Fix review comments

MarkDaoust · MarkDaoust · commit 159cf5ab85ad · 2018-07-19T12:52:58.000-07:00
diff --git a/samples/core/guide/autograph.ipynb b/samples/core/guide/autograph.ipynb
@@ -13,8 +13,8 @@
       "toc_visible": true
     },
     "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
+      "name": "python2",
+      "display_name": "Python 2"
     }
   },
   "cells": [
@@ -191,7 +191,11 @@
       "source": [
         "## Automatically convert Python control flow\n",
         "\n",
-        "AutoGraph will convert much of the Python language into the equivalent TensorFlow graph building code. It converts a function like:"
+        "AutoGraph will convert much of the Python language into the equivalent TensorFlow graph building code. \n",
+        "\n",
+        "Note: In real applications batching is essential for performance. The best code to convert to AutoGraph is code where the control flow is decided at the _batch_ level. If making decisions at the individual _example_ level, you must index and batch the examples to maintain performance while applying the control flow logic. \n",
+        "\n",
+        "AutoGraph converts a function like:"
       ]
     },
     {
@@ -321,9 +325,8 @@
         "\n",
         "tf_sum_even = autograph.to_graph(sum_even)\n",
         "\n",
-        "with tf.Graph().as_default():  \n",
-        "  with tf.Session():\n",
-        "    print('Graph result: %d\\n\\n' % tf_sum_even(tf.constant([10,12,15,20])).eval())"
+        "with tf.Graph().as_default(), tf.Session() as sess:\n",
+        "    print('Graph result: %d\\n\\n' % sess.run(tf_sum_even(tf.constant([10,12,15,20]))))"
       ],
       "execution_count": 0,
       "outputs": []
@@ -393,7 +396,7 @@
       "source": [
         "## Examples\n",
         "\n",
-        "Let's demonstrate some useful Python language features."
+        "Let's demonstrate some useful Python language features.\n"
       ]
     },
     {
@@ -419,14 +422,13 @@
         "@autograph.convert()\n",
         "def inverse(x):\n",
         "  assert x != 0.0, 'Do not pass zero!'\n",
-        "  return 1.0/x\n",
+        "  return 1.0 / x\n",
         "\n",
-        "with tf.Graph().as_default():  \n",
-        "  with tf.Session():\n",
-        "    try:\n",
-        "      print(inverse(tf.constant(0.0)).eval())\n",
-        "    except tf.errors.InvalidArgumentError as e:\n",
-        "      print('Got error message:\\n    %s' % e.message)"
+        "with tf.Graph().as_default(), tf.Session() as sess:\n",
+        "  try:\n",
+        "    print(sess.run(inverse(tf.constant(0.0))))\n",
+        "  except tf.errors.InvalidArgumentError as e:\n",
+        "    print('Got error message:\\n    %s' % e.message)"
       ],
       "execution_count": 0,
       "outputs": []
@@ -459,9 +461,8 @@
         "    i += 1\n",
         "  return n\n",
         "    \n",
-        "with tf.Graph().as_default():\n",
-        "  with tf.Session():\n",
-        "    count(tf.constant(5)).eval()"
+        "with tf.Graph().as_default(), tf.Session() as sess:\n",
+        "    sess.run(count(tf.constant(5)))"
       ],
       "execution_count": 0,
       "outputs": []
@@ -499,9 +500,8 @@
         "  return autograph.stack(z) \n",
         "\n",
         "\n",
-        "with tf.Graph().as_default():  \n",
-        "  with tf.Session():\n",
-        "    print(arange(tf.constant(10)).eval())"
+        "with tf.Graph().as_default(), tf.Session() as sess:\n",
+        "    sess.run(arange(tf.constant(10)))"
       ],
       "execution_count": 0,
       "outputs": []
@@ -655,14 +655,14 @@
       "source": [
         "## Interoperation with `tf.Keras`\n",
         "\n",
-        "Now that you've seen the basics, let's build some real model components with autograph.\n",
+        "Now that you've seen the basics, let's build some model components with autograph.\n",
         "\n",
-        "It's relatively simple to integrate `autograph` with `tf.keras`. But remember that batchng is essential for performance. So the best candidate code for conversion to autograph is code where the control flow is decided at the _batch_ level. If decisions are made at the individual _example_ level you will still need to index and batch your examples to maintain performance while appling the control flow logic. \n",
+        "It's relatively simple to integrate `autograph` with `tf.keras`. \n",
         "\n",
         "\n",
         "### Stateless functions\n",
         "\n",
-        "For stateless functions like `collatz`, below, the easiest way to include them in a keras model is to wrap them up as a layer uisng `tf.keras.layers.Lambda`."
+        "For stateless functions, like `collatz` shown below, the easiest way to include them in a keras model is to wrap them up as a layer uisng `tf.keras.layers.Lambda`."
       ]
     },
     {
@@ -711,7 +711,7 @@
         "\n",
         "<!--TODO(markdaoust) link to full examples  or these referenced models.-->\n",
         "\n",
-        "The easiest way to use autograph is keras layers and models is to `@autograph.convert()` the `call` method. See the [keras guide](https://tensorflow.org/guide/keras#build_advanced_models) for details on how to build on these classes. \n",
+        "The easiest way to use AutoGraph with Keras layers and models is to `@autograph.convert()` the `call` method. See the [TensorFlow Keras guide](https://tensorflow.org/guide/keras#build_advanced_models) for details on how to build on these classes. \n",
         "\n",
         "Here is a simple example of the [stocastic network depth](https://arxiv.org/abs/1603.09382) technique :"
       ]
@@ -866,9 +866,9 @@
       "source": [
         "## Advanced example: An in-graph training loop\n",
         "\n",
-        "Since writing control flow in AutoGraph is easy, running a training loop in a TensorFlow graph should also be easy.  \n",
+        "The previous section showed that AutoGraph can be used inside Keras layers and models. Keras models can also be used in AutoGraph code.\n",
         "\n",
-        "Important: While this example wraps a `tf.keras.Model` using AutoGraph, `tf.contrib.autograph` is compatible with `tf.keras` and can be used in [Keras custom layers and models](https://tensorflow.org/guide/keras#build_advanced_models). \n",
+        "Since writing control flow in AutoGraph is easy, running a training loop in a TensorFlow graph should also be easy.  \n",
         "\n",
         "This example shows how to train a simple Keras model on MNIST with the entire training process—loading batches, calculating gradients, updating parameters, calculating validation accuracy, and repeating until convergence—is performed in-graph."
       ]
@@ -1083,4 +1083,4 @@
       "outputs": []
     }
   ]
-}
+}
