C4W2_1_Time_series_training_data_labels_preprocessing.ipynb

{
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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "C4W2-1-Time series-training data-labels-preprocessing",
      "provenance": [],
      "collapsed_sections": [],
      "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/georgezoto/TensorFlow-in-Practice/blob/master/C4W2_1_Time_series_training_data_labels_preprocessing.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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "s6eq-RBcQ_Zr",
        "colab": {}
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BOjujz601HcS",
        "outputId": "dbaa1c2b-21d9-4405-8d62-2811d6c9f99d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-rc3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "asEdslR_05O_",
        "outputId": "e572945a-00a3-417e-db63-e522aaf99e58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "dataset = tf.data.Dataset.range(10)\n",
        "for val in dataset:\n",
        "   print(val.numpy())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
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            "9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Lrv_ghSt1lgQ",
        "outputId": "a5ec3ba7-a9d4-4d5e-a55e-4a36162feabf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "dataset = tf.data.Dataset.range(10)\n",
        "dataset = dataset.window(5, shift=1)\n",
        "for window_dataset in dataset:\n",
        "  for val in window_dataset:\n",
        "    print(val.numpy(), end=\" \")\n",
        "  print()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 1 2 3 4 \n",
            "1 2 3 4 5 \n",
            "2 3 4 5 6 \n",
            "3 4 5 6 7 \n",
            "4 5 6 7 8 \n",
            "5 6 7 8 9 \n",
            "6 7 8 9 \n",
            "7 8 9 \n",
            "8 9 \n",
            "9 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QLEq6MG-2DN2",
        "outputId": "d21f364c-d78d-4e66-e3fd-9d697ae5c854",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "dataset = tf.data.Dataset.range(10)\n",
        "dataset = dataset.window(5, shift=1, drop_remainder=True)\n",
        "for window_dataset in dataset:\n",
        "  for val in window_dataset:\n",
        "    print(val.numpy(), end=\" \")\n",
        "  print()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 1 2 3 4 \n",
            "1 2 3 4 5 \n",
            "2 3 4 5 6 \n",
            "3 4 5 6 7 \n",
            "4 5 6 7 8 \n",
            "5 6 7 8 9 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PJ9CAHlJ2ODe",
        "outputId": "cd9cc40d-0179-4797-b85f-d87841c06052",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "dataset = tf.data.Dataset.range(10)\n",
        "dataset = dataset.window(5, shift=1, drop_remainder=True)\n",
        "dataset = dataset.flat_map(lambda window: window.batch(5))\n",
        "for window in dataset:\n",
        "  print(window.numpy())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 2 3 4]\n",
            "[1 2 3 4 5]\n",
            "[2 3 4 5 6]\n",
            "[3 4 5 6 7]\n",
            "[4 5 6 7 8]\n",
            "[5 6 7 8 9]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DryEZ2Mz2nNV",
        "outputId": "e4c56ab1-739a-42e3-cf19-a0fd483d0ac6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "dataset = tf.data.Dataset.range(10)\n",
        "dataset = dataset.window(5, shift=1, drop_remainder=True)\n",
        "dataset = dataset.flat_map(lambda window: window.batch(5))\n",
        "dataset = dataset.map(lambda window: (window[:-1], window[-1:]))\n",
        "for x,y in dataset:\n",
        "  print(x.numpy(), y.numpy())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 2 3] [4]\n",
            "[1 2 3 4] [5]\n",
            "[2 3 4 5] [6]\n",
            "[3 4 5 6] [7]\n",
            "[4 5 6 7] [8]\n",
            "[5 6 7 8] [9]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1tl-0BOKkEtk",
        "outputId": "50e7f857-bcc7-433f-9568-31d7b62060f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "dataset = tf.data.Dataset.range(10)\n",
        "dataset = dataset.window(5, shift=1, drop_remainder=True)\n",
        "dataset = dataset.flat_map(lambda window: window.batch(5))\n",
        "dataset = dataset.map(lambda window: (window[:-1], window[-1:]))\n",
        "dataset = dataset.shuffle(buffer_size=10)\n",
        "for x,y in dataset:\n",
        "  print(x.numpy(), y.numpy())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 2 3] [4]\n",
            "[2 3 4 5] [6]\n",
            "[5 6 7 8] [9]\n",
            "[1 2 3 4] [5]\n",
            "[4 5 6 7] [8]\n",
            "[3 4 5 6] [7]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Wa0PNwxMGapy",
        "outputId": "0d80dac9-3abe-4592-d5ed-b21013130a16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "dataset = tf.data.Dataset.range(10)\n",
        "dataset = dataset.window(5, shift=1, drop_remainder=True)\n",
        "dataset = dataset.flat_map(lambda window: window.batch(5))\n",
        "dataset = dataset.map(lambda window: (window[:-1], window[-1:]))\n",
        "dataset = dataset.shuffle(buffer_size=10)\n",
        "dataset = dataset.batch(2).prefetch(1)\n",
        "for x,y in dataset:\n",
        "  print(\"x = \", x.numpy())\n",
        "  print(\"y = \", y.numpy())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x =  [[0 1 2 3]\n",
            " [5 6 7 8]]\n",
            "y =  [[4]\n",
            " [9]]\n",
            "x =  [[1 2 3 4]\n",
            " [2 3 4 5]]\n",
            "y =  [[5]\n",
            " [6]]\n",
            "x =  [[3 4 5 6]\n",
            " [4 5 6 7]]\n",
            "y =  [[7]\n",
            " [8]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKv6P-ChgIOm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}