Update eager notebooks with colab/github buttons. Change titles. Open leftnav.

5 files changed, 953 insertions, 653 deletions

diff --git a/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb
index 51d10a7784..99d141bf2b 100644
--- a/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb
+++ b/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb
@@ -1,27 +1,101 @@
 {
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Eager execution basics",
+      "version": "0.3.2",
+      "views": {},
+      "default_view": {},
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    }
+  },
   "cells": [
     {
+      "metadata": {
+        "id": "iPpI7RaYoZuE",
+        "colab_type": "text"
+      },
       "cell_type": "markdown",
+      "source": [
+        "##### Copyright 2018 The TensorFlow Authors."
+      ]
+    },
+    {
       "metadata": {
-        "colab_type": "text",
-        "id": "U9i2Dsh-ziXr"
+        "id": "hro2InpHobKk",
+        "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": "U9i2Dsh-ziXr",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Eager execution basics"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Hndw-YcxoOJK",
+        "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/1_basics.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/1_basics.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": "6sILUVbHoSgH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
       "source": [
-        "# An introduction to TensorFlow\n",
-        "\n",
         "This is an introductory tutorial for using TensorFlow. It will cover:\n",
         "\n",
         "* Importing required packages\n",
         "* Creating and using Tensors\n",
-        "* Using GPU acceleration\n"
+        "* Using GPU acceleration"
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "z1JcS5iBXMRO"
+        "id": "z1JcS5iBXMRO",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Import TensorFlow\n",
         "\n",
@@ -30,32 +104,32 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "RlIWhyeLoYnG",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "RlIWhyeLoYnG"
+        "cellView": "code"
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "import tensorflow as tf\n",
         "\n",
         "tf.enable_eager_execution()"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "H9UySOPLXdaw"
+        "id": "H9UySOPLXdaw",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Tensors\n",
         "\n",
@@ -63,10 +137,9 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "ngUe237Wt48W",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -74,7 +147,8 @@
           },
           "height": 125
         },
-        "colab_type": "code",
+        "cellView": "code",
+        "outputId": "b1a1cd60-4eb3-443d-cd6b-68406390784e",
         "executionInfo": {
           "elapsed": 320,
           "status": "ok",
@@ -85,13 +159,22 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "ngUe237Wt48W",
-        "outputId": "b1a1cd60-4eb3-443d-cd6b-68406390784e"
+        }
       },
+      "cell_type": "code",
+      "source": [
+        "print(tf.add(1, 2))\n",
+        "print(tf.add([1, 2], [3, 4]))\n",
+        "print(tf.square(5))\n",
+        "print(tf.reduce_sum([1, 2, 3]))\n",
+        "print(tf.encode_base64(\"hello world\"))\n",
+        "\n",
+        "# Operator overloading is also supported\n",
+        "print(tf.square(2) + tf.square(3))"
+      ],
+      "execution_count": 0,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "tf.Tensor(3, shape=(), dtype=int32)\n",
@@ -100,34 +183,25 @@
             "tf.Tensor(6, shape=(), dtype=int32)\n",
             "tf.Tensor(aGVsbG8gd29ybGQ, shape=(), dtype=string)\n",
             "tf.Tensor(13, shape=(), dtype=int32)\n"
-          ]
+          ],
+          "name": "stdout"
         }
-      ],
-      "source": [
-        "print(tf.add(1, 2))\n",
-        "print(tf.add([1, 2], [3, 4]))\n",
-        "print(tf.square(5))\n",
-        "print(tf.reduce_sum([1, 2, 3]))\n",
-        "print(tf.encode_base64(\"hello world\"))\n",
-        "\n",
-        "# Operator overloading is also supported\n",
-        "print(tf.square(2) + tf.square(3))"
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "IDY4WsYRhP81"
+        "id": "IDY4WsYRhP81",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "Each Tensor has a shape and a datatype"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "srYWH1MdJNG7",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -135,7 +209,7 @@
           },
           "height": 53
         },
-        "colab_type": "code",
+        "outputId": "5e4ac41c-5115-4e50-eba0-42e249c16561",
         "executionInfo": {
           "elapsed": 215,
           "status": "ok",
@@ -146,32 +220,32 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "srYWH1MdJNG7",
-        "outputId": "5e4ac41c-5115-4e50-eba0-42e249c16561"
+        }
       },
+      "cell_type": "code",
+      "source": [
+        "x = tf.matmul([[1]], [[2, 3]])\n",
+        "print(x.shape)\n",
+        "print(x.dtype)"
+      ],
+      "execution_count": 0,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "(1, 2)\n",
-            "\u003cdtype: 'int32'\u003e\n"
-          ]
+            "<dtype: 'int32'>\n"
+          ],
+          "name": "stdout"
         }
-      ],
-      "source": [
-        "x = tf.matmul([[1]], [[2, 3]])\n",
-        "print(x.shape)\n",
-        "print(x.dtype)"
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "eBPw8e8vrsom"
+        "id": "eBPw8e8vrsom",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "The most obvious differences between NumPy arrays and TensorFlow Tensors are:\n",
         "\n",
@@ -180,11 +254,11 @@
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "Dwi1tdW3JBw6"
+        "id": "Dwi1tdW3JBw6",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "### NumPy Compatibility\n",
         "\n",
@@ -197,9 +271,9 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "lCUWzso6mbqR",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -207,7 +281,7 @@
           },
           "height": 251
         },
-        "colab_type": "code",
+        "outputId": "fd0a22bc-8249-49dd-fcbd-63161cc47e46",
         "executionInfo": {
           "elapsed": 238,
           "status": "ok",
@@ -218,13 +292,28 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "lCUWzso6mbqR",
-        "outputId": "fd0a22bc-8249-49dd-fcbd-63161cc47e46"
+        }
       },
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "ndarray = np.ones([3, 3])\n",
+        "\n",
+        "print(\"TensorFlow operations convert numpy arrays to Tensors automatically\")\n",
+        "tensor = tf.multiply(ndarray, 42)\n",
+        "print(tensor)\n",
+        "\n",
+        "\n",
+        "print(\"And NumPy operations convert Tensors to numpy arrays automatically\")\n",
+        "print(np.add(tensor, 1))\n",
+        "\n",
+        "print(\"The .numpy() method explicitly converts a Tensor to a numpy array\")\n",
+        "print(tensor.numpy())"
+      ],
+      "execution_count": 0,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "TensorFlow operations convert numpy arrays to Tensors automatically\n",
@@ -240,32 +329,17 @@
             "[[ 42.  42.  42.]\n",
             " [ 42.  42.  42.]\n",
             " [ 42.  42.  42.]]\n"
-          ]
+          ],
+          "name": "stdout"
         }
-      ],
-      "source": [
-        "import numpy as np\n",
-        "\n",
-        "ndarray = np.ones([3, 3])\n",
-        "\n",
-        "print(\"TensorFlow operations convert numpy arrays to Tensors automatically\")\n",
-        "tensor = tf.multiply(ndarray, 42)\n",
-        "print(tensor)\n",
-        "\n",
-        "\n",
-        "print(\"And NumPy operations convert Tensors to numpy arrays automatically\")\n",
-        "print(np.add(tensor, 1))\n",
-        "\n",
-        "print(\"The .numpy() method explicitly converts a Tensor to a numpy array\")\n",
-        "print(tensor.numpy())"
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "PBNP8yTRfu_X"
+        "id": "PBNP8yTRfu_X",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## GPU acceleration\n",
         "\n",
@@ -273,10 +347,9 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "3Twf_Rw-gQFM",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -284,7 +357,8 @@
           },
           "height": 53
         },
-        "colab_type": "code",
+        "cellView": "code",
+        "outputId": "2239ae2b-adf3-4895-b1f3-464cf5361d1b",
         "executionInfo": {
           "elapsed": 340,
           "status": "ok",
@@ -295,20 +369,9 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "3Twf_Rw-gQFM",
-        "outputId": "2239ae2b-adf3-4895-b1f3-464cf5361d1b"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Is there a GPU available:  False\n",
-            "Is the Tensor on GPU #0:   False\n"
-          ]
         }
-      ],
+      },
+      "cell_type": "code",
       "source": [
         "x = tf.random_uniform([3, 3])\n",
         "\n",
@@ -317,26 +380,37 @@
         "\n",
         "print(\"Is the Tensor on GPU #0:  \"),\n",
         "print(x.device.endswith('GPU:0'))"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Is there a GPU available:  False\n",
+            "Is the Tensor on GPU #0:   False\n"
+          ],
+          "name": "stdout"
+        }
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "vpgYzgVXW2Ud"
+        "id": "vpgYzgVXW2Ud",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "### Device Names\n",
         "\n",
-        "The `Tensor.device` property provides a fully qualified string name of the device hosting the contents of the Tensor. This name encodes a bunch of details, such as an identifier of the network address of the host on which this program is executing and the device within that host. This is required for distributed execution of TensorFlow programs, but we'll skip that for now. The string will end with `GPU:\u003cN\u003e` if the tensor is placed on the `N`-th tensor on the host."
+        "The `Tensor.device` property provides a fully qualified string name of the device hosting the contents of the Tensor. This name encodes a bunch of details, such as an identifier of the network address of the host on which this program is executing and the device within that host. This is required for distributed execution of TensorFlow programs, but we'll skip that for now. The string will end with `GPU:<N>` if the tensor is placed on the `N`-th tensor on the host."
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "ZWZQCimzuqyP"
+        "id": "ZWZQCimzuqyP",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "\n",
         "\n",
@@ -346,9 +420,9 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "RjkNZTuauy-Q",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -356,7 +430,7 @@
           },
           "height": 53
         },
-        "colab_type": "code",
+        "outputId": "2e613293-ccac-4db2-b793-8ceb5b5adcfd",
         "executionInfo": {
           "elapsed": 1762,
           "status": "ok",
@@ -367,20 +441,9 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "RjkNZTuauy-Q",
-        "outputId": "2e613293-ccac-4db2-b793-8ceb5b5adcfd"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "On CPU:\n",
-            "10 loops, best of 3: 35.8 ms per loop\n"
-          ]
         }
-      ],
+      },
+      "cell_type": "code",
       "source": [
         "def time_matmul(x):\n",
         "  %timeit tf.matmul(x, x)\n",
@@ -398,14 +461,25 @@
         "    x = tf.random_uniform([1000, 1000])\n",
         "    assert x.device.endswith(\"GPU:0\")\n",
         "    time_matmul(x)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "On CPU:\n",
+            "10 loops, best of 3: 35.8 ms per loop\n"
+          ],
+          "name": "stdout"
+        }
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "YEOJTNiOvnpQ"
+        "id": "YEOJTNiOvnpQ",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Next Steps\n",
         "\n",
@@ -413,17 +487,5 @@
         "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."
       ]
     }
-  ],
-  "metadata": {
-    "colab": {
-      "collapsed_sections": [],
-      "default_view": {},
-      "name": "TensorFlow: An introduction",
-      "provenance": [],
-      "version": "0.3.2",
-      "views": {}
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
+  ]
+}
\ No newline at end of file
diff --git a/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb
index 9c1af9c208..d7bcf749e8 100644
--- a/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb
+++ b/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb
@@ -1,54 +1,128 @@
 {
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Automatic differentiation and gradient tape",
+      "version": "0.3.2",
+      "views": {},
+      "default_view": {},
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    }
+  },
   "cells": [
     {
+      "metadata": {
+        "id": "t09eeeR5prIJ",
+        "colab_type": "text"
+      },
       "cell_type": "markdown",
+      "source": [
+        "##### Copyright 2018 The TensorFlow Authors."
+      ]
+    },
+    {
       "metadata": {
-        "colab_type": "text",
-        "id": "vDJ4XzMqodTy"
+        "id": "GCCk8_dHpuNf",
+        "colab_type": "code",
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "cellView": "form"
       },
+      "cell_type": "code",
       "source": [
-        "# Automatic Differentiation\n",
-        "\n",
-        "In the previous tutorial we introduced `Tensor`s and operations on them. In this tutorial we will cover [automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation), a key technique for optimizing machine learning models."
+        "#@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": "xh8WkEwWpnm7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Automatic differentiation and gradient tape"
       ]
     },
     {
+      "metadata": {
+        "id": "idv0bPeCp325",
+        "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/2_gradients.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/2_gradients.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /><span>View source on GitHub</span></a></td></table>"
+      ]
+    },
+    {
       "metadata": {
-        "colab_type": "text",
-        "id": "GQJysDM__Qb0"
+        "id": "vDJ4XzMqodTy",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
+      "source": [
+        "In the previous tutorial we introduced `Tensor`s and operations on them. In this tutorial we will cover [automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation), a key technique for optimizing machine learning models."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "GQJysDM__Qb0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
       "source": [
         "## Setup\n"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "OiMPZStlibBv",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "OiMPZStlibBv"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "import tensorflow as tf\n",
         "tf.enable_eager_execution()\n",
         "\n",
         "tfe = tf.contrib.eager # Shorthand for some symbols"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "1CLWJl0QliB0"
+        "id": "1CLWJl0QliB0",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Derivatives of a function\n",
         "\n",
@@ -56,19 +130,17 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "9FViq92UX7P8",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "9FViq92UX7P8"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "from math import pi\n",
         "\n",
@@ -82,15 +154,17 @@
         "# with respect to its arguments. Since f() has a single argument,\n",
         "# grad_f will return a list with a single element.\n",
         "grad_f = tfe.gradients_function(f)\n",
-        "assert tf.abs(grad_f(pi/2)[0]).numpy() \u003c 1e-7"
-      ]
+        "assert tf.abs(grad_f(pi/2)[0]).numpy() < 1e-7"
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "v9fPs8RyopCf"
+        "id": "v9fPs8RyopCf",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "### Higher-order gradients\n",
         "\n",
@@ -98,9 +172,9 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "3D0ZvnGYo0rW",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -108,7 +182,7 @@
           },
           "height": 276
         },
-        "colab_type": "code",
+        "outputId": "e23f8cc6-6813-4944-f20f-825b8a03c2ff",
         "executionInfo": {
           "elapsed": 730,
           "status": "ok",
@@ -119,24 +193,9 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "3D0ZvnGYo0rW",
-        "outputId": "e23f8cc6-6813-4944-f20f-825b8a03c2ff"
-      },
-      "outputs": [
-        {
-          "data": {
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5nzUnrbzzj51MmZnKmUvGhbCnbQn1eH79RR77d5Zx+Q2zSEju+1F0u7YUsXVj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\nF3bGGIswXNgZYyzC/Be68EGj7hfMcwAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "\u003cmatplotlib.figure.Figure at 0x7f385e198650\u003e"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "output_type": "display_data"
         }
-      ],
+      },
+      "cell_type": "code",
       "source": [
         "def f(x):\n",
         "  return tf.square(tf.sin(x))\n",
@@ -154,14 +213,29 @@
         "plt.plot(x, grad(grad(grad(f)))(x), label=\"third derivative\")\n",
         "plt.legend()\n",
         "plt.show()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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5nzUnrbzzj51MmZnKmUvGhbCnbQn1eH79RR77d5Zx+Q2zSEju+1F0u7YUsXVj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\nF3bGGIswXNgZYyzC/Be68EGj7hfMcwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f385e198650>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "-39gouo7mtgu"
+        "id": "-39gouo7mtgu",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Gradient tapes\n",
         "\n",
@@ -172,19 +246,17 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "MH0UfjympWf7",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "MH0UfjympWf7"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "def f(x, y):\n",
         "  output = 1\n",
@@ -200,14 +272,16 @@
         "assert g(3.0, 2).numpy() == 6.0   # And its gradient will be 2 * x\n",
         "assert f(4.0, 3).numpy() == 64.0  # f(x, 3) is essentially x * x * x\n",
         "assert g(4.0, 3).numpy() == 48.0  # And its gradient will be 3 * x * x"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "aNmR5-jhpX2t"
+        "id": "aNmR5-jhpX2t",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "At times it may be inconvenient to encapsulate computation of interest into a function. For example, if you want the gradient of the output with respect to intermediate values computed in the function. In such cases, the slightly more verbose but explicit [tf.GradientTape](https://www.tensorflow.org/api_docs/python/tf/GradientTape) context is useful. All computation inside the context of a `tf.GradientTape` is \"recorded\".\n",
         "\n",
@@ -215,19 +289,17 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "bAFeIE8EuVIq",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "bAFeIE8EuVIq"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "x = tf.ones((2, 2))\n",
         "  \n",
@@ -249,14 +321,16 @@
         "for i in [0, 1]:\n",
         "  for j in [0, 1]:\n",
         "    assert dz_dx[i][j].numpy() == 8.0"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "DK05KXrAAld3"
+        "id": "DK05KXrAAld3",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "### Higher-order gradients\n",
         "\n",
@@ -264,19 +338,17 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "cPQgthZ7ugRJ",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "cPQgthZ7ugRJ"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "# TODO(ashankar): Should we use the persistent tape here instead? Follow up on Tom and Alex's discussion\n",
         "\n",
@@ -293,31 +365,21 @@
         "\n",
         "assert dy_dx.numpy() == 3.0\n",
         "assert d2y_dx2.numpy() == 6.0"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "4U1KKzUpNl58"
+        "id": "4U1KKzUpNl58",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Next Steps\n",
         "\n",
         "In this tutorial we covered gradient computation in TensorFlow. With that we have enough of the primitives required to build an train neural networks, which we will cover in the [next tutorial](https://github.com/tensorflow/models/tree/master/official/contrib/eager/python/examples/notebooks/3_neural_networks.ipynb)."
       ]
     }
-  ],
-  "metadata": {
-    "colab": {
-      "collapsed_sections": [],
-      "default_view": {},
-      "name": "Automatic Differentiation",
-      "provenance": [],
-      "version": "0.3.2",
-      "views": {}
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
+  ]
+}
\ No newline at end of file
diff --git a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb
index d268cbcd91..37f2f4f402 100644
--- a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb
+++ b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb
@@ -1,14 +1,88 @@
 {
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Datasets",
+      "version": "0.3.2",
+      "views": {},
+      "default_view": {},
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    }
+  },
   "cells": [
     {
+      "metadata": {
+        "id": "hvpDiXDuuNEu",
+        "colab_type": "text"
+      },
       "cell_type": "markdown",
+      "source": [
+        "##### Copyright 2018 The TensorFlow Authors."
+      ]
+    },
+    {
       "metadata": {
-        "colab_type": "text",
-        "id": "U9i2Dsh-ziXr"
+        "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/3_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/3_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": [
-        "# Eager Execution Tutorial: Importing Data\n",
-        "\n",
         "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",
@@ -22,65 +96,53 @@
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "z1JcS5iBXMRO"
-      },
-      "source": [
-        "# Setup: Enable eager execution\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 0,
-      "metadata": {
-        "cellView": "code",
+        "id": "RlIWhyeLoYnG",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "RlIWhyeLoYnG"
+        "cellView": "code"
       },
-      "outputs": [],
+      "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": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "H9UySOPLXdaw"
+        "id": "H9UySOPLXdaw",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
-        "# Step 1: Create a source `Dataset`\n",
+        "## 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."
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "WPTUfGq6kJ5w",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "WPTUfGq6kJ5w"
+        "cellView": "code"
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "ds_tensors = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6])\n",
         "\n",
@@ -93,57 +155,59 @@
         "Line 3\n",
         "  \"\"\")\n",
         "ds_file = tf.data.TextLineDataset(filename)\n"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "twBfWd5xyu_d"
+        "id": "twBfWd5xyu_d",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
-        "# Step 2: Apply transformations\n",
+        "## 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."
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "ngUe237Wt48W",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "ngUe237Wt48W"
+        "cellView": "code"
       },
-      "outputs": [],
+      "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": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "IDY4WsYRhP81"
+        "id": "IDY4WsYRhP81",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
-        "# Step 3: Iterate\n",
+        "## 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."
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "lCUWzso6mbqR",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -152,7 +216,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 153
         },
-        "colab_type": "code",
+        "outputId": "8e4b0298-d27d-4ac7-e26a-ef94af0594ec",
         "executionInfo": {
           "elapsed": 388,
           "status": "ok",
@@ -163,13 +227,21 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "lCUWzso6mbqR",
-        "outputId": "8e4b0298-d27d-4ac7-e26a-ef94af0594ec"
+        }
       },
+      "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": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Elements of ds_tensors:\n",
@@ -180,30 +252,10 @@
             "Elements in ds_file:\n",
             "tf.Tensor(['Line 1' 'Line 2'], shape=(2,), dtype=string)\n",
             "tf.Tensor(['Line 3' '  '], shape=(2,), dtype=string)\n"
-          ]
+          ],
+          "name": "stdout"
         }
-      ],
-      "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)"
       ]
     }
-  ],
-  "metadata": {
-    "colab": {
-      "collapsed_sections": [],
-      "default_view": {},
-      "name": "Eager Execution Tutorial: Importing Data",
-      "provenance": [],
-      "version": "0.3.2",
-      "views": {}
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
+  ]
+}
\ No newline at end of file
diff --git a/tensorflow/contrib/eager/python/examples/notebooks/3_training_models.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/3_training_models.ipynb
index 84f1d031d4..ca0262eade 100644
--- a/tensorflow/contrib/eager/python/examples/notebooks/3_training_models.ipynb
+++ b/tensorflow/contrib/eager/python/examples/notebooks/3_training_models.ipynb
@@ -1,14 +1,88 @@
 {
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Variables, models, and training",
+      "version": "0.3.2",
+      "views": {},
+      "default_view": {},
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    }
+  },
   "cells": [
     {
+      "metadata": {
+        "id": "5rmpybwysXGV",
+        "colab_type": "text"
+      },
       "cell_type": "markdown",
+      "source": [
+        "##### Copyright 2018 The TensorFlow Authors."
+      ]
+    },
+    {
       "metadata": {
-        "colab_type": "text",
-        "id": "k2o3TTG4TFpt"
+        "id": "m8y3rGtQsYP2",
+        "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": "hrXv0rU9sIma",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Variables, models, and training"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7S0BwJ_8sLu7",
+        "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/3_training_models.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/3_training_models.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": "k2o3TTG4TFpt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
       "source": [
-        "# Training Models\n",
-        "\n",
         "In the previous tutorial we covered the TensorFlow APIs for automatic differentiation, a basic building block for machine learning.\n",
         "In this tutorial we will use the TensorFlow primitives introduced in the prior tutorials to do some simple machine learning.\n",
         "\n",
@@ -16,41 +90,41 @@
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "3LXMVuV0VhDr"
+        "id": "3LXMVuV0VhDr",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Setup"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "PJ64L90aVir3",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "PJ64L90aVir3"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "import tensorflow as tf\n",
         "tf.enable_eager_execution()\n",
         "tfe = tf.contrib.eager # Shorthand for some symbols"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "eMAWbDJFVmMk"
+        "id": "eMAWbDJFVmMk",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Variables\n",
         "\n",
@@ -58,33 +132,33 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "VkJwtLS_Jbn8",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "VkJwtLS_Jbn8"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "# Using python state\n",
         "x = tf.zeros([10, 10])\n",
         "x += 2  # This is equivalent to x = x + 2, which does not mutate the original\n",
         "        # value of x\n",
         "print(x)"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "wfneTXy7JcUz"
+        "id": "wfneTXy7JcUz",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "TensorFlow, however, has stateful operations built in, and these are often more pleasant to use than low-level Python representations of your state. To represent weights in a model, for example, it's often convenient and efficient to use TensorFlow variables.\n",
         "\n",
@@ -92,19 +166,17 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "itxmrMil6DQi",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "itxmrMil6DQi"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "v = tfe.Variable(1.0)\n",
         "assert v.numpy() == 1.0\n",
@@ -116,14 +188,16 @@
         "# Use `v` in a TensorFlow operation like tf.square() and reassign\n",
         "v.assign(tf.square(v))\n",
         "assert v.numpy() == 9.0"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "-paSaeq1JzwC"
+        "id": "-paSaeq1JzwC",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "Computations using Variables are automatically traced when computing gradients. For Variables representing embeddings TensorFlow will do sparse updates by default, which are more computation and memory efficient.\n",
         "\n",
@@ -131,11 +205,11 @@
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "BMiFcDzE7Qu3"
+        "id": "BMiFcDzE7Qu3",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Example: Fitting a linear model\n",
         "\n",
@@ -150,11 +224,11 @@
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "gFzH64Jn9PIm"
+        "id": "gFzH64Jn9PIm",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "### Define the model\n",
         "\n",
@@ -162,19 +236,17 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "_WRu7Pze7wk8",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "_WRu7Pze7wk8"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "class Model(object):\n",
         "  def __init__(self):\n",
@@ -189,14 +261,16 @@
         "model = Model()\n",
         "\n",
         "assert model(3.0).numpy() == 15.0"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "xa6j_yXa-j79"
+        "id": "xa6j_yXa-j79",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "### Define a loss function\n",
         "\n",
@@ -204,30 +278,30 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "Y0ysUFGY924U",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "Y0ysUFGY924U"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "def loss(predicted_y, desired_y):\n",
         "  return tf.reduce_mean(tf.square(predicted_y - desired_y))"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "qutT_fkl_CBc"
+        "id": "qutT_fkl_CBc",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "### Obtain training data\n",
         "\n",
@@ -235,19 +309,17 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "gxPTb-kt_N5m",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "gxPTb-kt_N5m"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "TRUE_W = 3.0\n",
         "TRUE_b = 2.0\n",
@@ -256,22 +328,24 @@
         "inputs  = tf.random_normal(shape=[NUM_EXAMPLES])\n",
         "noise   = tf.random_normal(shape=[NUM_EXAMPLES])\n",
         "outputs = inputs * TRUE_W + TRUE_b + noise"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "-50nq-wPBsAW"
+        "id": "-50nq-wPBsAW",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "Before we train the model let's visualize where the model stands right now. We'll plot the model's predictions in red and the training data in blue."
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "_eb83LtrB4nt",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -279,7 +353,7 @@
           },
           "height": 293
         },
-        "colab_type": "code",
+        "outputId": "3873f508-72fb-41e7-a7f5-3f513deefe38",
         "executionInfo": {
           "elapsed": 1210,
           "status": "ok",
@@ -290,48 +364,48 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "_eb83LtrB4nt",
-        "outputId": "3873f508-72fb-41e7-a7f5-3f513deefe38"
+        }
       },
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.scatter(inputs, outputs, c='b')\n",
+        "plt.scatter(inputs, model(inputs), c='r')\n",
+        "plt.show()\n",
+        "\n",
+        "print('Current loss: '),\n",
+        "print(loss(model(inputs), outputs).numpy())"
+      ],
+      "execution_count": 0,
       "outputs": [
         {
+          "output_type": "display_data",
           "data": {
             "image/png": 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WOvbtC8JorCQ5Of6M9lJj2hj4agsEdyGCLwhNEEcxj4jowlVXnbk1gb92kkR6MM/yzCrs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\noZkggi8IgtBMEMEXBEFoJvw//5K32R/vBHAAAAAASUVORK5CYII=\n",
             "text/plain": [
-              "\u003cmatplotlib.figure.Figure at 0x7f5be3c99f50\u003e"
+              "<matplotlib.figure.Figure at 0x7f5be3c99f50>"
             ]
           },
           "metadata": {
             "tags": []
-          },
-          "output_type": "display_data"
+          }
         },
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Current loss:  9.48636\n"
-          ]
+          ],
+          "name": "stdout"
         }
-      ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "plt.scatter(inputs, outputs, c='b')\n",
-        "plt.scatter(inputs, model(inputs), c='r')\n",
-        "plt.show()\n",
-        "\n",
-        "print('Current loss: '),\n",
-        "print(loss(model(inputs), outputs).numpy())"
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "sSDP-yeq_4jE"
+        "id": "sSDP-yeq_4jE",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "### Define a training loop\n",
         "\n",
@@ -339,19 +413,17 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "MBIACgdnA55X",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "MBIACgdnA55X"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "def train(model, inputs, outputs, learning_rate):\n",
         "  with tf.GradientTape() as t:\n",
@@ -359,22 +431,24 @@
         "  dW, db = t.gradient(current_loss, [model.W, model.b])\n",
         "  model.W.assign_sub(learning_rate * dW)\n",
         "  model.b.assign_sub(learning_rate * db)"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "RwWPaJryD2aN"
+        "id": "RwWPaJryD2aN",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "Finally, let's repeatedly run through the training data and see how `W` and `b` evolve."
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "XdfkR223D9dW",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -382,7 +456,7 @@
           },
           "height": 446
         },
-        "colab_type": "code",
+        "outputId": "c43591ae-d5ac-4f2b-a8e7-bfce607e0919",
         "executionInfo": {
           "elapsed": 569,
           "status": "ok",
@@ -393,13 +467,36 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "XdfkR223D9dW",
-        "outputId": "c43591ae-d5ac-4f2b-a8e7-bfce607e0919"
+        }
       },
+      "cell_type": "code",
+      "source": [
+        "model = Model()\n",
+        "\n",
+        "# Collect the history of W-values and b-values to plot later\n",
+        "Ws, bs = [], []\n",
+        "epochs = range(10)\n",
+        "for epoch in epochs:\n",
+        "  Ws.append(model.W.numpy())\n",
+        "  bs.append(model.b.numpy())\n",
+        "  current_loss = loss(model(inputs), outputs)\n",
+        "\n",
+        "  train(model, inputs, outputs, learning_rate=0.1)\n",
+        "  print('Epoch %2d: W=%1.2f b=%1.2f, loss=%2.5f' %\n",
+        "        (epoch, Ws[-1], bs[-1], current_loss))\n",
+        "\n",
+        "# Let's plot it all\n",
+        "plt.plot(epochs, Ws, 'r',\n",
+        "         epochs, bs, 'b')\n",
+        "plt.plot([TRUE_W] * len(epochs), 'r--',\n",
+        "         [TRUE_b] * len(epochs), 'b--')\n",
+        "plt.legend(['W', 'b', 'true W', 'true_b'])\n",
+        "plt.show()\n",
+        "  "
+      ],
+      "execution_count": 0,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Epoch  0: W=5.00 b=0.00, loss=9.48636\n",
@@ -412,52 +509,29 @@
             "Epoch  7: W=3.38 b=1.62, loss=1.34756\n",
             "Epoch  8: W=3.30 b=1.70, loss=1.23463\n",
             "Epoch  9: W=3.24 b=1.76, loss=1.16460\n"
-          ]
+          ],
+          "name": "stdout"
         },
         {
+          "output_type": "display_data",
           "data": {
             "image/png": 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kWJNHwjJ8BOTwcJXTE7XM3z8ACQkDMH/+fRg6NAnDhyc3+rrBYMCCBY9g0aL5iIrqjG7d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\n",
             "text/plain": [
-              "\u003cmatplotlib.figure.Figure at 0x7f5be4b8ec50\u003e"
+              "<matplotlib.figure.Figure at 0x7f5be4b8ec50>"
             ]
           },
           "metadata": {
             "tags": []
-          },
-          "output_type": "display_data"
+          }
         }
-      ],
-      "source": [
-        "model = Model()\n",
-        "\n",
-        "# Collect the history of W-values and b-values to plot later\n",
-        "Ws, bs = [], []\n",
-        "epochs = range(10)\n",
-        "for epoch in epochs:\n",
-        "  Ws.append(model.W.numpy())\n",
-        "  bs.append(model.b.numpy())\n",
-        "  current_loss = loss(model(inputs), outputs)\n",
-        "\n",
-        "  train(model, inputs, outputs, learning_rate=0.1)\n",
-        "  print('Epoch %2d: W=%1.2f b=%1.2f, loss=%2.5f' %\n",
-        "        (epoch, Ws[-1], bs[-1], current_loss))\n",
-        "\n",
-        "# Let's plot it all\n",
-        "plt.plot(epochs, Ws, 'r',\n",
-        "         epochs, bs, 'b')\n",
-        "plt.plot([TRUE_W] * len(epochs), 'r--',\n",
-        "         [TRUE_b] * len(epochs), 'b--')\n",
-        "plt.legend(['W', 'b', 'true W', 'true_b'])\n",
-        "plt.show()\n",
-        "  "
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "vPnIVuaSJwWz"
+        "id": "vPnIVuaSJwWz",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Next Steps\n",
         "\n",
@@ -469,17 +543,5 @@
         "The [next tutorial](TODO) will cover these higher level APIs."
       ]
     }
-  ],
-  "metadata": {
-    "colab": {
-      "collapsed_sections": [],
-      "default_view": {},
-      "name": "Training Models",
-      "provenance": [],
-      "version": "0.3.2",
-      "views": {}
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
+  ]
+}
\ No newline at end of file
diff --git a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb
index 5749f22ac5..3378054be4 100644
--- a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb
+++ b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb
@@ -1,44 +1,122 @@
 {
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Custom layers",
+      "version": "0.3.2",
+      "views": {},
+      "default_view": {},
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
   "cells": [
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "tDnwEv8FtJm7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "##### Copyright 2018 The TensorFlow Authors."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JlknJBWQtKkI",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "pwX7Fii1rwsJ"
+        "cellView": "form"
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
-        "import tensorflow as tf\n",
-        "tf.enable_eager_execution()\n",
-        "tfe = tf.contrib.eager\n"
-      ]
+        "#@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": "60RdWsg1tETW",
+        "colab_type": "text"
+      },
       "cell_type": "markdown",
+      "source": [
+        "# Custom layers"
+      ]
+    },
+    {
       "metadata": {
-        "colab_type": "text",
-        "id": "UEu3q4jmpKVT"
+        "id": "BcJg7Enms86w",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
-        "# High level API\n",
-        "\n",
-        "We recommend using `tf.keras` as a high-level API for building neural networks. That said, most TensorFlow APIs are usable with eager execution.\n",
-        "\n"
+        "<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/4_high_level.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/4_high_level.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": "UEu3q4jmpKVT",
+        "colab_type": "text"
+      },
       "cell_type": "markdown",
+      "source": [
+        "We recommend using `tf.keras` as a high-level API for building neural networks. That said, most TensorFlow APIs are usable with eager execution.\n"
+      ]
+    },
+    {
       "metadata": {
-        "colab_type": "text",
-        "id": "zSFfVVjkrrsI"
+        "id": "pwX7Fii1rwsJ",
+        "colab_type": "code",
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        }
       },
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf\n",
+        "tfe = tf.contrib.eager\n",
+        "\n",
+        "tf.enable_eager_execution()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zSFfVVjkrrsI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
       "source": [
         "## Layers: common sets of useful operations\n",
         "\n",
@@ -50,19 +128,17 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "8PyXlPl-4TzQ",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "8PyXlPl-4TzQ"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "# In the tf.keras.layers package, layers are objects. To construct a layer,\n",
         "# simply construct the object. Most layers take as a first argument the number\n",
@@ -72,23 +148,25 @@
         "# the first time the layer is used, but it can be provided if you want to \n",
         "# specify it manually, which is useful in some complex models.\n",
         "layer = tf.keras.layers.Dense(10, input_shape=(None, 5))"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "Fn69xxPO5Psr"
+        "id": "Fn69xxPO5Psr",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "The full list of pre-existing layers can be seen in [the documentation](https://www.tensorflow.org/api_docs/python/tf/keras/layers). It includes Dense (a fully-connected layer),\n",
         "Conv2D, LSTM, BatchNormalization, Dropout, and many others."
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 3,
       "metadata": {
+        "id": "E3XKNknP5Mhb",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -96,7 +174,7 @@
           },
           "height": 204
         },
-        "colab_type": "code",
+        "outputId": "c5d52434-d980-4488-efa7-5660819d0207",
         "executionInfo": {
           "elapsed": 244,
           "status": "ok",
@@ -107,15 +185,20 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "E3XKNknP5Mhb",
-        "outputId": "c5d52434-d980-4488-efa7-5660819d0207"
+        }
       },
+      "cell_type": "code",
+      "source": [
+        "# To use a layer, simply call it.\n",
+        "layer(tf.zeros([10, 5]))"
+      ],
+      "execution_count": 0,
       "outputs": [
         {
+          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "\u003ctf.Tensor: id=30, shape=(10, 10), dtype=float32, numpy=\n",
+              "<tf.Tensor: id=30, shape=(10, 10), dtype=float32, numpy=\n",
               "array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
               "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
               "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
@@ -125,25 +208,20 @@
               "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
               "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
               "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
-              "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]], dtype=float32)\u003e"
+              "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]], dtype=float32)>"
             ]
           },
-          "execution_count": 3,
           "metadata": {
             "tags": []
           },
-          "output_type": "execute_result"
+          "execution_count": 3
         }
-      ],
-      "source": [
-        "# To use a layer, simply call it.\n",
-        "layer(tf.zeros([10, 5]))"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 4,
       "metadata": {
+        "id": "Wt_Nsv-L5t2s",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -151,7 +229,7 @@
           },
           "height": 221
         },
-        "colab_type": "code",
+        "outputId": "f0d96dce-0128-4080-bfe2-0ee6fbc0ad90",
         "executionInfo": {
           "elapsed": 320,
           "status": "ok",
@@ -162,15 +240,22 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "Wt_Nsv-L5t2s",
-        "outputId": "f0d96dce-0128-4080-bfe2-0ee6fbc0ad90"
+        }
       },
+      "cell_type": "code",
+      "source": [
+        "# Layers have many useful methods. For example, you can inspect all variables\n",
+        "# in a layer by calling layer.variables. In this case a fully-connected layer\n",
+        "# will have variables for weights and biases.\n",
+        "layer.variables"
+      ],
+      "execution_count": 0,
       "outputs": [
         {
+          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "[\u003ctf.Variable 'dense_1/kernel:0' shape=(5, 10) dtype=float32, numpy=\n",
+              "[<tf.Variable 'dense_1/kernel:0' shape=(5, 10) dtype=float32, numpy=\n",
               " array([[ 0.43788117, -0.62099844, -0.30525017, -0.59352523,  0.1783089 ,\n",
               "          0.47078604, -0.23620895, -0.30482283,  0.01366901, -0.1288507 ],\n",
               "        [ 0.18407935, -0.56550485,  0.54180616, -0.42254075,  0.3702994 ,\n",
@@ -180,28 +265,21 @@
               "        [ 0.35752094,  0.44161648,  0.61500639, -0.12653333,  0.41629118,\n",
               "          0.36193585,  0.066082  , -0.59253877,  0.47318751,  0.17115968],\n",
               "        [-0.22554061, -0.17727301,  0.5525015 ,  0.3678053 , -0.00454676,\n",
-              "          0.24066836, -0.53640735,  0.13792562, -0.10727292,  0.59708995]], dtype=float32)\u003e,\n",
-              " \u003ctf.Variable 'dense_1/bias:0' shape=(10,) dtype=float32, numpy=array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.], dtype=float32)\u003e]"
+              "          0.24066836, -0.53640735,  0.13792562, -0.10727292,  0.59708995]], dtype=float32)>,\n",
+              " <tf.Variable 'dense_1/bias:0' shape=(10,) dtype=float32, numpy=array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.], dtype=float32)>]"
             ]
           },
-          "execution_count": 4,
           "metadata": {
             "tags": []
           },
-          "output_type": "execute_result"
+          "execution_count": 4
         }
-      ],
-      "source": [
-        "# Layers have many useful methods. For example, you can inspect all variables\n",
-        "# in a layer by calling layer.variables. In this case a fully-connected layer\n",
-        "# will have variables for weights and biases.\n",
-        "layer.variables"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 5,
       "metadata": {
+        "id": "6ilvKjz8_4MQ",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -209,7 +287,7 @@
           },
           "height": 221
         },
-        "colab_type": "code",
+        "outputId": "f647fced-c2d7-41a3-c237-242036784665",
         "executionInfo": {
           "elapsed": 226,
           "status": "ok",
@@ -220,15 +298,20 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "6ilvKjz8_4MQ",
-        "outputId": "f647fced-c2d7-41a3-c237-242036784665"
+        }
       },
+      "cell_type": "code",
+      "source": [
+        "# The variables are also accessible through nice accessors\n",
+        "layer.kernel, layer.bias"
+      ],
+      "execution_count": 0,
       "outputs": [
         {
+          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "(\u003ctf.Variable 'dense_1/kernel:0' shape=(5, 10) dtype=float32, numpy=\n",
+              "(<tf.Variable 'dense_1/kernel:0' shape=(5, 10) dtype=float32, numpy=\n",
               " array([[ 0.43788117, -0.62099844, -0.30525017, -0.59352523,  0.1783089 ,\n",
               "          0.47078604, -0.23620895, -0.30482283,  0.01366901, -0.1288507 ],\n",
               "        [ 0.18407935, -0.56550485,  0.54180616, -0.42254075,  0.3702994 ,\n",
@@ -238,28 +321,23 @@
               "        [ 0.35752094,  0.44161648,  0.61500639, -0.12653333,  0.41629118,\n",
               "          0.36193585,  0.066082  , -0.59253877,  0.47318751,  0.17115968],\n",
               "        [-0.22554061, -0.17727301,  0.5525015 ,  0.3678053 , -0.00454676,\n",
-              "          0.24066836, -0.53640735,  0.13792562, -0.10727292,  0.59708995]], dtype=float32)\u003e,\n",
-              " \u003ctf.Variable 'dense_1/bias:0' shape=(10,) dtype=float32, numpy=array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.], dtype=float32)\u003e)"
+              "          0.24066836, -0.53640735,  0.13792562, -0.10727292,  0.59708995]], dtype=float32)>,\n",
+              " <tf.Variable 'dense_1/bias:0' shape=(10,) dtype=float32, numpy=array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.], dtype=float32)>)"
             ]
           },
-          "execution_count": 5,
           "metadata": {
             "tags": []
           },
-          "output_type": "execute_result"
+          "execution_count": 5
         }
-      ],
-      "source": [
-        "# The variables are also accessible through nice accessors\n",
-        "layer.kernel, layer.bias"
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "O0kDbE54-5VS"
+        "id": "O0kDbE54-5VS",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Implementing custom layers\n",
         "The best way to implement your own layer is extending the tf.keras.Layer class and implementing:\n",
@@ -271,9 +349,9 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 7,
       "metadata": {
+        "id": "5Byl3n1k5kIy",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -281,7 +359,7 @@
           },
           "height": 391
         },
-        "colab_type": "code",
+        "outputId": "6e7f9285-649a-4132-82ce-73ea92f15862",
         "executionInfo": {
           "elapsed": 251,
           "status": "ok",
@@ -292,13 +370,30 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "5Byl3n1k5kIy",
-        "outputId": "6e7f9285-649a-4132-82ce-73ea92f15862"
+        }
       },
+      "cell_type": "code",
+      "source": [
+        "class MyDenseLayer(tf.keras.layers.Layer):\n",
+        "  def __init__(self, num_outputs):\n",
+        "    super(MyDenseLayer, self).__init__()\n",
+        "    self.num_outputs = num_outputs\n",
+        "    \n",
+        "  def build(self, input_shape):\n",
+        "    self.kernel = self.add_variable(\"kernel\", \n",
+        "                                    shape=[input_shape[-1].value, \n",
+        "                                           self.num_outputs])\n",
+        "    \n",
+        "  def call(self, input):\n",
+        "    return tf.matmul(input, self.kernel)\n",
+        "  \n",
+        "layer = MyDenseLayer(10)\n",
+        "print(layer(tf.zeros([10, 5])))\n",
+        "print(layer.variables)"
+      ],
+      "execution_count": 0,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "tf.Tensor(\n",
@@ -312,7 +407,7 @@
             " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
             " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
             " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]], shape=(10, 10), dtype=float32)\n",
-            "[\u003ctf.Variable 'my_dense_layer_1/kernel:0' shape=(5, 10) dtype=float32, numpy=\n",
+            "[<tf.Variable 'my_dense_layer_1/kernel:0' shape=(5, 10) dtype=float32, numpy=\n",
             "array([[-0.4011991 ,  0.22458655, -0.33237562, -0.25117266,  0.33528614,\n",
             "        -0.01392961,  0.58580834, -0.16346583,  0.28465688, -0.47191954],\n",
             "       [-0.52922136,  0.22416979, -0.58209574, -0.60914612,  0.05226624,\n",
@@ -322,35 +417,18 @@
             "       [ 0.34073615, -0.59835428,  0.06498981, -0.44489855, -0.34302285,\n",
             "         0.20969599,  0.35527444, -0.03173476, -0.22227573,  0.09303057],\n",
             "       [ 0.41764337, -0.06435019, -0.52509922, -0.39957345,  0.56811184,\n",
-            "         0.23481232, -0.61666459,  0.31144124, -0.11532354, -0.42421889]], dtype=float32)\u003e]\n"
-          ]
+            "         0.23481232, -0.61666459,  0.31144124, -0.11532354, -0.42421889]], dtype=float32)>]\n"
+          ],
+          "name": "stdout"
         }
-      ],
-      "source": [
-        "class MyDenseLayer(tf.keras.layers.Layer):\n",
-        "  def __init__(self, num_outputs):\n",
-        "    super(MyDenseLayer, self).__init__()\n",
-        "    self.num_outputs = num_outputs\n",
-        "    \n",
-        "  def build(self, input_shape):\n",
-        "    self.kernel = self.add_variable(\"kernel\", \n",
-        "                                    shape=[input_shape[-1].value, \n",
-        "                                           self.num_outputs])\n",
-        "    \n",
-        "  def call(self, input):\n",
-        "    return tf.matmul(input, self.kernel)\n",
-        "  \n",
-        "layer = MyDenseLayer(10)\n",
-        "print(layer(tf.zeros([10, 5])))\n",
-        "print(layer.variables)"
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "tk8E2vY0-z4Z"
+        "id": "tk8E2vY0-z4Z",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`.\n",
         "\n",
@@ -358,11 +436,11 @@
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "Qhg4KlbKrs3G"
+        "id": "Qhg4KlbKrs3G",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Models: composing layers\n",
         "\n",
@@ -372,9 +450,9 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 9,
       "metadata": {
+        "id": "N30DTXiRASlb",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -382,7 +460,7 @@
           },
           "height": 190
         },
-        "colab_type": "code",
+        "outputId": "a8b23a8e-5cf9-4bbf-f93b-6c763d74e2b3",
         "executionInfo": {
           "elapsed": 420,
           "status": "ok",
@@ -393,27 +471,9 @@
             "userId": ""
           },
           "user_tz": 420
-        },
-        "id": "N30DTXiRASlb",
-        "outputId": "a8b23a8e-5cf9-4bbf-f93b-6c763d74e2b3"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "tf.Tensor(\n",
-            "[[[[ 0.  0.  0.]\n",
-            "   [ 0.  0.  0.]\n",
-            "   [ 0.  0.  0.]]\n",
-            "\n",
-            "  [[ 0.  0.  0.]\n",
-            "   [ 0.  0.  0.]\n",
-            "   [ 0.  0.  0.]]]], shape=(1, 2, 3, 3), dtype=float32)\n",
-            "['resnet_identity_block_1/conv2d_3/kernel:0', 'resnet_identity_block_1/conv2d_3/bias:0', 'resnet_identity_block_1/batch_normalization_3/gamma:0', 'resnet_identity_block_1/batch_normalization_3/beta:0', 'resnet_identity_block_1/conv2d_4/kernel:0', 'resnet_identity_block_1/conv2d_4/bias:0', 'resnet_identity_block_1/batch_normalization_4/gamma:0', 'resnet_identity_block_1/batch_normalization_4/beta:0', 'resnet_identity_block_1/conv2d_5/kernel:0', 'resnet_identity_block_1/conv2d_5/bias:0', 'resnet_identity_block_1/batch_normalization_5/gamma:0', 'resnet_identity_block_1/batch_normalization_5/beta:0', 'resnet_identity_block_1/batch_normalization_3/moving_mean:0', 'resnet_identity_block_1/batch_normalization_3/moving_variance:0', 'resnet_identity_block_1/batch_normalization_4/moving_mean:0', 'resnet_identity_block_1/batch_normalization_4/moving_variance:0', 'resnet_identity_block_1/batch_normalization_5/moving_mean:0', 'resnet_identity_block_1/batch_normalization_5/moving_variance:0']\n"
-          ]
         }
-      ],
+      },
+      "cell_type": "code",
       "source": [
         "class ResnetIdentityBlock(tf.keras.Model):\n",
         "  def __init__(self, kernel_size, filters):\n",
@@ -448,22 +508,40 @@
         "block = ResnetIdentityBlock(1, [1, 2, 3])\n",
         "print(block(tf.zeros([1, 2, 3, 3])))\n",
         "print([x.name for x in block.variables])"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "tf.Tensor(\n",
+            "[[[[ 0.  0.  0.]\n",
+            "   [ 0.  0.  0.]\n",
+            "   [ 0.  0.  0.]]\n",
+            "\n",
+            "  [[ 0.  0.  0.]\n",
+            "   [ 0.  0.  0.]\n",
+            "   [ 0.  0.  0.]]]], shape=(1, 2, 3, 3), dtype=float32)\n",
+            "['resnet_identity_block_1/conv2d_3/kernel:0', 'resnet_identity_block_1/conv2d_3/bias:0', 'resnet_identity_block_1/batch_normalization_3/gamma:0', 'resnet_identity_block_1/batch_normalization_3/beta:0', 'resnet_identity_block_1/conv2d_4/kernel:0', 'resnet_identity_block_1/conv2d_4/bias:0', 'resnet_identity_block_1/batch_normalization_4/gamma:0', 'resnet_identity_block_1/batch_normalization_4/beta:0', 'resnet_identity_block_1/conv2d_5/kernel:0', 'resnet_identity_block_1/conv2d_5/bias:0', 'resnet_identity_block_1/batch_normalization_5/gamma:0', 'resnet_identity_block_1/batch_normalization_5/beta:0', 'resnet_identity_block_1/batch_normalization_3/moving_mean:0', 'resnet_identity_block_1/batch_normalization_3/moving_variance:0', 'resnet_identity_block_1/batch_normalization_4/moving_mean:0', 'resnet_identity_block_1/batch_normalization_4/moving_variance:0', 'resnet_identity_block_1/batch_normalization_5/moving_mean:0', 'resnet_identity_block_1/batch_normalization_5/moving_variance:0']\n"
+          ],
+          "name": "stdout"
+        }
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "wYfucVw65PMj"
+        "id": "wYfucVw65PMj",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "Much of the time, however, models which compose many layers simply call one layer after the other. This can be done in very little code using tf.keras.Sequential"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "L9frk7Ur4uvJ",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
@@ -472,7 +550,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 153
         },
-        "colab_type": "code",
+        "outputId": "882e9076-b6d9-4380-bb1e-7c6b57d54c39",
         "executionInfo": {
           "elapsed": 361,
           "status": "ok",
@@ -483,69 +561,53 @@
             "userId": "108023195365833072773"
           },
           "user_tz": 420
-        },
-        "id": "L9frk7Ur4uvJ",
-        "outputId": "882e9076-b6d9-4380-bb1e-7c6b57d54c39"
+        }
       },
+      "cell_type": "code",
+      "source": [
+        " my_seq = tf.keras.Sequential([tf.keras.layers.Conv2D(1, (1, 1)),\n",
+        "                               tf.keras.layers.BatchNormalization(),\n",
+        "                               tf.keras.layers.Conv2D(2, 1, \n",
+        "                                                      padding='same'),\n",
+        "                               tf.keras.layers.BatchNormalization(),\n",
+        "                               tf.keras.layers.Conv2D(3, (1, 1)),\n",
+        "                               tf.keras.layers.BatchNormalization()])\n",
+        "my_seq(tf.zeros([1, 2, 3, 3]))"
+      ],
+      "execution_count": 0,
       "outputs": [
         {
+          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "\u003ctf.Tensor: id=1423, shape=(1, 2, 3, 3), dtype=float32, numpy=\n",
+              "<tf.Tensor: id=1423, shape=(1, 2, 3, 3), dtype=float32, numpy=\n",
               "array([[[[0., 0., 0.],\n",
               "         [0., 0., 0.],\n",
               "         [0., 0., 0.]],\n",
               "\n",
               "        [[0., 0., 0.],\n",
               "         [0., 0., 0.],\n",
-              "         [0., 0., 0.]]]], dtype=float32)\u003e"
+              "         [0., 0., 0.]]]], dtype=float32)>"
             ]
           },
-          "execution_count": 26,
           "metadata": {
             "tags": []
           },
-          "output_type": "execute_result"
+          "execution_count": 26
         }
-      ],
-      "source": [
-        " my_seq = tf.keras.Sequential([tf.keras.layers.Conv2D(1, (1, 1)),\n",
-        "                               tf.keras.layers.BatchNormalization(),\n",
-        "                               tf.keras.layers.Conv2D(2, 1, \n",
-        "                                                      padding='same'),\n",
-        "                               tf.keras.layers.BatchNormalization(),\n",
-        "                               tf.keras.layers.Conv2D(3, (1, 1)),\n",
-        "                               tf.keras.layers.BatchNormalization()])\n",
-        "my_seq(tf.zeros([1, 2, 3, 3]))"
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "c5YwYcnuK-wc"
+        "id": "c5YwYcnuK-wc",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "# Next steps\n",
         "\n",
         "Now you can go back to the previous notebook and adapt the linear regression example to use layers and models to be better structured."
       ]
     }
-  ],
-  "metadata": {
-    "colab": {
-      "collapsed_sections": [],
-      "default_view": {},
-      "name": "4 - High level API - TensorFlow Eager.ipynb",
-      "provenance": [],
-      "version": "0.3.2",
-      "views": {}
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
+  ]
+}
\ No newline at end of file