Sunspot.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled3.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJUVAduy_eaX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "try:\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1B8eI7jB__QM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "a139c495-9043-4d44-daba-933551a73390"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMIHS58-AF2U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "outputId": "53f2e084-c315-46e6-a23f-e5e767be42ea"
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/Sunspots.csv \\\n",
        "    -O /tmp/sunspots.csv\n",
        "#Loading the data"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-25 04:04:37--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/Sunspots.csv\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 108.177.119.128, 2a00:1450:4013:c04::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|108.177.119.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 70827 (69K) [application/octet-stream]\n",
            "Saving to: ‘/tmp/sunspots.csv’\n",
            "\n",
            "\r/tmp/sunspots.csv     0%[                    ]       0  --.-KB/s               \r/tmp/sunspots.csv   100%[===================>]  69.17K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2020-05-25 04:04:37 (118 MB/s) - ‘/tmp/sunspots.csv’ saved [70827/70827]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RUNRFAqAPCX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "c126cada-6303-4656-9624-956826039c86"
      },
      "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)\n",
        "import csv\n",
        "time_step = []\n",
        "sunspots = []\n",
        "\n",
        "with open('/tmp/sunspots.csv') as csvfile:\n",
        "  reader = csv.reader(csvfile, delimiter=',')\n",
        "  next(reader)\n",
        "  for row in reader:\n",
        "    sunspots.append(float(row[2]))\n",
        "    time_step.append(int(row[0]))\n",
        "\n",
        "series = np.array(sunspots)\n",
        "time = np.array(time_step)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time, series)\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZ4j0DukAVOi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "93316d1d-ba79-47fd-8ebe-9cad08f9ceed"
      },
      "source": [
        "series = np.array(sunspots)\n",
        "time = np.array(time_step)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time, series)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Bi5XUTjAzLF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "split_time = 3000#Training period\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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I6_o4JMzA_Na",
        "colab_type": "code",
        "colab": {}
      },
      "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TwjJ7LElBGEz",
        "colab_type": "code",
        "colab": {}
      },
      "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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7KA2jsxBK6D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3986fb60-f1aa-45ff-d341-f2dd2e304464"
      },
      "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"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<PrefetchDataset shapes: ((None, None, 1), (None, None, 1)), types: (tf.float64, tf.float64)>\n",
            "(3000,)\n",
            "Epoch 1/100\n",
            "12/12 [==============================] - 3s 287ms/step - loss: 79.8340 - mae: 80.3314 - lr: 1.0000e-08\n",
            "Epoch 2/100\n",
            "12/12 [==============================] - 3s 282ms/step - loss: 78.0944 - mae: 78.5918 - lr: 1.1220e-08\n",
            "Epoch 3/100\n",
            "12/12 [==============================] - 3s 284ms/step - loss: 75.4519 - mae: 75.9497 - lr: 1.2589e-08\n",
            "Epoch 4/100\n",
            "12/12 [==============================] - 3s 288ms/step - loss: 72.2679 - mae: 72.7658 - lr: 1.4125e-08\n",
            "Epoch 5/100\n",
            "12/12 [==============================] - 3s 283ms/step - loss: 68.7693 - mae: 69.2672 - lr: 1.5849e-08\n",
            "Epoch 6/100\n",
            "12/12 [==============================] - 3s 284ms/step - loss: 65.1128 - mae: 65.6099 - lr: 1.7783e-08\n",
            "Epoch 7/100\n",
            "12/12 [==============================] - 3s 283ms/step - loss: 61.5272 - mae: 62.0241 - lr: 1.9953e-08\n",
            "Epoch 8/100\n",
            "12/12 [==============================] - 3s 280ms/step - loss: 58.1406 - mae: 58.6369 - lr: 2.2387e-08\n",
            "Epoch 9/100\n",
            "12/12 [==============================] - 3s 282ms/step - loss: 55.0732 - mae: 55.5697 - lr: 2.5119e-08\n",
            "Epoch 10/100\n",
            "12/12 [==============================] - 3s 282ms/step - loss: 52.3436 - mae: 52.8399 - lr: 2.8184e-08\n",
            "Epoch 11/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 49.9148 - mae: 50.4113 - lr: 3.1623e-08\n",
            "Epoch 12/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 47.8592 - mae: 48.3560 - lr: 3.5481e-08\n",
            "Epoch 13/100\n",
            "12/12 [==============================] - 3s 276ms/step - loss: 46.0553 - mae: 46.5517 - lr: 3.9811e-08\n",
            "Epoch 14/100\n",
            "12/12 [==============================] - 3s 284ms/step - loss: 44.5444 - mae: 45.0406 - lr: 4.4668e-08\n",
            "Epoch 15/100\n",
            "12/12 [==============================] - 3s 279ms/step - loss: 43.3078 - mae: 43.8045 - lr: 5.0119e-08\n",
            "Epoch 16/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 42.2855 - mae: 42.7826 - lr: 5.6234e-08\n",
            "Epoch 17/100\n",
            "12/12 [==============================] - 3s 273ms/step - loss: 41.3797 - mae: 41.8767 - lr: 6.3096e-08\n",
            "Epoch 18/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 40.5481 - mae: 41.0453 - lr: 7.0795e-08\n",
            "Epoch 19/100\n",
            "12/12 [==============================] - 3s 282ms/step - loss: 39.7690 - mae: 40.2659 - lr: 7.9433e-08\n",
            "Epoch 20/100\n",
            "12/12 [==============================] - 3s 289ms/step - loss: 39.0213 - mae: 39.5181 - lr: 8.9125e-08\n",
            "Epoch 21/100\n",
            "12/12 [==============================] - 3s 287ms/step - loss: 38.2735 - mae: 38.7699 - lr: 1.0000e-07\n",
            "Epoch 22/100\n",
            "12/12 [==============================] - 3s 276ms/step - loss: 37.4213 - mae: 37.9177 - lr: 1.1220e-07\n",
            "Epoch 23/100\n",
            "12/12 [==============================] - 3s 279ms/step - loss: 36.3874 - mae: 36.8841 - lr: 1.2589e-07\n",
            "Epoch 24/100\n",
            "12/12 [==============================] - 3s 280ms/step - loss: 35.3032 - mae: 35.7995 - lr: 1.4125e-07\n",
            "Epoch 25/100\n",
            "12/12 [==============================] - 3s 278ms/step - loss: 34.0848 - mae: 34.5806 - lr: 1.5849e-07\n",
            "Epoch 26/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 32.9497 - mae: 33.4454 - lr: 1.7783e-07\n",
            "Epoch 27/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 31.8641 - mae: 32.3596 - lr: 1.9953e-07\n",
            "Epoch 28/100\n",
            "12/12 [==============================] - 3s 283ms/step - loss: 31.1204 - mae: 31.6161 - lr: 2.2387e-07\n",
            "Epoch 29/100\n",
            "12/12 [==============================] - 3s 287ms/step - loss: 30.3506 - mae: 30.8462 - lr: 2.5119e-07\n",
            "Epoch 30/100\n",
            "12/12 [==============================] - 3s 278ms/step - loss: 29.6234 - mae: 30.1187 - lr: 2.8184e-07\n",
            "Epoch 31/100\n",
            "12/12 [==============================] - 3s 277ms/step - loss: 29.0391 - mae: 29.5344 - lr: 3.1623e-07\n",
            "Epoch 32/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 28.5973 - mae: 29.0926 - lr: 3.5481e-07\n",
            "Epoch 33/100\n",
            "12/12 [==============================] - 3s 286ms/step - loss: 28.1780 - mae: 28.6731 - lr: 3.9811e-07\n",
            "Epoch 34/100\n",
            "12/12 [==============================] - 3s 285ms/step - loss: 27.9753 - mae: 28.4699 - lr: 4.4668e-07\n",
            "Epoch 35/100\n",
            "12/12 [==============================] - 3s 284ms/step - loss: 27.8942 - mae: 28.3888 - lr: 5.0119e-07\n",
            "Epoch 36/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 27.4666 - mae: 27.9612 - lr: 5.6234e-07\n",
            "Epoch 37/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 27.0270 - mae: 27.5215 - lr: 6.3096e-07\n",
            "Epoch 38/100\n",
            "12/12 [==============================] - 3s 280ms/step - loss: 26.5197 - mae: 27.0143 - lr: 7.0795e-07\n",
            "Epoch 39/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 26.3692 - mae: 26.8638 - lr: 7.9433e-07\n",
            "Epoch 40/100\n",
            "12/12 [==============================] - 3s 289ms/step - loss: 25.8427 - mae: 26.3372 - lr: 8.9125e-07\n",
            "Epoch 41/100\n",
            "12/12 [==============================] - 3s 285ms/step - loss: 25.5105 - mae: 26.0048 - lr: 1.0000e-06\n",
            "Epoch 42/100\n",
            "12/12 [==============================] - 3s 283ms/step - loss: 25.1186 - mae: 25.6130 - lr: 1.1220e-06\n",
            "Epoch 43/100\n",
            "12/12 [==============================] - 3s 289ms/step - loss: 24.6611 - mae: 25.1554 - lr: 1.2589e-06\n",
            "Epoch 44/100\n",
            "12/12 [==============================] - 3s 284ms/step - loss: 24.4240 - mae: 24.9186 - lr: 1.4125e-06\n",
            "Epoch 45/100\n",
            "12/12 [==============================] - 3s 282ms/step - loss: 24.7842 - mae: 25.2789 - lr: 1.5849e-06\n",
            "Epoch 46/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 24.1148 - mae: 24.6087 - lr: 1.7783e-06\n",
            "Epoch 47/100\n",
            "12/12 [==============================] - 3s 285ms/step - loss: 23.3096 - mae: 23.8037 - lr: 1.9953e-06\n",
            "Epoch 48/100\n",
            "12/12 [==============================] - 3s 286ms/step - loss: 22.9947 - mae: 23.4887 - lr: 2.2387e-06\n",
            "Epoch 49/100\n",
            "12/12 [==============================] - 3s 290ms/step - loss: 22.5345 - mae: 23.0283 - lr: 2.5119e-06\n",
            "Epoch 50/100\n",
            "12/12 [==============================] - 3s 284ms/step - loss: 22.1644 - mae: 22.6581 - lr: 2.8184e-06\n",
            "Epoch 51/100\n",
            "12/12 [==============================] - 3s 289ms/step - loss: 22.1919 - mae: 22.6858 - lr: 3.1623e-06\n",
            "Epoch 52/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 21.7631 - mae: 22.2570 - lr: 3.5481e-06\n",
            "Epoch 53/100\n",
            "12/12 [==============================] - 3s 279ms/step - loss: 21.6876 - mae: 22.1816 - lr: 3.9811e-06\n",
            "Epoch 54/100\n",
            "12/12 [==============================] - 3s 279ms/step - loss: 21.8647 - mae: 22.3586 - lr: 4.4668e-06\n",
            "Epoch 55/100\n",
            "12/12 [==============================] - 3s 280ms/step - loss: 22.4406 - mae: 22.9342 - lr: 5.0119e-06\n",
            "Epoch 56/100\n",
            "12/12 [==============================] - 3s 284ms/step - loss: 22.3569 - mae: 22.8512 - lr: 5.6234e-06\n",
            "Epoch 57/100\n",
            "12/12 [==============================] - 3s 289ms/step - loss: 21.3050 - mae: 21.7984 - lr: 6.3096e-06\n",
            "Epoch 58/100\n",
            "12/12 [==============================] - 3s 288ms/step - loss: 20.5180 - mae: 21.0114 - lr: 7.0795e-06\n",
            "Epoch 59/100\n",
            "12/12 [==============================] - 3s 287ms/step - loss: 20.4598 - mae: 20.9529 - lr: 7.9433e-06\n",
            "Epoch 60/100\n",
            "12/12 [==============================] - 3s 282ms/step - loss: 20.8196 - mae: 21.3127 - lr: 8.9125e-06\n",
            "Epoch 61/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 20.8843 - mae: 21.3779 - lr: 1.0000e-05\n",
            "Epoch 62/100\n",
            "12/12 [==============================] - 3s 284ms/step - loss: 20.0488 - mae: 20.5423 - lr: 1.1220e-05\n",
            "Epoch 63/100\n",
            "12/12 [==============================] - 3s 286ms/step - loss: 20.2019 - mae: 20.6950 - lr: 1.2589e-05\n",
            "Epoch 64/100\n",
            "12/12 [==============================] - 3s 288ms/step - loss: 19.3769 - mae: 19.8702 - lr: 1.4125e-05\n",
            "Epoch 65/100\n",
            "12/12 [==============================] - 3s 288ms/step - loss: 22.7590 - mae: 23.2534 - lr: 1.5849e-05\n",
            "Epoch 66/100\n",
            "12/12 [==============================] - 3s 283ms/step - loss: 21.0860 - mae: 21.5796 - lr: 1.7783e-05\n",
            "Epoch 67/100\n",
            "12/12 [==============================] - 3s 282ms/step - loss: 20.0275 - mae: 20.5208 - lr: 1.9953e-05\n",
            "Epoch 68/100\n",
            "12/12 [==============================] - 3s 283ms/step - loss: 21.4155 - mae: 21.9089 - lr: 2.2387e-05\n",
            "Epoch 69/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 19.4559 - mae: 19.9490 - lr: 2.5119e-05\n",
            "Epoch 70/100\n",
            "12/12 [==============================] - 3s 280ms/step - loss: 21.8080 - mae: 22.3018 - lr: 2.8184e-05\n",
            "Epoch 71/100\n",
            "12/12 [==============================] - 3s 282ms/step - loss: 20.4448 - mae: 20.9383 - lr: 3.1623e-05\n",
            "Epoch 72/100\n",
            "12/12 [==============================] - 3s 282ms/step - loss: 22.5032 - mae: 22.9970 - lr: 3.5481e-05\n",
            "Epoch 73/100\n",
            "12/12 [==============================] - 3s 283ms/step - loss: 20.4577 - mae: 20.9508 - lr: 3.9811e-05\n",
            "Epoch 74/100\n",
            "12/12 [==============================] - 3s 285ms/step - loss: 21.4358 - mae: 21.9294 - lr: 4.4668e-05\n",
            "Epoch 75/100\n",
            "12/12 [==============================] - 3s 283ms/step - loss: 24.1796 - mae: 24.6737 - lr: 5.0119e-05\n",
            "Epoch 76/100\n",
            "12/12 [==============================] - 3s 285ms/step - loss: 22.5941 - mae: 23.0878 - lr: 5.6234e-05\n",
            "Epoch 77/100\n",
            "12/12 [==============================] - 3s 286ms/step - loss: 21.2353 - mae: 21.7283 - lr: 6.3096e-05\n",
            "Epoch 78/100\n",
            "12/12 [==============================] - 3s 287ms/step - loss: 22.7952 - mae: 23.2889 - lr: 7.0795e-05\n",
            "Epoch 79/100\n",
            "12/12 [==============================] - 3s 285ms/step - loss: 19.7659 - mae: 20.2589 - lr: 7.9433e-05\n",
            "Epoch 80/100\n",
            "12/12 [==============================] - 3s 284ms/step - loss: 22.8839 - mae: 23.3773 - lr: 8.9125e-05\n",
            "Epoch 81/100\n",
            "12/12 [==============================] - 3s 279ms/step - loss: 38.7277 - mae: 39.2244 - lr: 1.0000e-04\n",
            "Epoch 82/100\n",
            "12/12 [==============================] - 3s 287ms/step - loss: 43.9011 - mae: 44.3984 - lr: 1.1220e-04\n",
            "Epoch 83/100\n",
            "12/12 [==============================] - 4s 298ms/step - loss: 38.4261 - mae: 38.9222 - lr: 1.2589e-04\n",
            "Epoch 84/100\n",
            "12/12 [==============================] - 4s 294ms/step - loss: 34.5216 - mae: 35.0177 - lr: 1.4125e-04\n",
            "Epoch 85/100\n",
            "12/12 [==============================] - 3s 284ms/step - loss: 33.7845 - mae: 34.2805 - lr: 1.5849e-04\n",
            "Epoch 86/100\n",
            "12/12 [==============================] - 3s 287ms/step - loss: 35.2180 - mae: 35.7145 - lr: 1.7783e-04\n",
            "Epoch 87/100\n",
            "12/12 [==============================] - 3s 284ms/step - loss: 43.9936 - mae: 44.4906 - lr: 1.9953e-04\n",
            "Epoch 88/100\n",
            "12/12 [==============================] - 3s 286ms/step - loss: 31.6561 - mae: 32.1515 - lr: 2.2387e-04\n",
            "Epoch 89/100\n",
            "12/12 [==============================] - 3s 285ms/step - loss: 56.8778 - mae: 57.3750 - lr: 2.5119e-04\n",
            "Epoch 90/100\n",
            "12/12 [==============================] - 3s 283ms/step - loss: 58.6312 - mae: 59.1293 - lr: 2.8184e-04\n",
            "Epoch 91/100\n",
            "12/12 [==============================] - 3s 283ms/step - loss: 51.7272 - mae: 52.2252 - lr: 3.1623e-04\n",
            "Epoch 92/100\n",
            "12/12 [==============================] - 3s 280ms/step - loss: 53.9708 - mae: 54.4687 - lr: 3.5481e-04\n",
            "Epoch 93/100\n",
            "12/12 [==============================] - 3s 281ms/step - loss: 65.6057 - mae: 66.1042 - lr: 3.9811e-04\n",
            "Epoch 94/100\n",
            "12/12 [==============================] - 3s 280ms/step - loss: 54.3253 - mae: 54.8233 - lr: 4.4668e-04\n",
            "Epoch 95/100\n",
            "12/12 [==============================] - 3s 280ms/step - loss: 59.3857 - mae: 59.8838 - lr: 5.0119e-04\n",
            "Epoch 96/100\n",
            "12/12 [==============================] - 3s 285ms/step - loss: 48.9564 - mae: 49.4539 - lr: 5.6234e-04\n",
            "Epoch 97/100\n",
            "12/12 [==============================] - 3s 284ms/step - loss: 65.9034 - mae: 66.4017 - lr: 6.3096e-04\n",
            "Epoch 98/100\n",
            "12/12 [==============================] - 4s 293ms/step - loss: 55.1943 - mae: 55.6924 - lr: 7.0795e-04\n",
            "Epoch 99/100\n",
            "12/12 [==============================] - 3s 288ms/step - loss: 55.3806 - mae: 55.8787 - lr: 7.9433e-04\n",
            "Epoch 100/100\n",
            "12/12 [==============================] - 3s 282ms/step - loss: 55.7757 - mae: 56.2743 - lr: 8.9125e-04\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0T8ara10BPNV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "cd2bec1c-7730-4dc3-b809-ec6fa5b97050"
      },
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "plt.axis([1e-8, 1e-4, 0, 60])\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1e-08, 0.0001, 0.0, 60.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5-GdKriDhKJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "olr=history.history[\"lr\"][history.history[\"loss\"].index(min(history.history[\"loss\"]))]#optimum learning rate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIb64izqDtRk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cd4760bc-56ee-470f-82aa-8abe7c55f9c4"
      },
      "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=olr, momentum=0.9)\n",
        "model.compile(loss=tf.keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "history = model.fit(train_set,epochs=500)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "30/30 [==============================] - 3s 116ms/step - loss: 35.9376 - mae: 36.4332\n",
            "Epoch 2/500\n",
            "30/30 [==============================] - 5s 154ms/step - loss: 23.0771 - mae: 23.5707\n",
            "Epoch 3/500\n",
            "30/30 [==============================] - 6s 187ms/step - loss: 21.8749 - mae: 22.3679\n",
            "Epoch 4/500\n",
            "30/30 [==============================] - 5s 151ms/step - loss: 20.4145 - mae: 20.9073\n",
            "Epoch 5/500\n",
            "30/30 [==============================] - 4s 119ms/step - loss: 19.5466 - mae: 20.0388\n",
            "Epoch 6/500\n",
            "30/30 [==============================] - 4s 119ms/step - loss: 19.3623 - mae: 19.8544\n",
            "Epoch 7/500\n",
            "30/30 [==============================] - 4s 121ms/step - loss: 18.3478 - mae: 18.8394\n",
            "Epoch 8/500\n",
            "30/30 [==============================] - 4s 119ms/step - loss: 18.2873 - mae: 18.7791\n",
            "Epoch 9/500\n",
            "30/30 [==============================] - 4s 121ms/step - loss: 18.6480 - mae: 19.1393\n",
            "Epoch 10/500\n",
            "30/30 [==============================] - 4s 120ms/step - loss: 18.1239 - mae: 18.6155\n",
            "Epoch 11/500\n",
            "30/30 [==============================] - 4s 119ms/step - loss: 18.1845 - mae: 18.6759\n",
            "Epoch 12/500\n",
            "30/30 [==============================] - 4s 120ms/step - loss: 17.9513 - mae: 18.4427\n",
            "Epoch 13/500\n",
            "30/30 [==============================] - 4s 121ms/step - loss: 17.8472 - mae: 18.3386\n",
            "Epoch 14/500\n",
            "30/30 [==============================] - 4s 121ms/step - loss: 18.1512 - mae: 18.6425\n",
            "Epoch 15/500\n",
            "30/30 [==============================] - 4s 122ms/step - loss: 17.5467 - mae: 18.0382\n",
            "Epoch 16/500\n",
            "30/30 [==============================] - 4s 121ms/step - loss: 17.8949 - mae: 18.3862\n",
            "Epoch 17/500\n",
            "30/30 [==============================] - 4s 120ms/step - loss: 17.3874 - mae: 17.8783\n",
            "Epoch 18/500\n",
            "30/30 [==============================] - 4s 119ms/step - loss: 17.2284 - mae: 17.7191\n",
            "Epoch 19/500\n",
            "30/30 [==============================] - 4s 119ms/step - loss: 17.3597 - mae: 17.8508\n",
            "Epoch 20/500\n",
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            "Epoch 499/500\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOH8NWZDEb5f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rnn_forecast = model_forecast(model, series[..., np.newaxis], window_size)\n",
        "rnn_forecast = rnn_forecast[split_time - window_size:-1, -1, 0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CeTa-KImNJOL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "43296173-e6f7-4aa3-b922-c0a906b8b182"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, rnn_forecast)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AMSWjhONcAL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "af81bfb6-d6b1-47a7-afda-3fbcfd456db2"
      },
      "source": [
        "tf.keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16.281103"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rHfpShm_NzYY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "842dc29d-e603-427f-a8df-8121c0d6ca0f"
      },
      "source": [
        "import matplotlib.image  as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\n",
        "loss=history.history['loss']\n",
        "\n",
        "epochs=range(len(loss)) # Get number of epochs\n",
        "\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, loss, 'r')\n",
        "plt.title('Training loss')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend([\"Loss\"])\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "\n",
        "\n",
        "zoomed_loss = loss[200:]\n",
        "zoomed_epochs = range(200,500)\n",
        "\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(zoomed_epochs, zoomed_loss, 'r')\n",
        "plt.title('Training loss')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend([\"Loss\"])\n",
        "\n",
        "plt.figure()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WRYH3H3DN6JD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}