Copy_of_S+P_Week_2_Lesson_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of S+P Week 2 Lesson 2.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RhuthuHegde/Tensorflow-coursera/blob/master/Copy_of_S%2BP_Week_2_Lesson_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%204%20-%20S%2BP/S%2BP%20Week%202%20Lesson%202.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-pwam8szRReT",
        "colab": {}
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vX2xbu7KctbX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5832664d-1d84-4614-ebd5-686175e93797"
      },
      "source": [
        "!pip install tensorflow==2.0.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/46/0f/7bd55361168bb32796b360ad15a25de6966c9c1beb58a8e30c01c8279862/tensorflow-2.0.0-cp36-cp36m-manylinux2010_x86_64.whl (86.3MB)\n",
            "\u001b[K     |████████████████████████████████| 86.3MB 80kB/s \n",
            "\u001b[?25hRequirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.31.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.15.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.2.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.34.2)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.18.5)\n",
            "Collecting tensorflow-estimator<2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fc/08/8b927337b7019c374719145d1dceba21a8bb909b93b1ad6f8fb7d22c1ca1/tensorflow_estimator-2.0.1-py2.py3-none-any.whl (449kB)\n",
            "\u001b[K     |████████████████████████████████| 450kB 39.9MB/s \n",
            "\u001b[?25hCollecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
            "Collecting keras-applications>=1.0.8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 6.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (3.3.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.9.0)\n",
            "Collecting tensorboard<2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/54/99b9d5d52d5cb732f099baaaf7740403e83fe6b0cedde940fabd2b13d75a/tensorboard-2.0.2-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 37.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.12.1)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (3.12.4)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.8.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.1.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.1.2)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==2.0.0) (2.10.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (49.2.0)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2.23.0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.17.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.2.2)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.0.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.4.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2020.6.20)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.24.3)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (4.1.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (4.6)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.7.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.3.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.4.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.1.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.1.0)\n",
            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=ca9486f2556ae6f581ed3bd567aaea785a6bbcb385f38295f068c0ef96af1c1e\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "Successfully built gast\n",
            "\u001b[31mERROR: tensorflow-probability 0.11.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tensorflow-estimator, gast, keras-applications, tensorboard, tensorflow\n",
            "  Found existing installation: tensorflow-estimator 2.3.0\n",
            "    Uninstalling tensorflow-estimator-2.3.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.3.0\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "  Found existing installation: tensorboard 2.3.0\n",
            "    Uninstalling tensorboard-2.3.0:\n",
            "      Successfully uninstalled tensorboard-2.3.0\n",
            "  Found existing installation: tensorflow 2.3.0\n",
            "    Uninstalling tensorflow-2.3.0:\n",
            "      Successfully uninstalled tensorflow-2.3.0\n",
            "Successfully installed gast-0.2.2 keras-applications-1.0.8 tensorboard-2.0.2 tensorflow-2.0.0 tensorflow-estimator-2.0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BOjujz601HcS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a680b42d-d420-444e-8c13-d43419dbedd2"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zswl7jRtGzkk",
        "colab": {}
      },
      "source": [
        "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",
        "\n",
        "def trend(time, slope=0):\n",
        "    return slope * time\n",
        "\n",
        "def seasonal_pattern(season_time):\n",
        "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
        "    return np.where(season_time < 0.4,\n",
        "                    np.cos(season_time * 2 * np.pi),\n",
        "                    1 / np.exp(3 * season_time))\n",
        "\n",
        "def seasonality(time, period, amplitude=1, phase=0):\n",
        "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
        "    season_time = ((time + phase) % period) / period\n",
        "    return amplitude * seasonal_pattern(season_time)\n",
        "\n",
        "def noise(time, noise_level=1, seed=None):\n",
        "    rnd = np.random.RandomState(seed)\n",
        "    return rnd.randn(len(time)) * noise_level\n",
        "\n",
        "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
        "baseline = 10\n",
        "series = trend(time, 0.1)  \n",
        "baseline = 10\n",
        "amplitude = 40\n",
        "slope = 0.05\n",
        "noise_level = 5\n",
        "\n",
        "# Create the series\n",
        "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
        "# Update with noise\n",
        "series += noise(time, noise_level, seed=42)\n",
        "\n",
        "split_time = 1000\n",
        "time_train = time[:split_time]\n",
        "x_train = series[:split_time]\n",
        "time_valid = time[split_time:]\n",
        "x_valid = series[split_time:]\n",
        "\n",
        "window_size = 20\n",
        "batch_size = 32\n",
        "shuffle_buffer_size = 1000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4sTTIOCbyShY",
        "colab": {}
      },
      "source": [
        "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
        "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
        "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
        "  dataset = dataset.batch(batch_size).prefetch(1)\n",
        "  return dataset"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ou-WmE2AXu6B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "8b7a1d34-9934-4be3-d589-71a536e1c156"
      },
      "source": [
        "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
        "print(dataset)\n",
        "l0 = tf.keras.layers.Dense(1, input_shape=[window_size])\n",
        "model = tf.keras.models.Sequential([l0])\n",
        "\n",
        "\n",
        "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(lr=1e-6, momentum=0.9))\n",
        "model.fit(dataset,epochs=100,verbose=0)\n",
        "\n",
        "print(\"Layer weights {}\".format(l0.get_weights()))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<PrefetchDataset shapes: ((None, None), (None,)), types: (tf.float32, tf.float32)>\n",
            "Layer weights [array([[-0.06761044],\n",
            "       [ 0.0042758 ],\n",
            "       [ 0.1005431 ],\n",
            "       [-0.08719928],\n",
            "       [ 0.00094026],\n",
            "       [ 0.04537581],\n",
            "       [ 0.06139471],\n",
            "       [-0.03153139],\n",
            "       [-0.06558639],\n",
            "       [-0.03209908],\n",
            "       [ 0.06599976],\n",
            "       [ 0.03331076],\n",
            "       [-0.11756367],\n",
            "       [ 0.07439977],\n",
            "       [ 0.05913175],\n",
            "       [ 0.04914911],\n",
            "       [-0.07483651],\n",
            "       [ 0.21841483],\n",
            "       [ 0.2746769 ],\n",
            "       [ 0.45575893]], dtype=float32), array([0.0120432], dtype=float32)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-gtVJuLVxR-M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "293ee8be-08ee-45a4-d171-ed69539d3002"
      },
      "source": [
        "forecast = []\n",
        "\n",
        "for time in range(len(series) - window_size):\n",
        "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
        "\n",
        "forecast = forecast[split_time-window_size:]\n",
        "results = np.array(forecast)[:, 0, 0]\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, results)"
      ],
      "execution_count": null,
      "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": {
        "colab_type": "code",
        "id": "hR2BO0Dai_ZT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a12d9b0d-845a-4cd4-af2b-1e0f2307ad09"
      },
      "source": [
        "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.385023"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWf6mUdPdvqH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}