvLiaSRg86qFkXoSCGaaiozVVGSsRpZl2v2dvNd8gJcvvMHrdXu4vezmuPt6Q350kg6zfu5HFecq8gVF3C9Qv2BSzrCII9/sNDs6SeJ8Sx/9asOVTXGJSXdZ6fYE4hywhtM3atpZjXz1ZKYqdZLWbv+MH7+KLMscrmrHbjGwLH/sH9BVhal89qblRGWZX79VO2vHdLFS36p0v7f3zN7nJRAsFCRJIsOWzk1FN+AyOdnTtFdLM6t4w17sBtu8NGu65qjmqzy/8lo5E7TdnAsWrfgaDTpWFKZwvqWPynNKl5sjtvEoL0NxwGrs8I76+KFpZ2+CtLPFqCczRUnVtM7CHLPK+ZY+uvsCrClJx6Af/+Nav9RNcY6Tg2faqW0S7ldDUR3Puqex1UoguNgw6gxcV3A1wUiQ1+reor6viRZvGwC+kH9eUs4wKLgWvTnOA3s2yLUr7l9FrsJZfZ3JsGjFF+DGjcrs2LlmZVjaEYt88zOUenB92+hD1EPTzokiX5NJT5rTgkEv0do1e+J7+Kyyn3O0lPNwJElixzZl3+avdlcrIGpcAAAgAElEQVQjT8DD+lKhWRVfz8A49xQIFhdbcy7HbrDxpwuv850Dj/Po/n+hN+DBG/JNal/uTKJGo8lz0AC1OXsD/2fz31DkKpj115ooi1p8VxWlkhsz3JAAh2W4+PaP+lh1NthhNeIdCGsipqaqzUY9Op1ERoqNli7/jItcKBzl8Nl23qtswWTQUV48/sC8yrKCFFYXp3G6rkeL+i91fANhbba7exorJQWCixGLwcyOpbeyMnUZxa5CwnKEEx2nkJHn3GBj8JgsrMtYw4aMill/Lb1OT0aCbu/5ZFGLryRJfCAW/dqtRnQ6pa6Rm25HksYWXzXyzUxVFjEEY+NKgVAECTDF1gxmpljxB8JaR/VMEIlGefg/9vGDF47T5Qlw9doczMbJeWN/7JpiJOC53TVxG5zqWvv47TvnJrTVaTHRPGTJhpiHFlyKbMxax19VfI7bSm4C4FhHJQB2w/w1Zt5bfjcfKrpu3l5/PlnU4gtKV2uq06w1RwGYjHqyUm00tPePGrH2+0PodRJpTqUWoY4bBUIRTCa91qCQmTrzdd+eviBtPX6Ksp38n89s5K7rJ7+KrCAzic2rMqlv62f/yVbt9l+8WsWuPeeoqu+ZseO9GIjbcOULabPfAsGlRoEzD4POwGl1rd48Rb6XOotefI0GHd/69Ea+dMfauNvzMxz4AxE6exPX//r9IRxWI/ZYk5bq7xwIReOi0KyY+LbMYN23q085puUFySzJSppyJ+JHryrGoJd44a1awpEonb0DnI2J7snz3TN2vBcDzR3K56PuYe4VqWfBJYoxtp4wHFUCivlquLrUWfTiC+C0m7ROZ5Xx6r79/hAOmxF7bEOSFvkGw5iHbEPKjO31be2aufEVNS2a6pxeB2B6spVt63Lp6B3gzSNN7D05aL5xqu7SEl818l1ZqNTOu0TqWXAJU5JcqH0txHd+uCTENxF57pj4to8U30g0incgTJLViM2cKPId9CbR0s4zGfl6YuI7Azt6b76iELNRz+/ePc87x1sw6CVy0u2ca/IwEFQayYaOVS1Wmju9OG1G7aJrIlutBILFSmlykfa1bR6sJQWXsPiOFfl6/UqU67DGR76yLBMMRTCbBt82l92E2aSf0ZpvV2wUZrqRLyhR/wc25uPxBmnp8rG2JJ11ZelEojJn63t44a1aHnzybWqaRlrPLRaCoQgdPQNkp9k1VzLRdCW4lClyLkFCKWeJyHd+uGTFNyXJjN1iSCi+qruVw2rEZlEj3zDhiEwkKsfVfCVJIivFRmu3f8Y6iNWUaIpzZizfbtxUoF1EbF6VxYolKQC8dqiRl/bVEYnKvPDm4nXEaunyIQPZ6UJ8BQJQmqxyHIrVohDf+eGSFV9JkijNddHW7aemMT7q648tSoiv+YbirCWHkplqJRSO0u2ZmT/oXZ4BDHodScPq1FPFZjFwz43L2LwykzUlaZTmujDodRyv7SQSlUl3WTh1oZszi7QO3BRzMstJs2krJYX4Ci511rnXYDVYSbNM3ENAMHNcsuIL8MHLFbeT3717Pu72fi3yNWGLia93IKzt8jWbholvzGayuWt0u8rJ0NUXIDXJPKN+q5tWZHLfLaswGnSYjHpKcxV3mfVL3Xz+1nIAfv1W7aJ0xOqIdbRnpFhx2U3oJEkYbQgueW4svJbHrvyWGDWaJy5p8V1WkMLS/GSO1XRyrnnQB1lNOydZjZr4jhX5FuUoQjYT4zuhcBSPN0jqDKWcR+OailwKMhzcdV0ZxTlO1pakcbahl6qGxVf7VXc3u+xmdDoJl8M0Y1kKgeBiRSfp0OsmZ94jmDkuafEFuGVrIQC79pwjGlWiPq9/cP+vfUjNdyCYWHxXFaZgNuo5fLZ92pGjGpGlJM2u0fjlKzN55M83kRabe1WdwN490Tyrrzsf9HiV9zTZoexfTkky09MfuORcvgQCwcLhkhffFUtSWJrn4nhtJ9995n1au3yaVaTDasRi0qOTJHxD087DxNdo0LO6JI22bj+N7dNLPXdrnc5zu19zWUEKKUlmDpxuJxQefdXixUhvfxCdJJFkGxTfSFSmvrWfn7x4ala3UgkEAkEiLnnxlSSJL3xsDZctdXO2voev/3gvLx+oB5S0syRJ2CwGvEPTzqaRqZr1S9OBwS1EU6Vrhgw2JotOJ3H5ykz8gTBHqzvn9LVnm57+AE77oLd3Sqzp6p+fPcJbR5t57VDDfB6eQCC4BLnkxReUCHfnR8v5i5tXsq4sndJcF5ctc2sCaLMY8A2ECYQUP+BESw7WlqRj0Escmq74qpHvDBhsTJYrVimjB+9Vtoxzz4sHWZbp9QZxOQbfT3XcSM1wXGpWmwKBYP4xjH+XSwNJkriiPIsryrNGfM9uMdDlCTAQVMw3TMaR1yxWs4EVS1I5XttJe48fd/LUOgjnK/IFxXgkz23nWE0nnb0DWj34YsYfCBMKR0m2m7TbMmLd6evK0gmGo1Se66K7L6CJskAgEMw2IvKdADaLkXAkqjlfWUyJr1kuW6bsi9w7jchR7cKd65qvylVrcohEZf7u3/fy3O5qItGLe/uPusN3aORbUZbGV+5cy+dvLae8SJlxPHle7D0WCARzhxDfCaAabXTGUsLmBJEvwMblGZiMOvYca55yJ22XZwCTUYfNPD9Jies25HHPjcuwW428uLeOvZWt4z9oAaNuL1I7nQH0Oh3lxWkYDTpt0YIQX4FAMJcI8Z0AzljK8vVYY85oi+2tZgOXr8iko3dgyn/MFYMNy4wabEwGnSRx7bpcvnTHGgDOXOR7f3u8IyPfoeS57TjtJk6e716UBiMCgWBhMiHxHRgY4Prrr+eFF14gFArx1a9+lTvuuINPf/rT9PYuPlOG4XxkSyEf2JiPyahHgjFrg9dU5ALw5pGmSb+OPxCm3x+at5TzUPLcDiwmPdUXuemGarAxtOY7FEmSWFmYQq83OO0xMYFAIJgoExLfp556CpfLBcCzzz5LSkoKv/rVr7jppps4ePDgrB7gQiDJZuLPrivj//7VFr79uU1aw04iirKTyHM7OFLVwYv7LvCn/XUTXtn37gmlVlyWlzwjxz0ddDqJklwXLV0+PDGv64sRdXXgaJEvwKpY6vnUBdH1LBAI5oZxxbempobq6mq2bdsGwBtvvMEtt9wCwMc//nGuu+66WT3AhYTdYiQ3tgd4NCRJYts6pWnpuTdq+OXr1Tz5/DHCkbEbl6JRmZcP1GHQ67h2Xe5MHvaUKctTLrhqLuLotzeWdh5a8x1OYVYSAI0dU4983zraxP5TF3d9XCAQzB3jiu9jjz3GQw89pP2/sbGRt956i3vuuYcHH3yQnp6LuyY4G1xTkcPO28r5wu2rWb/UTVVDL8+/WTPmY96vaqe9Z4At5ZlajXm+KctVxHe6fs8eb1AzKJlr1Iarsd7TjBQrEtDaNTWnq6gs8/TLZ/jhbyp56+jkyw0CgeDSY8yW2l27dlFRUUF+fr52myzLFBUV8YUvfIF/+7d/40c/+hFf+9rXxnyRlBQbBsPMG3i73Ukz/pwzxYcyFeG66rJ8vvL4W/xpfz0rS9xs35Cf8P6vv6/80f6zG1csmPNKclrRPXuU8619kzqmofcdCIb54r/uYdOqLB68a/1sHOaY9PnDOO0msrNcY97PnWqjvdc/4jwnct4eb5BwRGnW+ulLp8lIc3DVAsleDGWh/FzNBOJcFibiXCbOmOK7e/du6uvr2b17Ny0tLZhMJtLT09m4cSMAV155JU8++eS4L9I9C965bncS7e19M/68s8Hnb1nJo08f5vFnDuPx+Nm6Ojvu+4eqOzl1vos1JWlYdCyo8yrIcFBV30NjUw+mBF3ewVCEX7x6litWZbGsIGXE51LX2ke/P8TBky20tXnmvIu7y+MnzWkZ9z11uyxUnuuirqEba2zMa6I/Y2q6uizPxYXWPv7rdydYnuec/sHPIBfT78t4iHNZmIhzSfw8ozFm2vnxxx/n+eef59lnn2XHjh3s3LmT7du3s2fPHgAqKyspKiqa9gEudnLdDv7mrnXYLAb+6w+n+N275zXzirP1Pfzo18dwWI3cfcPSeT7SkZTmuYhEZc63JP5BPHGui7eONvP4c8eoaRqZnm7v8QPg8YVoj+3VVQmFo7Nq4hEIRfAHImM2W6lkpSpNdFNZsuCJ1ZWXF6RQluuio3cA38DEmuwEAsGlyaTnfO+55x7efPNN7rrrLl599VXuu+++2TiuRceSrCT+5q51uBwmfv1WLY8+fZh/23WCf/3VMaIy7LytnPQpWlLOJssLUgA4Xpt42YI6ihQIRXj82aM0tMWLdFu3X/u6tnFQnGVZ5ts/PcD//8KJmT5kDc1gYwI1dFV8W6ZQ91XF12k3kZ+hXOk2iLElgUAwBhO2UXrggQe0r5944olZOZjFTkFmEt/+3OX8/JWz7DvZSm2ThySbkftuq2D5kpT5PryErCpKxWTUceB0G7dfXTwibVzV2INOkvj49lKeea2KX756lk8NieBbh4hvTaOHzbHlDU0dXhrbvfT0zd5S+0TWkqORmapc+LR2+ce550hU8XXZTZozWX1bP0vz539kTCAQLEzEYoU5xmE1cv8tq/jw5iVYTHrSXBYyMpwLtlZiNupZW5LOgdNt1Lf1U5A5WMMIhSNcaOkjP9PB9Rvy+MPeCxw5284915dpIt0WS+Ma9BLVQ9LSJ2Mztd4BxVjEYTVO+RjrWvs4cLqN264qQq8bTOYMzvhOIPKNzW5PpeNZnYN22k1avbi+bWF+ngKBYGEg7CXnibwMB+nJ1nmzkZwMG5dnAHDgdFvc7edb+ghHZMpyXZpTVE9fIG5etq3HT0qSmSVZSTS09WsjR6eGrPEbmpqeCr95+xx/eO8CZ+vix940d6sJRL6pTgsGvW5KaefeIZFvdpoNvU6ivi0+7RyNylP2+xYIBIsPIb6CcVldkobJqOPg6bY4/2O13lsaM+NYuURdUqAIaygcodsTIDPFSklOrHGr2UMkGuVM/VDxnXo3fFSWORvzn65u8sR9ry4WfWanju5IpqLTSWSmWGnp8iHLMsdqOnjnWBOnL3QTCo/dFDa05mvQ68hOs9PY3k80Ovhe/eCF4zzyX/uFf7RAIACE+AomgNmoZ01JOq3dfurb+rXbVfON0pgZx8pCpW6tLpVo7xlARjGxKIndp6bJw4WWfvyBCOmxfcHTiXwb2714B5RVjzWN8d3WNY0eLCY9Oen2CT1XVqqNgWCEP7x3gcefO8Z3fnqA7z7z/rgGKb3eIEaDDotJGcXKz3AQDEdpi3V61zZ5OFLdQUO7l+5ZrHEvNgKhCL98vUq8Z4JFiRBfwYS4fIWSen75QD2gdCtXN/aS5jST6lRENNVpIS/DwZm6HsKRqCaqGSk2SnKUude9lS2aDeM1FTlAfFMWKGLWOWwsaTRO1w1G0DWNvVpk2e8P0dLlozjHiU43sdR+ZixCfuGtWqxmPZ+7ZRUmg27cDVUebxCnzaSVEPIzFAtS9ULlpf112n0vDBvZqm7o5cV9F0REnIBjNZ38aX89e4RrmGARIsRXMCHWlbnJz3Dw7okWLrT0Udvkod8fonTYEoiKMjeBUITaJo+WTs5ItpLqtLB1dRYN7V5NwLeuzkavk2jrGUw7hyNRHn36EH//kwP4A+Fxj+tMrM5bkuvEOxDWara1sRR0Sc7YzlZDUTueAe6+YRm3XVNKcY6TxnbvqHO7sizT5wvG2VcOim8fbT1+Dp1p09ZQDp+X3vV2Lc+9UUOl2Cc8ArVhrr13ej0BAsFCRIivYELodMo4EcB/v3iKf/3VMQA2xSJilbVL3YAStbT2qJGvImqfvWkFt2wtBJS552SHmTSXJS7t/OaRJtq6/fT7Q+w51jzmMan13jSnmc0rlRGmmkZP7F8lBV2SO3GnqSWxTu4NyzPYvCoTUOrZMkq6PBG+QJhwRMaVQHyPVnfyPy+dRpbh9muKAbjQGi++6hpD9YIk0TlOdCvWYkNtmOvomVgWRCC4mBDiK5gwKwtTWVOSRl1rP15/iM9+aDnrytxx91lTmo7DauTVQ/VaI5Q7Zh6ikyRuu6qYhz+9gb+6rRxQhLnPF8IfCDMQDPO7d89jNuoxGXS8cqBuzG1QTe1e+v0hlhWkaCKrumzVxv4tnkTkW5CZxLc+s4H7PrJSSyGX5iqR/WjLJQabrQZHpZx2EylJZurb+jl5vps8t51r1+WS6jTHpZ37/SGtU/pEbRdNCbYqvXqwgQeffJvG9v4R31vs9HqVyLdDRL6CRYgQX8GkuOv6MpblJ3PfLau4am3OiO/bLEbuuq6MYChKY7s3bvZVpSjbqbl5ZSYrdda2bj+vHGzA4w1y46Z8rlyTTacnwMFh401DUeu9y/KTyXM7MBl11DT2EpVlaps9ZKXaJj0/XJjlxKAf/LUozXUiAdUNibd3De10HsoDH1vNZ29aztfvXs/ffWoDBr2OJZlJ9HqDWgORKrbZacp78MrBkdHvgdOtRKIy71d1TOo8FgPqhUlXX2DclZwCwcWGEF/BpMhMsfG1T67n8pWZo95n86pMyouUsSM15Twa6vdrmz28tO8CDquRGzcV8IFNBUgS/Gl/4nQsoAnS8iUpGPQ6irKU+uyBU234AxGtyWs62CxGctx2aps9CQVAFQinLV58C7OcXLUmh7K8ZK3euyS2N1iNftV56A9uKiDdZeHdEy34Bgbr3N6BkFa7Hq/pazGipp1lGbo8IvUsWFwI8RXMOJIk8akPLsNpM2re0KOhiu8Lb9bgD0S4+YolWM0GMpKtrCpM5UJrnyZwQ2nq8HLqQjfLC5K1tPba0nRk4Ee/rQTQxpumS2mui2AoGjdmpTJa5JuIQlV8Y3Xfpli9Ny/DwZbyLELhaFz39qnz3ahN0FUNvQSCE9uJ3O8PLYru6aGf+/ClHALBxY4QX8GskO6y8v0vbOX2q4vHvJ8qvt6BMKlOM9euH9yDq84Pn0vQ7PTG4UYAtq/P0277wKZ8dt5WzpqSNNJdFtaUpE37PIYeh2oqIssyz75ezf5TrZq1pGsC4rskS4nEByNfRcyz02ysLFQNSgYjXLUDenlBMpGozJn6xKnvoZyp6+ZLT+zhneMtEzq3hUo0qnSRq3T0iLqvYHEhxFcwawz1WR6NdJcVdQr31q1FGA2DO4OLY2nj2ub4Zid/IMw7J5pJdpioKEvXbtdJEhuWZ/DlHWv57l9u0eaPp0tZbEHC8XPKZqfqxl5e2l/H0y+f1eaRJxL5uuwmkh0mzrV4kGWZpk4f6S4LFpOB4hwnZpNecweTZZkTtV3YzAZuumIJMLHU88sH6pFlOFpzcdeIPb4gsjz4vnaIyFewyBDiK5hXjAYdhdlOCjIdbFmdFfe9wmxFfIdGvpFolBf31TEQjLBtXW5cc9RskZFspTjHSWVtFx29fi2q7PeHOHC6HZhY5AtKfbq3P8i+k614vEHNfcug17EsP5mWLh9dngFau/10egZYWZjCsvxkjAbduLPAnb0DHKlWRLe6ofeiTj2r9V61bt8uIl/BIkNsNRLMO1+/ez2yLI+IlB1WI5kpVmqb+4jKMmfqevjpi6dp6/FjNeu5JkG39Wxxzdocaps8vH6okQOnW3HajPT7w4QjUQx6aURH92jcuLGAvZWtPPNaFQC5Q6wvVxamcqymk5Pnu7XZ3vLiNIwGPUvzk6k818Uzr1aRkmTmusty47IEAG8ebUSWwWrW0+sN0tE7oNXDLzbUMaPCrCSO1XSKyFew6BCRr2DeMeh1I4REpTjHiT8Qpqndy3/8/iSdngG2rcvl7z+7aUJ7emeKTSsysZr1/Gl/Hf5AhKvW5nDZMmXG2Wk3TXg71ZKsJMqLUunzKeKaEye+SnPaG+83suvt2pintlK3vixmXvLKwXqefaOal4Z1gYcjUd460oTdYuBDlytp6upRZpMvBtTINyXJQprTctHUfP2BMG8cbiASFaNRgrER4itY0KgmGb949SzdfQG2r8/jUzcu0+aE5wqzSc8Vq7JQE7lbV2dzw4Z8YOSY0Xh8OFbDhXjxzU2347SbONfsIRSKct8tK7V1iNdU5PCdz1/BN+65DIfVyEv76vDGLC9lWeaXr1fj8YXYujpba96qbpwd8e3tD8y68YW2ptFhIj3ZgscXmnC393yy51gzP3v5LEcW8Fx2OBKdkHWrYHYR4itY0BTF6r6n63rQ6yRu3JQ/b8dyTYXSiV2a6yIr1UZJrpOPbCnkQ5uXjPPIeJbmJ1OW58Js0pOTNii+kiRp89E7ri2Ncw+TJImMZCuluS4+tLkAfyDMn/YrDmC/eKWK1w41kJtu58NXLKEg04HJoBvVlaupw6vtVZ4Kjz93jH/82aFZ3U+sRr4uu4l0l3KhNVzw/YEweytbJryEYy5Q55HbF7Al5q/31PK3T70rBHieETVfwYImP8OBQS8RjshcsSprxjqYp3osX7pjjeZIJUkSHx1nlCoRkiTxxTvW0O8PYTbFp9t3XFvKphUZrC4efUxq+/o8Xt5fz8v763njcCPegTB5bjt/fdc6kmJReFG2k7P1PfgGwtgsg7/m1Q29PPr0IfIzHTz0yfVYTJP7E9DZOxA3p5wX87GeKEqNfPxrfrXm63KYcScrn3l77wC5bgeyLPPc7mpeP9xIIBjBaTfx4I61monJfKJG7AvpgmA4F1r68A6E6eoLkDvBXgXBzCMiX8GCxmjQUZilWDx+aHPBfB8Oa0vTyUixTft57BYjmQmex2U3saYkfcwastmo59arigiGoxgNOq7fkMff3LUuLv2tLoT46Uun+c7PD/PO0aZYeroKGahr7efff3eScCRK9yTsG0/Exq0AqsZJa7f1+HnvRAvhSJRINMpPXjzFX37/TX78u0rO1vfQ1DH6fuNebxBJgiSrcTDyjdV9T57r4sW9dVhNerauzqLPG+SxXxwesc95PuiNbWLqXMCOXKoxjPcSXdixUBCXPYIFz2dvWk6XJ0D2kBTtpc62ilzKi1JJdVrQJRDqpfnJ/OG9CxyIeWN/9+mDXL0mm5omD+uXuvEHwrxf1cF939sNwNqSNL54xxqissxP/niaXLeDD14+8mLnRO3guFNVQw/XrssdcR/1e0/86hjegTC/f+88qU4Llee6MBl17K1sZW9lq3bfv7vnshFuZL39yo5knU7SMg0NMUcw1Wf7zu2lbF6ZxeriNH74m0p++855Hrxz7QTfwbGpaeqlqd2b0L98LLTIdwGLr3qMl+q2rIWCEF/Bgic7zS6ENwFqRJiI8qJUPvfhFbjsioD94IXj7D7ShF4ncee1JTisRn7y4mk83iAeX4ijNZ28e6KFtm4/75xowWTUsW1dDhaTgTN13UiSRHGOk5MXukh3WRgIRuIcv1q7/dQ09tLc6aPfH+S9ylYiEZl1ZekcqeqgudPHqqJUdt5WTnVjL0erO+j1Bjl0pp3DZ9tHiq83qO1Xzkm3YzToON8cWxcZE191BeSmFZm88GattlQj0cXIZPnFK1Wca/ZQnOMk1z3x1Lpaq16oXtSRaJT+WKe9EN/5RYivQLAIkSSJrauztf9/87OX848/2ccHNhZoafOdH10NKI1MD//Hfn7+ylmtESsYivL+2Q5WFqXyz88eJRKR+cjWQvyBCJtXZdHtCXCkuoPuvgC/fquWt4/H7162mPR84fbVrC5O41yzh5rGXs0UZXVxGquL0wiEIhyt3sOJc13suHbwsf5AmEAogsuudHob9DoKMh2ca+ojEIpQ29iL2agnM3UwbV+a5+LdEy00d/riZqenwkAwrFmA7j3ZyseumZj4hsIRfLEmJu9AGH8gPOH577mi3xfSOvZF2nl+WVg/GQKBYFZYu9TNv37xqoTNTukuK3dsK+Hnr5xFJ0n8+YeX859/OMV7lS00dXoJhZV68G/ePgfA6qI0mju9HKnu4HfvnOPt483kpCs7i/MzHCTZjKQkmbVmrqJsp9a1PhSzUc+yfBeV57vp7Q9oc9tqTXKoa1hRtpOaRkXE69v6Kc5xxkW4pbmK+FY39ExbfGuaPFon997KVj56dfGEomk16lXp8gxMKmqeC4YuqxCR7/wyIfEdGBjg5ptvZufOnezfv5/KykqSkxW/28997nNs27ZtNo9RIBDMAGN1GV+7PpeuvgHy3Q42r8rijfcbqTzfRXVjL06bkVuvKuZnfzqDXiexfEkydqvyp2P3kSYk4N6bV1CYNfkVjquK0qg8303l+S62lCuR+tAZX5XimHjvOdZMNCprKWeV0rzY8ovGXm0kbCiq1eZojWy+gRChcBSXw0xVbIFFqtNMp2eA6oZelsb8vcdCPW69TiISlemcY/FV1lHK2Cyj77D2+IT4LhQmJL5PPfUULtdgTeYrX/kK11577RiPEAgEFxM6SWLHtlLt/1esyqK2ycNAMMKHr1jCtetyNQGzmAwUZiVpI2BXrsmekvCCUpt+9g04cW5QfFUf5+QhDmZq5HzojNJAVpAZL2o56XasZkNCV6+oLPOP/3MIm1nPFz62RtuvPJQnnz9OY4eX79x/BWfre5CAO68t5Ye/qWTvydZJiW+e28GF1j46PYk7uWeDqCzz6M8PEY3K/H/3Xj7qRYZHRL4LhnFHjWpqaqiurhbRrUBwCbFxRQZ6nYTFpNc6mrevz9NWOBoNepYXpGC3GLj9mpIpv06u247LYeLkuS4t1Xu0RhlnWjZE8DJSrNjMBsIR5T7DI1+dJFGS66S12x8X3QGcutDNuWYPlee7eWrXCfr9IY5UdVAXm1f2+IKcre+h3x/ilYP11DR5yHXb2bAsA5fdxIFTrRMaxVLHjNRtXHM563u0uoPGdi/NnT6aOn2j3s/jHRRcIb7zy7ji+9hjj/HQQw/F3fb000/zqU99igcffJCurvHXnAkEgosLp83E/besYudt5aOmMf/ytnL+8S82T3ijUyIkSaK8MBWPL0R9az+hcKlpDGgAABptSURBVITjtZ1kJFvJdce7fxVlK4Jr0OvibDlV1L3Lw+d93z6mNIPluR0cq+nki/+6hyeeP8b3nnmfUDjCyXNdWhPS7989TygcZWl+MjqdxMblGXgHwpyu6x73XNTIVxXfmep4lmV53A1VrxwY9PqurO0c9X7TiXzDkSgHT7cJ3+oZYsy0865du6ioqCA/f9DS79ZbbyU5OZkVK1bw4x//mB/84Ad861vfGvNFUlJsGEYxzp8Obvf8O9rMFOJcFiaX8rl8aI7OfdvGAt450cKbx5u5qiKXQDDC1i25ZGTEp7JXlbqpPN9NYXYS2VmuEc+zYWU2u/aco7HTzwe2KMfe7wty+Gw7eRkO/uXL1/C9pw/R3TeAQa/j1Pkuatu8VMVWVm4uz2LviRbtudzuJLZvWsKrhxo4VdfLtZsKxzyPQCwqr1iRhe7F0/T6QhN6z8e6TzAU4fOPvcblq7K4/6NrEt6npqGH03U9FOe6qG3s5Wyjh0+O8pyBWARvMujwByJxr93TF+DwmVauvSw/Ydr6d3tq+fGuE3xhRwU3jmKpein/vkyWMcV39+7d1NfXs3v3blpaWjCZTHz7299mxYoVAGzfvp1HHnlk3Bfp7h49DTJV3O4k2tv7Zvx55wNxLgsTcS5zQ1GGnfwMB28eaqAplgpekecacbyZTqUGXJybnPBcUu0GzCY9L++7wLa12TisRl471EAoHOWKVZn0efx8/paVADS29/Pwf+7nD2/XUtfSh8tu4hPXlfH+2XYCwQiZLjPt7X24k4w4rEbePd7Ex64uGrPrubVDMQGRwmFSHCZau7wjjjMUjrL3ZAvlRWmkJJnH/VzO1HXT3u3n9QP13LplCXqdjnAkik6S0OmUY/nly2cAuGVLIc/truZ4TQdNzT0JN4W1dSl/i7NSbTR2eGlr82hC+8yrVbxysB6zTkpY437rsBJdv3+qhfUlqSO+v5B/xibLTJ3LWAI+Ztr58ccf5/nnn+fZZ59lx44d7Ny5k2eeeYb6euVD2LdvH2VlZdM+QIFAcOmikyQ+elUxMnC2oReX3URx7sgGrvLiNG7clM9to9SYLSYDt24tot8f4vk3a+j3h3jj/Ub0Oklr5lLJdTsoyk7iRG0XHl+I8uJUHFYjn/ngcm67qoiUJEXo9TodFaXp9PYHqY1FyKPR6w1g0Ouwmg2kOi0jbDtlWeZ//nSa//7jab790wPjPh/AmVjntS8QprbJQzgS5eH/2MdTu04AEAhGOHimjcwUK+XFqZQXpRIKRzlbn9hq0+MNYjHpSUkyE4nK+AODCzbq2xSxUWvhQ+n3h7TnrJ7AcQvGZ9Jzvp/85Cf58pe/jNVqxWaz8eijj87GcQkEgkuItaVpFGU7OdfsYd1Sd8II02jQ8fHtZWNGJddvyOOdE828eaSJI1WKi9aVq7MT1qWvXJ3NuWbledRFFpevzBxxv/VL3bx9vJnDZ9u1unIier1BXLHdzmkuC1UNvbR2++nsHSDNaebkhW7eOd5CmtNCV98A3/n5Yb7tMJPlHH0v9Zm6Hu3rYzWd9PYHae3209bjp7c/QHWjh1A4yoblGegkiVVFqfxpfz0nznWyqmhkdOrxBnHaTTisSh2/fyCkLd5ojEXu6r9DOVbToTXEtXb56PeHtOcQTI0Ji+8DDzygff3888/PysEIBIJLE0mSuOv6Mv7z9ye5ZpJ+ykMx6HXc84FlfOfnh+nzhfjo1cV8eJT65KaVmTzzWjWRaFTbgZyIVUUpmI16Dp9tZ8e2koT1UFmW6e0PUhjbrJQW27719/99IC76TbIZ+frd67nQ0seTLxzn92/Xcu9NKxK+bjgSpaaxl4xkK119Axyv6dQsNmUZDp1t51wsClXXTy7NS8Zo0HHiXBcfH/Z80ahMny+EO8WKXRVfX4iMZCseb5C+mO1kY/tI8X0/tp94w/IMDp5uo6axl7Wl6aO+Z4LxEQ5XAoFgQVCa6+LR+6+Y9vMszU/mKx9fi9NmoiBz9Jqb3WLkrutK8QcjY0ZxRoOeNSVpHDjdRuW5LsoTrHv0DoSJRGWcsQhbddmymPRsKc/FOxCitdvPHdeUkOq0kJJkJiPZyvtn2gjfuCyhAcr5lj6C4SirilNp6/JReb5be+6mDi97T7bS3OEl2WGiMNYJbjLqWbEkhWM1nbR2++I2Z/X7Q0RlGZdtSOQb63geGu02dvQjy7J2kREKRzhR20VGipUrV2cr4tvkEeI7TYT4CgSCRUd50ej7kIdybWxueTxu3lLIwTNtPPNaFX+/JGWEWKozvqpF5qYVmaQkmSnMco7Y2QxKpL+mNI1XDzZwpr6HVQki77Oxeu+y/GQyk62a+N60eQm7jzRSFTMU2bYuNy5Nv3F5BsdqOjlwqo2btxRqt6tjRk67CYdNEV/V37mxvR8Y7ILu8gRIcynR+8nz3QRCEdaVpVMSq8UvhPWNM017j590l2XMdZ4zidjnKxAIBOOQn+HgmrU5NHf62P1+I/5AmOZOL40dXro8A/TEhC05FvnqdBLLClISCq+KGjkeq048l6uK79L8ZFaXKBcTdouBy5a52bg8Q7vf+rL4CHRdWToGvcT+U21xt/f6hoivJXHkuyZ2TI0d/drj/rS/DlAuKOwWI9lpNmqbPUSjY88eX0ycqevmaz98T1vBOReIyFcgEAgmwG1XF7PvVBvPvFrFL16tivteVmzDktMxccORZfnJWM16jlZ38GfXlcZFXOFIlKqGHjJTrCQ7zLjsJm7avIRctx2TUc9lyzJ45tUqzCY9ywpS4p7XZjFSXpTGkeoOmju92jrOuMh3eNq53YtOktiwzM3B0200tHtZU5LOqQvdnK7rYU1JmmbxWZLjovl4M40dXvIzFtbiiKmiNt7pdXMT9YKIfAUCgWBCOG0mPnF9GdnpdsqLU7l6bTbb1uVSlO2kJTY/m2wfvXN5OAa9joqlGbT1+LXHq7x2qAF/IMKaEiUSlSSJO7aVcMWqLABSksz82XVlfPKGpRgNI/+Mq5Hx0EhOE19bfLezLMs0dnjJTLWyJNYw1tiu1H137akF4NYri7TnUVPPamS+kPENhDhwum1ch7C2mBdFxpAa+WwjIl+BQCCYIFtXZ8ftSQal0/lIdQfVjb0Jx3vGYtPKTN473szR6k4tQu3yDLDr7XM4rEY+srVw1MfesDF/1O9VlKVj0Ot4eX89x2o6SY/Vb0FZ1Ti027m7L4A/EGZVUSruZCsmg47Gdi/vV3VQ1dBLRWl63EpIdSzr0Jk2rrtsYjXz+eLpl8+y92QrX/14xZifTWu3sswjI9k6V4cmIl+BQCCYDpIksa7MzY5tpQmj0LHYuDILnSSx/1Srdtv/vlZFIBjhjm0lU56ltZoNbKvIIRiOUNfax/5TbVoN2Gk3xqWd1XpvXrodnSSRk26nscPLj35biUGv47ariuKeO9VpoTTXxZn6Hq3RbCHS2u1jX+x9PXVhbG/utm4fyQ7TmDX6mUaIr0AgEMwTLoeZlUUpnG/po6XLR3VjLwfPKGYeV67JHv8JxuATNyzlx39zLT/6623svK2cVKcZp91ESpIZo0GH2aTH6w9pc73qsopct51IVEaW4YsfW51wXGvj8gxt1ng0QuEIP3jhOEdiM8KzSSQaxTcQvyjixb0XULPNZ+pHF99QWOnuzpzDlDMI8RUIBIJ5ZXPMVWvfyVZ+/+55AO7YVjKmj/RkkCSJDcsz+M79V/DofZs1z2eHxYjHF+S9yhYk0AxC1pak47SbeOBjqxPONINitgFwcIzu4JpGD4fPtvPW0aYZOY+x+PnLZ/nqv71LR6+SPu7yDPDO8RYyU20UZiVxvrmPQDCS8LFtPQPIKGsr5xIhvgKBQDCPrCtzYzLoeO1QA8dqOlma50q42GC6qL7TKg6rkZ7+IPVt/VxRnkV6rN65YXkG//KFrVptNxEpSWbK8lycqRs99ax6RA9vJptp2nr8vHW0mUAwwqsHGwD4/XsXiERlPrx5CcuXpBCJylQ3xc8mh8KKGKvNVpmpIvIVCASCSwar2UBFWbo29jPUGGM2cVgVITYZdNx+dXHc9yZiNLFheQYysH+U6LeuTZkVbu/xx1lszjR/fO88UVlGr5N462gT1Y29vHmkkaxUG5tXZbIsdiEz1Cf7lYP1fP77b3LyfBetXXPfbAVCfAUCgWDeURc6LMlKmnTH9FRx2JSZ5Bs3FZDqtIxz75FsWpGJXiex52hzwlEeNfKNRGXae/zTO9hR6Oj1887xFrJSbdyytZCBYIR//uURZBk+vr0Ug15HWV4ykqQYaQAcqergf1+tQpbh7ePNtMWOba4jXzFqJBAIBPPM2pJ0br+6mIrS9DmzN9xSrswMf2hzwZQe77KbqChN59DZds639JGRMTiOFApHaOoYTDe3dPm0UaqZ5KV9dUSiMjdvWcKaknT+sPcCA8EIKwtTWBNzBbNZDBRkJnGu2cNv3j7HS/vqMBp0WEx6jlR1aEYhIvIVCASCSwydTuLmLYXkzaFj1OriNO6/ZRUW09RjsKtiG6j2DGuqauzwEpVlbS9yS+fYdd9XDtaz72TriNsvtPSNauYRDEV4r7KVZIeJy1dm4rAa2b4uD6NBx59tL4u7iFlekEw4IvObt88RiUb5i4+sZEt5NgPBCFUNvXM+ZgQi8hUIBALBFCkvSiUlyczek60MBMLa7XWtSr134/IMXj5QT/MYTVceX5D/fbUKh83IxhUZWpf3mbpuvv/Lo8iyzDfuuSzO6APg8Nl2/IEw29cvQa9T4sg7tpVw85Yl2Czx89E3birAYjKQ53awrCAZh9VISpKFl2K+1XM9ZgQi8hUIBALBFNHpJK5ao0SQX/6XN/m3Xx+nrrWPC7F674ZlipiO1fFcWduFDPT5QjTFZo7rWvt44vljyLJMJCrz7787SSAUPyr09vFmgDjHMZ1OGiG8AMkOM7deWcRly9yawUhRdhJpTiUyn+sxIxDiKxAIBIJpoPpbd/T6OXimnX957iinL3SjkySWZDlwp1jHTDsfrx3c6nTqQjdRWeap31TiD0S49+aV3LAhn5YuH8++Xq3dr7N3gFPnuynNc2lLLSaLJElctkyZV57rZisQaWeBQCAQTINkh5mHP72B9HQHP/tDJc+9UUNvf5Bctx2jQU92qo0j1R30+YIk2eK3PkWjMifOdWE1G/AHwpy60E1BpoPWLh9XrMri8pWZrF+azskLXbzxfiPuZCs3bsrnlYP1yMCVq6fnAnbdZXm0dPnYsMw9reeZCiLyFQgEAsG0kSSJD24qYPMqZWyqIENxzMpKU6LKRKnncy0e+v0hNixzk5Fi5Ux9N2/GmreuitlrGg16vvSxNaQkmXn2jWr+/r8P8PKBehxWY9xe46ngTrby5R1r53SbkYoQX4FAIBDMCJIk8ZkPLufmLYV86HJlhElNCydKPR+vUVLOq4vTWLEkBX8gwr7KVtzJFpYWDLp8pSdb+crHK7BbDNS19VNRms63PrMhzrHrYuPiPXKBQCAQLDhMRn2cY5Yqvok6no/XdqGTJFYWphKVZd480oSM0kQ13Ns6N93Ow5/eQJcnwLKC5Dmbh54tROQrEAgEglkjz23HoJfYd7IV/5BxpNZuH+eaPZTlubBZDCxfkgKABGwtT1zLzUixsXxJykUvvCDEVyAQCASziM1i5KbNS+juC7Brzznt9jcONwJwdYVi1OG0mbh6bQ7XbcgjzTV5u8uLDZF2FggEAsGs8uErlrD3ZCuvHqpnS3kWWWk23j7WjNNmZMOywaapz3xo+Twe5dwiIl+BQCAQzCpGg557blyGLMPjvzrKr3bX4AuEuboiB6Ph0pShCZ31wMAA119/PS+88IJ22549e1i2bNmsHZhAIBAIFg+rClO567oyPN4grx1qQJJgW0XufB/WvDGhtPNTTz2Fy+XS/h8IBPjxj3+M2z33g8kCgUAguDi5YWM+uW47//67k6wtTZvSKsPFwriRb01NDdXV1Wzbtk277Yc//CGf+MQnMJlMoz9QIBAIBIJhrCxM5ftf2MqnPnjp1HcTIcmJtiAP4b777uPhhx9m165d5Obmsm7dOr773e/y1FNPsX37dl5//fVxXyQcjmAwzO26JoFAIBAIFipjpp137dpFRUUF+fn52m2PPvoo3/zmNyf1It3dY+9ynApudxLt7X0z/rzzgTiXhYk4l4WJOJeFiTiXxM8zGmOK7+7du6mvr2f37t20tLRgMBjQ6XT89V//NQBtbW3cfffdPP3009M+SIFAIBAILhXGFN/HH39c+/rJJ58kNzeX22+/Xbtt+/btQngFAoFAIJgkl+aAlUAgEAgE88iEHa4eeOCBEbdNpNlKIBAIBAJBPCLyFQgEAoFgjhHiKxAIBALBHCPEVyAQCASCOUaIr0AgEAgEc4wQX4FAIBAI5hghvgKBQCAQzDHjejsLBAKBQCCYWUTkKxAIBALBHCPEVyAQCASCOUaIr0AgEAgEc4wQX4FAIBAI5hghvgKBQCAQzDFCfAUCgUAgmGMmvNVoofBP//RPHD16FEmS+MY3vsGaNWvm+5AmzXe/+10OHTpEOBzm/vvv5/XXX6eyspLk5GQAPve5z7Ft27b5PcgJsG/fPr70pS9RVlYGwNKlS7n33nv527/9WyKRCG63m+9973uYTKZ5PtLxee655/jtb3+r/f/EiROUl5fj8/mw2WwAfO1rX6O8vHy+DnFczp49y86dO/nMZz7D3XffTXNzc8LP4re//S0//elP0el03HnnnezYsWO+D30Eic7l61//OuFwGIPBwPe+9z3cbjerVq1i/fr12uN+8pOfoNfr5/HIRzL8XB566KGEv+8X4+fyxS9+ke7ubgB6enqoqKjg/vvv5yMf+Yj2u5KSksITTzwxn4edkOF/h1evXj23vy/yRcS+ffvk++67T5ZlWa6urpbvvPPOeT6iyfPee+/J9957ryzLstzV1SVfc8018te+9jX59ddfn+cjmzx79+6VH3jggbjbHnroIfmPf/yjLMuy/P3vf1/++c9/Ph+HNi327dsnP/LII/Ldd98tnzlzZr4PZ0J4vV757rvvlr/5zf/X3v2FNPX/cRx/nuZsThe2aqsuVtFFjZJIVsyioFVGRZb2b9IaghBRasZiGYTbXRnd9A8qqaAsCHYRQoFRVxEm0cA0iIjdjC5sM3PLFtY434vwkD9P5X582fF8+TzuzoddvD68fX8+fD47zNPynTt3ZFlWr8XIyIhcWVkpp1IpOZPJyNu3b5eHhoa0jD6B2lyCwaD88OFDWZZluaOjQ25ra5NlWZZXr16tWc7JUJuLWr/rtS6/amlpkXt7e+V4PC5XV1drkHDy1NbhfPeLrq6du7u72bRpEwCLFy9meHiYL1++aJwqN6tWreLChQsAzJgxg0wmQzab1TjVv6enp4eNGzcCsGHDBrq7uzVOlLsrV65w5MgRrWPkpLCwkPb2dmw2mzKmVove3l7KysqwWCyYTCbKy8uJRqNaxValNpdQKMSWLVuAnyepz58/axUvJ2pzUaPXuoyJxWKk02nd3ESqrcP57hddbb7JZJKZM2cqz1arlUQioWGi3BkMBuUaMxKJsH79egwGAx0dHfj9fo4fP86nT580Tjl579+/5/Dhw9TW1vL8+XMymYxyzTxr1izd1ef169fMmzePOXPmAHDx4kUOHDhAa2sr37590zjd7xUUFGAymcaNqdUimUxitVqVz0zFHlKbi9lsxmAwkM1muXfvHjt27ABgdHSUQCCA1+vl1q1bWsT9I7W5ABP6Xa91GXP79m18Pp/ynEwmaWpqwuv1jvs6Z6pQW4fz3S+6+873V7KOfxnzyZMnRCIRbt68SX9/P6WlpTidTq5fv87ly5dpbW3VOuJfLVy4kIaGBrZu3Uo8Hsfv9487xeuxPpFIhOrqagD8fj9LlizB4XAQCoW4e/cu9fX1Gif8//yuFnqqUTabJRgM4na7qaioACAYDFJVVYUkSfh8PlwuF2VlZRon/bOdO3dO6PeVK1eO+4ye6jI6OsqrV68Ih8MAlJaWcuzYMaqqqkin0+zduxe32/3X078Wfl2HKysrlfF89IuuTr42m41kMqk8f/z4UTmh6MmzZ8+4evUq7e3tWCwWKioqcDqdAHg8Ht69e6dxwsmx2+1s27YNSZJwOBzMnj2b4eFh5YQ4MDAwJRvuT3p6epSFcPPmzTgcDkBfdRljNpsn1EKth/RSo1OnTrFgwQIaGhqUsdraWoqLizGbzbjdbl3USK3f9VyXly9fjrtuLikpYffu3RiNRqxWK8uXLycWi2mYUN3/rsP57hddbb5r166lq6sLgDdv3mCz2SgpKdE4VW7S6TTnzp3j2rVrytuOjY2NxONx4OfiP/b28FTX2dnJjRs3AEgkEgwODlJTU6PU6PHjx6xbt07LiDkZGBiguLiYwsJCZFmmrq6OVCoF6KsuY9asWTOhFitWrKCvr49UKsXIyAjRaBSXy6Vx0r/r7OzEaDTS1NSkjMViMQKBALIs8+PHD6LRqC5qpNbveq0LQF9fH0uXLlWeX7x4wZkzZwD4+vUrb9++ZdGiRVrFU6W2Due7X3R17VxeXs6yZcvwer1IkkQoFNI6Us4ePXrE0NAQzc3NylhNTQ3Nzc0UFRVhNpuVP9ypzuPxcOLECZ4+fcr3798Jh8M4nU5OnjzJ/fv3mT9/Prt27dI65qQlEgnl+x1Jkti3bx91dXUUFRVht9tpbGzUOOHv9ff309bWxocPHygoKKCrq4vz58/T0tIyrhZGo5FAIEB9fT2SJHH06FEsFovW8cdRm8vg4CDTp0/n4MGDwM8XLsPhMHPnzmXPnj1MmzYNj8cz5V74UZuLz+eb0O8mk0mXdbl06RKJREK5IQJwuVw8ePCA/fv3k81mOXToEHa7XcPkE6mtw2fPnuX06dN56xfxLwUFQRAEIc90de0sCIIgCP8FYvMVBEEQhDwTm68gCIIg5JnYfAVBEAQhz8TmKwiCIAh5JjZfQRAEQcgzsfkKgiAIQp6JzVcQBEEQ8uwfDKK80QVH9ZwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RqLjapNgNEdX"
      },
      "source": [
        "### Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXHXFQd_K0tX"
      },
      "source": [
        "def model_forecast(model, data, window_size):\r\n",
        "    ds = tf.data.Dataset.from_tensor_slices(data)\r\n",
        "    ds = ds.window(window_size, shift=1, drop_remainder=True)\r\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size))\r\n",
        "    ds = ds.batch(32).prefetch(1)\r\n",
        "    forecast = model.predict(ds)\r\n",
        "    return forecast"
      ],
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEdC1C_zPDwU"
      },
      "source": [
        "rnn_forecast = model_forecast(model, data[..., np.newaxis], WINDOW_SIZE)\r\n",
        "rnn_forecast = rnn_forecast[split_index - WINDOW_SIZE:-1, -1, 0]"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fPANfKlTNX5A",
        "outputId": "65bbc28d-b36f-4729-ef91-dacec6221434"
      },
      "source": [
        "# Overall Error\r\n",
        "error = tf.keras.metrics.mean_absolute_error(test_data, rnn_forecast).numpy()\r\n",
        "print(error)"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "41.32207\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YSJgY7xtQH7C"
      },
      "source": [
        "### Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "hBWTXn9dOyv-",
        "outputId": "79c387de-2c26-495b-f08b-ee5985f23df7"
      },
      "source": [
        "plt.figure(figsize=(15, 6))\r\n",
        "\r\n",
        "plt.plot(list(range(split_index,len(data))), test_data, label = 'Test Data')\r\n",
        "plt.plot(list(range(split_index,len(data))), rnn_forecast, label = 'Predictions')\r\n",
        "#plt.fill_between(range(split_index,len(data)), results - error, results + error, alpha = 0.5, color = 'red')\r\n",
        "plt.legend()\r\n",
        "plt.show()\r\n",
        "\r\n",
        "plt.figure(figsize=(15, 6))\r\n",
        "# Plotting with Confidence Intervals\r\n",
        "#plt.plot(list(range(split_index,len(data))), test_data, label = 'Test Data')\r\n",
        "plt.plot(list(range(split_index,len(data))), rnn_forecast, label = 'Predictions', color = 'k', linestyle = '--')\r\n",
        "plt.fill_between(range(split_index,len(data)), rnn_forecast - error, rnn_forecast + error, alpha = 0.5, color = 'orange')\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83sZFh1KQWD9"
      },
      "source": [
        "# Unfortunately, the DNN model was more efficient than the combination model in this case. Sometimes, simpler models can give much better result! We can compare these results with standard statistical models like SARIMA or ARIMA as an extension."
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3PIcao8RuFI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}