transformer.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "transformer.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vvshyer/tensorflow2.0_learning/blob/master/transformer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
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      "metadata": {
        "id": "dC3Kt-7NrxYt",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        }
      },
      "source": [
        "!pip install tensorflow-gpu==2.0.0-beta0"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu==2.0.0-beta0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e8/7e/87c4c94686cda7066f52cbca4c344248516490acdd6b258ec6b8a805d956/tensorflow_gpu-2.0.0b0-cp36-cp36m-manylinux1_x86_64.whl (348.8MB)\n",
            "\u001b[K     |████████████████████████████████| 348.9MB 64kB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (1.11.2)\n",
            "Collecting tf-estimator-nightly<1.14.0.dev2019060502,>=1.14.0.dev2019060501 (from tensorflow-gpu==2.0.0-beta0)\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 31.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (0.33.4)\n",
            "Collecting tb-nightly<1.14.0a20190604,>=1.14.0a20190603 (from tensorflow-gpu==2.0.0-beta0)\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 32.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (1.12.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (1.0.8)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (0.1.7)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (3.7.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (1.15.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (0.2.2)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (1.1.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (1.16.4)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (0.7.1)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta0) (0.8.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow-gpu==2.0.0-beta0) (41.0.1)\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-gpu==2.0.0-beta0) (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-gpu==2.0.0-beta0) (0.15.5)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0-beta0) (2.8.0)\n",
            "Installing collected packages: tf-estimator-nightly, tb-nightly, tensorflow-gpu\n",
            "Successfully installed tb-nightly-1.14.0a20190603 tensorflow-gpu-2.0.0b0 tf-estimator-nightly-1.14.0.dev2019060501\n"
          ],
          "name": "stdout"
        },
        {
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          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "tensorboard",
                  "tensorflow",
                  "tensorflow_estimator"
                ]
              }
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          "metadata": {
            "tags": []
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    {
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      "metadata": {
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        "colab_type": "code",
        "outputId": "d8126ecc-bafd-4ace-ac4b-6542283da7e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import sklearn\n",
        "import pandas as pd\n",
        "import os\n",
        "import sys\n",
        "import time\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "\n",
        "print(tf.__version__)\n",
        "print(sys.version_info)\n",
        "for module in mpl, np, pd, sklearn, tf, keras:\n",
        "    print(module.__name__, module.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0-beta0\n",
            "sys.version_info(major=3, minor=6, micro=8, releaselevel='final', serial=0)\n",
            "matplotlib 3.0.3\n",
            "numpy 1.16.4\n",
            "pandas 0.24.2\n",
            "sklearn 0.21.3\n",
            "tensorflow 2.0.0-beta0\n",
            "tensorflow.python.keras.api._v2.keras 2.2.4-tf\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQcfw4MqpEUL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 1. loads data\n",
        "# 2. preprocesses data -> dataset\n",
        "# 3. tools\n",
        "#    3.1 generates position embedding\n",
        "#    3.2 create mask. (a. padding, b. decoder)\n",
        "#    3.3 scaled_dot_product_attention\n",
        "# 4. builds model\n",
        "#    4.1 multiheadAttention\n",
        "#    4.2 EncoderLayer\n",
        "#    4.3 DecoderLayer\n",
        "#    4.4 EncoderModel\n",
        "#    4.5 DecoderModel\n",
        "#    4.6 Transformer\n",
        "# 5. optimizer & loss\n",
        "# 6. train step -> train\n",
        "# 7.Evaluate and Visualize"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cR43FX3dpl9l",
        "colab_type": "code",
        "outputId": "4ee15731-f594-4786-ab4d-ebd5810c143d",
        "colab": {
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 *
 * Copyright (c) 2014 Dan Le Van
 *
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    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,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");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

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 */",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "text/css"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "\n",
        "examples, info = tfds.load('ted_hrlr_translate/pt_to_en',\n",
        "                                     with_info = True,\n",
        "                                     as_supervised = True)\n",
        "\n",
        "train_examples, val_examples = examples['train'], examples['validation']\n",
        "print(info)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset ted_hrlr_translate (124.94 MiB) to /root/tensorflow_datasets/ted_hrlr_translate/pt_to_en/0.0.1...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "665458a9f35e4a50a207361b5dfef493",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Completed...', max=1, style=ProgressStyl…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "40c9f25bddcf4f3b8e33222800dc7da2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Size...', max=1, style=ProgressStyle(des…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "627ae5079bb74d4f9f78365b3d01fbce",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', description='Extraction completed...', max=1, style=Prog…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3a15735f165c41548103e8b674d8484a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "81949bffc43a49bd8221e5d4209b2f94",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Shuffling...', max=1, style=ProgressStyle(description_width='…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0821 07:32:57.671226 140112910727040 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use eager execution and: \n",
            "`tf.data.TFRecordDataset(path)`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c2fd8c6976c047158515b514b96221e1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f701a84cd13b43398b0b1d9ee8ac0552",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Writing...', max=51785, style=ProgressStyle(description_width…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9fe94144d0e34a13a4ef4c0b34c4355c",
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            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a8357c2048dd46478862fe144847f3a7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Shuffling...', max=1, style=ProgressStyle(description_width='…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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            "\r"
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            "\r"
          ],
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        {
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          "output_type": "stream",
          "text": [
            "\r\u001b[1mDataset ted_hrlr_translate downloaded and prepared to /root/tensorflow_datasets/ted_hrlr_translate/pt_to_en/0.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "W0821 07:33:00.803310 140112910727040 dataset_builder.py:439] Warning: Setting shuffle_files=True because split=TRAIN and shuffle_files=None. This behavior will be deprecated on 2019-08-06, at which point shuffle_files=False will be the default for all splits.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "tfds.core.DatasetInfo(\n",
            "    name='ted_hrlr_translate',\n",
            "    version=0.0.1,\n",
            "    description='Data sets derived from TED talk transcripts for comparing similar language pairs\n",
            "where one is high resource and the other is low resource.\n",
            "',\n",
            "    urls=['https://github.com/neulab/word-embeddings-for-nmt'],\n",
            "    features=Translation({\n",
            "        'en': Text(shape=(), dtype=tf.string),\n",
            "        'pt': Text(shape=(), dtype=tf.string),\n",
            "    }),\n",
            "    total_num_examples=54781,\n",
            "    splits={\n",
            "        'test': 1803,\n",
            "        'train': 51785,\n",
            "        'validation': 1193,\n",
            "    },\n",
            "    supervised_keys=('pt', 'en'),\n",
            "    citation=\"\"\"@inproceedings{Ye2018WordEmbeddings,\n",
            "      author  = {Ye, Qi and Devendra, Sachan and Matthieu, Felix and Sarguna, Padmanabhan and Graham, Neubig},\n",
            "      title   = {When and Why are pre-trained word embeddings useful for Neural Machine Translation},\n",
            "      booktitle = {HLT-NAACL},\n",
            "      year    = {2018},\n",
            "      }\"\"\",\n",
            "    redistribution_info=,\n",
            ")\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-EuH0vBgqDOh",
        "colab_type": "code",
        "outputId": "901cdad7-b783-49ba-b0d0-05b79ac361de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "for pt, en in train_examples.take(5):\n",
        "    print(pt.numpy())\n",
        "    print(en.numpy())\n",
        "    print()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "b'os astr\\xc3\\xb3nomos acreditam que cada estrela da gal\\xc3\\xa1xia tem um planeta , e especulam que at\\xc3\\xa9 um quinto deles tem um planeta do tipo da terra que poder\\xc3\\xa1 ter vida , mas ainda n\\xc3\\xa3o vimos nenhum deles .'\n",
            "b\"astronomers now believe that every star in the galaxy has a planet , and they speculate that up to one fifth of them have an earth-like planet that might be able to harbor life , but we have n't seen any of them .\"\n",
            "\n",
            "b'o problema \\xc3\\xa9 que nunca vivi l\\xc3\\xa1 um \\xc3\\xbanico dia .'\n",
            "b\"except , i 've never lived one day of my life there .\"\n",
            "\n",
            "b'agora aqui temos imagens sendo extra\\xc3\\xaddas em tempo real diretamente do feed ,'\n",
            "b'now here are live images being pulled straight from the feed .'\n",
            "\n",
            "b'agora : um , dois , tr\\xc3\\xaas , vai .'\n",
            "b'so : one , two , three , go .'\n",
            "\n",
            "b'eventualmente , vamos ver se teremos todos os sentidos humanos empregues , e se vamos ter meios para viver a hist\\xc3\\xb3ria qualquer que seja a via escolhida .'\n",
            "b'eventually , we can see if we will have all of our human senses employed , and we will have agency to live the story in any path we choose .'\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R5a16y9frZP2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "en_tokenizer = tfds.features.text.SubwordTextEncoder.build_from_corpus(\n",
        "    (en.numpy() for pt, en in train_examples),\n",
        "    target_vocab_size = 2**13)\n",
        "\n",
        "pt_tokenizer = tfds.features.text.SubwordTextEncoder.build_from_corpus(\n",
        "    (pt.numpy() for pt, en in train_examples),\n",
        "    target_vocab_size = 2**13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZhQvlUwtgwi",
        "colab_type": "code",
        "outputId": "2706cf08-e6bc-49e5-e39d-90bdf408fca4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "sample_string = \"Transformer is awesome.\"\n",
        "\n",
        "tokenized_string = en_tokenizer.encode(sample_string)\n",
        "print('Tokenized string is {}'.format(tokenized_string))\n",
        "\n",
        "origin_string = en_tokenizer.decode(tokenized_string)\n",
        "print('The original string is {}'.format(origin_string))\n",
        "\n",
        "assert origin_string == sample_string\n",
        "\n",
        "for token in tokenized_string:\n",
        "    print('{} --> \"{}\"'.format(token, en_tokenizer.decode([token])))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tokenized string is [7915, 1248, 7946, 7194, 13, 2799, 7877]\n",
            "The original string is Transformer is awesome.\n",
            "7915 --> \"T\"\n",
            "1248 --> \"ran\"\n",
            "7946 --> \"s\"\n",
            "7194 --> \"former \"\n",
            "13 --> \"is \"\n",
            "2799 --> \"awesome\"\n",
            "7877 --> \".\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kv-Cz46gvDG7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "buffer_size = 20000\n",
        "batch_size = 64\n",
        "max_length = 40\n",
        "\n",
        "def encode_to_subword(pt_sentence, en_sentence):\n",
        "    pt_sequence = [pt_tokenizer.vocab_size] \\\n",
        "    + pt_tokenizer.encode(pt_sentence.numpy()) \\\n",
        "    + [pt_tokenizer.vocab_size + 1]\n",
        "    \n",
        "    en_sequence = [en_tokenizer.vocab_size] \\\n",
        "    + en_tokenizer.encode(en_sentence.numpy()) \\\n",
        "    + [en_tokenizer.vocab_size + 1]\n",
        "    \n",
        "    return pt_sequence, en_sequence\n",
        "\n",
        "def filter_by_max_length(pt, en):\n",
        "    return tf.logical_and(tf.size(pt) <= max_length,\n",
        "                          tf.size(en) <= max_length)\n",
        "\n",
        "def tf_encode_to_subword(pt_sentence, en_sentence):\n",
        "    return tf.py_function(encode_to_subword,\n",
        "                          [pt_sentence, en_sentence],\n",
        "                          [tf.int64, tf.int64])\n",
        "\n",
        "train_dataset = train_examples.map(tf_encode_to_subword)\n",
        "train_dataset = train_dataset.filter(filter_by_max_length)\n",
        "train_dataset = train_dataset.shuffle(\n",
        "    buffer_size).padded_batch(batch_size, padded_shapes=([-1], [-1]))\n",
        "\n",
        "valid_dataset = val_examples.map(tf_encode_to_subword)\n",
        "valid_dataset = valid_dataset.filter(\n",
        "    filter_by_max_length).padded_batch(\n",
        "    batch_size, padded_shapes=([-1], [-1]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-WrNLRMwnke",
        "colab_type": "code",
        "outputId": "e0d127de-1479-4abd-c31c-b6140b63d679",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "for pt_batch, en_batch in valid_dataset.take(5):\n",
        "    print(pt_batch.shape, en_batch.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0821 07:35:58.534578 140109837195008 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string\n",
            "W0821 07:35:58.536026 140109837195008 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string\n",
            "W0821 07:35:58.542167 140109845587712 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string\n",
            "W0821 07:35:58.544516 140109845587712 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string\n",
            "W0821 07:35:58.548218 140109837195008 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(64, 40) (64, 40)\n",
            "(64, 38) (64, 40)\n",
            "(64, 40) (64, 40)\n",
            "(64, 39) (64, 39)\n",
            "(64, 37) (64, 38)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x6Dxi1_8w_Hn",
        "colab_type": "code",
        "outputId": "183e1200-423d-44ab-b33d-2e5217765bc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# PE(pos, 2i)   = sin(pos / 10000^(2i/d_model))\n",
        "# PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model))\n",
        "\n",
        "# pos.shape: [sentence_length, 1]\n",
        "# i.shape  : [1, d_model]\n",
        "# result.shape: [sentence_length, d_model]\n",
        "def get_angles(pos, i, d_model):\n",
        "    angle_rates = 1/ np.power(10000,\n",
        "                              (2 * (i // 2)) / np.float32(d_model))\n",
        "    return pos * angle_rates\n",
        "\n",
        "def get_position_embedding(sentence_length, d_model):\n",
        "    angle_rads = get_angles(np.arange(sentence_length)[:, np.newaxis],\n",
        "                            np.arange(d_model)[np.newaxis, :],\n",
        "                            d_model)\n",
        "    # 取所有偶数位 0, 2, 4, ...\n",
        "    # sines.shape: [sentence_length, d_model / 2]\n",
        "    sines = np.sin(angle_rads[:, 0::2])\n",
        "    # 取所有奇数位 1, 3, 5, ...\n",
        "    # coses.shape: [sentence_length, d_model / 2]\n",
        "    coses = np.cos(angle_rads[:, 1::2])\n",
        "    \n",
        "    # position_embedding.shape: [sentence_length, d_model]\n",
        "    position_embedding = np.concatenate([sines, coses], axis = -1)\n",
        "    # position_embedding.shape: [1, sentence_length, d_model]\n",
        "    position_embedding = position_embedding[np.newaxis, ...]\n",
        "    \n",
        "    return tf.cast(position_embedding, dtype=tf.float32)\n",
        "\n",
        "position_embedding = get_position_embedding(50, 512)\n",
        "print(position_embedding.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 50, 512)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AHPFeL5y8nI",
        "colab_type": "code",
        "outputId": "e1351e3a-621c-4848-c3e1-b54df040f0ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "def plot_position_embedding(position_embedding):\n",
        "    plt.pcolormesh(position_embedding[0], cmap = 'RdBu')\n",
        "    plt.xlabel('Depth')\n",
        "    plt.xlim((0, 512))\n",
        "    plt.ylabel('Position')\n",
        "    plt.colorbar()\n",
        "    plt.show()\n",
        "    \n",
        "plot_position_embedding(position_embedding)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bW90hcCSaq3UUUtE64vbdzdbAEk0lY7YWn7fE4/fbcxHsbbO1vig2UXLQUTzz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\nKaUGkP8FDDDoBdR2Fq0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1ZNZVlHzaXT",
        "colab_type": "code",
        "outputId": "b9b8cf9f-303e-43db-f1bd-66260d0c9a65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "# 生成mask\n",
        "# 1. padding mask\n",
        "# 2. look ahead\n",
        "\n",
        "def create_padding_mask(batch_data):\n",
        "    padding_mask = tf.cast(tf.math.equal(batch_data, 0), tf.float32)\n",
        "    # [batch_size, 1, 1, seq_len]\n",
        "    return padding_mask[:, tf.newaxis, tf.newaxis, :]\n",
        "\n",
        "x = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])\n",
        "create_padding_mask(x)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=313192, shape=(3, 1, 1, 5), dtype=float32, numpy=\n",
              "array([[[[0., 0., 1., 1., 0.]]],\n",
              "\n",
              "\n",
              "       [[[0., 0., 0., 1., 1.]]],\n",
              "\n",
              "\n",
              "       [[[1., 1., 1., 0., 0.]]]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zs2Z5t8N1-F3",
        "colab_type": "code",
        "outputId": "ffb2db7d-e0a8-4481-8731-ebaa04f9835d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# attention_weights.shape: [3, 3]\n",
        "# [[1, 2, 3],     [[1, 0, 0],\n",
        "#  [4, 5, 6], -->  [4, 5, 0],\n",
        "#  [7, 8, 9]]      [7, 8, 9]]\n",
        "def create_look_ahead_mask(size):\n",
        "    mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)\n",
        "    return mask # (seq_len, seq_len)\n",
        "\n",
        "create_look_ahead_mask(3)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=313201, shape=(3, 3), dtype=float32, numpy=\n",
              "array([[0., 1., 1.],\n",
              "       [0., 0., 1.],\n",
              "       [0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ko8tXXKU2sR4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 缩放点积注意力\n",
        "def scaled_dot_product_attention(q, k, v, mask):\n",
        "    \"\"\"\n",
        "    Args:\n",
        "    - q: shape == (..., seq_len_q, depth)\n",
        "    - k: shape == (..., seq_len_k, depth)\n",
        "    - v: shape == (..., seq_len_v, depth_v)\n",
        "    - seq_len_k == seq_len_v\n",
        "    - mask: shape == (..., seq_len_q, seq_len_k)\n",
        "    Returns:\n",
        "    - output: weighted sum\n",
        "    - attention_weights: weights of attention\n",
        "    \"\"\"\n",
        "    \n",
        "    # matmul_qk.shape: (..., seq_len_q, seq_len_k)\n",
        "    matmul_qk = tf.matmul(q, k, transpose_b = True)\n",
        "    \n",
        "    dk = tf.cast(tf.shape(k)[-1], tf.float32)\n",
        "    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\n",
        "    \n",
        "    if mask is not None:\n",
        "        # 使得softmax后值趋近于0\n",
        "        scaled_attention_logits += (mask * -1e9)\n",
        "        \n",
        "    # attention_weights.shape: (..., seq_len_q, seq_len_k)\n",
        "    attention_weights = tf.nn.softmax(\n",
        "        scaled_attention_logits, axis=-1)\n",
        "    \n",
        "    # output.shape: (..., seq_len_q, depth_v)\n",
        "    output = tf.matmul(attention_weights, v)\n",
        "    \n",
        "    return output, attention_weights\n",
        "\n",
        "def print_scaled_dot_product_attention(q, k, v):\n",
        "    temp_out, temp_att = scaled_dot_product_attention(q, k, v, None)\n",
        "    print(\"Attention weights are:\")\n",
        "    print(temp_att)\n",
        "    print(\"Output is:\")\n",
        "    print(temp_out)\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NxPsPDYj5T-W",
        "colab_type": "code",
        "outputId": "7887baec-5f13-4516-fb0e-9a94d6ac5453",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "temp_k = tf.constant([[10,0,0],\n",
        "                      [0,10,0],\n",
        "                      [0,0,10],\n",
        "                      [0,0,10]], dtype=tf.float32)  # (4, 3)\n",
        "\n",
        "temp_v = tf.constant([[   1,0],\n",
        "                      [  10,0],\n",
        "                      [ 100,5],\n",
        "                      [1000,6]], dtype=tf.float32)  # (4, 2)\n",
        "\n",
        "# This `query` aligns with the second `key`,\n",
        "# so the second `value` is returned.\n",
        "temp_q1 = tf.constant([[0, 10, 0]], dtype=tf.float32)  # (1, 3)\n",
        "\n",
        "np.set_printoptions(suppress=True) # 四舍五入\n",
        "print_scaled_dot_product_attention(temp_q1, temp_k, temp_v)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Attention weights are:\n",
            "tf.Tensor([[0. 1. 0. 0.]], shape=(1, 4), dtype=float32)\n",
            "Output is:\n",
            "tf.Tensor([[10.  0.]], shape=(1, 2), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymaYzh4650ST",
        "colab_type": "code",
        "outputId": "a48c6eaa-8db9-49c1-b7d0-4a0ed469bde9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# This query aligns with a repeated key (third and fourth), \n",
        "# so all associated values get averaged.\n",
        "temp_q2 = tf.constant([[0, 0, 10]], dtype=tf.float32)  # (1, 3)\n",
        "print_scaled_dot_product_attention(temp_q2, temp_k, temp_v)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Attention weights are:\n",
            "tf.Tensor([[0.  0.  0.5 0.5]], shape=(1, 4), dtype=float32)\n",
            "Output is:\n",
            "tf.Tensor([[550.    5.5]], shape=(1, 2), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nexB28rV6OHK",
        "colab_type": "code",
        "outputId": "6dcd9482-5530-4aa1-8edb-0ffe51e60785",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# This query aligns equally with the first and second key, \n",
        "# so their values get averaged.\n",
        "temp_q3 = tf.constant([[10, 10, 0]], dtype=tf.float32)  # (1, 3)\n",
        "print_scaled_dot_product_attention(temp_q3, temp_k, temp_v)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Attention weights are:\n",
            "tf.Tensor([[0.5 0.5 0.  0. ]], shape=(1, 4), dtype=float32)\n",
            "Output is:\n",
            "tf.Tensor([[5.5 0. ]], shape=(1, 2), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-eOO6o06e7Q",
        "colab_type": "code",
        "outputId": "d8c357cc-f227-4b7d-af09-efa610a44d7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "temp_q4 = tf.constant([[0, 10, 0],\n",
        "                       [0, 0, 10],\n",
        "                       [10, 10, 0]], dtype=tf.float32)  # (3, 3)\n",
        "print_scaled_dot_product_attention(temp_q4, temp_k, temp_v)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Attention weights are:\n",
            "tf.Tensor(\n",
            "[[0.  1.  0.  0. ]\n",
            " [0.  0.  0.5 0.5]\n",
            " [0.5 0.5 0.  0. ]], shape=(3, 4), dtype=float32)\n",
            "Output is:\n",
            "tf.Tensor(\n",
            "[[ 10.    0. ]\n",
            " [550.    5.5]\n",
            " [  5.5   0. ]], shape=(3, 2), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNZGCEu86oTR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# multihead attention\n",
        "class MultiHeadAttention(keras.layers.Layer):\n",
        "    \"\"\"\n",
        "    理论上:\n",
        "    x -> Wq0 -> q0\n",
        "    x -> Wk0 -> k0\n",
        "    x -> Wv0 -> v0\n",
        "    \n",
        "    实战中:\n",
        "    q -> Wq0 -> q0\n",
        "    k -> Wk0 -> k0\n",
        "    v -> Wv0 -> v0\n",
        "    \n",
        "    实战中技巧\n",
        "    q -> Wq -> Q -> split -> q0, q1, q2...\n",
        "    k -> Wk -> K -> split -> k0, k1, k2...\n",
        "    v -> Wv -> V -> split -> v0, v1, v2...\n",
        "    \"\"\"\n",
        "    \n",
        "    def __init__(self, d_model, num_heads):\n",
        "        super(MultiHeadAttention, self).__init__()\n",
        "        self.num_heads = num_heads\n",
        "        self.d_model = d_model\n",
        "        assert d_model % self.num_heads == 0\n",
        "        \n",
        "        self.depth = self.d_model // self.num_heads\n",
        "        \n",
        "        self.WQ = keras.layers.Dense(self.d_model)\n",
        "        self.WK = keras.layers.Dense(self.d_model)\n",
        "        self.WV = keras.layers.Dense(self.d_model)\n",
        "        \n",
        "        self.dense = keras.layers.Dense(self.d_model)\n",
        "    \n",
        "    def split_heads(self, x, batch_size):\n",
        "        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))\n",
        "        return tf.transpose(x, perm=[0, 2, 1, 3])\n",
        "    \n",
        "    def call(self, q, k, v, mask):\n",
        "        batch_size = tf.shape(q)[0]\n",
        "        \n",
        "        q = self.WQ(q) # q.shape: (batch_size, seq_len_q, d_model)\n",
        "        k = self.WK(k) # k.shape: (batch_size, seq_len_k, d_model)\n",
        "        v = self.WV(v) # v.shape: (batch_size, seq_len_v, d_model) \n",
        "        \n",
        "        # q.shape: (batch_size, num_heads, seq_len_q, depth)\n",
        "        q = self.split_heads(q, batch_size)\n",
        "        # k.shape: (batch_size, num_heads, seq_len_k, depth)\n",
        "        k = self.split_heads(k, batch_size)  \n",
        "        # v.shape: (batch_size, num_heads, seq_len_v, depth)\n",
        "        v = self.split_heads(v, batch_size)\n",
        "        \n",
        "        # scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)\n",
        "        # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)\n",
        "        scaled_attention_outputs, attention_weights = scaled_dot_product_attention(q, k, v, mask)\n",
        "        \n",
        "        # (batch_size, seq_len_q, num_heads, depth)\n",
        "        scaled_attention_outputs = tf.transpose(\n",
        "            scaled_attention_outputs, perm=[0, 2, 1, 3])\n",
        "        \n",
        "        # (batch_size, seq_len_q, d_model)\n",
        "        concat_attention = tf.reshape(scaled_attention_outputs,\n",
        "                                      (batch_size, -1, self.d_model))\n",
        "        \n",
        "        # (batch_size, seq_len_q, d_model)\n",
        "        output = self.dense(concat_attention)\n",
        "        \n",
        "        return output, attention_weights"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DzpsFB99jTt",
        "colab_type": "code",
        "outputId": "ea4e951e-4ded-4a93-b49f-3fe630d2af87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "temp_mha = MultiHeadAttention(d_model=512, num_heads=8)\n",
        "y = tf.random.uniform((1, 60, 512))  # (batch_size, encoder_sequence, d_model)\n",
        "output, attention_weights = temp_mha(y, y, y, mask=None)\n",
        "print(output.shape)\n",
        "print(attention_weights.shape)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 60, 512)\n",
            "(1, 8, 60, 60)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgW1SU4x-Nr6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "48902eba-14b6-45a2-bf72-cc60fb522b80"
      },
      "source": [
        "def feed_forward_network(d_model, dff):\n",
        "    # dff: dim of feed forward network.\n",
        "    return keras.Sequential([\n",
        "        keras.layers.Dense(dff, activation='relu'),\n",
        "        keras.layers.Dense(d_model)\n",
        "    ])\n",
        "\n",
        "sample_ffn = feed_forward_network(512, 2048)\n",
        "sample_ffn(tf.random.uniform((64, 50, 512))).shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([64, 50, 512])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KrydLX_tHK3v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "869a9753-7cec-4e45-b690-be55a2061189"
      },
      "source": [
        "# EncoderLayer实现\n",
        "class EncoderLayer(keras.layers.Layer):\n",
        "    \"\"\"\n",
        "    x -> self attention -> add & normalize & dropout\n",
        "    -> feed_forward -> add & normalize & dropout\n",
        "    \n",
        "    \"\"\"\n",
        "    def __init__(self, d_model, num_heads, dff, rate=0.1):\n",
        "        super(EncoderLayer, self).__init__()\n",
        "        self.mha = MultiHeadAttention(d_model, num_heads)\n",
        "        self.ffn = feed_forward_network(d_model, dff)\n",
        "        \n",
        "        self.layer_norm1 = keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "        self.layer_norm2 = keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "        \n",
        "        self.dropout1 = keras.layers.Dropout(rate)\n",
        "        self.dropout2 = keras.layers.Dropout(rate)\n",
        "    \n",
        "    def call(self, x, training, encoder_padding_mask):\n",
        "        # x.shape          : (batch_size, seq_len, dim=d_model)\n",
        "        # attn_output.shape: (batch_size, seq_len, d_model)\n",
        "        # out1.shape       : (batch_size, seq_len, d_model)\n",
        "        attn_output, _ = self.mha(x, x, x, encoder_padding_mask)\n",
        "        attn_output = self.dropout1(attn_output, training=training)\n",
        "        out1 = self.layer_norm1(x + attn_output)\n",
        "        \n",
        "        # ffn_output.shape : (batch_size, seq_len, d_model)\n",
        "        # out2.shape       : (batch_size, seq_len, d_model)\n",
        "        ffn_output = self.ffn(out1)\n",
        "        ffn_output = self.dropout2(ffn_output, training=training)\n",
        "        out2 = self.layer_norm2(out1 + ffn_output)\n",
        "        \n",
        "        return out2\n",
        "\n",
        "sample_encoder_layer = EncoderLayer(512, 8, 2048)\n",
        "sample_input = tf.random.uniform((64, 50, 512))\n",
        "sample_output = sample_encoder_layer(sample_input, False, None)\n",
        "print(sample_output.shape)\n",
        "    "
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 50, 512)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJ79sMKdJNmE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "679eeede-795e-424f-8c6a-87b9405a101a"
      },
      "source": [
        "# DecoderLayer实现\n",
        "class DecoderLayer(keras.layers.Layer):\n",
        "    def __init__(self, d_model, num_heads, dff, rate=0.1):\n",
        "        \"\"\"\n",
        "        x -> self attention -> add & normalize & dropout -> out1\n",
        "        out1, encoding_outputs -> attention -> add & normalize & dropout -> out2\n",
        "        out2 -> ffn -> add & normalize & dropout -> out3\n",
        "        \"\"\"\n",
        "        super(DecoderLayer, self).__init__()\n",
        "        \n",
        "        self.mha1 = MultiHeadAttention(d_model, num_heads)\n",
        "        self.mha2 = MultiHeadAttention(d_model, num_heads)\n",
        "        \n",
        "        self.ffn = feed_forward_network(d_model, dff)\n",
        "        \n",
        "        self.layer_norm1 = keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "        self.layer_norm2 = keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "        self.layer_norm3 = keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "        \n",
        "        self.dropout1 = keras.layers.Dropout(rate)\n",
        "        self.dropout2 = keras.layers.Dropout(rate)\n",
        "        self.dropout3 = keras.layers.Dropout(rate)\n",
        "    \n",
        "    def call(self, x, encoding_outputs, training, decoder_mask, encoder_decoder_padding_mask):\n",
        "        # decoder_mask: 由look_ahead_mask和decoder_padding_mask合并而来\n",
        "        # x.shape               : (batch_size, target_seq_len, d_model)\n",
        "        # encoding_outputs.shape: (batch_size, input_seq_len, d_model)\n",
        "        \n",
        "        # attn1, out1.shape: (batch_size, target_seq_len, d_model)\n",
        "        attn1, attn_weights1 = self.mha1(x, x, x, decoder_mask)\n",
        "        attn1 = self.dropout1(attn1, training = training)\n",
        "        out1 = self.layer_norm1(attn1 + x)\n",
        "        \n",
        "        # attn2, out2.shape: (batch_size, target_seq_len, d_model)\n",
        "        attn2, attn_weights2 = self.mha2(\n",
        "            out1, encoding_outputs, encoding_outputs, encoder_decoder_padding_mask)\n",
        "        attn2 = self.dropout2(attn2, training = training)\n",
        "        out2 = self.layer_norm2(attn2 + out1)\n",
        "        \n",
        "        # ffn_output, out3.shape: (batch_size, target_seq_len, d_model)\n",
        "        ffn_output = self.ffn(out2)\n",
        "        ffn_output = self.dropout3(ffn_output, training = training)\n",
        "        out3 = self.layer_norm3(ffn_output + out2)\n",
        "        \n",
        "        return out3, attn_weights1, attn_weights2\n",
        "    \n",
        "sample_decoder_layer = DecoderLayer(512, 8, 2048)\n",
        "sample_decoder_input = tf.random.uniform((64, 60, 512))\n",
        "sample_decoder_output, sample_decoder_attn_weights1, sample_decoder_attn_weights2 = sample_decoder_layer(\n",
        "    sample_decoder_input, sample_output, False, None, None)\n",
        "\n",
        "print(sample_decoder_output.shape)\n",
        "print(sample_decoder_attn_weights1.shape)\n",
        "print(sample_decoder_attn_weights2.shape)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 60, 512)\n",
            "(64, 8, 60, 60)\n",
            "(64, 8, 60, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hv5eTcVfOUZ8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d1a424f5-9903-4faa-9021-df7dab44b17a"
      },
      "source": [
        "# EncoderModel的实现\n",
        "class EncoderModel(keras.layers.Layer):\n",
        "    def __init__(self, num_layers, input_vocab_size, max_length,\n",
        "                 d_model, num_heads, dff, rate=0.1):\n",
        "        super(EncoderModel, self).__init__()\n",
        "        self.d_model = d_model\n",
        "        self.num_layers = num_layers\n",
        "        self.max_length = max_length\n",
        "        \n",
        "        self.embedding = keras.layers.Embedding(input_vocab_size,\n",
        "                                                self.d_model)\n",
        "        # position_embedding.shape: (1, max_length, d_model)\n",
        "        self.position_embedding = get_position_embedding(max_length,\n",
        "                                                         self.d_model)\n",
        "        self.dropout = keras.layers.Dropout(rate)\n",
        "        self.encoder_layers = [\n",
        "            EncoderLayer(d_model, num_heads, dff, rate)\n",
        "            for _ in range(self.num_layers)]\n",
        "    \n",
        "    def call(self, x, training, encoder_padding_mask):\n",
        "        # x.shape: (batch_size, input_seq_len)\n",
        "        input_seq_len = tf.shape(x)[1]\n",
        "        tf.debugging.assert_less_equal(input_seq_len, self.max_length, \n",
        "                                       \"input_seq_len should be less or equal to self.max_length\")\n",
        "        \n",
        "        # x.shape: (batch_size, input_seq_len, d_model)\n",
        "        x = self.embedding(x)\n",
        "        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n",
        "        x += self.position_embedding[:, :input_seq_len, :]\n",
        "        \n",
        "        x = self.dropout(x, training = training)\n",
        "        \n",
        "        for i in range(self.num_layers):\n",
        "            x = self.encoder_layers[i](x, training, encoder_padding_mask)\n",
        "            \n",
        "        # x.shape: (batch_size, input_seq_len, d_model)\n",
        "        return x\n",
        "\n",
        "sample_encoder_model = EncoderModel(2, 8500, max_length, 512, 8, 2048)\n",
        "sample_encoder_model_input = tf.random.uniform((64, 37))\n",
        "sample_encoder_model_output = sample_encoder_model(\n",
        "    sample_encoder_model_input, False, encoder_padding_mask=None)\n",
        "print(sample_encoder_model_output.shape)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 37, 512)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JT9U17iGQ4Nm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "33f666c9-3b98-452c-a3d6-671d70d05aab"
      },
      "source": [
        "# DecoderModel的实现\n",
        "class DecoderModel(keras.layers.Layer):\n",
        "    def __init__(self, num_layers, target_vocab_size,\n",
        "                 max_length, d_model, num_heads, dff, rate=0.1):\n",
        "        super(DecoderModel, self).__init__()\n",
        "        self.num_layers = num_layers\n",
        "        self.max_length = max_length\n",
        "        self.d_model = d_model\n",
        "        \n",
        "        self.embedding = keras.layers.Embedding(target_vocab_size, d_model)\n",
        "        self.position_embedding = get_position_embedding(max_length, d_model)\n",
        "        \n",
        "        self.dropout = keras.layers.Dropout(rate)\n",
        "        self.decoder_layers = [\n",
        "            DecoderLayer(d_model, num_heads, dff, rate)\n",
        "            for _ in range(self.num_layers)]\n",
        "    \n",
        "    def call(self, x, encoding_outputs, training, decoder_mask, encoder_decoder_padding_mask):\n",
        "        # x.shape: (batch_size, output_seq_len)\n",
        "        output_seq_len = tf.shape(x)[1]\n",
        "        tf.debugging.assert_less_equal(output_seq_len, self.max_length, \n",
        "                                       \"output_seq_len should be less or equal to self.max_length\")\n",
        "        \n",
        "        attention_weights = {}\n",
        "        \n",
        "        # x.shape: (batch_size, output_seq_len, d_model)\n",
        "        x = self.embedding(x)\n",
        "        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n",
        "        x += self.position_embedding[:, :output_seq_len, :]\n",
        "        \n",
        "        x = self.dropout(x, training = training)\n",
        "        \n",
        "        for i in range(self.num_layers):\n",
        "            x, attn1, attn2 = self.decoder_layers[i](x, encoding_outputs, training,\n",
        "                                                  decoder_mask, encoder_decoder_padding_mask)\n",
        "            attention_weights['decoder_layer{}_att1'.format(i+1)] = attn1\n",
        "            attention_weights['decoder_layer{}_att2'.format(i+1)] = attn2\n",
        "        \n",
        "        # x.shape: (batch_size, output_seq_len, d_model)\n",
        "        return x, attention_weights\n",
        "    \n",
        "sample_decoder_model = DecoderModel(2, 8000, max_length, 512, 8, 2048)\n",
        "\n",
        "sample_decoder_model_input = tf.random.uniform((64, 35))\n",
        "\n",
        "sample_decoder_model_output, sample_decoder_model_att = sample_decoder_model(\n",
        "    sample_decoder_model_input, sample_encoder_model_output,\n",
        "    training = False, decoder_mask = None,\n",
        "    encoder_decoder_padding_mask = None)\n",
        "\n",
        "print(sample_decoder_model_output.shape)\n",
        "for key in sample_decoder_model_att:\n",
        "    print(sample_decoder_model_att[key].shape)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 35, 512)\n",
            "(64, 8, 35, 35)\n",
            "(64, 8, 35, 37)\n",
            "(64, 8, 35, 35)\n",
            "(64, 8, 35, 37)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2zrKAFC5THfT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "a5a8bafc-e6b0-4526-d843-48e8fc810117"
      },
      "source": [
        "# Transformer的实现\n",
        "class Transformer(keras.Model):\n",
        "    def __init__(self, num_layers, input_vocab_size, target_vocab_size,\n",
        "                 max_length, d_model, num_heads, dff, rate=0.1):\n",
        "        super(Transformer, self).__init__()\n",
        "        \n",
        "        self.encoder_model = EncoderModel(\n",
        "            num_layers, input_vocab_size, max_length, d_model, num_heads, dff, rate)\n",
        "        \n",
        "        self.decoder_model = DecoderModel(\n",
        "            num_layers, target_vocab_size, max_length, d_model, num_heads, dff, rate)\n",
        "        \n",
        "        self.final_layer = keras.layers.Dense(target_vocab_size)\n",
        "        \n",
        "    def call(self, inp, tar, training, encoder_padding_mask,\n",
        "             decoder_mask, encoder_decoder_padding_mask):\n",
        "        # encoding_outputs.shape: (batch_size, input_seq_len, d_model)\n",
        "        encoding_outputs = self.encoder_model(inp, training, encoder_decoder_padding_mask)\n",
        "        \n",
        "        # decoding_outputs.shape: (batch_size, output_seq_len, d_model)\n",
        "        decoding_outputs, attention_weights = self.decoder_model(\n",
        "            tar, encoding_outputs, training, decoder_mask, encoder_decoder_padding_mask)\n",
        "        \n",
        "        # predictions.shape: (batch_size, output_seq_len, target_vocab_size)\n",
        "        predictions = self.final_layer(decoding_outputs)\n",
        "        \n",
        "        return predictions, attention_weights\n",
        "    \n",
        "sample_transformer = Transformer(2, 8500, 8000, max_length, \n",
        "                                 512, 8, 2048, rate = 0.1)\n",
        "temp_input = tf.random.uniform((64, 26))\n",
        "temp_target = tf.random.uniform((64, 31))\n",
        "\n",
        "predictions, attention_weights = sample_transformer(\n",
        "    temp_input, temp_target, training = False,\n",
        "    encoder_padding_mask = None,\n",
        "    decoder_mask = None,\n",
        "    encoder_decoder_padding_mask = None)\n",
        "\n",
        "print(predictions.shape)\n",
        "for key in attention_weights:\n",
        "    print(key, attention_weights[key].shape)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 31, 8000)\n",
            "decoder_layer1_att1 (64, 8, 31, 31)\n",
            "decoder_layer1_att2 (64, 8, 31, 26)\n",
            "decoder_layer2_att1 (64, 8, 31, 31)\n",
            "decoder_layer2_att2 (64, 8, 31, 26)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBQiV0ZnVxNf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 模型的训练\n",
        "# 1. initializes model\n",
        "# 2. define loss optimizer, learning_rate schedule\n",
        "# 3. train_step\n",
        "# 4. train_process"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQk2N2bXWEDV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_layers = 4\n",
        "d_model = 128\n",
        "dff = 512\n",
        "num_heads = 8\n",
        "\n",
        "input_vocab_size = pt_tokenizer.vocab_size + 2\n",
        "target_vocab_size = en_tokenizer.vocab_size + 2\n",
        "\n",
        "dropout_rate = 0.1\n",
        "\n",
        "transformer = Transformer(num_layers, \n",
        "                          input_vocab_size,\n",
        "                          target_vocab_size,\n",
        "                          max_length,\n",
        "                          d_model,\n",
        "                          num_heads, dff, dropout_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bo1wSZl5Wg2-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# learning_rate = (d_model ** -0.5) * min(step_num ** (-0.5), step_num * warm_up_steps ** (-1.5))\n",
        "class CustomizedSchedule(keras.optimizers.schedules.LearningRateSchedule):\n",
        "    def __init__(self, d_model, warmup_steps = 4000):\n",
        "        super(CustomizedSchedule, self).__init__()\n",
        "        \n",
        "        self.d_model = tf.cast(d_model, tf.float32)\n",
        "        self.warmup_steps = warmup_steps\n",
        "    \n",
        "    def __call__(self, step):\n",
        "        arg1 = tf.math.rsqrt(step)\n",
        "        arg2 = step * (self.warmup_steps ** (-1.5))\n",
        "        arg3 = tf.math.rsqrt(self.d_model)\n",
        "        \n",
        "        return arg3 * tf.math.minimum(arg1, arg2)\n",
        "    \n",
        "learning_rate = CustomizedSchedule(d_model)\n",
        "optimizer = keras.optimizers.Adam(learning_rate,\n",
        "                                  beta_1 = 0.9, beta_2 = 0.98, epsilon = 1e-9)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4A5MN-0XX2-e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "84204951-80af-49bd-823b-30b9b3881099"
      },
      "source": [
        "temp_learning_rate_schedule = CustomizedSchedule(d_model)\n",
        "\n",
        "plt.plot(temp_learning_rate_schedule(tf.range(40000, dtype=tf.float32)))\n",
        "plt.ylabel(\"Learning_rate\")\n",
        "plt.xlabel(\"Train step\")"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Train step')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aVY823a5DScaNrdzciWnM2/HGSJoVurZw21SISAKQiTcBoK19W2uvAP7ilp8E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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWknX2dyYKOf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss_object = keras.losses.SparseCategoricalCrossentropy(\n",
        "    from_logits = True, reduction='none')\n",
        "\n",
        "def loss_function(real, pred):\n",
        "    mask = tf.math.logical_not(tf.math.equal(real, 0))\n",
        "    loss_ = loss_object(real, pred)\n",
        "    \n",
        "    mask = tf.cast(mask, dtype=loss_.dtype)\n",
        "    loss_ *= mask\n",
        "    \n",
        "    return tf.reduce_mean(loss_)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YTg0dc4lYwqD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_masks(inp, tar):\n",
        "    \"\"\"\n",
        "    Encoder:\n",
        "      - encoder_padding_mask (self attention of EncoderLayer)\n",
        "    Decoder:\n",
        "      - look_ahead_mask (self attention of DecoderLayer)\n",
        "      - encoder_decoder_padding_mask (encoder-decoder attention of DecoderLayer)\n",
        "      - decoder_padding_mask (self attention of DecoderLayer)\n",
        "    \"\"\"\n",
        "    \n",
        "    encoder_padding_mask = create_padding_mask(inp)\n",
        "    encoder_decoder_padding_mask = create_padding_mask(inp)\n",
        "    \n",
        "    look_ahead_mask = create_look_ahead_mask(tf.shape(tar)[1])\n",
        "    decoder_padding_mask = create_padding_mask(tar)\n",
        "    decoder_mask = tf.maximum(decoder_padding_mask,\n",
        "                              look_ahead_mask)\n",
        "\n",
        "    return encoder_padding_mask, decoder_mask, encoder_decoder_padding_mask"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Axn6BZTraEs6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp_inp, temp_tar = iter(train_dataset.take(1)).next()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VI4YUB7KaJuL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c51e9f16-f377-42bf-d296-7e2488de9ebb"
      },
      "source": [
        "print(temp_inp.shape)\n",
        "print(temp_tar.shape)\n",
        "\n",
        "create_masks(temp_inp, temp_tar)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 33)\n",
            "(64, 32)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: id=600325, shape=(64, 1, 1, 33), dtype=float32, numpy=\n",
              " array([[[[0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        [[[0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        [[[0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        ...,\n",
              " \n",
              " \n",
              "        [[[0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        [[[0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        [[[0., 0., 0., ..., 1., 1., 1.]]]], dtype=float32)>,\n",
              " <tf.Tensor: id=600357, shape=(64, 1, 32, 32), dtype=float32, numpy=\n",
              " array([[[[0., 1., 1., ..., 1., 1., 1.],\n",
              "          [0., 0., 1., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        [[[0., 1., 1., ..., 1., 1., 1.],\n",
              "          [0., 0., 1., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        [[[0., 1., 1., ..., 1., 1., 1.],\n",
              "          [0., 0., 1., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        ...,\n",
              " \n",
              " \n",
              "        [[[0., 1., 1., ..., 1., 1., 1.],\n",
              "          [0., 0., 1., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        [[[0., 1., 1., ..., 1., 1., 1.],\n",
              "          [0., 0., 1., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        [[[0., 1., 1., ..., 1., 1., 1.],\n",
              "          [0., 0., 1., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          ...,\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.],\n",
              "          [0., 0., 0., ..., 1., 1., 1.]]]], dtype=float32)>,\n",
              " <tf.Tensor: id=600332, shape=(64, 1, 1, 33), dtype=float32, numpy=\n",
              " array([[[[0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        [[[0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        [[[0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        ...,\n",
              " \n",
              " \n",
              "        [[[0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        [[[0., 0., 0., ..., 1., 1., 1.]]],\n",
              " \n",
              " \n",
              "        [[[0., 0., 0., ..., 1., 1., 1.]]]], dtype=float32)>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJP8_6G4bDFt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "3aee05e5-1eff-4e4b-ba46-3ee8103f6289"
      },
      "source": [
        "train_loss = keras.metrics.Mean(name = 'train_loss')\n",
        "train_accuracy = keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n",
        "\n",
        "def train_step(inp, tar):\n",
        "    tar_inp = tar[:, :-1]\n",
        "    tar_real = tar[:, 1:]\n",
        "    \n",
        "    encoder_padding_mask, decoder_mask, encoder_decoder_padding_mask = create_masks(inp, tar_inp)\n",
        "    \n",
        "    with tf.GradientTape() as tape:\n",
        "        predictions, _ = transformer(inp, tar_inp, True, encoder_padding_mask,\n",
        "                                     decoder_mask,\n",
        "                                     encoder_decoder_padding_mask)\n",
        "        loss = loss_function(tar_real, predictions)\n",
        "        \n",
        "    gradients = tape.gradient(loss, transformer.trainable_variables)\n",
        "    optimizer.apply_gradients(\n",
        "        zip(gradients, transformer.trainable_variables))\n",
        "    \n",
        "    train_loss(loss)\n",
        "    train_accuracy(tar_real, predictions)\n",
        "    \n",
        "epochs = 20\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    start = time.time()\n",
        "    train_loss.reset_states()\n",
        "    train_accuracy.reset_states()\n",
        "    \n",
        "    for (batch, (inp, tar)) in enumerate(train_dataset):\n",
        "        train_step(inp, tar)\n",
        "        if batch % 100 == 0:\n",
        "            print('Epoch {} Batch {} Loss {:.4f} Accuracy {:.4f}'.format(\n",
        "                epoch+1, batch, train_loss.result(), train_accuracy.result()))\n",
        "            \n",
        "    print('Epoch {} Loss {:.4f} Accuracy {:.4f}'.format(\n",
        "        epoch + 1, train_loss.result(), train_accuracy.result()))\n",
        "    \n",
        "    print('Time take for 1 epoch: {} secs\\n'.format(time.time() - start))"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-59-3f137135b740>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m         \u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m100\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m             print('Epoch {} Batch {} Loss {:.4f} Accuracy {:.4f}'.format(\n",
            "\u001b[0;32m<ipython-input-59-3f137135b740>\u001b[0m in \u001b[0;36mtrain_step\u001b[0;34m(inp, tar)\u001b[0m\n\u001b[1;32m     11\u001b[0m         predictions, _ = transformer(inp, tar_inp, True, encoder_padding_mask,\n\u001b[1;32m     12\u001b[0m                                      \u001b[0mdecoder_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m                                      encoder_decoder_padding_mask)\n\u001b[0m\u001b[1;32m     14\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtar_real\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    710\u001b[0m           with base_layer_utils.autocast_context_manager(\n\u001b[1;32m    711\u001b[0m               input_list, self._mixed_precision_policy.should_cast_variables):\n\u001b[0;32m--> 712\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    713\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_mask_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-45-fed733776467>\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inp, tar, training, encoder_padding_mask, decoder_mask, encoder_decoder_padding_mask)\u001b[0m\n\u001b[1;32m     15\u001b[0m              decoder_mask, encoder_decoder_padding_mask):\n\u001b[1;32m     16\u001b[0m         \u001b[0;31m# encoding_outputs.shape: (batch_size, input_seq_len, d_model)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0mencoding_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoder_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoder_decoder_padding_mask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;31m# decoding_outputs.shape: (batch_size, output_seq_len, d_model)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    710\u001b[0m           with base_layer_utils.autocast_context_manager(\n\u001b[1;32m    711\u001b[0m               input_list, self._mixed_precision_policy.should_cast_variables):\n\u001b[0;32m--> 712\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    713\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_mask_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-43-5eaef62988a5>\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, x, training, encoder_padding_mask)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_layers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoder_layers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoder_padding_mask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0;31m# x.shape: (batch_size, input_seq_len, d_model)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    710\u001b[0m           with base_layer_utils.autocast_context_manager(\n\u001b[1;32m    711\u001b[0m               input_list, self._mixed_precision_policy.should_cast_variables):\n\u001b[0;32m--> 712\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    713\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_mask_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-41-2f5fe2c282b1>\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, x, training, encoder_padding_mask)\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;31m# attn_output.shape: (batch_size, seq_len, d_model)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;31m# out1.shape       : (batch_size, seq_len, d_model)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0mattn_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoder_padding_mask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m         \u001b[0mattn_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattn_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mout1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_norm1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mattn_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    710\u001b[0m           with base_layer_utils.autocast_context_manager(\n\u001b[1;32m    711\u001b[0m               input_list, self._mixed_precision_policy.should_cast_variables):\n\u001b[0;32m--> 712\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    713\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_mask_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-18-1c65c950f586>\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, q, k, v, mask)\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;31m# scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;31m# attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m         \u001b[0mscaled_attention_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_weights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaled_dot_product_attention\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m         \u001b[0;31m# (batch_size, seq_len_q, num_heads, depth)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-13-e18ac6f3b271>\u001b[0m in \u001b[0;36mscaled_dot_product_attention\u001b[0;34m(q, k, v, mask)\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;31m# 使得softmax后值趋近于0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0mscaled_attention_logits\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mle9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;31m# attention_weights.shape: (..., seq_len_q, seq_len_k)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'le9' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ht_eXcBAfNMK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}