{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "gQskE9NgL-ZB"
      },
      "source": [
        "Copyright 2018 The TensorFlow Datasets Authors, Licensed under the Apache License, Version 2.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "RPo1Cw2p83pb"
      },
      "outputs": [],
      "source": [
        "!pip install -q tensorflow-datasets tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "S-RWB9G48uJA"
      },
      "outputs": [],
      "source": [
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "# tfds works in both Eager and Graph modes\n",
        "tf.enable_eager_execution()\n",
        "\n",
        "# See available datasets\n",
        "print(tfds.list_builders())\n",
        "\n",
        "# Construct a tf.data.Dataset\n",
        "dataset = tfds.load(name=\"mnist\", split=tfds.Split.TRAIN)\n",
        "\n",
        "# Build your input pipeline\n",
        "dataset = dataset.shuffle(1024).batch(32).prefetch(tf.data.experimental.AUTOTUNE)\n",
        "for features in dataset.take(1):\n",
        "  image, label = features[\"image\"], features[\"label\"]"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "tensorflow/datasets",
      "provenance": [],
      "version": "0.3.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}