S+P_Week_4_Exercise_Question.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": " S+P Week 4 Exercise Question.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/soumyadiptapete/Coursera_TF_specialization/blob/master/S%2BP_Week_4_Exercise_Question.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNkzTFfynsmV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "outputId": "3a495756-ff6b-469b-fc43-409284a6577b"
      },
      "source": [
        "!pip install tensorflow==2.0.0b1"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==2.0.0b1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/29/6c/2c9a5c4d095c63c2fb37d20def0e4f92685f7aee9243d6aae25862694fd1/tensorflow-2.0.0b1-cp36-cp36m-manylinux1_x86_64.whl (87.9MB)\n",
            "\u001b[K     |████████████████████████████████| 87.9MB 92kB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (3.10.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (1.17.4)\n",
            "Collecting tb-nightly<1.14.0a20190604,>=1.14.0a20190603\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a4/96/571b875cd81dda9d5dfa1422a4f9d749e67c0a8d4f4f0b33a4e5f5f35e27/tb_nightly-1.14.0a20190603-py3-none-any.whl (3.1MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1MB 45.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (1.11.2)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (0.33.6)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (1.15.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (0.8.0)\n",
            "Collecting tf-estimator-nightly<1.14.0.dev2019060502,>=1.14.0.dev2019060501\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/32/dd/99c47dd007dcf10d63fd895611b063732646f23059c618a373e85019eb0e/tf_estimator_nightly-1.14.0.dev2019060501-py2.py3-none-any.whl (496kB)\n",
            "\u001b[K     |████████████████████████████████| 501kB 76.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (1.12.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (1.0.8)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (0.2.2)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (1.1.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (0.1.8)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (0.8.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0b1) (1.1.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow==2.0.0b1) (41.4.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow==2.0.0b1) (3.1.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow==2.0.0b1) (0.16.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow==2.0.0b1) (2.8.0)\n",
            "Installing collected packages: tb-nightly, tf-estimator-nightly, tensorflow\n",
            "  Found existing installation: tensorflow 1.15.0\n",
            "    Uninstalling tensorflow-1.15.0:\n",
            "      Successfully uninstalled tensorflow-1.15.0\n",
            "Successfully installed tb-nightly-1.14.0a20190603 tensorflow-2.0.0b1 tf-estimator-nightly-1.14.0.dev2019060501\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56XEQOGknrAk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "outputId": "3d31fb42-2b70-4580-f9b3-9c126742c562"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2.0.0-beta1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLl52leVp5wU",
        "colab_type": "code",
        "colab": {}
      },
      "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tP7oqUdkk0gY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "2ccb0213-e218-4ac9-9868-be27fb2d8c28"
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv \\\n",
        "    -O /tmp/daily-min-temperatures.csv"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-11-14 11:00:03--  https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 67921 (66K) [text/plain]\n",
            "Saving to: ‘/tmp/daily-min-temperatures.csv’\n",
            "\n",
            "\r          /tmp/dail   0%[                    ]       0  --.-KB/s               \r/tmp/daily-min-temp 100%[===================>]  66.33K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2019-11-14 11:00:03 (4.51 MB/s) - ‘/tmp/daily-min-temperatures.csv’ saved [67921/67921]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NcG9r1eClbTh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "69148026-2fdc-4bd4-aaf2-324e0e17305e"
      },
      "source": [
        "import csv\n",
        "time_step = []\n",
        "temps = []\n",
        "\n",
        "with open('/tmp/daily-min-temperatures.csv') as csvfile:\n",
        "      csv_reader = csv.reader(csvfile, delimiter=',')\n",
        "      next(csv_reader) #leave first line\n",
        "      line_count = 0\n",
        "      for row in csv_reader:\n",
        "        time_step.append(line_count)\n",
        "        temps.append(float(row[1])) #float command converts number to float\n",
        "        line_count+=1\n",
        "            \n",
        "series = np.array(temps)\n",
        "time = np.array(time_step)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time, series)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cJDOHQe+yOTdzgsvyDsfcPwOXPj6X8Q7hU5ifJ5U1hpXl5Yfx0sKyrO/lXWqU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\nYZiA+P8LhE/6qyPAPwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RR5tkCDUJqkJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2b1b6ee1-4057-47de-cc5d-a82101927652"
      },
      "source": [
        "len(time)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3650"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L92YRw_IpCFG",
        "colab_type": "code",
        "colab": {}
      },
      "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 = 30\n",
        "shuffle_buffer_size = 1000\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJwUUZscnG38",
        "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 window: window.batch(window_size+1)) #coverts each set into a window\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": "4XwGrf-A_wF0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def model_forecast(model, series, window_size):\n",
        "    series=tf.expand_dims(series,axis=-1)\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": "AclfYY3Mn6Ph",
        "colab_type": "code",
        "outputId": "b3903cdd-ca00-4c8e-8081-a4e2e9fc55e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "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",
        "\n",
        "\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "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 [==============================] - 5s 532ms/step - loss: 31.1593 - mae: 31.6401\n",
            "Epoch 2/100\n",
            "10/10 [==============================] - 3s 318ms/step - loss: 30.5752 - mae: 31.0652\n",
            "Epoch 3/100\n",
            "10/10 [==============================] - 3s 320ms/step - loss: 29.6785 - mae: 30.1734\n",
            "Epoch 4/100\n",
            "10/10 [==============================] - 3s 317ms/step - loss: 28.5556 - mae: 29.0553\n",
            "Epoch 5/100\n",
            "10/10 [==============================] - 3s 316ms/step - loss: 27.1878 - mae: 27.6946\n",
            "Epoch 6/100\n",
            "10/10 [==============================] - 3s 323ms/step - loss: 25.4788 - mae: 25.9970\n",
            "Epoch 7/100\n",
            "10/10 [==============================] - 3s 318ms/step - loss: 23.3038 - mae: 23.8389\n",
            "Epoch 8/100\n",
            "10/10 [==============================] - 3s 319ms/step - loss: 20.5453 - mae: 21.1001\n",
            "Epoch 9/100\n",
            "10/10 [==============================] - 3s 322ms/step - loss: 17.2259 - mae: 17.7962\n",
            "Epoch 10/100\n",
            "10/10 [==============================] - 3s 318ms/step - loss: 13.5191 - mae: 14.0931\n",
            "Epoch 11/100\n",
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            "Epoch 50/100\n",
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            "Epoch 61/100\n",
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            "Epoch 65/100\n",
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            "Epoch 69/100\n",
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            "Epoch 71/100\n",
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            "Epoch 75/100\n",
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            "Epoch 76/100\n",
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            "Epoch 78/100\n",
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            "Epoch 80/100\n",
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            "Epoch 81/100\n",
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            "Epoch 82/100\n",
            "10/10 [==============================] - 3s 317ms/step - loss: 12.9341 - mae: 13.1031\n",
            "Epoch 83/100\n",
            "10/10 [==============================] - 3s 323ms/step - loss: 18.5680 - mae: 18.7712\n",
            "Epoch 84/100\n",
            "10/10 [==============================] - 3s 318ms/step - loss: 19.1276 - mae: 20.1010\n",
            "Epoch 85/100\n",
            "10/10 [==============================] - 3s 320ms/step - loss: 25.0943 - mae: 25.9562\n",
            "Epoch 86/100\n",
            "10/10 [==============================] - 3s 314ms/step - loss: 27.2979 - mae: 27.0169\n",
            "Epoch 87/100\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 27.2493 - mae: 28.5461\n",
            "Epoch 88/100\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 32.8564 - mae: 32.9523\n",
            "Epoch 89/100\n",
            "10/10 [==============================] - 3s 324ms/step - loss: 66.9200 - mae: 67.1268\n",
            "Epoch 90/100\n",
            "10/10 [==============================] - 3s 318ms/step - loss: 28.2742 - mae: 27.3634\n",
            "Epoch 91/100\n",
            "10/10 [==============================] - 3s 320ms/step - loss: 31.7370 - mae: 33.5611\n",
            "Epoch 92/100\n",
            "10/10 [==============================] - 3s 319ms/step - loss: 49.3101 - mae: 50.8230\n",
            "Epoch 93/100\n",
            "10/10 [==============================] - 3s 313ms/step - loss: 15.8049 - mae: 16.1870\n",
            "Epoch 94/100\n",
            "10/10 [==============================] - 3s 317ms/step - loss: 17.4672 - mae: 17.4949\n",
            "Epoch 95/100\n",
            "10/10 [==============================] - 3s 318ms/step - loss: 20.4727 - mae: 20.6840\n",
            "Epoch 96/100\n",
            "10/10 [==============================] - 3s 321ms/step - loss: 22.7271 - mae: 23.1291\n",
            "Epoch 97/100\n",
            "10/10 [==============================] - 3s 316ms/step - loss: 25.9050 - mae: 26.1785\n",
            "Epoch 98/100\n",
            "10/10 [==============================] - 3s 319ms/step - loss: 29.9775 - mae: 29.6829\n",
            "Epoch 99/100\n",
            "10/10 [==============================] - 3s 322ms/step - loss: 31.2160 - mae: 32.2827\n",
            "Epoch 100/100\n",
            "10/10 [==============================] - 3s 317ms/step - loss: 37.8361 - mae: 38.4650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVcKmg7Q_7rD",
        "colab_type": "code",
        "outputId": "b86a2c25-9640-4b5e-d491-f39b45164e43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        }
      },
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "plt.axis([1e-8, 1e-4, 0, 60])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1e-08, 0.0001, 0, 60]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4mjt6maHQz5/q8hhLz6jjN5v2464uHhEpHndnX4eO9PPu2vOms7uth837O4tdiogEWHvP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\nQBT6IiIBotAXEQmQ/w97NMLUxeoOOAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsksvkcXAAgq",
        "colab_type": "code",
        "outputId": "0f2df310-de47-495d-f629-69f833b8ea2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "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=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",
        "\n",
        "optimizer = tf.keras.optimizers.SGD(lr=7*10**-6, momentum=0.9)\n",
        "model.compile(loss=tf.keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "history = model.fit(train_set,epochs=50)\n",
        "                                    \n",
        "# EXPECTED OUTPUT SHOULD SEE AN MAE OF <2 WITHIN ABOUT 30 EPOCHS"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "25/25 [==============================] - 6s 248ms/step - loss: 11.2535 - mae: 11.8933\n",
            "Epoch 2/50\n",
            "25/25 [==============================] - 4s 161ms/step - loss: 3.2388 - mae: 3.7197\n",
            "Epoch 3/50\n",
            "25/25 [==============================] - 4s 163ms/step - loss: 2.5461 - mae: 3.0091\n",
            "Epoch 4/50\n",
            "25/25 [==============================] - 4s 162ms/step - loss: 2.5260 - mae: 2.9892\n",
            "Epoch 5/50\n",
            "25/25 [==============================] - 4s 159ms/step - loss: 2.5835 - mae: 3.0495\n",
            "Epoch 6/50\n",
            "25/25 [==============================] - 4s 159ms/step - loss: 2.6366 - mae: 3.1063\n",
            "Epoch 7/50\n",
            "25/25 [==============================] - 4s 161ms/step - loss: 2.6103 - mae: 3.0793\n",
            "Epoch 8/50\n",
            "25/25 [==============================] - 4s 160ms/step - loss: 2.5769 - mae: 3.0445\n",
            "Epoch 9/50\n",
            "25/25 [==============================] - 4s 160ms/step - loss: 2.5555 - mae: 3.0224\n",
            "Epoch 10/50\n",
            "25/25 [==============================] - 4s 158ms/step - loss: 2.5502 - mae: 3.0175\n",
            "Epoch 11/50\n",
            "25/25 [==============================] - 4s 160ms/step - loss: 2.5249 - mae: 2.9915\n",
            "Epoch 12/50\n",
            "25/25 [==============================] - 4s 160ms/step - loss: 2.4932 - mae: 2.9585\n",
            "Epoch 13/50\n",
            "25/25 [==============================] - 4s 161ms/step - loss: 2.4672 - mae: 2.9316\n",
            "Epoch 14/50\n",
            "25/25 [==============================] - 4s 160ms/step - loss: 2.4454 - mae: 2.9090\n",
            "Epoch 15/50\n",
            "25/25 [==============================] - 4s 158ms/step - loss: 2.4266 - mae: 2.8896\n",
            "Epoch 16/50\n",
            "25/25 [==============================] - 4s 161ms/step - loss: 2.4090 - mae: 2.8713\n",
            "Epoch 17/50\n",
            "25/25 [==============================] - 4s 159ms/step - loss: 2.3942 - mae: 2.8561\n",
            "Epoch 18/50\n",
            "25/25 [==============================] - 4s 162ms/step - loss: 2.3808 - mae: 2.8424\n",
            "Epoch 19/50\n",
            "25/25 [==============================] - 4s 161ms/step - loss: 2.3680 - mae: 2.8291\n",
            "Epoch 20/50\n",
            "25/25 [==============================] - 4s 161ms/step - loss: 2.3558 - mae: 2.8167\n",
            "Epoch 21/50\n",
            "25/25 [==============================] - 4s 161ms/step - loss: 2.3451 - mae: 2.8056\n",
            "Epoch 22/50\n",
            "25/25 [==============================] - 4s 162ms/step - loss: 2.3361 - mae: 2.7964\n",
            "Epoch 23/50\n",
            "25/25 [==============================] - 4s 165ms/step - loss: 2.3279 - mae: 2.7880\n",
            "Epoch 24/50\n",
            "25/25 [==============================] - 4s 163ms/step - loss: 2.3207 - mae: 2.7805\n",
            "Epoch 25/50\n",
            "25/25 [==============================] - 4s 157ms/step - loss: 2.3138 - mae: 2.7735\n",
            "Epoch 26/50\n",
            "25/25 [==============================] - 4s 160ms/step - loss: 2.3068 - mae: 2.7664\n",
            "Epoch 27/50\n",
            "25/25 [==============================] - 4s 160ms/step - loss: 2.3007 - mae: 2.7601\n",
            "Epoch 28/50\n",
            "25/25 [==============================] - 4s 161ms/step - loss: 2.2951 - mae: 2.7544\n",
            "Epoch 29/50\n",
            "25/25 [==============================] - 4s 161ms/step - loss: 2.2902 - mae: 2.7493\n",
            "Epoch 30/50\n",
            "25/25 [==============================] - 4s 158ms/step - loss: 2.2856 - mae: 2.7447\n",
            "Epoch 31/50\n",
            "25/25 [==============================] - 4s 159ms/step - loss: 2.2815 - mae: 2.7405\n",
            "Epoch 32/50\n",
            "25/25 [==============================] - 4s 159ms/step - loss: 2.2776 - mae: 2.7366\n",
            "Epoch 33/50\n",
            "25/25 [==============================] - 4s 158ms/step - loss: 2.2741 - mae: 2.7330\n",
            "Epoch 34/50\n",
            "25/25 [==============================] - 4s 162ms/step - loss: 2.2712 - mae: 2.7300\n",
            "Epoch 35/50\n",
            "25/25 [==============================] - 4s 159ms/step - loss: 2.2682 - mae: 2.7270\n",
            "Epoch 36/50\n",
            "25/25 [==============================] - 4s 162ms/step - loss: 2.2653 - mae: 2.7240\n",
            "Epoch 37/50\n",
            "25/25 [==============================] - 4s 159ms/step - loss: 2.2626 - mae: 2.7212\n",
            "Epoch 38/50\n",
            "25/25 [==============================] - 4s 160ms/step - loss: 2.2599 - mae: 2.7184\n",
            "Epoch 39/50\n",
            "25/25 [==============================] - 4s 159ms/step - loss: 2.2574 - mae: 2.7159\n",
            "Epoch 40/50\n",
            "25/25 [==============================] - 4s 160ms/step - loss: 2.2551 - mae: 2.7135\n",
            "Epoch 41/50\n",
            "25/25 [==============================] - 4s 161ms/step - loss: 2.2528 - mae: 2.7111\n",
            "Epoch 42/50\n",
            "25/25 [==============================] - 4s 163ms/step - loss: 2.2505 - mae: 2.7087\n",
            "Epoch 43/50\n",
            "25/25 [==============================] - 4s 163ms/step - loss: 2.2484 - mae: 2.7066\n",
            "Epoch 44/50\n",
            "25/25 [==============================] - 4s 165ms/step - loss: 2.2463 - mae: 2.7044\n",
            "Epoch 45/50\n",
            "25/25 [==============================] - 4s 162ms/step - loss: 2.2442 - mae: 2.7022\n",
            "Epoch 46/50\n",
            "25/25 [==============================] - 4s 162ms/step - loss: 2.2422 - mae: 2.7002\n",
            "Epoch 47/50\n",
            "25/25 [==============================] - 4s 159ms/step - loss: 2.2402 - mae: 2.6982\n",
            "Epoch 48/50\n",
            "25/25 [==============================] - 4s 162ms/step - loss: 2.2385 - mae: 2.6963\n",
            "Epoch 49/50\n",
            "25/25 [==============================] - 4s 162ms/step - loss: 2.2369 - mae: 2.6947\n",
            "Epoch 50/50\n",
            "25/25 [==============================] - 4s 163ms/step - loss: 2.2352 - mae: 2.6929\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaC6NNMRp0lb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b8f85a93-1f14-48bd-c5a4-ffb149c4f0fe"
      },
      "source": [
        "rnn_forecast = model_forecast(model, series, window_size)\n",
        "print(rnn_forecast.shape)\n",
        "rnn_forecast = rnn_forecast[split_time - window_size:-1, -1, -1] %only the last poition of the 2nd and 3rd dimensions has the values"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3587, 64, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "izy6wl2S9d-2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "outputId": "a31495fc-1e5c-4a53-ddc8-8425f65475ec"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, rnn_forecast)\n",
        "\n",
        "# EXPECTED OUTPUT. PLOT SHOULD SHOW PROJECTIONS FOLLOWING ORIGINAL DATA CLOSELY"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FXcW58PH60BnTlbKUx6u2KbepsRyCuRC8uosIXqKtkzW8NhHjZy2lkyDBK4DBLhkUvrws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\n/wCq0qoPYcpnCQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "13XrorC5wQoE",
        "colab_type": "code",
        "outputId": "d317418e-5736-458a-bfea-495a684e7f11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf.keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()\n",
        "\n",
        "# EXPECTED OUTPUT MAE < 2 -- I GOT 1.789626"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0496097"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AOVzQXxCwkzP",
        "colab_type": "code",
        "outputId": "9932ee24-67cd-4cdb-a490-f0a255f00fd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(rnn_forecast)\n",
        "# EXPECTED OUTPUT -- ARRAY OF VALUES IN THE LOW TEENS"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[12.115385 13.009798 13.405204 ... 14.066222 14.130974 14.66313 ]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}