l08c05_forecasting_with_machine_learning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "l08c05_forecasting_with_machine_learning.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "vidayERjaO5q"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Za8-Nr5k11fh"
      },
      "source": [
        "##### Copyright 2018 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "Eq10uEbw0E4l"
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yHG6wUbvifuX"
      },
      "source": [
        "# Forecasting with machine learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQNmSYMPh1TB"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c05_forecasting_with_machine_learning.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c05_forecasting_with_machine_learning.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vidayERjaO5q"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqWabzlJ63nL"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "keras = tf.keras"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cg1hfKCPldZG"
      },
      "source": [
        "def plot_series(time, series, format=\"-\", start=0, end=None, label=None):\n",
        "    plt.plot(time[start:end], series[start:end], format, label=label)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    if label:\n",
        "        plt.legend(fontsize=14)\n",
        "    plt.grid(True)\n",
        "    \n",
        "def trend(time, slope=0):\n",
        "    return slope * time\n",
        "  \n",
        "  \n",
        "def seasonal_pattern(season_time):\n",
        "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
        "    return np.where(season_time < 0.4,\n",
        "                    np.cos(season_time * 2 * np.pi),\n",
        "                    1 / np.exp(3 * season_time))\n",
        "\n",
        "  \n",
        "def seasonality(time, period, amplitude=1, phase=0):\n",
        "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
        "    season_time = ((time + phase) % period) / period\n",
        "    return amplitude * seasonal_pattern(season_time)\n",
        "  \n",
        "  \n",
        "def white_noise(time, noise_level=1, seed=None):\n",
        "    rnd = np.random.RandomState(seed)\n",
        "    return rnd.randn(len(time)) * noise_level"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iL2DDjV3lel6",
        "outputId": "c229d694-78a0-4d0a-a745-cd5ca0b33d2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "time = np.arange(4 * 365 + 1)\n",
        "\n",
        "slope = 0.05\n",
        "baseline = 10\n",
        "amplitude = 40\n",
        "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
        "\n",
        "noise_level = 5\n",
        "noise = white_noise(time, noise_level, seed=42)\n",
        "\n",
        "series += noise\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time, series)\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViWVB9qd8OIR"
      },
      "source": [
        "## Forecasting with Machine Learning\n",
        "\n",
        "First, we will train a model to forecast the next step given the previous 30 steps, therefore, we need to create a dataset of 30-step windows for training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tl-0BOKkEtk"
      },
      "source": [
        "def window_dataset(series, window_size, batch_size=32,\n",
        "                   shuffle_buffer=1000):\n",
        "    dataset = tf.data.Dataset.from_tensor_slices(series)\n",
        "    dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "    dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
        "    dataset = dataset.shuffle(shuffle_buffer)\n",
        "    dataset = dataset.map(lambda window: (window[:-1], window[-1]))\n",
        "    dataset = dataset.batch(batch_size).prefetch(1)\n",
        "    return dataset"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zmp1JXKxk9Vb"
      },
      "source": [
        "split_time = 1000\n",
        "time_train = time[:split_time]\n",
        "x_train = series[:split_time]\n",
        "time_valid = time[split_time:]\n",
        "x_valid = series[split_time:]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T1IvwAFn8OIc"
      },
      "source": [
        "### Linear Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ieOKdcEQ0A6k",
        "outputId": "1cd242d1-a720-4f55-ab95-37430984dfd5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 30\n",
        "train_set = window_dataset(x_train, window_size)\n",
        "valid_set = window_dataset(x_valid, window_size)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "  keras.layers.Dense(1, input_shape=[window_size])\n",
        "])\n",
        "optimizer = keras.optimizers.SGD(lr=1e-5, momentum=0.9)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "model.fit(train_set, epochs=100, validation_data=valid_set)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "31/31 [==============================] - 3s 28ms/step - loss: 46.6813 - mae: 47.1784 - val_loss: 23.1475 - val_mae: 23.6473\n",
            "Epoch 2/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 11.9123 - mae: 12.4017 - val_loss: 10.6108 - val_mae: 11.0934\n",
            "Epoch 3/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 10.2063 - mae: 10.6947 - val_loss: 9.1115 - val_mae: 9.6013\n",
            "Epoch 4/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 9.8053 - mae: 10.2898 - val_loss: 9.0103 - val_mae: 9.4965\n",
            "Epoch 5/100\n",
            "31/31 [==============================] - 1s 10ms/step - loss: 9.7068 - mae: 10.1940 - val_loss: 8.9696 - val_mae: 9.4569\n",
            "Epoch 6/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 9.5858 - mae: 10.0706 - val_loss: 9.2814 - val_mae: 9.7695\n",
            "Epoch 7/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 9.5716 - mae: 10.0561 - val_loss: 8.7506 - val_mae: 9.2409\n",
            "Epoch 8/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 9.4335 - mae: 9.9208 - val_loss: 8.9409 - val_mae: 9.4259\n",
            "Epoch 9/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 9.2821 - mae: 9.7655 - val_loss: 8.7019 - val_mae: 9.1887\n",
            "Epoch 10/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 9.1588 - mae: 9.6455 - val_loss: 9.1858 - val_mae: 9.6757\n",
            "Epoch 11/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 9.1080 - mae: 9.5909 - val_loss: 8.3841 - val_mae: 8.8717\n",
            "Epoch 12/100\n",
            "31/31 [==============================] - 1s 10ms/step - loss: 9.0054 - mae: 9.4901 - val_loss: 8.3203 - val_mae: 8.8105\n",
            "Epoch 13/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 8.8572 - mae: 9.3423 - val_loss: 8.2304 - val_mae: 8.7186\n",
            "Epoch 14/100\n",
            "31/31 [==============================] - 1s 10ms/step - loss: 8.7560 - mae: 9.2400 - val_loss: 8.2015 - val_mae: 8.6929\n",
            "Epoch 15/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 8.7060 - mae: 9.1891 - val_loss: 8.0562 - val_mae: 8.5446\n",
            "Epoch 16/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 8.6244 - mae: 9.1106 - val_loss: 7.9800 - val_mae: 8.4679\n",
            "Epoch 17/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 8.4941 - mae: 8.9788 - val_loss: 7.8962 - val_mae: 8.3846\n",
            "Epoch 18/100\n",
            "31/31 [==============================] - 1s 10ms/step - loss: 8.3914 - mae: 8.8778 - val_loss: 7.8248 - val_mae: 8.3136\n",
            "Epoch 19/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 8.3789 - mae: 8.8672 - val_loss: 7.9163 - val_mae: 8.4018\n",
            "Epoch 20/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 8.2872 - mae: 8.7752 - val_loss: 7.9781 - val_mae: 8.4660\n",
            "Epoch 21/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 8.1183 - mae: 8.6040 - val_loss: 7.6067 - val_mae: 8.0948\n",
            "Epoch 22/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 8.1174 - mae: 8.6013 - val_loss: 7.6044 - val_mae: 8.0939\n",
            "Epoch 23/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.9768 - mae: 8.4606 - val_loss: 7.5181 - val_mae: 8.0027\n",
            "Epoch 24/100\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 7.9488 - mae: 8.4330 - val_loss: 7.4165 - val_mae: 7.9037\n",
            "Epoch 25/100\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 7.8248 - mae: 8.3076 - val_loss: 7.4864 - val_mae: 7.9701\n",
            "Epoch 26/100\n",
            "31/31 [==============================] - 1s 10ms/step - loss: 7.7746 - mae: 8.2588 - val_loss: 7.3600 - val_mae: 7.8449\n",
            "Epoch 27/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.6489 - mae: 8.1347 - val_loss: 7.4656 - val_mae: 7.9534\n",
            "Epoch 28/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.5881 - mae: 8.0748 - val_loss: 7.2633 - val_mae: 7.7468\n",
            "Epoch 29/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.5281 - mae: 8.0121 - val_loss: 7.2226 - val_mae: 7.7046\n",
            "Epoch 30/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.4287 - mae: 7.9117 - val_loss: 7.4429 - val_mae: 7.9314\n",
            "Epoch 31/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.4079 - mae: 7.8897 - val_loss: 7.0365 - val_mae: 7.5220\n",
            "Epoch 32/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.2735 - mae: 7.7602 - val_loss: 6.9825 - val_mae: 7.4673\n",
            "Epoch 33/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 7.2739 - mae: 7.7580 - val_loss: 6.9299 - val_mae: 7.4151\n",
            "Epoch 34/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.1570 - mae: 7.6430 - val_loss: 7.1487 - val_mae: 7.6338\n",
            "Epoch 35/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.1307 - mae: 7.6135 - val_loss: 6.8096 - val_mae: 7.2905\n",
            "Epoch 36/100\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 7.0562 - mae: 7.5384 - val_loss: 6.8589 - val_mae: 7.3451\n",
            "Epoch 37/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.9709 - mae: 7.4550 - val_loss: 6.6912 - val_mae: 7.1721\n",
            "Epoch 38/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.9274 - mae: 7.4110 - val_loss: 6.7854 - val_mae: 7.2720\n",
            "Epoch 39/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.8287 - mae: 7.3123 - val_loss: 7.0975 - val_mae: 7.5836\n",
            "Epoch 40/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 6.7947 - mae: 7.2743 - val_loss: 6.6359 - val_mae: 7.1221\n",
            "Epoch 41/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.7593 - mae: 7.2443 - val_loss: 6.6223 - val_mae: 7.1082\n",
            "Epoch 42/100\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 6.6816 - mae: 7.1641 - val_loss: 6.4618 - val_mae: 6.9384\n",
            "Epoch 43/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.5978 - mae: 7.0800 - val_loss: 6.6013 - val_mae: 7.0877\n",
            "Epoch 44/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 6.6256 - mae: 7.1066 - val_loss: 6.4921 - val_mae: 6.9772\n",
            "Epoch 45/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.5119 - mae: 6.9944 - val_loss: 6.4523 - val_mae: 6.9408\n",
            "Epoch 46/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.5113 - mae: 6.9923 - val_loss: 6.3676 - val_mae: 6.8539\n",
            "Epoch 47/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.4703 - mae: 6.9551 - val_loss: 6.4135 - val_mae: 6.9027\n",
            "Epoch 48/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.3575 - mae: 6.8423 - val_loss: 6.2646 - val_mae: 6.7469\n",
            "Epoch 49/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.3132 - mae: 6.7936 - val_loss: 6.5807 - val_mae: 7.0648\n",
            "Epoch 50/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.3524 - mae: 6.8329 - val_loss: 6.1738 - val_mae: 6.6523\n",
            "Epoch 51/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.2175 - mae: 6.7013 - val_loss: 6.5609 - val_mae: 7.0448\n",
            "Epoch 52/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.2483 - mae: 6.7297 - val_loss: 6.3995 - val_mae: 6.8871\n",
            "Epoch 53/100\n",
            "31/31 [==============================] - 1s 10ms/step - loss: 6.2216 - mae: 6.7043 - val_loss: 6.0807 - val_mae: 6.5592\n",
            "Epoch 54/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.0713 - mae: 6.5524 - val_loss: 6.0608 - val_mae: 6.5410\n",
            "Epoch 55/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.0764 - mae: 6.5575 - val_loss: 6.0446 - val_mae: 6.5258\n",
            "Epoch 56/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 6.0693 - mae: 6.5438 - val_loss: 5.9976 - val_mae: 6.4784\n",
            "Epoch 57/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.9879 - mae: 6.4677 - val_loss: 6.5870 - val_mae: 7.0729\n",
            "Epoch 58/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.9670 - mae: 6.4472 - val_loss: 6.2659 - val_mae: 6.7540\n",
            "Epoch 59/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.9528 - mae: 6.4379 - val_loss: 5.9457 - val_mae: 6.4270\n",
            "Epoch 60/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.8707 - mae: 6.3552 - val_loss: 5.9626 - val_mae: 6.4462\n",
            "Epoch 61/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.8656 - mae: 6.3480 - val_loss: 6.0507 - val_mae: 6.5383\n",
            "Epoch 62/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.8503 - mae: 6.3305 - val_loss: 5.9375 - val_mae: 6.4194\n",
            "Epoch 63/100\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.7668 - mae: 6.2473 - val_loss: 5.8891 - val_mae: 6.3675\n",
            "Epoch 64/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.7735 - mae: 6.2540 - val_loss: 5.7918 - val_mae: 6.2716\n",
            "Epoch 65/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.7196 - mae: 6.1992 - val_loss: 5.7822 - val_mae: 6.2677\n",
            "Epoch 66/100\n",
            "31/31 [==============================] - 1s 10ms/step - loss: 5.7195 - mae: 6.2001 - val_loss: 5.8370 - val_mae: 6.3169\n",
            "Epoch 67/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.7116 - mae: 6.1953 - val_loss: 5.8026 - val_mae: 6.2866\n",
            "Epoch 68/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.6455 - mae: 6.1273 - val_loss: 5.8970 - val_mae: 6.3825\n",
            "Epoch 69/100\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.5995 - mae: 6.0752 - val_loss: 6.1008 - val_mae: 6.5871\n",
            "Epoch 70/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.6110 - mae: 6.0918 - val_loss: 5.9595 - val_mae: 6.4444\n",
            "Epoch 71/100\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 5.5976 - mae: 6.0817 - val_loss: 5.6474 - val_mae: 6.1311\n",
            "Epoch 72/100\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.5355 - mae: 6.0157 - val_loss: 5.6318 - val_mae: 6.1172\n",
            "Epoch 73/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.5398 - mae: 6.0224 - val_loss: 5.6279 - val_mae: 6.1140\n",
            "Epoch 74/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.5468 - mae: 6.0275 - val_loss: 5.5921 - val_mae: 6.0779\n",
            "Epoch 75/100\n",
            "31/31 [==============================] - 1s 10ms/step - loss: 5.4855 - mae: 5.9659 - val_loss: 6.0816 - val_mae: 6.5651\n",
            "Epoch 76/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.4834 - mae: 5.9646 - val_loss: 5.6142 - val_mae: 6.0898\n",
            "Epoch 77/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.4842 - mae: 5.9646 - val_loss: 5.5331 - val_mae: 6.0186\n",
            "Epoch 78/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.4070 - mae: 5.8878 - val_loss: 5.5209 - val_mae: 6.0072\n",
            "Epoch 79/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.3568 - mae: 5.8389 - val_loss: 5.5470 - val_mae: 6.0356\n",
            "Epoch 80/100\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.3579 - mae: 5.8375 - val_loss: 5.5790 - val_mae: 6.0657\n",
            "Epoch 81/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.3304 - mae: 5.8088 - val_loss: 5.7429 - val_mae: 6.2243\n",
            "Epoch 82/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.2761 - mae: 5.7589 - val_loss: 5.7831 - val_mae: 6.2640\n",
            "Epoch 83/100\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.3007 - mae: 5.7804 - val_loss: 5.4424 - val_mae: 5.9216\n",
            "Epoch 84/100\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.2705 - mae: 5.7502 - val_loss: 5.6147 - val_mae: 6.0958\n",
            "Epoch 85/100\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.2405 - mae: 5.7215 - val_loss: 5.4449 - val_mae: 5.9350\n",
            "Epoch 86/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.2553 - mae: 5.7318 - val_loss: 5.3910 - val_mae: 5.8785\n",
            "Epoch 87/100\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.1897 - mae: 5.6698 - val_loss: 5.3836 - val_mae: 5.8726\n",
            "Epoch 88/100\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.1770 - mae: 5.6583 - val_loss: 5.5470 - val_mae: 6.0273\n",
            "Epoch 89/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.1712 - mae: 5.6501 - val_loss: 5.3334 - val_mae: 5.8172\n",
            "Epoch 90/100\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.1521 - mae: 5.6322 - val_loss: 5.3244 - val_mae: 5.8025\n",
            "Epoch 91/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.1463 - mae: 5.6261 - val_loss: 5.4182 - val_mae: 5.9050\n",
            "Epoch 92/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.1003 - mae: 5.5802 - val_loss: 5.3004 - val_mae: 5.7758\n",
            "Epoch 93/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.0947 - mae: 5.5750 - val_loss: 5.4387 - val_mae: 5.9227\n",
            "Epoch 94/100\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.0597 - mae: 5.5381 - val_loss: 5.3146 - val_mae: 5.8038\n",
            "Epoch 95/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.0638 - mae: 5.5395 - val_loss: 5.4179 - val_mae: 5.9012\n",
            "Epoch 96/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.0408 - mae: 5.5199 - val_loss: 5.2369 - val_mae: 5.7242\n",
            "Epoch 97/100\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.0147 - mae: 5.4941 - val_loss: 5.3345 - val_mae: 5.8204\n",
            "Epoch 98/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.0047 - mae: 5.4842 - val_loss: 5.2042 - val_mae: 5.6894\n",
            "Epoch 99/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.0098 - mae: 5.4891 - val_loss: 5.3813 - val_mae: 5.8612\n",
            "Epoch 100/100\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.0298 - mae: 5.5070 - val_loss: 5.7582 - val_mae: 6.2393\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff260630590>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3N8AGRM8OIc",
        "outputId": "d8a2b8ef-0165-4875-dff3-3d50630449ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 30\n",
        "train_set = window_dataset(x_train, window_size)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "  keras.layers.Dense(1, input_shape=[window_size])\n",
        "])\n",
        "\n",
        "lr_schedule = keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: 1e-6 * 10**(epoch / 30))\n",
        "optimizer = keras.optimizers.SGD(lr=1e-6, momentum=0.9)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "31/31 [==============================] - 1s 6ms/step - loss: 67.6867 - mae: 68.1863 - lr: 1.0000e-06\n",
            "Epoch 2/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 52.3572 - mae: 52.8561 - lr: 1.0798e-06\n",
            "Epoch 3/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 34.6465 - mae: 35.1446 - lr: 1.1659e-06\n",
            "Epoch 4/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 17.6521 - mae: 18.1484 - lr: 1.2589e-06\n",
            "Epoch 5/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 8.4604 - mae: 8.9488 - lr: 1.3594e-06\n",
            "Epoch 6/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.9765 - mae: 8.4627 - lr: 1.4678e-06\n",
            "Epoch 7/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.9201 - mae: 8.4035 - lr: 1.5849e-06\n",
            "Epoch 8/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.8785 - mae: 8.3609 - lr: 1.7113e-06\n",
            "Epoch 9/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.8191 - mae: 8.3014 - lr: 1.8478e-06\n",
            "Epoch 10/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 7.7701 - mae: 8.2547 - lr: 1.9953e-06\n",
            "Epoch 11/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.7249 - mae: 8.2097 - lr: 2.1544e-06\n",
            "Epoch 12/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 7.6673 - mae: 8.1525 - lr: 2.3263e-06\n",
            "Epoch 13/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 7.6269 - mae: 8.1136 - lr: 2.5119e-06\n",
            "Epoch 14/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 7.5684 - mae: 8.0530 - lr: 2.7123e-06\n",
            "Epoch 15/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.5128 - mae: 8.0016 - lr: 2.9286e-06\n",
            "Epoch 16/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.4312 - mae: 7.9150 - lr: 3.1623e-06\n",
            "Epoch 17/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.3864 - mae: 7.8736 - lr: 3.4145e-06\n",
            "Epoch 18/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.3137 - mae: 7.7950 - lr: 3.6869e-06\n",
            "Epoch 19/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.2457 - mae: 7.7273 - lr: 3.9811e-06\n",
            "Epoch 20/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.1735 - mae: 7.6544 - lr: 4.2987e-06\n",
            "Epoch 21/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.1804 - mae: 7.6662 - lr: 4.6416e-06\n",
            "Epoch 22/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 7.0853 - mae: 7.5706 - lr: 5.0119e-06\n",
            "Epoch 23/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.0073 - mae: 7.4884 - lr: 5.4117e-06\n",
            "Epoch 24/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.9851 - mae: 7.4691 - lr: 5.8434e-06\n",
            "Epoch 25/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.8725 - mae: 7.3567 - lr: 6.3096e-06\n",
            "Epoch 26/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.8170 - mae: 7.2973 - lr: 6.8129e-06\n",
            "Epoch 27/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.7869 - mae: 7.2673 - lr: 7.3564e-06\n",
            "Epoch 28/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.6955 - mae: 7.1753 - lr: 7.9433e-06\n",
            "Epoch 29/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.7099 - mae: 7.1941 - lr: 8.5770e-06\n",
            "Epoch 30/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.6355 - mae: 7.1132 - lr: 9.2612e-06\n",
            "Epoch 31/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.5437 - mae: 7.0231 - lr: 1.0000e-05\n",
            "Epoch 32/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.5368 - mae: 7.0210 - lr: 1.0798e-05\n",
            "Epoch 33/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.4093 - mae: 6.8926 - lr: 1.1659e-05\n",
            "Epoch 34/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.4816 - mae: 6.9675 - lr: 1.2589e-05\n",
            "Epoch 35/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.3046 - mae: 6.7875 - lr: 1.3594e-05\n",
            "Epoch 36/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.2577 - mae: 6.7419 - lr: 1.4678e-05\n",
            "Epoch 37/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.2191 - mae: 6.6986 - lr: 1.5849e-05\n",
            "Epoch 38/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.1605 - mae: 6.6405 - lr: 1.7113e-05\n",
            "Epoch 39/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.1336 - mae: 6.6142 - lr: 1.8478e-05\n",
            "Epoch 40/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.0521 - mae: 6.5349 - lr: 1.9953e-05\n",
            "Epoch 41/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.9596 - mae: 6.4395 - lr: 2.1544e-05\n",
            "Epoch 42/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.8883 - mae: 6.3673 - lr: 2.3263e-05\n",
            "Epoch 43/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.9301 - mae: 6.4072 - lr: 2.5119e-05\n",
            "Epoch 44/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.8831 - mae: 6.3622 - lr: 2.7123e-05\n",
            "Epoch 45/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.7877 - mae: 6.2663 - lr: 2.9286e-05\n",
            "Epoch 46/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.6731 - mae: 6.1480 - lr: 3.1623e-05\n",
            "Epoch 47/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.7843 - mae: 6.2660 - lr: 3.4145e-05\n",
            "Epoch 48/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.8111 - mae: 6.2936 - lr: 3.6869e-05\n",
            "Epoch 49/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.6778 - mae: 6.1561 - lr: 3.9811e-05\n",
            "Epoch 50/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.6273 - mae: 6.1083 - lr: 4.2987e-05\n",
            "Epoch 51/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.4036 - mae: 5.8842 - lr: 4.6416e-05\n",
            "Epoch 52/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.4779 - mae: 5.9611 - lr: 5.0119e-05\n",
            "Epoch 53/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.2399 - mae: 5.7194 - lr: 5.4117e-05\n",
            "Epoch 54/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.4156 - mae: 5.8931 - lr: 5.8434e-05\n",
            "Epoch 55/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.0955 - mae: 5.5773 - lr: 6.3096e-05\n",
            "Epoch 56/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.3713 - mae: 5.8517 - lr: 6.8129e-05\n",
            "Epoch 57/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 4.9477 - mae: 5.4255 - lr: 7.3564e-05\n",
            "Epoch 58/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.0357 - mae: 5.5195 - lr: 7.9433e-05\n",
            "Epoch 59/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.3320 - mae: 5.8154 - lr: 8.5770e-05\n",
            "Epoch 60/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.0502 - mae: 5.5320 - lr: 9.2612e-05\n",
            "Epoch 61/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.4312 - mae: 5.9079 - lr: 1.0000e-04\n",
            "Epoch 62/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 4.7412 - mae: 5.2177 - lr: 1.0798e-04\n",
            "Epoch 63/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 4.7009 - mae: 5.1809 - lr: 1.1659e-04\n",
            "Epoch 64/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.1741 - mae: 5.6554 - lr: 1.2589e-04\n",
            "Epoch 65/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.5444 - mae: 6.0204 - lr: 1.3594e-04\n",
            "Epoch 66/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.2933 - mae: 5.7770 - lr: 1.4678e-04\n",
            "Epoch 67/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.4325 - mae: 5.9125 - lr: 1.5849e-04\n",
            "Epoch 68/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 4.7728 - mae: 5.2524 - lr: 1.7113e-04\n",
            "Epoch 69/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.8575 - mae: 6.3436 - lr: 1.8478e-04\n",
            "Epoch 70/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.7263 - mae: 7.2108 - lr: 1.9953e-04\n",
            "Epoch 71/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 8.4035 - mae: 8.8914 - lr: 2.1544e-04\n",
            "Epoch 72/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.5493 - mae: 7.0333 - lr: 2.3263e-04\n",
            "Epoch 73/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 4.8068 - mae: 5.2873 - lr: 2.5119e-04\n",
            "Epoch 74/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.4627 - mae: 6.9477 - lr: 2.7123e-04\n",
            "Epoch 75/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 8.1859 - mae: 8.6720 - lr: 2.9286e-04\n",
            "Epoch 76/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 8.1732 - mae: 8.6607 - lr: 3.1623e-04\n",
            "Epoch 77/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 10.3116 - mae: 10.8020 - lr: 3.4145e-04\n",
            "Epoch 78/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 9.9555 - mae: 10.4477 - lr: 3.6869e-04\n",
            "Epoch 79/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 12.5410 - mae: 13.0331 - lr: 3.9811e-04\n",
            "Epoch 80/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 18.2217 - mae: 18.7183 - lr: 4.2987e-04\n",
            "Epoch 81/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 30.0461 - mae: 30.5423 - lr: 4.6416e-04\n",
            "Epoch 82/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 39.6340 - mae: 40.1314 - lr: 5.0119e-04\n",
            "Epoch 83/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 27.5702 - mae: 28.0679 - lr: 5.4117e-04\n",
            "Epoch 84/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 32.9483 - mae: 33.4435 - lr: 5.8434e-04\n",
            "Epoch 85/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 25.5016 - mae: 25.9967 - lr: 6.3096e-04\n",
            "Epoch 86/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 34.0204 - mae: 34.5167 - lr: 6.8129e-04\n",
            "Epoch 87/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 63.5979 - mae: 64.0947 - lr: 7.3564e-04\n",
            "Epoch 88/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 38.7950 - mae: 39.2876 - lr: 7.9433e-04\n",
            "Epoch 89/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 23.9960 - mae: 24.4910 - lr: 8.5770e-04\n",
            "Epoch 90/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 66.4214 - mae: 66.9184 - lr: 9.2612e-04\n",
            "Epoch 91/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 40.5205 - mae: 41.0183 - lr: 0.0010\n",
            "Epoch 92/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 34.2454 - mae: 34.7417 - lr: 0.0011\n",
            "Epoch 93/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 50.3738 - mae: 50.8703 - lr: 0.0012\n",
            "Epoch 94/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 44.5584 - mae: 45.0558 - lr: 0.0013\n",
            "Epoch 95/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 48.3764 - mae: 48.8740 - lr: 0.0014\n",
            "Epoch 96/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 82.9206 - mae: 83.4202 - lr: 0.0015\n",
            "Epoch 97/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 72.4634 - mae: 72.9607 - lr: 0.0016\n",
            "Epoch 98/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 129.2921 - mae: 129.7917 - lr: 0.0017\n",
            "Epoch 99/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 95.1654 - mae: 95.6645 - lr: 0.0018\n",
            "Epoch 100/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 41.3524 - mae: 41.8480 - lr: 0.0020\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PF9e7IDm8OId",
        "outputId": "4f6757dd-0705-4e77-eeff-b54b8692fb48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "plt.axis([1e-6, 1e-3, 0, 20])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1e-06, 0.001, 0.0, 20.0)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMNwyIFE8OIf",
        "outputId": "edb7d538-ea6c-4ad8-c662-a8f56e9052ab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 30\n",
        "train_set = window_dataset(x_train, window_size)\n",
        "valid_set = window_dataset(x_valid, window_size)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "  keras.layers.Dense(1, input_shape=[window_size])\n",
        "])\n",
        "optimizer = keras.optimizers.SGD(lr=1e-5, momentum=0.9)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "early_stopping = keras.callbacks.EarlyStopping(patience=10)\n",
        "model.fit(train_set, epochs=500,\n",
        "          validation_data=valid_set,\n",
        "          callbacks=[early_stopping])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 46.6813 - mae: 47.1784 - val_loss: 23.1475 - val_mae: 23.6473\n",
            "Epoch 2/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 11.9123 - mae: 12.4017 - val_loss: 10.6108 - val_mae: 11.0934\n",
            "Epoch 3/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 10.2063 - mae: 10.6947 - val_loss: 9.1115 - val_mae: 9.6013\n",
            "Epoch 4/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 9.8053 - mae: 10.2898 - val_loss: 9.0103 - val_mae: 9.4965\n",
            "Epoch 5/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 9.7068 - mae: 10.1940 - val_loss: 8.9696 - val_mae: 9.4569\n",
            "Epoch 6/500\n",
            "31/31 [==============================] - 1s 10ms/step - loss: 9.5858 - mae: 10.0706 - val_loss: 9.2814 - val_mae: 9.7695\n",
            "Epoch 7/500\n",
            "31/31 [==============================] - 1s 10ms/step - loss: 9.5716 - mae: 10.0561 - val_loss: 8.7506 - val_mae: 9.2409\n",
            "Epoch 8/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 9.4335 - mae: 9.9208 - val_loss: 8.9409 - val_mae: 9.4259\n",
            "Epoch 9/500\n",
            "31/31 [==============================] - 1s 15ms/step - loss: 9.2821 - mae: 9.7655 - val_loss: 8.7019 - val_mae: 9.1887\n",
            "Epoch 10/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 9.1588 - mae: 9.6455 - val_loss: 9.1858 - val_mae: 9.6757\n",
            "Epoch 11/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 9.1080 - mae: 9.5909 - val_loss: 8.3841 - val_mae: 8.8717\n",
            "Epoch 12/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 9.0054 - mae: 9.4901 - val_loss: 8.3203 - val_mae: 8.8105\n",
            "Epoch 13/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 8.8572 - mae: 9.3423 - val_loss: 8.2304 - val_mae: 8.7186\n",
            "Epoch 14/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 8.7560 - mae: 9.2400 - val_loss: 8.2015 - val_mae: 8.6929\n",
            "Epoch 15/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 8.7060 - mae: 9.1891 - val_loss: 8.0562 - val_mae: 8.5446\n",
            "Epoch 16/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 8.6244 - mae: 9.1106 - val_loss: 7.9800 - val_mae: 8.4679\n",
            "Epoch 17/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 8.4941 - mae: 8.9788 - val_loss: 7.8962 - val_mae: 8.3846\n",
            "Epoch 18/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 8.3914 - mae: 8.8778 - val_loss: 7.8248 - val_mae: 8.3136\n",
            "Epoch 19/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 8.3789 - mae: 8.8672 - val_loss: 7.9163 - val_mae: 8.4018\n",
            "Epoch 20/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 8.2872 - mae: 8.7752 - val_loss: 7.9781 - val_mae: 8.4660\n",
            "Epoch 21/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 8.1183 - mae: 8.6040 - val_loss: 7.6067 - val_mae: 8.0948\n",
            "Epoch 22/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 8.1174 - mae: 8.6013 - val_loss: 7.6044 - val_mae: 8.0939\n",
            "Epoch 23/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 7.9768 - mae: 8.4606 - val_loss: 7.5181 - val_mae: 8.0027\n",
            "Epoch 24/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 7.9488 - mae: 8.4330 - val_loss: 7.4165 - val_mae: 7.9037\n",
            "Epoch 25/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 7.8248 - mae: 8.3076 - val_loss: 7.4864 - val_mae: 7.9701\n",
            "Epoch 26/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.7746 - mae: 8.2588 - val_loss: 7.3600 - val_mae: 7.8449\n",
            "Epoch 27/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 7.6489 - mae: 8.1347 - val_loss: 7.4656 - val_mae: 7.9534\n",
            "Epoch 28/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 7.5881 - mae: 8.0748 - val_loss: 7.2633 - val_mae: 7.7468\n",
            "Epoch 29/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 7.5281 - mae: 8.0121 - val_loss: 7.2226 - val_mae: 7.7046\n",
            "Epoch 30/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.4287 - mae: 7.9117 - val_loss: 7.4429 - val_mae: 7.9314\n",
            "Epoch 31/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.4079 - mae: 7.8897 - val_loss: 7.0365 - val_mae: 7.5220\n",
            "Epoch 32/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.2735 - mae: 7.7602 - val_loss: 6.9825 - val_mae: 7.4673\n",
            "Epoch 33/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 7.2739 - mae: 7.7580 - val_loss: 6.9299 - val_mae: 7.4151\n",
            "Epoch 34/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 7.1570 - mae: 7.6430 - val_loss: 7.1487 - val_mae: 7.6338\n",
            "Epoch 35/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.1307 - mae: 7.6135 - val_loss: 6.8096 - val_mae: 7.2905\n",
            "Epoch 36/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.0562 - mae: 7.5384 - val_loss: 6.8589 - val_mae: 7.3451\n",
            "Epoch 37/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 6.9709 - mae: 7.4550 - val_loss: 6.6912 - val_mae: 7.1721\n",
            "Epoch 38/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 6.9274 - mae: 7.4110 - val_loss: 6.7854 - val_mae: 7.2720\n",
            "Epoch 39/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 6.8287 - mae: 7.3123 - val_loss: 7.0975 - val_mae: 7.5836\n",
            "Epoch 40/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.7947 - mae: 7.2743 - val_loss: 6.6359 - val_mae: 7.1221\n",
            "Epoch 41/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.7593 - mae: 7.2443 - val_loss: 6.6223 - val_mae: 7.1082\n",
            "Epoch 42/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.6816 - mae: 7.1641 - val_loss: 6.4618 - val_mae: 6.9384\n",
            "Epoch 43/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 6.5978 - mae: 7.0800 - val_loss: 6.6013 - val_mae: 7.0877\n",
            "Epoch 44/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 6.6256 - mae: 7.1066 - val_loss: 6.4921 - val_mae: 6.9772\n",
            "Epoch 45/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.5119 - mae: 6.9944 - val_loss: 6.4523 - val_mae: 6.9408\n",
            "Epoch 46/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.5113 - mae: 6.9923 - val_loss: 6.3676 - val_mae: 6.8539\n",
            "Epoch 47/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.4703 - mae: 6.9551 - val_loss: 6.4135 - val_mae: 6.9027\n",
            "Epoch 48/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 6.3575 - mae: 6.8423 - val_loss: 6.2646 - val_mae: 6.7469\n",
            "Epoch 49/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 6.3132 - mae: 6.7936 - val_loss: 6.5807 - val_mae: 7.0648\n",
            "Epoch 50/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.3524 - mae: 6.8329 - val_loss: 6.1738 - val_mae: 6.6523\n",
            "Epoch 51/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.2175 - mae: 6.7013 - val_loss: 6.5609 - val_mae: 7.0448\n",
            "Epoch 52/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.2483 - mae: 6.7297 - val_loss: 6.3995 - val_mae: 6.8871\n",
            "Epoch 53/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 6.2216 - mae: 6.7043 - val_loss: 6.0807 - val_mae: 6.5592\n",
            "Epoch 54/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.0713 - mae: 6.5524 - val_loss: 6.0608 - val_mae: 6.5410\n",
            "Epoch 55/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.0764 - mae: 6.5575 - val_loss: 6.0446 - val_mae: 6.5258\n",
            "Epoch 56/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.0693 - mae: 6.5438 - val_loss: 5.9976 - val_mae: 6.4784\n",
            "Epoch 57/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.9879 - mae: 6.4677 - val_loss: 6.5870 - val_mae: 7.0729\n",
            "Epoch 58/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.9670 - mae: 6.4472 - val_loss: 6.2659 - val_mae: 6.7540\n",
            "Epoch 59/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.9528 - mae: 6.4379 - val_loss: 5.9457 - val_mae: 6.4270\n",
            "Epoch 60/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.8707 - mae: 6.3552 - val_loss: 5.9626 - val_mae: 6.4462\n",
            "Epoch 61/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.8656 - mae: 6.3480 - val_loss: 6.0507 - val_mae: 6.5383\n",
            "Epoch 62/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.8503 - mae: 6.3305 - val_loss: 5.9375 - val_mae: 6.4194\n",
            "Epoch 63/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.7668 - mae: 6.2473 - val_loss: 5.8891 - val_mae: 6.3675\n",
            "Epoch 64/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.7735 - mae: 6.2540 - val_loss: 5.7918 - val_mae: 6.2716\n",
            "Epoch 65/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.7196 - mae: 6.1992 - val_loss: 5.7822 - val_mae: 6.2677\n",
            "Epoch 66/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.7195 - mae: 6.2001 - val_loss: 5.8370 - val_mae: 6.3169\n",
            "Epoch 67/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.7116 - mae: 6.1953 - val_loss: 5.8026 - val_mae: 6.2866\n",
            "Epoch 68/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.6455 - mae: 6.1273 - val_loss: 5.8970 - val_mae: 6.3825\n",
            "Epoch 69/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.5995 - mae: 6.0752 - val_loss: 6.1008 - val_mae: 6.5871\n",
            "Epoch 70/500\n",
            "31/31 [==============================] - 1s 10ms/step - loss: 5.6110 - mae: 6.0918 - val_loss: 5.9595 - val_mae: 6.4444\n",
            "Epoch 71/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.5976 - mae: 6.0817 - val_loss: 5.6474 - val_mae: 6.1311\n",
            "Epoch 72/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.5355 - mae: 6.0157 - val_loss: 5.6318 - val_mae: 6.1172\n",
            "Epoch 73/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.5398 - mae: 6.0224 - val_loss: 5.6279 - val_mae: 6.1140\n",
            "Epoch 74/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.5468 - mae: 6.0275 - val_loss: 5.5921 - val_mae: 6.0779\n",
            "Epoch 75/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.4855 - mae: 5.9659 - val_loss: 6.0816 - val_mae: 6.5651\n",
            "Epoch 76/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.4834 - mae: 5.9646 - val_loss: 5.6142 - val_mae: 6.0898\n",
            "Epoch 77/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.4842 - mae: 5.9646 - val_loss: 5.5331 - val_mae: 6.0186\n",
            "Epoch 78/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.4070 - mae: 5.8878 - val_loss: 5.5209 - val_mae: 6.0072\n",
            "Epoch 79/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.3568 - mae: 5.8389 - val_loss: 5.5470 - val_mae: 6.0356\n",
            "Epoch 80/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.3579 - mae: 5.8375 - val_loss: 5.5790 - val_mae: 6.0657\n",
            "Epoch 81/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.3304 - mae: 5.8088 - val_loss: 5.7429 - val_mae: 6.2243\n",
            "Epoch 82/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.2761 - mae: 5.7589 - val_loss: 5.7831 - val_mae: 6.2640\n",
            "Epoch 83/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.3007 - mae: 5.7804 - val_loss: 5.4424 - val_mae: 5.9216\n",
            "Epoch 84/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.2705 - mae: 5.7502 - val_loss: 5.6147 - val_mae: 6.0958\n",
            "Epoch 85/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.2405 - mae: 5.7215 - val_loss: 5.4449 - val_mae: 5.9350\n",
            "Epoch 86/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.2553 - mae: 5.7318 - val_loss: 5.3910 - val_mae: 5.8785\n",
            "Epoch 87/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.1897 - mae: 5.6698 - val_loss: 5.3836 - val_mae: 5.8726\n",
            "Epoch 88/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.1770 - mae: 5.6583 - val_loss: 5.5470 - val_mae: 6.0273\n",
            "Epoch 89/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.1712 - mae: 5.6501 - val_loss: 5.3334 - val_mae: 5.8172\n",
            "Epoch 90/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.1521 - mae: 5.6322 - val_loss: 5.3244 - val_mae: 5.8025\n",
            "Epoch 91/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.1463 - mae: 5.6261 - val_loss: 5.4182 - val_mae: 5.9050\n",
            "Epoch 92/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.1003 - mae: 5.5802 - val_loss: 5.3004 - val_mae: 5.7758\n",
            "Epoch 93/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.0947 - mae: 5.5750 - val_loss: 5.4387 - val_mae: 5.9227\n",
            "Epoch 94/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.0597 - mae: 5.5381 - val_loss: 5.3146 - val_mae: 5.8038\n",
            "Epoch 95/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.0638 - mae: 5.5395 - val_loss: 5.4179 - val_mae: 5.9012\n",
            "Epoch 96/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.0408 - mae: 5.5199 - val_loss: 5.2369 - val_mae: 5.7242\n",
            "Epoch 97/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.0147 - mae: 5.4941 - val_loss: 5.3345 - val_mae: 5.8204\n",
            "Epoch 98/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.0047 - mae: 5.4842 - val_loss: 5.2042 - val_mae: 5.6894\n",
            "Epoch 99/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.0098 - mae: 5.4891 - val_loss: 5.3813 - val_mae: 5.8612\n",
            "Epoch 100/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.0298 - mae: 5.5070 - val_loss: 5.7582 - val_mae: 6.2393\n",
            "Epoch 101/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.9522 - mae: 5.4295 - val_loss: 5.2437 - val_mae: 5.7239\n",
            "Epoch 102/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.0311 - mae: 5.5083 - val_loss: 5.1543 - val_mae: 5.6401\n",
            "Epoch 103/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.9436 - mae: 5.4193 - val_loss: 5.1390 - val_mae: 5.6240\n",
            "Epoch 104/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.9450 - mae: 5.4215 - val_loss: 5.3353 - val_mae: 5.8157\n",
            "Epoch 105/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.9048 - mae: 5.3807 - val_loss: 5.1165 - val_mae: 5.6013\n",
            "Epoch 106/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.9047 - mae: 5.3825 - val_loss: 5.3300 - val_mae: 5.8103\n",
            "Epoch 107/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.9422 - mae: 5.4200 - val_loss: 5.1152 - val_mae: 5.6036\n",
            "Epoch 108/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.8532 - mae: 5.3330 - val_loss: 5.2007 - val_mae: 5.6834\n",
            "Epoch 109/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.8442 - mae: 5.3215 - val_loss: 5.6814 - val_mae: 6.1621\n",
            "Epoch 110/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.9187 - mae: 5.3915 - val_loss: 5.1091 - val_mae: 5.5870\n",
            "Epoch 111/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.8404 - mae: 5.3195 - val_loss: 5.0761 - val_mae: 5.5654\n",
            "Epoch 112/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.8411 - mae: 5.3162 - val_loss: 5.0675 - val_mae: 5.5446\n",
            "Epoch 113/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.8452 - mae: 5.3206 - val_loss: 5.0255 - val_mae: 5.5094\n",
            "Epoch 114/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.8270 - mae: 5.3031 - val_loss: 5.3017 - val_mae: 5.7803\n",
            "Epoch 115/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.8297 - mae: 5.3081 - val_loss: 5.0467 - val_mae: 5.5351\n",
            "Epoch 116/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.7711 - mae: 5.2484 - val_loss: 5.0539 - val_mae: 5.5312\n",
            "Epoch 117/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.8465 - mae: 5.3216 - val_loss: 4.9959 - val_mae: 5.4835\n",
            "Epoch 118/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.8112 - mae: 5.2919 - val_loss: 5.1245 - val_mae: 5.6024\n",
            "Epoch 119/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.7606 - mae: 5.2337 - val_loss: 4.9780 - val_mae: 5.4659\n",
            "Epoch 120/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7182 - mae: 5.1950 - val_loss: 5.1574 - val_mae: 5.6361\n",
            "Epoch 121/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7128 - mae: 5.1854 - val_loss: 5.0212 - val_mae: 5.5043\n",
            "Epoch 122/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.6972 - mae: 5.1724 - val_loss: 5.1985 - val_mae: 5.6792\n",
            "Epoch 123/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.7283 - mae: 5.2057 - val_loss: 5.0452 - val_mae: 5.5233\n",
            "Epoch 124/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.7393 - mae: 5.2107 - val_loss: 4.9138 - val_mae: 5.3958\n",
            "Epoch 125/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.7014 - mae: 5.1780 - val_loss: 5.0025 - val_mae: 5.4797\n",
            "Epoch 126/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.6727 - mae: 5.1444 - val_loss: 4.9720 - val_mae: 5.4553\n",
            "Epoch 127/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6785 - mae: 5.1538 - val_loss: 4.9888 - val_mae: 5.4690\n",
            "Epoch 128/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.6817 - mae: 5.1575 - val_loss: 4.8904 - val_mae: 5.3673\n",
            "Epoch 129/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.6582 - mae: 5.1312 - val_loss: 4.9069 - val_mae: 5.3925\n",
            "Epoch 130/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6306 - mae: 5.0997 - val_loss: 4.9076 - val_mae: 5.3829\n",
            "Epoch 131/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.7226 - mae: 5.1940 - val_loss: 4.9074 - val_mae: 5.3918\n",
            "Epoch 132/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.6231 - mae: 5.0918 - val_loss: 5.1379 - val_mae: 5.6248\n",
            "Epoch 133/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6515 - mae: 5.1254 - val_loss: 4.8386 - val_mae: 5.3224\n",
            "Epoch 134/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.6095 - mae: 5.0818 - val_loss: 4.8331 - val_mae: 5.3102\n",
            "Epoch 135/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.5996 - mae: 5.0718 - val_loss: 4.8614 - val_mae: 5.3453\n",
            "Epoch 136/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.6100 - mae: 5.0800 - val_loss: 5.0160 - val_mae: 5.4992\n",
            "Epoch 137/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.5966 - mae: 5.0682 - val_loss: 5.0807 - val_mae: 5.5635\n",
            "Epoch 138/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6450 - mae: 5.1179 - val_loss: 4.8129 - val_mae: 5.2862\n",
            "Epoch 139/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.5723 - mae: 5.0471 - val_loss: 4.7926 - val_mae: 5.2739\n",
            "Epoch 140/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6689 - mae: 5.1466 - val_loss: 5.1160 - val_mae: 5.5957\n",
            "Epoch 141/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.5723 - mae: 5.0447 - val_loss: 4.8136 - val_mae: 5.2859\n",
            "Epoch 142/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.5720 - mae: 5.0456 - val_loss: 5.7264 - val_mae: 6.2045\n",
            "Epoch 143/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.6472 - mae: 5.1223 - val_loss: 4.8583 - val_mae: 5.3399\n",
            "Epoch 144/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.5445 - mae: 5.0164 - val_loss: 4.8452 - val_mae: 5.3258\n",
            "Epoch 145/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.5868 - mae: 5.0586 - val_loss: 4.7969 - val_mae: 5.2798\n",
            "Epoch 146/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.5272 - mae: 4.9983 - val_loss: 4.8206 - val_mae: 5.3032\n",
            "Epoch 147/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.5625 - mae: 5.0318 - val_loss: 4.7734 - val_mae: 5.2451\n",
            "Epoch 148/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.5522 - mae: 5.0233 - val_loss: 5.1678 - val_mae: 5.6437\n",
            "Epoch 149/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.5501 - mae: 5.0251 - val_loss: 4.7534 - val_mae: 5.2344\n",
            "Epoch 150/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.5521 - mae: 5.0240 - val_loss: 4.8183 - val_mae: 5.3010\n",
            "Epoch 151/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.5316 - mae: 5.0044 - val_loss: 5.3566 - val_mae: 5.8310\n",
            "Epoch 152/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.6382 - mae: 5.1124 - val_loss: 4.7275 - val_mae: 5.1992\n",
            "Epoch 153/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.5020 - mae: 4.9728 - val_loss: 5.2011 - val_mae: 5.6760\n",
            "Epoch 154/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.5167 - mae: 4.9930 - val_loss: 4.7310 - val_mae: 5.2126\n",
            "Epoch 155/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.4790 - mae: 4.9509 - val_loss: 4.7013 - val_mae: 5.1804\n",
            "Epoch 156/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.4868 - mae: 4.9600 - val_loss: 4.7211 - val_mae: 5.2024\n",
            "Epoch 157/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.5016 - mae: 4.9724 - val_loss: 4.6946 - val_mae: 5.1724\n",
            "Epoch 158/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.5500 - mae: 5.0233 - val_loss: 4.7235 - val_mae: 5.2067\n",
            "Epoch 159/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4735 - mae: 4.9471 - val_loss: 4.6757 - val_mae: 5.1516\n",
            "Epoch 160/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.5259 - mae: 4.9974 - val_loss: 4.7655 - val_mae: 5.2488\n",
            "Epoch 161/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4464 - mae: 4.9209 - val_loss: 5.4095 - val_mae: 5.8857\n",
            "Epoch 162/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.5273 - mae: 4.9983 - val_loss: 4.6621 - val_mae: 5.1371\n",
            "Epoch 163/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.4546 - mae: 4.9246 - val_loss: 4.7085 - val_mae: 5.1911\n",
            "Epoch 164/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4700 - mae: 4.9418 - val_loss: 4.6595 - val_mae: 5.1350\n",
            "Epoch 165/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4530 - mae: 4.9248 - val_loss: 4.8008 - val_mae: 5.2830\n",
            "Epoch 166/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.4511 - mae: 4.9258 - val_loss: 4.7053 - val_mae: 5.1887\n",
            "Epoch 167/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.5651 - mae: 5.0375 - val_loss: 5.1332 - val_mae: 5.6077\n",
            "Epoch 168/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4725 - mae: 4.9456 - val_loss: 4.6341 - val_mae: 5.1058\n",
            "Epoch 169/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4865 - mae: 4.9617 - val_loss: 4.7576 - val_mae: 5.2403\n",
            "Epoch 170/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.5042 - mae: 4.9783 - val_loss: 4.7814 - val_mae: 5.2656\n",
            "Epoch 171/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.5055 - mae: 4.9804 - val_loss: 4.8188 - val_mae: 5.3011\n",
            "Epoch 172/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.4858 - mae: 4.9600 - val_loss: 4.6572 - val_mae: 5.1380\n",
            "Epoch 173/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4433 - mae: 4.9140 - val_loss: 4.6161 - val_mae: 5.0884\n",
            "Epoch 174/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4774 - mae: 4.9516 - val_loss: 4.6350 - val_mae: 5.1081\n",
            "Epoch 175/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4290 - mae: 4.9024 - val_loss: 4.6213 - val_mae: 5.0976\n",
            "Epoch 176/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4001 - mae: 4.8742 - val_loss: 4.6523 - val_mae: 5.1264\n",
            "Epoch 177/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.4482 - mae: 4.9211 - val_loss: 4.6043 - val_mae: 5.0767\n",
            "Epoch 178/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4317 - mae: 4.9051 - val_loss: 4.6467 - val_mae: 5.1281\n",
            "Epoch 179/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4621 - mae: 4.9342 - val_loss: 4.6114 - val_mae: 5.0877\n",
            "Epoch 180/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4161 - mae: 4.8884 - val_loss: 4.5969 - val_mae: 5.0690\n",
            "Epoch 181/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4198 - mae: 4.8926 - val_loss: 4.5894 - val_mae: 5.0606\n",
            "Epoch 182/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4022 - mae: 4.8755 - val_loss: 4.7191 - val_mae: 5.2014\n",
            "Epoch 183/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4486 - mae: 4.9255 - val_loss: 4.5837 - val_mae: 5.0557\n",
            "Epoch 184/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.4380 - mae: 4.9142 - val_loss: 4.5838 - val_mae: 5.0551\n",
            "Epoch 185/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4476 - mae: 4.9214 - val_loss: 4.7708 - val_mae: 5.2549\n",
            "Epoch 186/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4405 - mae: 4.9176 - val_loss: 4.9912 - val_mae: 5.4687\n",
            "Epoch 187/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.3811 - mae: 4.8573 - val_loss: 4.8905 - val_mae: 5.3692\n",
            "Epoch 188/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.4497 - mae: 4.9265 - val_loss: 4.5827 - val_mae: 5.0545\n",
            "Epoch 189/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.5007 - mae: 4.9756 - val_loss: 4.7732 - val_mae: 5.2578\n",
            "Epoch 190/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.3986 - mae: 4.8711 - val_loss: 4.8453 - val_mae: 5.3263\n",
            "Epoch 191/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.3835 - mae: 4.8578 - val_loss: 4.6002 - val_mae: 5.0768\n",
            "Epoch 192/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.3800 - mae: 4.8554 - val_loss: 4.7555 - val_mae: 5.2396\n",
            "Epoch 193/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.3818 - mae: 4.8545 - val_loss: 4.5631 - val_mae: 5.0343\n",
            "Epoch 194/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4697 - mae: 4.9466 - val_loss: 4.7384 - val_mae: 5.2223\n",
            "Epoch 195/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4709 - mae: 4.9428 - val_loss: 4.5827 - val_mae: 5.0551\n",
            "Epoch 196/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.3815 - mae: 4.8552 - val_loss: 4.7013 - val_mae: 5.1849\n",
            "Epoch 197/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.4011 - mae: 4.8736 - val_loss: 4.5743 - val_mae: 5.0461\n",
            "Epoch 198/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4076 - mae: 4.8821 - val_loss: 4.5431 - val_mae: 5.0146\n",
            "Epoch 199/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.5170 - mae: 4.9934 - val_loss: 4.5683 - val_mae: 5.0389\n",
            "Epoch 200/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.4195 - mae: 4.8977 - val_loss: 4.6315 - val_mae: 5.1100\n",
            "Epoch 201/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4168 - mae: 4.8918 - val_loss: 4.5691 - val_mae: 5.0402\n",
            "Epoch 202/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.3472 - mae: 4.8217 - val_loss: 4.6169 - val_mae: 5.0947\n",
            "Epoch 203/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.3676 - mae: 4.8384 - val_loss: 4.5815 - val_mae: 5.0592\n",
            "Epoch 204/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.3772 - mae: 4.8520 - val_loss: 4.5358 - val_mae: 5.0070\n",
            "Epoch 205/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4084 - mae: 4.8794 - val_loss: 4.7380 - val_mae: 5.2216\n",
            "Epoch 206/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4137 - mae: 4.8906 - val_loss: 4.5333 - val_mae: 5.0044\n",
            "Epoch 207/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.4037 - mae: 4.8780 - val_loss: 4.7536 - val_mae: 5.2365\n",
            "Epoch 208/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.3536 - mae: 4.8272 - val_loss: 4.5232 - val_mae: 4.9928\n",
            "Epoch 209/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.3745 - mae: 4.8499 - val_loss: 4.5576 - val_mae: 5.0345\n",
            "Epoch 210/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.4488 - mae: 4.9273 - val_loss: 4.6756 - val_mae: 5.1585\n",
            "Epoch 211/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.4027 - mae: 4.8753 - val_loss: 4.6527 - val_mae: 5.1350\n",
            "Epoch 212/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.3545 - mae: 4.8304 - val_loss: 4.5231 - val_mae: 4.9928\n",
            "Epoch 213/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.3551 - mae: 4.8273 - val_loss: 4.5538 - val_mae: 5.0317\n",
            "Epoch 214/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.3685 - mae: 4.8410 - val_loss: 4.5293 - val_mae: 5.0028\n",
            "Epoch 215/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.3293 - mae: 4.8023 - val_loss: 4.7867 - val_mae: 5.2720\n",
            "Epoch 216/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.4600 - mae: 4.9395 - val_loss: 4.5836 - val_mae: 5.0635\n",
            "Epoch 217/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.3834 - mae: 4.8599 - val_loss: 4.5237 - val_mae: 4.9967\n",
            "Epoch 218/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.3884 - mae: 4.8623 - val_loss: 4.5975 - val_mae: 5.0777\n",
            "Epoch 219/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.3504 - mae: 4.8238 - val_loss: 4.5702 - val_mae: 5.0497\n",
            "Epoch 220/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.3777 - mae: 4.8527 - val_loss: 5.0052 - val_mae: 5.4833\n",
            "Epoch 221/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.3693 - mae: 4.8429 - val_loss: 4.6059 - val_mae: 5.0877\n",
            "Epoch 222/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.3657 - mae: 4.8410 - val_loss: 4.7673 - val_mae: 5.2517\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff1f09c9fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_eaAX9g_jS5W"
      },
      "source": [
        "def model_forecast(model, series, window_size):\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size))\n",
        "    ds = ds.batch(32).prefetch(1)\n",
        "    forecast = model.predict(ds)\n",
        "    return forecast"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FnIWROQ08OIj"
      },
      "source": [
        "lin_forecast = model_forecast(model, series[split_time - window_size:-1], window_size)[:, 0]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xd7Tj_fA8OIk",
        "outputId": "ee80f769-5c46-4e45-9943-d33cdb69b2e7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "lin_forecast.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(461,)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-nftslfgQJs",
        "outputId": "b68a7916-28ea-4096-cfd8-3239552e3515",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, lin_forecast)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4E_jXktf7iv",
        "outputId": "3ad18530-d760-4bf1-8af7-4f0f7c37ac0f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "keras.metrics.mean_absolute_error(x_valid, lin_forecast).numpy()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.1662674"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nEM33dZ8OIp"
      },
      "source": [
        "### Dense Model Forecasting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RhGTv4G_8OIp",
        "outputId": "f2a8468e-150f-4984-a701-5d3bfa6c7c17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 30\n",
        "train_set = window_dataset(x_train, window_size)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "  keras.layers.Dense(10, activation=\"relu\", input_shape=[window_size]),\n",
        "  keras.layers.Dense(10, activation=\"relu\"),\n",
        "  keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "lr_schedule = keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: 1e-7 * 10**(epoch / 20))\n",
        "optimizer = keras.optimizers.SGD(lr=1e-7, momentum=0.9)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "31/31 [==============================] - 1s 6ms/step - loss: 43.8270 - mae: 44.3262 - lr: 1.0000e-07\n",
            "Epoch 2/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 41.5210 - mae: 42.0202 - lr: 1.1220e-07\n",
            "Epoch 3/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 38.6253 - mae: 39.1248 - lr: 1.2589e-07\n",
            "Epoch 4/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 35.3202 - mae: 35.8201 - lr: 1.4125e-07\n",
            "Epoch 5/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 31.5905 - mae: 32.0903 - lr: 1.5849e-07\n",
            "Epoch 6/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 27.4033 - mae: 27.9022 - lr: 1.7783e-07\n",
            "Epoch 7/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 22.7830 - mae: 23.2804 - lr: 1.9953e-07\n",
            "Epoch 8/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 17.7632 - mae: 18.2594 - lr: 2.2387e-07\n",
            "Epoch 9/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 12.6771 - mae: 13.1682 - lr: 2.5119e-07\n",
            "Epoch 10/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 8.5974 - mae: 9.0856 - lr: 2.8184e-07\n",
            "Epoch 11/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.8492 - mae: 7.3315 - lr: 3.1623e-07\n",
            "Epoch 12/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.6324 - mae: 7.1175 - lr: 3.5481e-07\n",
            "Epoch 13/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.6240 - mae: 7.1077 - lr: 3.9811e-07\n",
            "Epoch 14/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.6150 - mae: 7.0992 - lr: 4.4668e-07\n",
            "Epoch 15/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.6021 - mae: 7.0864 - lr: 5.0119e-07\n",
            "Epoch 16/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.5907 - mae: 7.0744 - lr: 5.6234e-07\n",
            "Epoch 17/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.5780 - mae: 7.0608 - lr: 6.3096e-07\n",
            "Epoch 18/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.5633 - mae: 7.0463 - lr: 7.0795e-07\n",
            "Epoch 19/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.5439 - mae: 7.0269 - lr: 7.9433e-07\n",
            "Epoch 20/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.5262 - mae: 7.0090 - lr: 8.9125e-07\n",
            "Epoch 21/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.5036 - mae: 6.9854 - lr: 1.0000e-06\n",
            "Epoch 22/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.4846 - mae: 6.9654 - lr: 1.1220e-06\n",
            "Epoch 23/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.4581 - mae: 6.9401 - lr: 1.2589e-06\n",
            "Epoch 24/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.4487 - mae: 6.9276 - lr: 1.4125e-06\n",
            "Epoch 25/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.4031 - mae: 6.8834 - lr: 1.5849e-06\n",
            "Epoch 26/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.3747 - mae: 6.8538 - lr: 1.7783e-06\n",
            "Epoch 27/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.3502 - mae: 6.8289 - lr: 1.9953e-06\n",
            "Epoch 28/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.2981 - mae: 6.7802 - lr: 2.2387e-06\n",
            "Epoch 29/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.2842 - mae: 6.7625 - lr: 2.5119e-06\n",
            "Epoch 30/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.2212 - mae: 6.6986 - lr: 2.8184e-06\n",
            "Epoch 31/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.2325 - mae: 6.7086 - lr: 3.1623e-06\n",
            "Epoch 32/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.1728 - mae: 6.6505 - lr: 3.5481e-06\n",
            "Epoch 33/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.1273 - mae: 6.6050 - lr: 3.9811e-06\n",
            "Epoch 34/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.1122 - mae: 6.5893 - lr: 4.4668e-06\n",
            "Epoch 35/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.1123 - mae: 6.5921 - lr: 5.0119e-06\n",
            "Epoch 36/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.0450 - mae: 6.5217 - lr: 5.6234e-06\n",
            "Epoch 37/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.0152 - mae: 6.4915 - lr: 6.3096e-06\n",
            "Epoch 38/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.0342 - mae: 6.5135 - lr: 7.0795e-06\n",
            "Epoch 39/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.0322 - mae: 6.5112 - lr: 7.9433e-06\n",
            "Epoch 40/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.9744 - mae: 6.4509 - lr: 8.9125e-06\n",
            "Epoch 41/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.9914 - mae: 6.4690 - lr: 1.0000e-05\n",
            "Epoch 42/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.8855 - mae: 6.3654 - lr: 1.1220e-05\n",
            "Epoch 43/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.8357 - mae: 6.3136 - lr: 1.2589e-05\n",
            "Epoch 44/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.8881 - mae: 6.3705 - lr: 1.4125e-05\n",
            "Epoch 45/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.7689 - mae: 6.2470 - lr: 1.5849e-05\n",
            "Epoch 46/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.7805 - mae: 6.2587 - lr: 1.7783e-05\n",
            "Epoch 47/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.8350 - mae: 6.3123 - lr: 1.9953e-05\n",
            "Epoch 48/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.7155 - mae: 6.1955 - lr: 2.2387e-05\n",
            "Epoch 49/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.7046 - mae: 6.1883 - lr: 2.5119e-05\n",
            "Epoch 50/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.7044 - mae: 6.1864 - lr: 2.8184e-05\n",
            "Epoch 51/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.7984 - mae: 6.2822 - lr: 3.1623e-05\n",
            "Epoch 52/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.8983 - mae: 6.3823 - lr: 3.5481e-05\n",
            "Epoch 53/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.7021 - mae: 6.1834 - lr: 3.9811e-05\n",
            "Epoch 54/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.4734 - mae: 5.9536 - lr: 4.4668e-05\n",
            "Epoch 55/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.7361 - mae: 6.2165 - lr: 5.0119e-05\n",
            "Epoch 56/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.7525 - mae: 6.2323 - lr: 5.6234e-05\n",
            "Epoch 57/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.4157 - mae: 5.8924 - lr: 6.3096e-05\n",
            "Epoch 58/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.5249 - mae: 6.0040 - lr: 7.0795e-05\n",
            "Epoch 59/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.2515 - mae: 5.7304 - lr: 7.9433e-05\n",
            "Epoch 60/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.3036 - mae: 5.7855 - lr: 8.9125e-05\n",
            "Epoch 61/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.6514 - mae: 6.1299 - lr: 1.0000e-04\n",
            "Epoch 62/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.2128 - mae: 5.6914 - lr: 1.1220e-04\n",
            "Epoch 63/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 4.9886 - mae: 5.4681 - lr: 1.2589e-04\n",
            "Epoch 64/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.0251 - mae: 5.5016 - lr: 1.4125e-04\n",
            "Epoch 65/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.1802 - mae: 5.6566 - lr: 1.5849e-04\n",
            "Epoch 66/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.1127 - mae: 5.5876 - lr: 1.7783e-04\n",
            "Epoch 67/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 4.9667 - mae: 5.4448 - lr: 1.9953e-04\n",
            "Epoch 68/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 4.9382 - mae: 5.4169 - lr: 2.2387e-04\n",
            "Epoch 69/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 4.9660 - mae: 5.4450 - lr: 2.5119e-04\n",
            "Epoch 70/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.3608 - mae: 5.8445 - lr: 2.8184e-04\n",
            "Epoch 71/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.3278 - mae: 6.8128 - lr: 3.1623e-04\n",
            "Epoch 72/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 6.0064 - mae: 6.4910 - lr: 3.5481e-04\n",
            "Epoch 73/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 5.7309 - mae: 6.2157 - lr: 3.9811e-04\n",
            "Epoch 74/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.5764 - mae: 7.0584 - lr: 4.4668e-04\n",
            "Epoch 75/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 5.1845 - mae: 5.6637 - lr: 5.0119e-04\n",
            "Epoch 76/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.1811 - mae: 6.6652 - lr: 5.6234e-04\n",
            "Epoch 77/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.5606 - mae: 7.0461 - lr: 6.3096e-04\n",
            "Epoch 78/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.2083 - mae: 6.6933 - lr: 7.0795e-04\n",
            "Epoch 79/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 7.8671 - mae: 8.3551 - lr: 7.9433e-04\n",
            "Epoch 80/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 7.0522 - mae: 7.5407 - lr: 8.9125e-04\n",
            "Epoch 81/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 6.3656 - mae: 6.8484 - lr: 0.0010\n",
            "Epoch 82/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 14.2397 - mae: 14.7328 - lr: 0.0011\n",
            "Epoch 83/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 14.4624 - mae: 14.9582 - lr: 0.0013\n",
            "Epoch 84/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 11.1048 - mae: 11.5950 - lr: 0.0014\n",
            "Epoch 85/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 12.5111 - mae: 13.0017 - lr: 0.0016\n",
            "Epoch 86/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 32.0651 - mae: 32.5639 - lr: 0.0018\n",
            "Epoch 87/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 37.2791 - mae: 37.7784 - lr: 0.0020\n",
            "Epoch 88/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 39.1740 - mae: 39.6728 - lr: 0.0022\n",
            "Epoch 89/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 26.2678 - mae: 26.7615 - lr: 0.0025\n",
            "Epoch 90/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 40.4333 - mae: 40.9321 - lr: 0.0028\n",
            "Epoch 91/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 39.5200 - mae: 40.0180 - lr: 0.0032\n",
            "Epoch 92/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 38.6073 - mae: 39.1054 - lr: 0.0035\n",
            "Epoch 93/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 37.6340 - mae: 38.1325 - lr: 0.0040\n",
            "Epoch 94/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 36.5443 - mae: 37.0422 - lr: 0.0045\n",
            "Epoch 95/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 35.3848 - mae: 35.8837 - lr: 0.0050\n",
            "Epoch 96/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 34.0680 - mae: 34.5667 - lr: 0.0056\n",
            "Epoch 97/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 32.6286 - mae: 33.1268 - lr: 0.0063\n",
            "Epoch 98/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 31.0530 - mae: 31.5510 - lr: 0.0071\n",
            "Epoch 99/100\n",
            "31/31 [==============================] - 0s 5ms/step - loss: 29.3157 - mae: 29.8141 - lr: 0.0079\n",
            "Epoch 100/100\n",
            "31/31 [==============================] - 0s 6ms/step - loss: 27.3015 - mae: 27.7991 - lr: 0.0089\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5g-nC_em8OIq",
        "outputId": "8bdcf8fd-9cc9-4887-d9da-171677df6119",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "plt.axis([1e-7, 5e-3, 0, 30])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1e-07, 0.005, 0.0, 30.0)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7t0VrCH8OIr",
        "outputId": "ead4b3be-84ba-4ec1-e4d5-81ffaf20c688",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "window_size = 30\n",
        "train_set = window_dataset(x_train, window_size)\n",
        "valid_set = window_dataset(x_valid, window_size)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "  keras.layers.Dense(10, activation=\"relu\", input_shape=[window_size]),\n",
        "  keras.layers.Dense(10, activation=\"relu\"),\n",
        "  keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "optimizer = keras.optimizers.SGD(lr=1e-5, momentum=0.9)\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "early_stopping = keras.callbacks.EarlyStopping(patience=10)\n",
        "model.fit(train_set, epochs=500,\n",
        "          validation_data=valid_set,\n",
        "          callbacks=[early_stopping])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 20.4049 - mae: 20.8986 - val_loss: 9.5125 - val_mae: 10.0037\n",
            "Epoch 2/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 8.6179 - mae: 9.1027 - val_loss: 7.5804 - val_mae: 8.0585\n",
            "Epoch 3/500\n",
            "31/31 [==============================] - 1s 18ms/step - loss: 7.9849 - mae: 8.4702 - val_loss: 7.2411 - val_mae: 7.7189\n",
            "Epoch 4/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 7.6780 - mae: 8.1603 - val_loss: 7.0971 - val_mae: 7.5798\n",
            "Epoch 5/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 7.4285 - mae: 7.9136 - val_loss: 6.6877 - val_mae: 7.1663\n",
            "Epoch 6/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.2165 - mae: 7.6989 - val_loss: 6.7449 - val_mae: 7.2319\n",
            "Epoch 7/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 7.0491 - mae: 7.5318 - val_loss: 6.3436 - val_mae: 6.8156\n",
            "Epoch 8/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 6.9028 - mae: 7.3848 - val_loss: 6.5575 - val_mae: 7.0435\n",
            "Epoch 9/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.7285 - mae: 7.2134 - val_loss: 6.1564 - val_mae: 6.6327\n",
            "Epoch 10/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.5739 - mae: 7.0545 - val_loss: 6.1365 - val_mae: 6.6166\n",
            "Epoch 11/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 6.4917 - mae: 6.9751 - val_loss: 6.0710 - val_mae: 6.5529\n",
            "Epoch 12/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.3698 - mae: 6.8519 - val_loss: 6.0569 - val_mae: 6.5385\n",
            "Epoch 13/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 6.2862 - mae: 6.7685 - val_loss: 5.8964 - val_mae: 6.3704\n",
            "Epoch 14/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 6.2392 - mae: 6.7235 - val_loss: 5.9185 - val_mae: 6.4008\n",
            "Epoch 15/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 6.2024 - mae: 6.6859 - val_loss: 5.8176 - val_mae: 6.2957\n",
            "Epoch 16/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.1559 - mae: 6.6389 - val_loss: 5.8143 - val_mae: 6.2939\n",
            "Epoch 17/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.1469 - mae: 6.6283 - val_loss: 5.7770 - val_mae: 6.2569\n",
            "Epoch 18/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 6.0487 - mae: 6.5312 - val_loss: 5.8036 - val_mae: 6.2811\n",
            "Epoch 19/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 6.0462 - mae: 6.5311 - val_loss: 5.8459 - val_mae: 6.3204\n",
            "Epoch 20/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.9691 - mae: 6.4515 - val_loss: 5.7281 - val_mae: 6.2104\n",
            "Epoch 21/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.9475 - mae: 6.4314 - val_loss: 5.6406 - val_mae: 6.1186\n",
            "Epoch 22/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.9024 - mae: 6.3848 - val_loss: 5.6382 - val_mae: 6.1190\n",
            "Epoch 23/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.8469 - mae: 6.3291 - val_loss: 5.9977 - val_mae: 6.4816\n",
            "Epoch 24/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.8802 - mae: 6.3612 - val_loss: 5.6908 - val_mae: 6.1675\n",
            "Epoch 25/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.7917 - mae: 6.2766 - val_loss: 5.6000 - val_mae: 6.0798\n",
            "Epoch 26/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.8217 - mae: 6.3069 - val_loss: 5.5738 - val_mae: 6.0552\n",
            "Epoch 27/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.7690 - mae: 6.2534 - val_loss: 5.7255 - val_mae: 6.2086\n",
            "Epoch 28/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.8085 - mae: 6.2930 - val_loss: 5.5122 - val_mae: 5.9927\n",
            "Epoch 29/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.7321 - mae: 6.2148 - val_loss: 5.6341 - val_mae: 6.1126\n",
            "Epoch 30/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.6991 - mae: 6.1808 - val_loss: 5.5333 - val_mae: 6.0103\n",
            "Epoch 31/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.6720 - mae: 6.1514 - val_loss: 5.5871 - val_mae: 6.0643\n",
            "Epoch 32/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.6642 - mae: 6.1479 - val_loss: 5.6710 - val_mae: 6.1541\n",
            "Epoch 33/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.6133 - mae: 6.0965 - val_loss: 5.5384 - val_mae: 6.0146\n",
            "Epoch 34/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.6377 - mae: 6.1193 - val_loss: 5.5215 - val_mae: 5.9972\n",
            "Epoch 35/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.5874 - mae: 6.0645 - val_loss: 5.3812 - val_mae: 5.8583\n",
            "Epoch 36/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.6027 - mae: 6.0879 - val_loss: 5.4237 - val_mae: 5.9048\n",
            "Epoch 37/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.6141 - mae: 6.0915 - val_loss: 5.4910 - val_mae: 5.9729\n",
            "Epoch 38/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.5909 - mae: 6.0724 - val_loss: 5.8065 - val_mae: 6.2873\n",
            "Epoch 39/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.5284 - mae: 6.0080 - val_loss: 6.0727 - val_mae: 6.5558\n",
            "Epoch 40/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.5446 - mae: 6.0267 - val_loss: 5.3343 - val_mae: 5.8174\n",
            "Epoch 41/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.5197 - mae: 6.0011 - val_loss: 5.3013 - val_mae: 5.7805\n",
            "Epoch 42/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.4749 - mae: 5.9566 - val_loss: 5.3074 - val_mae: 5.7903\n",
            "Epoch 43/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.4665 - mae: 5.9480 - val_loss: 5.3096 - val_mae: 5.7900\n",
            "Epoch 44/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.4397 - mae: 5.9217 - val_loss: 5.3484 - val_mae: 5.8242\n",
            "Epoch 45/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.4373 - mae: 5.9183 - val_loss: 5.6576 - val_mae: 6.1352\n",
            "Epoch 46/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.4640 - mae: 5.9452 - val_loss: 5.7575 - val_mae: 6.2423\n",
            "Epoch 47/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.4859 - mae: 5.9670 - val_loss: 5.9034 - val_mae: 6.3868\n",
            "Epoch 48/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.4198 - mae: 5.9016 - val_loss: 5.2066 - val_mae: 5.6867\n",
            "Epoch 49/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.3805 - mae: 5.8602 - val_loss: 5.2942 - val_mae: 5.7708\n",
            "Epoch 50/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.4262 - mae: 5.9075 - val_loss: 5.4608 - val_mae: 5.9444\n",
            "Epoch 51/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.3880 - mae: 5.8703 - val_loss: 5.4590 - val_mae: 5.9310\n",
            "Epoch 52/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.3822 - mae: 5.8613 - val_loss: 5.5157 - val_mae: 5.9895\n",
            "Epoch 53/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.3546 - mae: 5.8377 - val_loss: 5.2389 - val_mae: 5.7165\n",
            "Epoch 54/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.3461 - mae: 5.8288 - val_loss: 5.1781 - val_mae: 5.6589\n",
            "Epoch 55/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.2768 - mae: 5.7589 - val_loss: 5.1607 - val_mae: 5.6413\n",
            "Epoch 56/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.2539 - mae: 5.7373 - val_loss: 5.4277 - val_mae: 5.9119\n",
            "Epoch 57/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.3347 - mae: 5.8184 - val_loss: 5.7525 - val_mae: 6.2344\n",
            "Epoch 58/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.2696 - mae: 5.7501 - val_loss: 5.2096 - val_mae: 5.6877\n",
            "Epoch 59/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.2272 - mae: 5.7103 - val_loss: 5.2327 - val_mae: 5.7112\n",
            "Epoch 60/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.2658 - mae: 5.7458 - val_loss: 5.0744 - val_mae: 5.5567\n",
            "Epoch 61/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.2125 - mae: 5.6927 - val_loss: 5.6904 - val_mae: 6.1728\n",
            "Epoch 62/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.2602 - mae: 5.7420 - val_loss: 5.1116 - val_mae: 5.5904\n",
            "Epoch 63/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.1874 - mae: 5.6697 - val_loss: 5.0692 - val_mae: 5.5508\n",
            "Epoch 64/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.1751 - mae: 5.6555 - val_loss: 5.2834 - val_mae: 5.7662\n",
            "Epoch 65/500\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 5.2088 - mae: 5.6886 - val_loss: 5.0484 - val_mae: 5.5301\n",
            "Epoch 66/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.1744 - mae: 5.6535 - val_loss: 5.1831 - val_mae: 5.6589\n",
            "Epoch 67/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.1534 - mae: 5.6338 - val_loss: 5.0369 - val_mae: 5.5184\n",
            "Epoch 68/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.1575 - mae: 5.6382 - val_loss: 5.0227 - val_mae: 5.5027\n",
            "Epoch 69/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.1541 - mae: 5.6362 - val_loss: 5.1051 - val_mae: 5.5804\n",
            "Epoch 70/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.1305 - mae: 5.6106 - val_loss: 5.2578 - val_mae: 5.7416\n",
            "Epoch 71/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 5.0891 - mae: 5.5636 - val_loss: 5.0369 - val_mae: 5.5136\n",
            "Epoch 72/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.0904 - mae: 5.5700 - val_loss: 5.1257 - val_mae: 5.5994\n",
            "Epoch 73/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.1134 - mae: 5.5959 - val_loss: 5.0332 - val_mae: 5.5098\n",
            "Epoch 74/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.1160 - mae: 5.5939 - val_loss: 5.0354 - val_mae: 5.5141\n",
            "Epoch 75/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.0561 - mae: 5.5355 - val_loss: 4.9754 - val_mae: 5.4559\n",
            "Epoch 76/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.0500 - mae: 5.5295 - val_loss: 4.9669 - val_mae: 5.4473\n",
            "Epoch 77/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 5.0397 - mae: 5.5198 - val_loss: 4.9841 - val_mae: 5.4657\n",
            "Epoch 78/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.0633 - mae: 5.5433 - val_loss: 4.9915 - val_mae: 5.4689\n",
            "Epoch 79/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 5.0161 - mae: 5.4968 - val_loss: 5.2080 - val_mae: 5.6843\n",
            "Epoch 80/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 5.0274 - mae: 5.5082 - val_loss: 4.9782 - val_mae: 5.4590\n",
            "Epoch 81/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.9629 - mae: 5.4395 - val_loss: 4.9678 - val_mae: 5.4490\n",
            "Epoch 82/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.9942 - mae: 5.4722 - val_loss: 5.2049 - val_mae: 5.6827\n",
            "Epoch 83/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.9730 - mae: 5.4516 - val_loss: 5.0278 - val_mae: 5.4998\n",
            "Epoch 84/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.9698 - mae: 5.4500 - val_loss: 4.9309 - val_mae: 5.4122\n",
            "Epoch 85/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.9676 - mae: 5.4458 - val_loss: 4.9285 - val_mae: 5.4099\n",
            "Epoch 86/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.9898 - mae: 5.4698 - val_loss: 4.9307 - val_mae: 5.4135\n",
            "Epoch 87/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.9521 - mae: 5.4326 - val_loss: 5.0003 - val_mae: 5.4712\n",
            "Epoch 88/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.9209 - mae: 5.4001 - val_loss: 4.9297 - val_mae: 5.4065\n",
            "Epoch 89/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.9469 - mae: 5.4234 - val_loss: 4.9102 - val_mae: 5.3918\n",
            "Epoch 90/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.9656 - mae: 5.4439 - val_loss: 4.9475 - val_mae: 5.4207\n",
            "Epoch 91/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.9077 - mae: 5.3837 - val_loss: 5.2979 - val_mae: 5.7770\n",
            "Epoch 92/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.9466 - mae: 5.4274 - val_loss: 4.9204 - val_mae: 5.4007\n",
            "Epoch 93/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.8801 - mae: 5.3606 - val_loss: 5.2442 - val_mae: 5.7227\n",
            "Epoch 94/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.8994 - mae: 5.3776 - val_loss: 5.0214 - val_mae: 5.4928\n",
            "Epoch 95/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.8727 - mae: 5.3513 - val_loss: 4.8865 - val_mae: 5.3652\n",
            "Epoch 96/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.8795 - mae: 5.3570 - val_loss: 4.8865 - val_mae: 5.3673\n",
            "Epoch 97/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.8868 - mae: 5.3668 - val_loss: 4.8741 - val_mae: 5.3554\n",
            "Epoch 98/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.8808 - mae: 5.3592 - val_loss: 4.8602 - val_mae: 5.3408\n",
            "Epoch 99/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.8471 - mae: 5.3256 - val_loss: 4.8552 - val_mae: 5.3356\n",
            "Epoch 100/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.8479 - mae: 5.3255 - val_loss: 4.8525 - val_mae: 5.3327\n",
            "Epoch 101/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.8653 - mae: 5.3406 - val_loss: 4.8489 - val_mae: 5.3295\n",
            "Epoch 102/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.8551 - mae: 5.3323 - val_loss: 4.8471 - val_mae: 5.3287\n",
            "Epoch 103/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.8306 - mae: 5.3099 - val_loss: 4.8481 - val_mae: 5.3292\n",
            "Epoch 104/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.8556 - mae: 5.3320 - val_loss: 4.8353 - val_mae: 5.3144\n",
            "Epoch 105/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.8136 - mae: 5.2912 - val_loss: 5.3163 - val_mae: 5.8016\n",
            "Epoch 106/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.9170 - mae: 5.3932 - val_loss: 4.9734 - val_mae: 5.4454\n",
            "Epoch 107/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.8636 - mae: 5.3387 - val_loss: 4.9875 - val_mae: 5.4577\n",
            "Epoch 108/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.8161 - mae: 5.2935 - val_loss: 4.8249 - val_mae: 5.3072\n",
            "Epoch 109/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7934 - mae: 5.2684 - val_loss: 5.3332 - val_mae: 5.8128\n",
            "Epoch 110/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.8247 - mae: 5.3031 - val_loss: 4.8576 - val_mae: 5.3363\n",
            "Epoch 111/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.8192 - mae: 5.2967 - val_loss: 4.8143 - val_mae: 5.2924\n",
            "Epoch 112/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.8120 - mae: 5.2869 - val_loss: 4.8396 - val_mae: 5.3194\n",
            "Epoch 113/500\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 4.7951 - mae: 5.2737 - val_loss: 4.8295 - val_mae: 5.3095\n",
            "Epoch 114/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.8072 - mae: 5.2841 - val_loss: 4.9524 - val_mae: 5.4232\n",
            "Epoch 115/500\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 4.7906 - mae: 5.2679 - val_loss: 4.8346 - val_mae: 5.3121\n",
            "Epoch 116/500\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 4.7736 - mae: 5.2540 - val_loss: 4.8053 - val_mae: 5.2867\n",
            "Epoch 117/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7790 - mae: 5.2556 - val_loss: 4.9063 - val_mae: 5.3782\n",
            "Epoch 118/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.9092 - mae: 5.3913 - val_loss: 4.8032 - val_mae: 5.2814\n",
            "Epoch 119/500\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 4.7897 - mae: 5.2676 - val_loss: 4.8939 - val_mae: 5.3669\n",
            "Epoch 120/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7613 - mae: 5.2389 - val_loss: 4.8447 - val_mae: 5.3209\n",
            "Epoch 121/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7511 - mae: 5.2276 - val_loss: 4.8397 - val_mae: 5.3164\n",
            "Epoch 122/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7653 - mae: 5.2440 - val_loss: 4.9183 - val_mae: 5.3906\n",
            "Epoch 123/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7959 - mae: 5.2753 - val_loss: 4.7707 - val_mae: 5.2476\n",
            "Epoch 124/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7766 - mae: 5.2513 - val_loss: 4.7665 - val_mae: 5.2440\n",
            "Epoch 125/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.7814 - mae: 5.2602 - val_loss: 4.7709 - val_mae: 5.2495\n",
            "Epoch 126/500\n",
            "31/31 [==============================] - 1s 18ms/step - loss: 4.7547 - mae: 5.2327 - val_loss: 4.7648 - val_mae: 5.2428\n",
            "Epoch 127/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7337 - mae: 5.2090 - val_loss: 4.8925 - val_mae: 5.3660\n",
            "Epoch 128/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7231 - mae: 5.2004 - val_loss: 4.7861 - val_mae: 5.2643\n",
            "Epoch 129/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.7312 - mae: 5.2095 - val_loss: 4.7655 - val_mae: 5.2437\n",
            "Epoch 130/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.7267 - mae: 5.2040 - val_loss: 4.7819 - val_mae: 5.2601\n",
            "Epoch 131/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.8444 - mae: 5.3232 - val_loss: 4.9965 - val_mae: 5.4721\n",
            "Epoch 132/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.7132 - mae: 5.1888 - val_loss: 4.8432 - val_mae: 5.3246\n",
            "Epoch 133/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7116 - mae: 5.1851 - val_loss: 4.7525 - val_mae: 5.2311\n",
            "Epoch 134/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.7332 - mae: 5.2071 - val_loss: 4.7661 - val_mae: 5.2428\n",
            "Epoch 135/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6982 - mae: 5.1762 - val_loss: 4.7733 - val_mae: 5.2507\n",
            "Epoch 136/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.7233 - mae: 5.2013 - val_loss: 4.7833 - val_mae: 5.2613\n",
            "Epoch 137/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6844 - mae: 5.1625 - val_loss: 4.7817 - val_mae: 5.2603\n",
            "Epoch 138/500\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 4.7006 - mae: 5.1761 - val_loss: 4.7402 - val_mae: 5.2175\n",
            "Epoch 139/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7188 - mae: 5.1966 - val_loss: 4.7549 - val_mae: 5.2319\n",
            "Epoch 140/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7832 - mae: 5.2612 - val_loss: 4.7764 - val_mae: 5.2548\n",
            "Epoch 141/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6848 - mae: 5.1601 - val_loss: 4.7251 - val_mae: 5.2023\n",
            "Epoch 142/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6835 - mae: 5.1576 - val_loss: 5.0954 - val_mae: 5.5717\n",
            "Epoch 143/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6978 - mae: 5.1762 - val_loss: 4.9924 - val_mae: 5.4680\n",
            "Epoch 144/500\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 4.6690 - mae: 5.1458 - val_loss: 4.7208 - val_mae: 5.1963\n",
            "Epoch 145/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6886 - mae: 5.1673 - val_loss: 4.7512 - val_mae: 5.2292\n",
            "Epoch 146/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6627 - mae: 5.1376 - val_loss: 4.7311 - val_mae: 5.2084\n",
            "Epoch 147/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6506 - mae: 5.1225 - val_loss: 4.9450 - val_mae: 5.4194\n",
            "Epoch 148/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6968 - mae: 5.1762 - val_loss: 4.7605 - val_mae: 5.2392\n",
            "Epoch 149/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6838 - mae: 5.1613 - val_loss: 4.9710 - val_mae: 5.4454\n",
            "Epoch 150/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6996 - mae: 5.1714 - val_loss: 4.7135 - val_mae: 5.1892\n",
            "Epoch 151/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6514 - mae: 5.1285 - val_loss: 4.7589 - val_mae: 5.2363\n",
            "Epoch 152/500\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 4.6623 - mae: 5.1385 - val_loss: 4.7050 - val_mae: 5.1791\n",
            "Epoch 153/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6448 - mae: 5.1220 - val_loss: 4.7148 - val_mae: 5.1915\n",
            "Epoch 154/500\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 4.6684 - mae: 5.1441 - val_loss: 4.8884 - val_mae: 5.3626\n",
            "Epoch 155/500\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 4.6418 - mae: 5.1171 - val_loss: 4.6947 - val_mae: 5.1698\n",
            "Epoch 156/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6204 - mae: 5.0927 - val_loss: 4.7574 - val_mae: 5.2344\n",
            "Epoch 157/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6318 - mae: 5.1044 - val_loss: 4.7524 - val_mae: 5.2307\n",
            "Epoch 158/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.7601 - mae: 5.2390 - val_loss: 4.7316 - val_mae: 5.2102\n",
            "Epoch 159/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6336 - mae: 5.1116 - val_loss: 4.6982 - val_mae: 5.1747\n",
            "Epoch 160/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6979 - mae: 5.1753 - val_loss: 4.7169 - val_mae: 5.1950\n",
            "Epoch 161/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6180 - mae: 5.0925 - val_loss: 5.3132 - val_mae: 5.7911\n",
            "Epoch 162/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6749 - mae: 5.1497 - val_loss: 4.6884 - val_mae: 5.1635\n",
            "Epoch 163/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6078 - mae: 5.0830 - val_loss: 4.8182 - val_mae: 5.2940\n",
            "Epoch 164/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.6889 - mae: 5.1660 - val_loss: 4.8031 - val_mae: 5.2860\n",
            "Epoch 165/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.6872 - mae: 5.1613 - val_loss: 5.1839 - val_mae: 5.6627\n",
            "Epoch 166/500\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 4.6009 - mae: 5.0765 - val_loss: 5.0716 - val_mae: 5.5501\n",
            "Epoch 167/500\n",
            "31/31 [==============================] - 1s 11ms/step - loss: 4.6209 - mae: 5.0946 - val_loss: 4.8871 - val_mae: 5.3612\n",
            "Epoch 168/500\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 4.6476 - mae: 5.1252 - val_loss: 4.7961 - val_mae: 5.2717\n",
            "Epoch 169/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6302 - mae: 5.1063 - val_loss: 4.9232 - val_mae: 5.4075\n",
            "Epoch 170/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.6589 - mae: 5.1350 - val_loss: 4.8675 - val_mae: 5.3426\n",
            "Epoch 171/500\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 4.6239 - mae: 5.0990 - val_loss: 5.2334 - val_mae: 5.7113\n",
            "Epoch 172/500\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 4.5918 - mae: 5.0683 - val_loss: 4.8114 - val_mae: 5.2875\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff1f05b4790>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RqQbX6DZ8OIu"
      },
      "source": [
        "dense_forecast = model_forecast(\n",
        "    model,\n",
        "    series[split_time - window_size:-1],\n",
        "    window_size)[:, 0]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98zwAuIo8OIv",
        "outputId": "da378aee-61dd-4449-cf71-89bce787316b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, dense_forecast)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EgkELN-58OIw",
        "outputId": "690f4f26-886e-48e5-b7e1-58901f2f2f12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "keras.metrics.mean_absolute_error(x_valid, dense_forecast).numpy()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.202554"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    }
  ]
}