Move datasets notebook as section in eager_intro

2 files changed, 126 insertions, 243 deletions

diff --git a/tensorflow/contrib/eager/python/examples/notebooks/datasets.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/datasets.ipynb
deleted file mode 100644
index ba6602cb16..0000000000
--- a/tensorflow/contrib/eager/python/examples/notebooks/datasets.ipynb
+++ /dev/null
@@ -1,237 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "datasets.ipynb",
-      "version": "0.3.2",
-      "views": {},
-      "default_view": {},
-      "provenance": [],
-      "private_outputs": true,
-      "collapsed_sections": [],
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
-  "cells": [
-    {
-      "metadata": {
-        "id": "hvpDiXDuuNEu",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "##### Copyright 2018 The TensorFlow Authors."
-      ]
-    },
-    {
-      "metadata": {
-        "id": "4FWzUGc7uQKr",
-        "colab_type": "code",
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          }
-        },
-        "cellView": "form"
-      },
-      "cell_type": "code",
-      "source": [
-        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-        "# you may not use this file except in compliance with the License.\n",
-        "# You may obtain a copy of the License at\n",
-        "#\n",
-        "# https://www.apache.org/licenses/LICENSE-2.0\n",
-        "#\n",
-        "# Unless required by applicable law or agreed to in writing, software\n",
-        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-        "# See the License for the specific language governing permissions and\n",
-        "# limitations under the License."
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "metadata": {
-        "id": "RRbeuncot80C",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "# Datasets"
-      ]
-    },
-    {
-      "metadata": {
-        "id": "i9rtB6DnuFw5",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "<table class=\"tfo-notebook-buttons\" align=\"left\"><td>\n",
-        "<a target=\"_blank\"  href=\"https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/datasets.ipynb\">\n",
-        "    <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /><span>Run in Google Colab</span></a>\n",
-        "</td><td>\n",
-        "<a target=\"_blank\"  href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/datasets.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /><span>View source on GitHub</span></a></td></table>"
-      ]
-    },
-    {
-      "metadata": {
-        "id": "U9i2Dsh-ziXr",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "This notebook demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/guide/datasets) to build pipelines to feed data to your program. It covers:\n",
-        "\n",
-        "* Creating a `Dataset`.\n",
-        "* Iteration over a `Dataset` with eager execution enabled.\n",
-        "\n",
-        "We recommend using the `Dataset`s API for building performant, complex input pipelines from simple, re-usable pieces that will feed your model's training or evaluation loops.\n",
-        "\n",
-        "If you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly simpler.\n",
-        "You can use Python iteration over the `tf.data.Dataset` object and do not need to explicitly create an `tf.data.Iterator` object.\n",
-        "As a result, the discussion on iterators in the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets) is not relevant when eager execution is enabled."
-      ]
-    },
-    {
-      "metadata": {
-        "id": "RlIWhyeLoYnG",
-        "colab_type": "code",
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          }
-        },
-        "cellView": "code"
-      },
-      "cell_type": "code",
-      "source": [
-        "# Import TensorFlow.\n",
-        "import tensorflow as tf\n",
-        "\n",
-        "# Enable eager execution\n",
-        "tf.enable_eager_execution()"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "metadata": {
-        "id": "H9UySOPLXdaw",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "## Create a source `Dataset`\n",
-        "\n",
-        "Create a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets#reading_input_data) for more information."
-      ]
-    },
-    {
-      "metadata": {
-        "id": "WPTUfGq6kJ5w",
-        "colab_type": "code",
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          }
-        },
-        "cellView": "code"
-      },
-      "cell_type": "code",
-      "source": [
-        "ds_tensors = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6])\n",
-        "\n",
-        "# Create a CSV file\n",
-        "import tempfile\n",
-        "_, filename = tempfile.mkstemp()\n",
-        "with open(filename, 'w') as f:\n",
-        "  f.write(\"\"\"Line 1\n",
-        "Line 2\n",
-        "Line 3\n",
-        "  \"\"\")\n",
-        "ds_file = tf.data.TextLineDataset(filename)\n"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "metadata": {
-        "id": "twBfWd5xyu_d",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "## Apply transformations\n",
-        "\n",
-        "Use the transformations functions like [`map`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map), [`batch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#batch), [`shuffle`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) etc. to apply transformations to the records of the dataset. See the [API documentation for `tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) for details."
-      ]
-    },
-    {
-      "metadata": {
-        "id": "ngUe237Wt48W",
-        "colab_type": "code",
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          }
-        },
-        "cellView": "code"
-      },
-      "cell_type": "code",
-      "source": [
-        "ds_tensors = ds_tensors.map(tf.square).shuffle(2).batch(2)\n",
-        "ds_file = ds_file.batch(2)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "metadata": {
-        "id": "IDY4WsYRhP81",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "## Iterate\n",
-        "\n",
-        "When eager execution is enabled `Dataset` objects support iteration.\n",
-        "If you're familiar with the use of `Dataset`s in TensorFlow graphs, note that there is no need for calls to `Dataset.make_one_shot_iterator()` or `get_next()` calls."
-      ]
-    },
-    {
-      "metadata": {
-        "id": "lCUWzso6mbqR",
-        "colab_type": "code",
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          }
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "print('Elements of ds_tensors:')\n",
-        "for x in ds_tensors:\n",
-        "  print(x)\n",
-        "\n",
-        "print('\\nElements in ds_file:')\n",
-        "for x in ds_file:\n",
-        "  print(x)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file
diff --git a/tensorflow/contrib/eager/python/examples/notebooks/eager_intro.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/eager_intro.ipynb
index a00893bc77..5311a33e6e 100644
--- a/tensorflow/contrib/eager/python/examples/notebooks/eager_intro.ipynb
+++ b/tensorflow/contrib/eager/python/examples/notebooks/eager_intro.ipynb
@@ -3,7 +3,7 @@
   "nbformat_minor": 0,
   "metadata": {
     "colab": {
-      "name": "Eager execution introduction",
+      "name": "eager_intro.ipynb",
       "version": "0.3.2",
       "views": {},
       "default_view": {},
@@ -92,7 +92,8 @@
         "\n",
         "* Importing required packages\n",
         "* Creating and using Tensors\n",
-        "* Using GPU acceleration"
+        "* Using GPU acceleration\n",
+        "* Datasets"
       ]
     },
     {
@@ -356,16 +357,135 @@
     },
     {
       "metadata": {
-        "id": "YEOJTNiOvnpQ",
+        "id": "o1K4dlhhHtQj",
         "colab_type": "text"
       },
       "cell_type": "markdown",
       "source": [
-        "## Next Steps\n",
+        "## Datasets\n",
         "\n",
-        "In this tutorial we covered the most fundamental concepts in TensorFlow - `Tensor`s, operations, and devices.\n",
-        "In [the next tutorial](https://github.com/tensorflow/models/tree/master/official/contrib/eager/python/examples/notebooks/2_gradients.ipynb) we will cover automatic differentiation - a building block required for training many machine learning models like neural networks."
+        "This section demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/guide/datasets) to build pipelines to feed data to your model. It covers:\n",
+        "\n",
+        "* Creating a `Dataset`.\n",
+        "* Iteration over a `Dataset` with eager execution enabled.\n",
+        "\n",
+        "We recommend using the `Dataset`s API for building performant, complex input pipelines from simple, re-usable pieces that will feed your model's training or evaluation loops.\n",
+        "\n",
+        "If you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly simpler.\n",
+        "You can use Python iteration over the `tf.data.Dataset` object and do not need to explicitly create an `tf.data.Iterator` object.\n",
+        "As a result, the discussion on iterators in the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets) is not relevant when eager execution is enabled."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "zI0fmOynH-Ne",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Create a source `Dataset`\n",
+        "\n",
+        "Create a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets#reading_input_data) for more information."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F04fVOHQIBiG",
+        "colab_type": "code",
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "ds_tensors = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6])\n",
+        "\n",
+        "# Create a CSV file\n",
+        "import tempfile\n",
+        "_, filename = tempfile.mkstemp()\n",
+        "\n",
+        "with open(filename, 'w') as f:\n",
+        "  f.write(\"\"\"Line 1\n",
+        "Line 2\n",
+        "Line 3\n",
+        "  \"\"\")\n",
+        "\n",
+        "ds_file = tf.data.TextLineDataset(filename)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "vbxIhC-5IPdf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Apply transformations\n",
+        "\n",
+        "Use the transformations functions like [`map`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map), [`batch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#batch), [`shuffle`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) etc. to apply transformations to the records of the dataset. See the [API documentation for `tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) for details."
       ]
+    },
+    {
+      "metadata": {
+        "id": "uXSDZWE-ISsd",
+        "colab_type": "code",
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "ds_tensors = ds_tensors.map(tf.square).shuffle(2).batch(2)\n",
+        "\n",
+        "ds_file = ds_file.batch(2)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "A8X1GNfoIZKJ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Iterate\n",
+        "\n",
+        "When eager execution is enabled `Dataset` objects support iteration.\n",
+        "If you're familiar with the use of `Dataset`s in TensorFlow graphs, note that there is no need for calls to `Dataset.make_one_shot_iterator()` or `get_next()` calls."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ws-WKRk5Ic6-",
+        "colab_type": "code",
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "print('Elements of ds_tensors:')\n",
+        "for x in ds_tensors:\n",
+        "  print(x)\n",
+        "\n",
+        "print('\\nElements in ds_file:')\n",
+        "for x in ds_file:\n",
+        "  print(x)"
+      ],
+      "execution_count": 0,
+      "outputs": []
     }
   ]
 }
\ No newline at end of file