TensorFlow In Practice/Course 4 - S+P/S+P Week 4 Exercise Answer.ipynb

{
  "cells": [
      {
      "cell_type": "code",
      "metadata": {
        "id": "zX4Kg8DUTKWO",
        "colab_type": "code",
        "colab": {}
      },
      "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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "id": "56XEQOGknrAk",
        "outputId": "0f446cf8-5d23-4dab-9783-08a1d284e3a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.0.0-beta1\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "sLl52leVp5wU"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
        "    plt.plot(time[start:end], series[start:end], format)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    plt.grid(True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "tP7oqUdkk0gY"
      },
      "outputs": [],
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv \\\n",
        "    -O /tmp/daily-min-temperatures.csv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "colab_type": "code",
        "id": "NcG9r1eClbTh",
        "outputId": "9c9c679a-f57c-4b0d-8b2a-f98a8e6fb9e3"
      },
      "outputs": [
        {
          "data": {
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j7DqZayEqbnxhCX7y2grUNbdZHmf1CNf/Y2Hou6PNlufVb9sj5ph/4WOlns7reG4KhIow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\nYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import csv\n",
        "time_step = []\n",
        "temps = []\n",
        "\n",
        "with open('/tmp/daily-min-temperatures.csv') as csvfile:\n",
        "  reader = csv.reader(csvfile, delimiter=',')\n",
        "  next(reader)\n",
        "  step=0\n",
        "  for row in reader:\n",
        "    temps.append(float(row[1]))\n",
        "    time_step.append(step)\n",
        "    step = step + 1\n",
        "\n",
        "series = np.array(temps)\n",
        "time = np.array(time_step)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time, series)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "L92YRw_IpCFG"
      },
      "outputs": [],
      "source": [
        "split_time = 2500\n",
        "time_train = time[:split_time]\n",
        "x_train = series[:split_time]\n",
        "time_valid = time[split_time:]\n",
        "x_valid = series[split_time:]\n",
        "\n",
        "window_size = 30\n",
        "batch_size = 32\n",
        "shuffle_buffer_size = 1000\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "lJwUUZscnG38"
      },
      "outputs": [],
      "source": [
        "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "    series = tf.expand_dims(series, axis=-1)\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size + 1))\n",
        "    ds = ds.shuffle(shuffle_buffer)\n",
        "    ds = ds.map(lambda w: (w[:-1], w[1:]))\n",
        "    return ds.batch(batch_size).prefetch(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "4XwGrf-A_wF0"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "colab_type": "code",
        "id": "AclfYY3Mn6Ph",
        "outputId": "dd1fef93-d819-4d56-df20-330169907e16"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0719 05:10:05.389573 140234944071552 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/util/random_seed.py:58: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<PrefetchDataset shapes: ((None, None, 1), (None, None, 1)), types: (tf.float64, tf.float64)>\n",
            "(2500,)\n",
            "Epoch 1/100\n",
            "10/10 [==============================] - 6s 624ms/step - loss: 31.1549 - mae: 31.6551\n",
            "Epoch 2/100\n",
            "10/10 [==============================] - 4s 364ms/step - loss: 30.5696 - mae: 31.0771\n",
            "Epoch 3/100\n",
            "10/10 [==============================] - 4s 358ms/step - loss: 29.6691 - mae: 30.1811\n",
            "Epoch 4/100\n",
            "10/10 [==============================] - 4s 366ms/step - loss: 28.5431 - mae: 29.0596\n",
            "Epoch 5/100\n",
            "10/10 [==============================] - 4s 361ms/step - loss: 27.1744 - mae: 27.6976\n",
            "Epoch 6/100\n",
            "10/10 [==============================] - 4s 367ms/step - loss: 25.4676 - mae: 26.0015\n",
            "Epoch 7/100\n",
            "10/10 [==============================] - 4s 375ms/step - loss: 23.2987 - mae: 23.8487\n",
            "Epoch 8/100\n",
            "10/10 [==============================] - 4s 369ms/step - loss: 20.5506 - mae: 21.1192\n",
            "Epoch 9/100\n",
            "10/10 [==============================] - 4s 372ms/step - loss: 17.2408 - mae: 17.8223\n",
            "Epoch 10/100\n",
            "10/10 [==============================] - 4s 365ms/step - loss: 13.5549 - mae: 14.1350\n",
            "Epoch 11/100\n",
            "10/10 [==============================] - 4s 370ms/step - loss: 10.0753 - mae: 10.6367\n",
            "Epoch 12/100\n",
            "10/10 [==============================] - 4s 365ms/step - loss: 7.5638 - mae: 8.0985\n",
            "Epoch 13/100\n",
            "10/10 [==============================] - 4s 363ms/step - loss: 6.2445 - mae: 6.7619\n",
            "Epoch 14/100\n",
            "10/10 [==============================] - 4s 374ms/step - loss: 5.6749 - mae: 6.1854\n",
            "Epoch 15/100\n",
            "10/10 [==============================] - 4s 363ms/step - loss: 5.3090 - mae: 5.8168\n",
            "Epoch 16/100\n",
            "10/10 [==============================] - 4s 361ms/step - loss: 4.9122 - mae: 5.4169\n",
            "Epoch 17/100\n",
            "10/10 [==============================] - 4s 356ms/step - loss: 4.5318 - mae: 5.0305\n",
            "Epoch 18/100\n",
            "10/10 [==============================] - 4s 367ms/step - loss: 4.2115 - mae: 4.7068\n",
            "Epoch 19/100\n",
            "10/10 [==============================] - 4s 374ms/step - loss: 3.9429 - mae: 4.4360\n",
            "Epoch 20/100\n",
            "10/10 [==============================] - 4s 358ms/step - loss: 3.7309 - mae: 4.2194\n",
            "Epoch 21/100\n",
            "10/10 [==============================] - 4s 363ms/step - loss: 3.5706 - mae: 4.0551\n",
            "Epoch 22/100\n",
            "10/10 [==============================] - 4s 366ms/step - loss: 3.4527 - mae: 3.9344\n",
            "Epoch 23/100\n",
            "10/10 [==============================] - 4s 362ms/step - loss: 3.3617 - mae: 3.8423\n",
            "Epoch 24/100\n",
            "10/10 [==============================] - 4s 358ms/step - loss: 3.2876 - mae: 3.7666\n",
            "Epoch 25/100\n",
            "10/10 [==============================] - 3s 344ms/step - loss: 3.2224 - mae: 3.6997\n",
            "Epoch 26/100\n",
            "10/10 [==============================] - 4s 360ms/step - loss: 3.1596 - mae: 3.6359\n",
            "Epoch 27/100\n",
            "10/10 [==============================] - 4s 360ms/step - loss: 3.0964 - mae: 3.5717\n",
            "Epoch 28/100\n",
            "10/10 [==============================] - 4s 362ms/step - loss: 3.0322 - mae: 3.5064\n",
            "Epoch 29/100\n",
            "10/10 [==============================] - 4s 352ms/step - loss: 2.9662 - mae: 3.4392\n",
            "Epoch 30/100\n",
            "10/10 [==============================] - 4s 359ms/step - loss: 2.9004 - mae: 3.3720\n",
            "Epoch 31/100\n",
            "10/10 [==============================] - 4s 361ms/step - loss: 2.8376 - mae: 3.3081\n",
            "Epoch 32/100\n",
            "10/10 [==============================] - 4s 375ms/step - loss: 2.7775 - mae: 3.2475\n",
            "Epoch 33/100\n",
            "10/10 [==============================] - 4s 360ms/step - loss: 2.7202 - mae: 3.1899\n",
            "Epoch 34/100\n",
            "10/10 [==============================] - 4s 367ms/step - loss: 2.6662 - mae: 3.1360\n",
            "Epoch 35/100\n",
            "10/10 [==============================] - 4s 379ms/step - loss: 2.6152 - mae: 3.0848\n",
            "Epoch 36/100\n",
            "10/10 [==============================] - 4s 357ms/step - loss: 2.5663 - mae: 3.0353\n",
            "Epoch 37/100\n",
            "10/10 [==============================] - 4s 366ms/step - loss: 2.5192 - mae: 2.9872\n",
            "Epoch 38/100\n",
            "10/10 [==============================] - 4s 367ms/step - loss: 2.4735 - mae: 2.9408\n",
            "Epoch 39/100\n",
            "10/10 [==============================] - 4s 372ms/step - loss: 2.4296 - mae: 2.8964\n",
            "Epoch 40/100\n",
            "10/10 [==============================] - 4s 371ms/step - loss: 2.3873 - mae: 2.8534\n",
            "Epoch 41/100\n",
            "10/10 [==============================] - 4s 361ms/step - loss: 2.3463 - mae: 2.8119\n",
            "Epoch 42/100\n",
            "10/10 [==============================] - 4s 354ms/step - loss: 2.3060 - mae: 2.7707\n",
            "Epoch 43/100\n",
            "10/10 [==============================] - 4s 375ms/step - loss: 2.2663 - mae: 2.7301\n",
            "Epoch 44/100\n",
            "10/10 [==============================] - 4s 359ms/step - loss: 2.2269 - mae: 2.6899\n",
            "Epoch 45/100\n",
            "10/10 [==============================] - 4s 366ms/step - loss: 2.1898 - mae: 2.6519\n",
            "Epoch 46/100\n",
            "10/10 [==============================] - 4s 367ms/step - loss: 2.1563 - mae: 2.6181\n",
            "Epoch 47/100\n",
            "10/10 [==============================] - 4s 363ms/step - loss: 2.1248 - mae: 2.5861\n",
            "Epoch 48/100\n",
            "10/10 [==============================] - 4s 367ms/step - loss: 2.0958 - mae: 2.5568\n",
            "Epoch 49/100\n",
            "10/10 [==============================] - 4s 363ms/step - loss: 2.0688 - mae: 2.5296\n",
            "Epoch 50/100\n",
            "10/10 [==============================] - 4s 370ms/step - loss: 2.0442 - mae: 2.5045\n",
            "Epoch 51/100\n",
            "10/10 [==============================] - 4s 371ms/step - loss: 2.0220 - mae: 2.4818\n",
            "Epoch 52/100\n",
            "10/10 [==============================] - 4s 362ms/step - loss: 2.0018 - mae: 2.4611\n",
            "Epoch 53/100\n",
            "10/10 [==============================] - 4s 361ms/step - loss: 1.9801 - mae: 2.4393\n",
            "Epoch 54/100\n",
            "10/10 [==============================] - 4s 369ms/step - loss: 1.9586 - mae: 2.4171\n",
            "Epoch 55/100\n",
            "10/10 [==============================] - 4s 358ms/step - loss: 1.9390 - mae: 2.3972\n",
            "Epoch 56/100\n",
            "10/10 [==============================] - 4s 366ms/step - loss: 1.9186 - mae: 2.3763\n",
            "Epoch 57/100\n",
            "10/10 [==============================] - 4s 366ms/step - loss: 1.8975 - mae: 2.3550\n",
            "Epoch 58/100\n",
            "10/10 [==============================] - 4s 359ms/step - loss: 1.8743 - mae: 2.3320\n",
            "Epoch 59/100\n",
            "10/10 [==============================] - 4s 363ms/step - loss: 1.8738 - mae: 2.3310\n",
            "Epoch 60/100\n",
            "10/10 [==============================] - 4s 354ms/step - loss: 2.1527 - mae: 2.6201\n",
            "Epoch 61/100\n",
            "10/10 [==============================] - 4s 357ms/step - loss: 2.6764 - mae: 3.1247\n",
            "Epoch 62/100\n",
            "10/10 [==============================] - 4s 360ms/step - loss: 2.9935 - mae: 3.4806\n",
            "Epoch 63/100\n",
            "10/10 [==============================] - 4s 360ms/step - loss: 3.5219 - mae: 3.9875\n",
            "Epoch 64/100\n",
            "10/10 [==============================] - 4s 361ms/step - loss: 3.8284 - mae: 4.2965\n",
            "Epoch 65/100\n",
            "10/10 [==============================] - 4s 357ms/step - loss: 4.1265 - mae: 4.5856\n",
            "Epoch 66/100\n",
            "10/10 [==============================] - 4s 359ms/step - loss: 4.3062 - mae: 4.7664\n",
            "Epoch 67/100\n",
            "10/10 [==============================] - 4s 358ms/step - loss: 4.3039 - mae: 4.6838\n",
            "Epoch 68/100\n",
            "10/10 [==============================] - 4s 363ms/step - loss: 4.8341 - mae: 5.2257\n",
            "Epoch 69/100\n",
            "10/10 [==============================] - 4s 370ms/step - loss: 10.3352 - mae: 10.7649\n",
            "Epoch 70/100\n",
            "10/10 [==============================] - 4s 367ms/step - loss: 5.4020 - mae: 5.8328\n",
            "Epoch 71/100\n",
            "10/10 [==============================] - 4s 363ms/step - loss: 5.9798 - mae: 6.4835\n",
            "Epoch 72/100\n",
            "10/10 [==============================] - 4s 356ms/step - loss: 5.4958 - mae: 6.0146\n",
            "Epoch 73/100\n",
            "10/10 [==============================] - 4s 362ms/step - loss: 4.4955 - mae: 5.0201\n",
            "Epoch 74/100\n",
            "10/10 [==============================] - 4s 370ms/step - loss: 4.4764 - mae: 5.0171\n",
            "Epoch 75/100\n",
            "10/10 [==============================] - 4s 380ms/step - loss: 4.2825 - mae: 4.7287\n",
            "Epoch 76/100\n",
            "10/10 [==============================] - 4s 361ms/step - loss: 4.2044 - mae: 4.6470\n",
            "Epoch 77/100\n",
            "10/10 [==============================] - 4s 370ms/step - loss: 4.4160 - mae: 4.9125\n",
            "Epoch 78/100\n",
            "10/10 [==============================] - 4s 361ms/step - loss: 4.3770 - mae: 4.8831\n",
            "Epoch 79/100\n",
            "10/10 [==============================] - 4s 361ms/step - loss: 5.0487 - mae: 5.5839\n",
            "Epoch 80/100\n",
            "10/10 [==============================] - 4s 390ms/step - loss: 10.0358 - mae: 10.8604\n",
            "Epoch 81/100\n",
            "10/10 [==============================] - 4s 370ms/step - loss: 3.1176 - mae: 3.6060\n",
            "Epoch 82/100\n",
            "10/10 [==============================] - 4s 371ms/step - loss: 3.0097 - mae: 3.4891\n",
            "Epoch 83/100\n",
            "10/10 [==============================] - 4s 361ms/step - loss: 2.7912 - mae: 3.2609\n",
            "Epoch 84/100\n",
            "10/10 [==============================] - 4s 357ms/step - loss: 4.3135 - mae: 4.7803\n",
            "Epoch 85/100\n",
            "10/10 [==============================] - 4s 357ms/step - loss: 5.3703 - mae: 5.8508\n",
            "Epoch 86/100\n",
            "10/10 [==============================] - 4s 360ms/step - loss: 6.5221 - mae: 7.0175\n",
            "Epoch 87/100\n",
            "10/10 [==============================] - 4s 361ms/step - loss: 7.1154 - mae: 7.7249\n",
            "Epoch 88/100\n",
            "10/10 [==============================] - 4s 368ms/step - loss: 8.9975 - mae: 9.4580\n",
            "Epoch 89/100\n",
            "10/10 [==============================] - 4s 360ms/step - loss: 9.8069 - mae: 10.1397\n",
            "Epoch 90/100\n",
            "10/10 [==============================] - 4s 354ms/step - loss: 11.1364 - mae: 11.6797\n",
            "Epoch 91/100\n",
            "10/10 [==============================] - 4s 356ms/step - loss: 12.5922 - mae: 13.2602\n",
            "Epoch 92/100\n",
            "10/10 [==============================] - 4s 357ms/step - loss: 14.2512 - mae: 14.8289\n",
            "Epoch 93/100\n",
            "10/10 [==============================] - 4s 358ms/step - loss: 13.0192 - mae: 13.8809\n",
            "Epoch 94/100\n",
            "10/10 [==============================] - 4s 356ms/step - loss: 61.8923 - mae: 63.5737\n",
            "Epoch 95/100\n",
            "10/10 [==============================] - 4s 366ms/step - loss: 30.5821 - mae: 31.4688\n",
            "Epoch 96/100\n",
            "10/10 [==============================] - 4s 357ms/step - loss: 45.9889 - mae: 46.9740\n",
            "Epoch 97/100\n",
            "10/10 [==============================] - 4s 355ms/step - loss: 51.7050 - mae: 51.8100\n",
            "Epoch 98/100\n",
            "10/10 [==============================] - 4s 361ms/step - loss: 59.1678 - mae: 57.9463\n",
            "Epoch 99/100\n",
            "10/10 [==============================] - 4s 373ms/step - loss: 66.1686 - mae: 68.4903\n",
            "Epoch 100/100\n",
            "10/10 [==============================] - 4s 360ms/step - loss: 74.1415 - mae: 72.0624\n"
          ]
        }
      ],
      "source": [
        "tf.keras.backend.clear_session()\n",
        "tf.random.set_seed(51)\n",
        "np.random.seed(51)\n",
        "window_size = 64\n",
        "batch_size = 256\n",
        "train_set = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
        "print(train_set)\n",
        "print(x_train.shape)\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv1D(filters=32, kernel_size=5,\n",
        "                      strides=1, padding=\"causal\",\n",
        "                      activation=\"relu\",\n",
        "                      input_shape=[None, 1]),\n",
        "  tf.keras.layers.LSTM(64, return_sequences=True),\n",
        "  tf.keras.layers.LSTM(64, return_sequences=True),\n",
        "  tf.keras.layers.Dense(30, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1),\n",
        "  tf.keras.layers.Lambda(lambda x: x * 400)\n",
        "])\n",
        "\n",
        "lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: 1e-8 * 10**(epoch / 20))\n",
        "optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
        "model.compile(loss=tf.keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "colab_type": "code",
        "id": "vVcKmg7Q_7rD",
        "outputId": "5e9b8029-e996-4a2b-e016-666c69865b11"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[1e-08, 0.0001, 0, 60]"
            ]
          },
          "execution_count": 13,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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brv1+EJ7b23qnevxkjx3ffqL1BvJv66/+brMvz8t0f6bTVkh92dfn9rc/+9Ie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\nIAp9EZEA+Q/EvnPaR7UhDAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "plt.axis([1e-8, 1e-4, 0, 60])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "colab_type": "code",
        "id": "QsksvkcXAAgq",
        "outputId": "70263fd4-3c3a-4e93-a451-ee942131e0d4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/150\n",
            "25/25 [==============================] - 6s 243ms/step - loss: 9.9624 - mae: 10.5789\n",
            "Epoch 2/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 2.5390 - mae: 3.0130\n",
            "Epoch 3/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.9265 - mae: 2.3815\n",
            "Epoch 4/150\n",
            "25/25 [==============================] - 3s 137ms/step - loss: 1.8597 - mae: 2.3125\n",
            "Epoch 5/150\n",
            "25/25 [==============================] - 3s 139ms/step - loss: 1.8181 - mae: 2.2696\n",
            "Epoch 6/150\n",
            "25/25 [==============================] - 3s 140ms/step - loss: 1.7882 - mae: 2.2385\n",
            "Epoch 7/150\n",
            "25/25 [==============================] - 4s 141ms/step - loss: 1.7618 - mae: 2.2112\n",
            "Epoch 8/150\n",
            "25/25 [==============================] - 3s 135ms/step - loss: 1.7382 - mae: 2.1870\n",
            "Epoch 9/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.7167 - mae: 2.1650\n",
            "Epoch 10/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.6976 - mae: 2.1454\n",
            "Epoch 11/150\n",
            "25/25 [==============================] - 3s 138ms/step - loss: 1.6808 - mae: 2.1281\n",
            "Epoch 12/150\n",
            "25/25 [==============================] - 3s 138ms/step - loss: 1.6661 - mae: 2.1128\n",
            "Epoch 13/150\n",
            "25/25 [==============================] - 4s 142ms/step - loss: 1.6531 - mae: 2.0993\n",
            "Epoch 14/150\n",
            "25/25 [==============================] - 4s 142ms/step - loss: 1.6417 - mae: 2.0872\n",
            "Epoch 15/150\n",
            "25/25 [==============================] - 3s 135ms/step - loss: 1.6315 - mae: 2.0764\n",
            "Epoch 16/150\n",
            "25/25 [==============================] - 3s 128ms/step - loss: 1.6223 - mae: 2.0667\n",
            "Epoch 17/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.6141 - mae: 2.0579\n",
            "Epoch 18/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.6067 - mae: 2.0500\n",
            "Epoch 19/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.6000 - mae: 2.0429\n",
            "Epoch 20/150\n",
            "25/25 [==============================] - 3s 138ms/step - loss: 1.5939 - mae: 2.0364\n",
            "Epoch 21/150\n",
            "25/25 [==============================] - 3s 137ms/step - loss: 1.5883 - mae: 2.0306\n",
            "Epoch 22/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.5833 - mae: 2.0254\n",
            "Epoch 23/150\n",
            "25/25 [==============================] - 4s 141ms/step - loss: 1.5787 - mae: 2.0207\n",
            "Epoch 24/150\n",
            "25/25 [==============================] - 3s 140ms/step - loss: 1.5745 - mae: 2.0163\n",
            "Epoch 25/150\n",
            "25/25 [==============================] - 4s 141ms/step - loss: 1.5707 - mae: 2.0124\n",
            "Epoch 26/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.5672 - mae: 2.0089\n",
            "Epoch 27/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.5640 - mae: 2.0056\n",
            "Epoch 28/150\n",
            "25/25 [==============================] - 3s 135ms/step - loss: 1.5610 - mae: 2.0026\n",
            "Epoch 29/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.5583 - mae: 1.9998\n",
            "Epoch 30/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.5558 - mae: 1.9973\n",
            "Epoch 31/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.5534 - mae: 1.9949\n",
            "Epoch 32/150\n",
            "25/25 [==============================] - 3s 139ms/step - loss: 1.5512 - mae: 1.9927\n",
            "Epoch 33/150\n",
            "25/25 [==============================] - 3s 139ms/step - loss: 1.5491 - mae: 1.9906\n",
            "Epoch 34/150\n",
            "25/25 [==============================] - 3s 138ms/step - loss: 1.5472 - mae: 1.9886\n",
            "Epoch 35/150\n",
            "25/25 [==============================] - 4s 140ms/step - loss: 1.5453 - mae: 1.9868\n",
            "Epoch 36/150\n",
            "25/25 [==============================] - 4s 141ms/step - loss: 1.5436 - mae: 1.9850\n",
            "Epoch 37/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.5419 - mae: 1.9833\n",
            "Epoch 38/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.5404 - mae: 1.9817\n",
            "Epoch 39/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.5389 - mae: 1.9803\n",
            "Epoch 40/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.5376 - mae: 1.9789\n",
            "Epoch 41/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.5363 - mae: 1.9776\n",
            "Epoch 42/150\n",
            "25/25 [==============================] - 3s 129ms/step - loss: 1.5350 - mae: 1.9764\n",
            "Epoch 43/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.5338 - mae: 1.9752\n",
            "Epoch 44/150\n",
            "25/25 [==============================] - 4s 140ms/step - loss: 1.5327 - mae: 1.9741\n",
            "Epoch 45/150\n",
            "25/25 [==============================] - 4s 141ms/step - loss: 1.5316 - mae: 1.9730\n",
            "Epoch 46/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.5305 - mae: 1.9719\n",
            "Epoch 47/150\n",
            "25/25 [==============================] - 3s 135ms/step - loss: 1.5295 - mae: 1.9708\n",
            "Epoch 48/150\n",
            "25/25 [==============================] - 3s 138ms/step - loss: 1.5286 - mae: 1.9698\n",
            "Epoch 49/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.5276 - mae: 1.9689\n",
            "Epoch 50/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.5267 - mae: 1.9680\n",
            "Epoch 51/150\n",
            "25/25 [==============================] - 3s 139ms/step - loss: 1.5259 - mae: 1.9672\n",
            "Epoch 52/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.5251 - mae: 1.9664\n",
            "Epoch 53/150\n",
            "25/25 [==============================] - 3s 135ms/step - loss: 1.5243 - mae: 1.9656\n",
            "Epoch 54/150\n",
            "25/25 [==============================] - 3s 137ms/step - loss: 1.5235 - mae: 1.9648\n",
            "Epoch 55/150\n",
            "25/25 [==============================] - 3s 137ms/step - loss: 1.5228 - mae: 1.9640\n",
            "Epoch 56/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.5221 - mae: 1.9633\n",
            "Epoch 57/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.5213 - mae: 1.9626\n",
            "Epoch 58/150\n",
            "25/25 [==============================] - 3s 137ms/step - loss: 1.5206 - mae: 1.9619\n",
            "Epoch 59/150\n",
            "25/25 [==============================] - 3s 135ms/step - loss: 1.5199 - mae: 1.9611\n",
            "Epoch 60/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.5193 - mae: 1.9604\n",
            "Epoch 61/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.5186 - mae: 1.9597\n",
            "Epoch 62/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.5179 - mae: 1.9589\n",
            "Epoch 63/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.5173 - mae: 1.9582\n",
            "Epoch 64/150\n",
            "25/25 [==============================] - 3s 135ms/step - loss: 1.5166 - mae: 1.9576\n",
            "Epoch 65/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.5161 - mae: 1.9570\n",
            "Epoch 66/150\n",
            "25/25 [==============================] - 3s 138ms/step - loss: 1.5155 - mae: 1.9564\n",
            "Epoch 67/150\n",
            "25/25 [==============================] - 3s 140ms/step - loss: 1.5149 - mae: 1.9557\n",
            "Epoch 68/150\n",
            "25/25 [==============================] - 4s 141ms/step - loss: 1.5142 - mae: 1.9550\n",
            "Epoch 69/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.5136 - mae: 1.9543\n",
            "Epoch 70/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.5129 - mae: 1.9536\n",
            "Epoch 71/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.5122 - mae: 1.9528\n",
            "Epoch 72/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.5115 - mae: 1.9521\n",
            "Epoch 73/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.5108 - mae: 1.9514\n",
            "Epoch 74/150\n",
            "25/25 [==============================] - 3s 135ms/step - loss: 1.5102 - mae: 1.9508\n",
            "Epoch 75/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.5097 - mae: 1.9503\n",
            "Epoch 76/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.5091 - mae: 1.9497\n",
            "Epoch 77/150\n",
            "25/25 [==============================] - 3s 137ms/step - loss: 1.5084 - mae: 1.9491\n",
            "Epoch 78/150\n",
            "25/25 [==============================] - 3s 128ms/step - loss: 1.5078 - mae: 1.9485\n",
            "Epoch 79/150\n",
            "25/25 [==============================] - 3s 129ms/step - loss: 1.5072 - mae: 1.9478\n",
            "Epoch 80/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.5065 - mae: 1.9471\n",
            "Epoch 81/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.5059 - mae: 1.9465\n",
            "Epoch 82/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.5053 - mae: 1.9459\n",
            "Epoch 83/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.5048 - mae: 1.9454\n",
            "Epoch 84/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.5043 - mae: 1.9448\n",
            "Epoch 85/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.5038 - mae: 1.9443\n",
            "Epoch 86/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.5033 - mae: 1.9438\n",
            "Epoch 87/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.5028 - mae: 1.9433\n",
            "Epoch 88/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.5023 - mae: 1.9428\n",
            "Epoch 89/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.5017 - mae: 1.9422\n",
            "Epoch 90/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.5012 - mae: 1.9416\n",
            "Epoch 91/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.5004 - mae: 1.9408\n",
            "Epoch 92/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.4992 - mae: 1.9396\n",
            "Epoch 93/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.4982 - mae: 1.9386\n",
            "Epoch 94/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.4975 - mae: 1.9378\n",
            "Epoch 95/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.4968 - mae: 1.9371\n",
            "Epoch 96/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.4962 - mae: 1.9364\n",
            "Epoch 97/150\n",
            "25/25 [==============================] - 3s 137ms/step - loss: 1.4955 - mae: 1.9358\n",
            "Epoch 98/150\n",
            "25/25 [==============================] - 3s 140ms/step - loss: 1.4949 - mae: 1.9351\n",
            "Epoch 99/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.4942 - mae: 1.9344\n",
            "Epoch 100/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.4934 - mae: 1.9336\n",
            "Epoch 101/150\n",
            "25/25 [==============================] - 3s 135ms/step - loss: 1.4927 - mae: 1.9328\n",
            "Epoch 102/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.4919 - mae: 1.9319\n",
            "Epoch 103/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.4913 - mae: 1.9313\n",
            "Epoch 104/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.4908 - mae: 1.9307\n",
            "Epoch 105/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.4903 - mae: 1.9302\n",
            "Epoch 106/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.4899 - mae: 1.9298\n",
            "Epoch 107/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.4895 - mae: 1.9293\n",
            "Epoch 108/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.4892 - mae: 1.9289\n",
            "Epoch 109/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.4888 - mae: 1.9285\n",
            "Epoch 110/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.4884 - mae: 1.9282\n",
            "Epoch 111/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.4881 - mae: 1.9278\n",
            "Epoch 112/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.4878 - mae: 1.9275\n",
            "Epoch 113/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.4874 - mae: 1.9271\n",
            "Epoch 114/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.4871 - mae: 1.9268\n",
            "Epoch 115/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.4868 - mae: 1.9265\n",
            "Epoch 116/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.4865 - mae: 1.9262\n",
            "Epoch 117/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.4862 - mae: 1.9258\n",
            "Epoch 118/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.4859 - mae: 1.9255\n",
            "Epoch 119/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.4856 - mae: 1.9252\n",
            "Epoch 120/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.4853 - mae: 1.9249\n",
            "Epoch 121/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.4851 - mae: 1.9247\n",
            "Epoch 122/150\n",
            "25/25 [==============================] - 3s 135ms/step - loss: 1.4848 - mae: 1.9243\n",
            "Epoch 123/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.4845 - mae: 1.9240\n",
            "Epoch 124/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.4842 - mae: 1.9238\n",
            "Epoch 125/150\n",
            "25/25 [==============================] - 3s 133ms/step - loss: 1.4840 - mae: 1.9235\n",
            "Epoch 126/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.4838 - mae: 1.9232\n",
            "Epoch 127/150\n",
            "25/25 [==============================] - 3s 129ms/step - loss: 1.4835 - mae: 1.9230\n",
            "Epoch 128/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.4833 - mae: 1.9228\n",
            "Epoch 129/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.4831 - mae: 1.9225\n",
            "Epoch 130/150\n",
            "25/25 [==============================] - 3s 129ms/step - loss: 1.4828 - mae: 1.9222\n",
            "Epoch 131/150\n",
            "25/25 [==============================] - 3s 128ms/step - loss: 1.4826 - mae: 1.9220\n",
            "Epoch 132/150\n",
            "25/25 [==============================] - 3s 127ms/step - loss: 1.4823 - mae: 1.9217\n",
            "Epoch 133/150\n",
            "25/25 [==============================] - 3s 129ms/step - loss: 1.4821 - mae: 1.9214\n",
            "Epoch 134/150\n",
            "25/25 [==============================] - 3s 129ms/step - loss: 1.4818 - mae: 1.9212\n",
            "Epoch 135/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.4815 - mae: 1.9209\n",
            "Epoch 136/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.4813 - mae: 1.9206\n",
            "Epoch 137/150\n",
            "25/25 [==============================] - 3s 127ms/step - loss: 1.4811 - mae: 1.9204\n",
            "Epoch 138/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.4808 - mae: 1.9201\n",
            "Epoch 139/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.4806 - mae: 1.9199\n",
            "Epoch 140/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.4804 - mae: 1.9197\n",
            "Epoch 141/150\n",
            "25/25 [==============================] - 3s 132ms/step - loss: 1.4802 - mae: 1.9194\n",
            "Epoch 142/150\n",
            "25/25 [==============================] - 3s 129ms/step - loss: 1.4799 - mae: 1.9192\n",
            "Epoch 143/150\n",
            "25/25 [==============================] - 3s 134ms/step - loss: 1.4797 - mae: 1.9189\n",
            "Epoch 144/150\n",
            "25/25 [==============================] - 3s 135ms/step - loss: 1.4795 - mae: 1.9187\n",
            "Epoch 145/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.4794 - mae: 1.9185\n",
            "Epoch 146/150\n",
            "25/25 [==============================] - 3s 131ms/step - loss: 1.4791 - mae: 1.9183\n",
            "Epoch 147/150\n",
            "25/25 [==============================] - 3s 130ms/step - loss: 1.4789 - mae: 1.9180\n",
            "Epoch 148/150\n",
            "25/25 [==============================] - 3s 136ms/step - loss: 1.4787 - mae: 1.9178\n",
            "Epoch 149/150\n",
            "25/25 [==============================] - 3s 137ms/step - loss: 1.4784 - mae: 1.9175\n",
            "Epoch 150/150\n",
            "25/25 [==============================] - 4s 143ms/step - loss: 1.4782 - mae: 1.9173\n"
          ]
        }
      ],
      "source": [
        "tf.keras.backend.clear_session()\n",
        "tf.random.set_seed(51)\n",
        "np.random.seed(51)\n",
        "train_set = windowed_dataset(x_train, window_size=60, batch_size=100, shuffle_buffer=shuffle_buffer_size)\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv1D(filters=60, kernel_size=5,\n",
        "                      strides=1, padding=\"causal\",\n",
        "                      activation=\"relu\",\n",
        "                      input_shape=[None, 1]),\n",
        "  tf.keras.layers.LSTM(60, return_sequences=True),\n",
        "  tf.keras.layers.LSTM(60, return_sequences=True),\n",
        "  tf.keras.layers.Dense(30, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1),\n",
        "  tf.keras.layers.Lambda(lambda x: x * 400)\n",
        "])\n",
        "\n",
        "\n",
        "optimizer = tf.keras.optimizers.SGD(lr=1e-5, momentum=0.9)\n",
        "model.compile(loss=tf.keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "history = model.fit(train_set,epochs=150)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "GaC6NNMRp0lb"
      },
      "outputs": [],
      "source": [
        "rnn_forecast = model_forecast(model, series[..., np.newaxis], window_size)\n",
        "rnn_forecast = rnn_forecast[split_time - window_size:-1, -1, 0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "colab_type": "code",
        "id": "PrktQX3hKYex",
        "outputId": "6f51b039-76a2-4c2d-9f37-c89c6aab20e2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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B0zNxM4jAT9Fr42u0QmbJuN2nwwXeaixGD6sgO+T6egWaMPf4mZWdykmCcdQT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zDC8qke6o5Wog+EgZ3EhJ02dRiuOGFvnHeYmY1qDYhHWuk7UCwmDABG5u7H/EP/DEoSaiUCAPfg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\n2AhjGIZhGIZpAf8frXwyiLneFFwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, rnn_forecast)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "id": "13XrorC5wQoE",
        "outputId": "7f5bda4a-160c-4c0f-e511-a5a1b6945d35"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.780626"
            ]
          },
          "execution_count": 18,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tf.keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "id": "AOVzQXxCwkzP",
        "outputId": "3b3fd11c-f9b3-4cbd-e32d-2d7d22b6ff7f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[11.636601 10.97607  12.159701 ... 13.589686 13.726407 14.940471]\n"
          ]
        }
      ],
      "source": [
        "print(rnn_forecast)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "S+P Week 4 Exercise Answer.ipynb",
      "provenance": [],
      "toc_visible": true,
      "version": "0.3.2"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}